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Abstract The importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives.

Amputation can have varying impacts on amputees depending on the extent of residual muscles left. Commercial prosthetics prioritize functional movements, neglecting patient-specific gestures related to the amputation type. Therefore, examining user-specific non-functional movements is pivotal. Rather than training on innumerable motions, prioritizing efficiently identifiable movements enhances prosthetic user-specificity. We examined 16 distinct wrist and finger motions using five classifiers (Wide Neural Networks, Ensemble Bagged Trees, Cubic SVM, Fine KNN, Logistic Regression Kernel). One-Way Anova testing ( $\text{p} < 0.05$ ) revealed significant classifier differences, with Wilcoxon signed-rank tests ( $\text{p} < 0.05$ ) distinguishing finger and wrist gestures. Ensemble Bagged Trees excelled ( $\text{p} < 0.05$ ) for finger (98.2% ± 0.81) and wrist (99% ± 0.73) movements; Fine KNN and Cubic SVM scored up to 96.3% ± 0.81 and 95.9% ± 1.43, respectively. Notably, Abduction of thumb and flexion of others consistently showed high accuracies for finger gestures. Wrist Radial Deviation (Clockwise) and Wrist Extension emerged as top wrist movements. Our study addresses the oversight of non-functional movements in myoelectric control. Our innovative approach focuses on selecting optimal finger and wrist movements for accurate gesture recognition, a departure from prior research neglecting these critical movements. By pinpointing these movements’ significance and performance, we provide a solution to enhance personalized prosthetic development. Our findings mark a pioneering step in leveraging accurate finger and wrist gestures to craft more effective and personalized myoelectric control systems. These insights bridge the gap in existing research and pave the way for improved prosthetic device design.

Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.

Short-term load forecasting is a challenging research problem and has a tremendous impact on electricity generation, transmission, and distribution. A robust forecasting algorithm can help power system operators to better tackle the ever-changing electric power demand. This paper presents a novel deep neural network for short-term electric load forecasting for the St. John’s campus of Memorial University of Newfoundland (MUN). The electric load data are obtained from the Memorial University of Newfoundland and combined with metrological data from St. John’s. This dataset is used to formulate a multivariate time-series forecasting problem. A novel deep learning algorithm is presented, consisting of a 1D Convolutional Neural Network, which is followed by an encoder–decoder-based network with attention. The input used for this model is the electric load consumption and metrological data, while the output is the hourly prediction of the next day. The model is compared with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM)-based Recurrent Neural Network. A CNN-based encoder–decoder model without attention is also tested. The proposed model shows a lower mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and higher R2 score. These evaluation metrics show an improved performance compared to GRU and LSTM-based RNNs as well as the CNN encoder–decoder model without attention.

This paper presents the design and optimization of a bidirectional Dual Active Bridge (DAB) converter for electric vehicle battery charging applications, encompassing both heavy and light electric vehicles. The core of this study is a 5.6 kW DAB converter that can seamlessly transition between 3.7 kW and 11.2 kW power outputs to accommodate different vehicle requirements without the need for circuit component changes. This flexibility is achieved through the novel integration of interleaved and parallel operation capabilities, allowing for efficient operation across a broad power range. Key innovations include the design of a high-frequency transformer with dual secondary outputs to facilitate power transfer at high currents up to 30 A, an optimized thermal design, and minimized stress on the circuit board. The use of next-generation power semiconductors and low-loss magnetic circuit elements has resulted in an optimized single-stage bidirectional converter design that showcases enhanced efficiency and competitiveness in the field. Furthermore, the converter’s design enables easy reconfiguration to meet the desired power output, vehicle type, and application needs, making it adaptable for future applications such as Vehicle-to-Grid (V2G) systems. The combination of these features—versatility in power output, efficient high-current transfer, innovative use of power semiconductors, and adaptability for future technologies—positions this DAB converter as a significant advancement in electric vehicle charging technology, offering a scalable solution to meet the evolving demands of electric mobility and renewable energy integration.

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications.

This paper presents a novel single-phase non-isolated bidirectional buck converter topology. The proposed converter uses a basic switching cell structure with a coupled inductor and an interleaving switching scheme. This article addresses a crucial challenge in bidirectional DC-DC conversion by prioritizing reducing output current ripples and minimizing filter inductor size. The employed method includes using MOSFETs with fast recovery diodes to mitigate reverse recovery and body diode losses. Furthermore, the optimization of the switching frequency of the output inductor to be twice the actual switching frequency contributes to reducing the component size of the converter. The coupled inductor also helps to reduce stress on components by distributing currents among its legs. The experimental result demonstrates the proposed converter has a very low ripple current as compared to the conventional converter. The low current ripples and smaller filter inductor size enabled by high-frequency operation have improved the efficiency and size of the converter. A common ground between input and output terminals ensures robust performance without common mode current concerns. Overall, the proposed converter represents a significant improvement in DC-DC converters, promising enhanced efficiency, reliability, and compactness in bidirectional DC-DC conversion systems. In order to verify the performance of the proposed converter, a 460 W buck converter prototype was built and tested.

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator.

Bitcoin has a reputation of being used for unlawful activities, such as money laundering, dark web transactions, and payments for ransomware in the context of smart cities. Blockchain technology prevents illegal transactions, but cannot detect these transactions. Anomaly detection is a fundamental technique for recognizing potential fraud. The heuristic and signature-based approaches were the foundation of earlier detection techniques, but tragically, these methods were insufficient to explore the entire complexity of anomaly detection. Machine Learning (ML) is a promising approach to anomaly detection, as it can be trained on large datasets of known malware samples to identify patterns and features of the transactions. Researchers are focusing on determining an efficient fraud and security threat detection model that overcomes the drawbacks of the existing methods. Therefore, ensemble learning can be applied to anomaly detection in Bitcoin by combining multiple ML classifiers. In the proposed model, the ADASYN-TL (Adaptive Synthetic + Tomek Link) balancing technique is used for data balancing. Random search, grid search and Bayesian optimization are used for hyperparameter tuning. The hyperparameters have a great impact on the performance of the model. For classification, we used the stacking model by combining Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. We used SHapley Additive exPlanation (SHAP) to interpret the predictions of the stacking model. The model also explores the performance of different classifiers using accuracy, F1-score, Area Under Curve-Receiver Operating Characteristic (AUC-ROC), precision, recall, False Positive Rate (FPR) and execution time, and ultimately selects the ideal model. The proposed model contributes to the development of effective fraud detection models that address the limitations of the existing algorithms. Our stacking model, which combines the prediction of multiple classifiers, achieved the highest F1-score of 97%, precision of 96%, recall of 98%, accuracy of 97%, AUC-ROC of 99% and FPR of 3%.

In healthcare, a huge amount is paid to meet the requirements of High-Need High-Cost (HNHC) patients, also known as super-utilizers. The major aim of the proposed study is to predict HNHC patients. This paper proposes hybrid Deep Learning (DL) models that identify HNHC patients, and help the management to manage their resources and budgets accordingly. We use the strategy of the Interquartile Range (IQR) that specifies the range of HNHC. The dataset used in this work has a lot of inconsistencies that are tackled by different techniques. Using three DL models, Gated Recurrent Unit (GRU), Fully Convolutional Network (FCN) and Vanilla Recurrent Neural Network (VRNN), we proposed 2 hybrid DL models, FCN-VRNN and GRU-FCN, for the prediction of HNHC patients. The performance of these models is evaluated based on different performance metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), R-Squared ( $R^{2}$ ) and execution time. The results depict that the proposed hybrid models show more competitive and outperforming results than the individual models. Proposed hybrid system model 1 FCN-VRNN achieves MSE of 0.4%, RMSE of 7%, MAPE of 29.8%, MAE of 3%, and $R^{2}$ of 99.4%. Proposed hybrid system model 2 GRU-FCN achieves MSE of 0.4%, RMSE of 6.5%, MAPE of 23.55%, MAE of 2.5%, and $R^{2}$ of 99.5%.

This paper aims to improve the performance of the conventional perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. As the oscillation around the maximum power point (MPP) is the main disadvantage of this technique, we introduce a modified P&O algorithm to conquer this handicap. The new algorithm recognizes approaching the peak of the photovoltaic (PV) array power curve and prevents the oscillation around the MPP. The key to achieve this goal is testing the change of output power in each cycle and comparing it with the change in array terminal power of the previous cycle. If a decrease in array terminal power is observed after an increase in the previous cycle or in the opposite direction, an increase in array terminal power is observed after a decrease in the previous cycle; it means we are at the peak of the power curve, so the duty cycle of the boost converter should remain the same as the previous cycle. Besides, an optimized duty cycle is introduced, which is adjusted based on the operating point of PV array. Furthermore, a DC-DC boost converter powered by a PV array simulator is used to test the proposed concept. When the irradiance changes, the proposed algorithm produces an average ηMPPT of nearly 3.1% greater than that of the conventional P&O algorithm and the incremental conductance (InC) algorithm. In addition, under strong partial shading conditions and drift avoidance tests, the proposed algorithm produces an average ηMPPT of nearly 9% and 8% greater than that of the conventional algorithms, respectively.

Aerosol is one of the major climate-forcing parameters which affect the Kingdom of Saudi Arabia in particular. The most relevant consideration that characterizes the aerosol properties and distribution is the Aerosol Optical Depth (AOD). In this study Modern Era Retrospective Analysis for Research and Applications (MERRA-2) AOD product from the year 1980–2021 is used to investigate aerosols pattern over the Kingdom of Saudi Arabia. The validation of the MERRA-2 AOD product is made by using AOD data retrieved from Aerosol Robotic Network (AERONET) stations located at Solar Village (SV) and at King Abdullah University of Science and Technology (KAUST). Various statistical analyses are performed to test the reliability of MERRA-2 data in the study region. The results of the statistical analysis indicate that MERRA-2 is highly correlated with both AERONET stations data. Thus, annual and seasonal aerosol climatology maps based on 41 years of MERRA-2 data are prepared and analyzed over the study region. The annual and seasonal aerosol climatology analysis of MERRA-2 data shows high density of AOD at southern and eastern regions while the low density emerges over the western and northern regions of the country during the study period. The results of the study are very encouraging, which increases our confidence level to use historical MERRA-2 AOD product to improve the knowledge on aerosols distribution over the region in future.

An AC-AC direct single-phase converter based on an impedance network is presented in this manuscript. It possesses all privileges of similar impedance source AC-AC converters such as buck-boost ability, maintaining or reversing the phase angle and sharing the same ground between input and output voltage. Furthermore, a magnetic coupling is exploited to provide high voltage gain by adjusting its turns ratio along with the duty cycle. A safe commutation strategy is implemented to avoid current and voltage spikes across switches needless to utilize snubber circuits. The presented converter offers continuous input current, and ac to ac conversion is done directly without using dc storage, making it appropriate for dynamic voltage restorer to compensate voltage sags and voltage swells. In addition, LC input and output filters are eliminated thanks to impedance network structure. In this regard, the presented topology offers good features in size and cost by reducing passive components compared to similar structures. Also, the comparative investigation shows that the proposed converter benefits from superior operational ranges among similar well-known topologies for the same conditions. Finally, theoretical analyses and operation modes of the proposed converter are discussed and testified by both simulation and experimental results.

Load forecasting is a critical aspect for power systems planning, operation and control. In this paper, as part of research efforts of an ambitious project at Memorial University of Newfoundland in St. John’s, Canada, to achieve more energy efficient and environmental friendly “Sustainable Campus”, we present a day-ahead load forecasting approach for the energy management system of the project. The hourly load consumption dataset from January 1, 2016 to March 31, 2020 is used in the paper, which was collected from two power meters on campus. Using the load consumption dataset along with the collected meteorological dataset, a total of 19 regression model-based day-ahead load forecasting algorithms for Memorial University of Newfoundland’s campus load are developed and evaluated in this paper. These 19 models belong to five families of regression models in MATLAB Regression Toolbox: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Ensemble of Trees. It is found that the family of GPR models shows the best load forecasting performance because they are nonparametric kernel-based probabilistic models. Two GPR models, Rational Quadratic GPR and Exponential GPR, are recommended as the best models for load forecasting through this study.

With advancements in renewable energy techno­logies, consumers are becoming prosumers, and renewable energy resources are being used in distributed networks. In an isolated distributed system, peer-to-peer (P2P) energy trading is one of the most promising energy management solutions. In this paper, we propose a P2P energy trading method for micro-grids using open resources and technology. The proposed setup comprises an Internet of Things (IoT) server to transfer energy amongst the peers without human intervention, and an Ethereum based private blockchain is suggested for money transfer in the form of cryptocurrency. The IoT server enables the peers to control and monitor self-produced energy. Arduino UNO, ACS 712 hall-effect current sensor, and a relay are the main components used in the hardware setup. The current sensor data is sent in real- time to Arduino for onward communication to the IoT server. A user-friendly interface has been developed on the server to perform various energy trading tasks. Peers have the choice to access the server remotely to perform energy trading tasks. The energy trading events can be shared amongst peers through e-mail notifications. For financial transactions, we utilized Ganache graphical user interface (GUI) a private Ethereum blockchain eliminating the need for financial institutions. The proposed peer-to-peer energy trading model has been successfully tested for energy trading between two peers. This paper provides details of the proposed hardware and software setup and explains how low-cost P2P energy trading can be achieved.

An open-source P2P energy trading platform facilitates energy trading amongst the peers. The proposed system provides real time data acquisition, monitoring and control of self-generated energy at a remote location. The trading activities are done on a web interface that uses a private Ethereum blockchain. A smart contract is deployed on the Ethereum blockchain and the trading activities performed on the web interface are recorded on a tamper-proof blockchain network. An internet of things platform is used to monitor and control the self-generated energy. Energy data is collected and processed by means of ESP32-S2 microcontrollers using field instrumentation devices which are connected to the voltage source and load. An open-source decentralized Peer-to-Peer (P2P) energy trading system, designed on the blockchain and internet of things (IoT) architecture is proposed. The hardware setup includes a relay, a current sensor, a voltage sensor, a Wi-Fi router and ESP32-S2 microcontroller. For data transfer the Message Queuing Telemetry Transport (MQTT) protocol is used over a local network. ESP32-S2 is set up as MQTT client and Node-Red IoT server is used as MQTT broker. Hypertext Transfer Protocol (http) request method is implemented to connect the Node-Red server with the web interface developed using React.JS library. The system design, implementation, testing, and results are presented in this paper.

Around 300 remote communities in Canada rely on diesel for their energy needs, a situation associated with high costs, high emissions, and accessibility problems. Various studies have addressed this problem by proposing renewable energy microgrids, which have a lot of potential due to the abundance and availability of renewable sources. However, there is a lack of studies regarding remote communities in Newfoundland and combined heat and power microgrids. This study chose Cartwright remote community based on consumption and available resources. Both distributed and centralized microgrid components were designed using MS Excel, Polysun, HOMER, and BEOPT. The final system included solar thermal, PV, wind energy, hydroelectric energy and fuel cells for energy generation, and hydrogen as an energy carrier for storage. The solar thermal distributed system reduced the thermal load by 35%. The microgrid reduced diesel consumption by 71% and CO2 emissions by 9000 tons. Renewable sources provided 100% of the electric load and 63.5% of the thermal load. The microgrid achieved a Levelized cost of −0.0245 $ \$ $ /kWh, which is only possible for combined heat and power systems.

With the advancement of technology it has been possible to detect diseases through non-invasive techniques. Previous studies showed us that diabetes mellitus and cardiometabolic syndrome are correlated disorders. Presence of one increased the possibility of existence of other. Efficient ways has been introduced to detect these disorders through facial image analysis using texture and color features. Furthermore, Human faces can be divided into three main categories based on the anatomical growth of their bones. Based on the knowledge the question arose that what could be the relation between facial types and these different categorical but linked disorders. Primary objective of this study is to analyze the facial types which have more probability of getting diabetes and cardiometabolic syndrome. Secondary objective includes the analysis of both genders i.e. male and female to detect which one of these genders are having more chances of these long lasting abnormalities. In the first step, data was acquired from four hospitals to make sure that it is accurate, both qualitatively and quantitatively. A few specifications were considered before capturing an image such as: frontal face, a distance of 3-4 feet between the camera and the subject, white background and neutral expressions. The age group of subjects was limited from of 30 to 80 years. The dataset consists of 198 participants including both male and female with equal number of subject in three groups namely diabetic, cardiometabolic and normal group. Facial index ratio was obtained after calculating the height and width of face using the anatomical landmarks described by researchers. After that each subject was classified into their respective group based on their facial ratio. Lastly, each group having different facial index ratio of project participant was analyzed statistically. Primary results showed that a large number of diabetic patients have wider and average faces also called mesofacial and brachifacial classes of face in medical terms. Further statistical analysis showed that there is a significant difference present between diabetic and normal group with P< 0.013 using CI of 95% and P

Bone is the tough, locomotive tissue of the body which is subjected to fractures very often. Advancements in Computer Aided Diagnosis (CAD) have leaded the researchers to develop robust algorithms to reduce human error in assessing different types of pathologies. This paper focuses on the data set release of the plain radiographic images of bone fractures which include both “Ground Truth Annotated” and “Non-Annotated Images”. The data was collected from the Radiology Department of Pakistan Institute of Medical Sciences, Islamabad, Pakistan in the form of digital images which were 8,345 in number, out of them 735 images were of bone fracture. The bone fractures were annotated manually to provide “truth” for the spatial location of bone fracture. The type of bone affected, area affected of the bone fractured, type of fracture, potential laterality of the affected bone and x-ray projection had also been identified and reported in the form of a data sheet. These features are expected to help training classifiers for CAD analysis of bone fractures. Frequency analysis of essential features had been performed to accentuate the data. The data set will provide grounds for the research community to manipulate for various CAD operations which may include estimation of precision of the developed CAD operators.

The performance of conventional repetitive controller (RC) deteriorates under frequency variations and system uncertainties. Due to limited bandwidth, it is also a trivial task to stabilize the conventional RC. This paper proposes a higher-order repetitive controller (HORC) with linear phase lead as a stabilizing compensator and zero-phase tracking error (ZPTE) compensator. The periodic signal generator, used by the HORC, offers relatively high gains in the neighborhood of tuned frequency and its harmonics. Stability conditions for higher-order repetitive (HOR) control system, including the phase lead compensator, are presented. The proposed solution is applied to repetitive current control of a two-level grid-connected inverter. Simulation and experimental results show that the HORC designed using the phase lead compensation is robust to frequency variation in reference/disturbance and system uncertainties.

Automated segmentation of brain tumour from multimodal MR images is pivotal for the analysis and monitoring of disease progression. As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes. Deep learning algorithms outperform on tasks of semantic segmentation as opposed to the more conventional, context-based computer vision approaches. Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation. In this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions. Both models were trained separately on the BraTS-19 challenge dataset and evaluated to yield segmentation maps which considerably differed from each other in terms of segmented tumour sub-regions and were ensembled variably to achieve the final prediction. The suggested ensemble achieved dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and tumour core, respectively, on the validation set, performing favourably in comparison to the state-of-the-art architectures currently available.

Pattern recognition (PR) algorithms have shown promising results for upper limb myoelectric control (MEC). Several studies have explored the efficacy of different pre and post processing techniques in implementing PR-based MECs. This paper explores the effect of segmentation type (disjoint and overlap) and segment size on the performance of PR-based MEC, for multiple datasets recorded with different recording devices. Two PR-based methods; linear discriminant analysis (LDA) and support vector machine (SVM) are used to classify hand gestures. Optimum values of segment size, step size and segmentation type were considered as performance measure for a robust MEC. Statistical analysis showed that optimum values of segment size for disjoint segmentation are between 250ms and 300ms for both LDA and SVM. For overlap segmentation, best results have been observed in the range of 250ms-300ms for LDA and 275ms-300ms for SVM. For both classifiers the step size of 20% achieved highest mean classification accuracy (MCA) on all datasets for overlap segmentation. Overall, there is no significant difference in MCA of disjoint and overlap segmentation for LDA (P-value = 0.15) but differ significantly in the case of SVM (P-value

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.

In this paper, the design, simulation, fabrication, and characterization study of a low-cost and directional hybrid four-element (2 × 2 configuration) Minkowski–Sierpinski fractal antenna array (MSFAA) for the high-efficiency IEEE 802.11ax WLANs (Wi-Fi 6E) and the sub-6 GHz 5G wireless system is presented. Each element of the array is separated by 0.7 λ0. The complete four-element fractal antenna array system includes designing the single-element Minkowski–Sierpinski fractal antenna using two different substrates for performance comparison and an equal-split Wilkinson power divider (WPD) to achieve power division and to form a feed network. The single-element antenna, four-element fractal antenna array, and WPDs are fabricated using a flame-resistant (FR4) glass epoxy substrate with a dielectric constant (εr) of 4.3 and thickness (h) of 1.66 mm. For performance comparison, a high-end Rogers thermoset microwave material (TMM4) substrate is also used, having εr = 4.5 and h = 1.524mm, respectively. The designed four-element fractal antenna array operates at the dual-band frequencies of 4.17 and 5.97 GHz, respectively. The various performance parameters of the antenna array, such as return loss, bandwidth, gain, and 2D and 3D radiation patterns, are analyzed using CST Microwave Studio. The fabricated four-element antenna array provides the bandwidth and gain characteristic of 85 MHz/4.19 dB and 182 MHz/9.61 dB at 4.17 and 5.97 GHz frequency bands, respectively. The proposed antenna array design gives an improvement in the bandwidth, gain, and radiation pattern in the boresight at both frequencies. In the IEEE 802.11 ax WLANs (Wi-Fi 6E) deployments and the upcoming 5G wireless and satellite communication systems, it is critical to have directional antenna arrays to focus the radiated power in any specific direction. Therefore, it is believed that the proposed dual-band four-element fractal antenna array with directional radiation patterns can be an ideal candidate for the high-efficiency IEEE 802.11ax WLANs (Wi-Fi 6E) and the upcoming 5G wireless and satellite communication systems.

The intimidating surge in the procurement of Distributed Energy Resources (DER) has increased the number of prosumers, creating a new possibility of local energy trading across the community. This project aims to formulate the peer-to-peer energy (P2P) sharing model to encourage the DERs to share surplus energy among the consumers. An effective pricing method is developed based on the supply-demand ratio (SDR) with the importance of self-optimization, which allows the prosumers to maximize their energy sharing and profits. To implement this pricing method, a simplified dynamic matchmaking algorithm has been deployed to introduce the Outstanding Prosumer to interact with existing consumers to increase the efficiency and profitability of the trade network. Consumers also benefit from this model, as they can pick the most economical energy supplier instead of relying on the utility grid. The prosumer with high excess energy and the consumer with the highest energy demand will be prioritized to maintain the SDR ratio to one or greater than one. Here, all the above-stated features of the peer-to-peer energy trading have been demonstrated with some calculations to back up some tangible results. Finally, a case study is simulated among the residents of Dhaka, Bangladesh, to demonstrate how peers can profit from participating in trading at a given time. Comparing the results with and without P2P trading, there has been a 17.54% reduction in an electric bill on a typical day of July, and a 49.53% reduction in the interaction with the grid.

A smart grid (SG), considered as a future electricity grid, utilizes bidirectional electricity and information flow to establish automated and widely distributed power generation. The SG provides a delivery network that has distributed energy sources, real-time asset monitoring, increased power quality, increased stability and reliability, and two-way information sharing. Furthermore, SG provides many advantages, such as demand response, distribution automation, optimized use of electricity, economical energy, real-time grid status monitoring, voltage regulation or VAR control, and electricity storage. In this survey, we explore the literature on smart Grid enabling technologies until 2022. We dig out four major systems: (1) the smart grid’s prominent features and challenges; (2) the smart grid standard system and legislations; (3) smart grid energy subsystem; and (4) the smart grid management system and protection system for new researchers for their future projects. The research challenges and future recommendations are also presented in the conclusion section to explore the new paradigm.

In distributed power generation systems, grid-connected inverters are becoming an attractive means of delivering the energy generated from renewable sources into the grid. However, the performance of the current controller drastically decreases in the presence of model uncertainty, grid harmonics, filter parametric, and grid impedance variations, which can jeopardize the entire system’s stability. This paper presents a novel design of a super-twisting integral sliding mode control (ST-ISMC) strategy for the first time in the application of a three-phase voltage source grid-connected inverter. The designed controller has shown robustness and maintains a low total harmonic distortion (THD) in the presence of filter parameters drift, grid impedance variation, and grid harmonics distortion. The super-twisting action is added to remove the chattering problem associated with the conventional SMC strategy, and integral action is adopted to improve the grid’s current steady-state error. The modeling and simulation of a complete system are carried out using MATLAB/SIMULINK. Finally, a real-world hardware prototype system is fabricated to demonstrate the performance and effectiveness of the proposed controller.

In recent years, the penetration of renewable power generations into the electrical grid has substantially increased. Continuous deployment of power electronic-based distributed generations and the reduction of traditional synchronous machines with their essential dynamics in modern power networks are very critical in this change. The use of power electronic inverters leads to the dissociation of sources and loads and lowering the power system inertia. Under power imbalance, this drop causes an elevated rate of change in frequency and frequency divergences, which has a notable impact on the system’s frequency stability. As a result, enhanced control techniques for grid-tied electronic converters are required to secure the power system’s stability and support. The virtual-synchronous generator (VSG) control is used to mimic the dynamics of a rotating synchronous generator and improve the power system’s stability. In this article, the problems of such low-inertia power systems, as well as the VSG technologies, are explored. This research also looks at different control orders and strategies for virtual-synchronous generators (VSG). In addition, the utilization of energy storage and critical matters in VSG and further research recommendations are explained.

This paper presents an automation strategy for multi-terminal HVDC (MT-HVDC) systems combining a dc optimal power flow (dc OPF) routine and a unified reference controller (URC). In the presented automatic framework, the dc OPF algorithm is implemented at the power dispatch center (PDC) of the MT-HVDC system to find optimal reference operation points of the power converters to minimize the losses during the operation of the MT-HVDC grid and solves the contradiction between minimizing losses and preventing commutation failure. At the local control systems, the operating points of the voltage-source converter (VSC) stations are tuned based on the calculations executed in the PDC, which enables fast response to power fluctuation and ensures a stable dc voltage. However, if the communication between the two control layers is lost, the MT-HVDC grid remains stable based on the pre-defined $V$ – $P$ droop characteristics for the power converter stations till the connection establishes again, and a set of new operating points is generated and sent. The static and dynamic simulations conducted on the CIGRE B4 HVDC test grid establish the efficient and effective grid control performance with the proposed automation strategy. The analysis shows that the proposed control scheme achieves the desired minimum losses while, at the same time, satisfying the system constraints.

In this study, we present a Direct Model Reference Adaptive Control (DMRAC) algorithm in a boost converter used in islanded microgrids (MG) with a solar photovoltaic (PV) system. Islanded types of microgrids have very sensitive voltage and frequency variability; therefore, a robust and adaptive controller is always desired to control such variations within the MG. A DC–DC boost converter with a modified MIT rule controller is proposed in this paper, which stabilizes output voltage variations in islanded MG. Since the boost converter is a non-minimum phase, the controller design that relies only on output voltage feedback becomes challenging. Even though output voltage control can be achieved using inductor current control, such current mode controllers may also require prior knowledge of the load resistance and more states, such as output and inductor currents in feedback. Here, two control loops are used to achieve a stable output voltage; a PID controller can regulate the output voltage at a fixed level, and the outer loop is designed to implement the MIT rule for a DMRAC. To ensure that the actual system is following the desired reference model, using only an output voltage feedback sensor, a DMRAC is devised to update the PID controller parameters in real-time. Compared to a DC–DC boost converter connected to the MG, a controller, such as the one introduced in this paper, is more successful in dealing with unknown parameter fluctuations and disturbance changes. The MATLAB/SIMULINK is used to design and simulate the controller with different load disturbances and input voltage variances. The hardware validation is also carried out to show the performance of the proposed controller. Our results suggest that the DMRAC provides robust regulation against parameter variations.

A low-cost, open-source peer-to-peer (P2P) energy trading system for a remote community is presented in this paper. As a result of its geographic location, this community has never been able to access electricity and other modern amenities. This study aims to design and implement a P2P energy trading system for this remote community that allows residents to take advantage of distributed energy resources. A Raspberry Pi 4 Model B (Pi4B) hosts the main server of the trading system that includes the user interface and a local Ethereum blockchain server. The Ethereum blockchain is used to deploy smart contracts. The Internet-of-Things (IoT) servers run on ESP32 microcontrollers. Sensors and actuators connected to the ESP32 are field instrumentation devices that facilitate acquiring, monitoring, and transferring energy data in real-time. To perform trading activities, React.JS open-source library was used to develop the blockchain-enabled user interface. An immutable blockchain network keeps track of all transactions. The proposed system runs on a local Wi-Fi network with restricted authorization for system security. Other security measures such as login credentials, private key, firewall, and secret recovery phrases are also considered for information security and data integrity. A Hypertext Transfer Protocol is implemented for communication between the servers and the client. This explains the overall system design, implementation, testing, and results.

The popularity of electric vehicles (EVs) is increasing day by day due to their environmentally friendly operation and high milage as compared to conventional fossil fuel vehicles. Almost all leading manufacturers are working on the development of EVs. The main problem associated with EVs is that charging many of these vehicles from the grid supply system imposes an extra burden on them, especially during peak hours, which results in high per-unit costs. As a solution, EV charging stations integrated with hybrid renewable energy resources (HREs) are being preferred, which utilize multi-energy systems to produce electricity. These charging stations can either be grid-tied or isolated. Isolated EV charging stations are operated without any interconnection to the main grid. These stations are also termed standalone or remote EV charging stations, and due to the absence of a grid supply, storage becomes compulsory for these systems. To attain maximum benefits from a storage system, it must be configured properly with the EV charging station. In this paper, different types of the latest energy storage systems (ESS) are discussed with a comprehensive review of configurations of these systems for multi-energy standalone EV charging stations. ESS in these charging stations is applied mainly in three different configurations, named single storage systems, multi-storage systems, and swappable storage systems. These configurations are discussed in detail with their pros and cons. Some important expectations from future energy storage systems are also highlighted.

This paper introduces a new single-phase AC-AC converter based on an impedance source circuit. Like the existing single-phase impedance source AC-AC converters, it has the buck-boost ability and direct ac conversion. The input and output voltage possesses the same ground, and the phase angle is maintained and reversed smoothly. The presented converter utilizes a coupled transformer which allows the designer to exploit the transformer’s turns ratio as a variable to attain the desired output voltage. Additionally, the used transformer provides an option to obtain higher voltage gain by decreasing the turns ratio. Hence, smaller size of the coupled inductors is required for the higher voltage cases. To eliminate the switching voltage and current spikes on the power switches, a safe commutation strategy is used instead of utilizing snubber circuits. Furthermore, the input current is continuous and sinusoidal with low harmonics thanks to embedding the input inductor in series with the input source. Additionally, a dynamic voltage restorer is presented based on the proposed converter to compensate the voltage sag and swell faults. Simulation results are provided to evaluate the theoretical analysis. Finally, a laboratory prototype has been fabricated to demonstrate the validation of the presented converter.

This scientific paper aims to increase the voltage source converter (VSC) control efficiency in a multi-terminal high voltage direct current (MTDC) network during dynamic operations. In the proposed study, the Mayfly algorithm (MA) is used to modify the control parameters of VSC stations. Traditional strategies that modify VSC control settings using approximate linear models fail to produce optimal results because VSCs are nonlinear characteristics of the MTDC system. Particle swarm optimization (PSO) may produce optimal outcomes, but it is prone to becoming stuck in a local optimum. To modify the proportional-integral (P.I.) control parameters of the VSC station, the Mayfly algorithm, a modified form of PSO, is used. The suggested algorithm’s objective function simultaneously optimizes both the outer and inner control layers. A four-terminal MTDC test system is developed in PSCAD/EMTDC to assess the benefits of the proposed algorithm. For VSCs, a comparison of classical, PSO, and proposed MA-based tuned parameters is carried out. The integral of time multiplied by absolute error (ITAE) criterion is used to compare the performance of classical, PSO, and a proposed algorithm for VSC controller parameters/gains. With an ITAE value of 6.8521 × 10−6, the MA-based proposed algorithm computes the optimal values and outperforms its predecessor to adjust the VSCs controller gains. For (i) wind farm power variation, (ii) AC grid load demand variation, and (iii) ultimate permanent VSC disconnection, steady-state and dynamic performances are evaluated. According to the results, the proposed algorithm based MTDC system performs well during transients.

Single-phase direct frequency converters are gaining attraction at the research and academic level as they are rapidly getting space over conventional multistage converters. The converters developed with a rectification and inversion process using a DC-link level are examples of multistage converters with some serious concerns such as an increase in the overall weight, losses, and cost. They also suffer from the low-reliability issue due to the issues involved with DC-link capacitors and problems linked with electromagnetic interference (EMI) caused by high-frequency pulse width modulation (PWM) switching. These problems are addressed with line frequency switching cycloconverters. In these converters, the power quality of the output voltage is improved by governing the amplitude of some selected output pulses or half-cycles. For this purpose, a low-frequency multiple tapping transformer may be used to obtain various voltage levels. However, its use is the main source of increased overall weight, losses, cost, and volume. In transformer eliminated topologies, high-frequency PWM control can be employed to control the magnitude of some selected half cycles of the output voltage. However, this approach may arise some problems related to EMI. In both control techniques, the attention is focused on the power quality of the output voltage only. The concern for the input current is ignored and not yet analyzed. This is one of the critical power quality concerns and requires further investigation. The magnitude control of the output half-cycles causes the variation in the amplitude of some half cycles of the input currents. As a result, all half cycles of the input current become non-symmetric. It generates harmonics that are always of low frequency and cannot be easily filtered out. It results in a high value of the harmonic factor (HF) of the input current. The improvement in the power quality of the output voltages severally degrades the power quality of the input currents. In this research, this problem is investigated with mathematically computed harmonic coefficients with a pulse selective approach. Also, a simple single-phase cycloconverter is introduced to improve the power quality index of the input current. The overall analysis is supported by the results obtained from a Simulink-based environment and a practically constructed prototype.

Variable voltage and frequency are required to govern the torque-speed characteristics of many industrial drive systems. Traditionally, this is achieved with a power converting system implemented with multistage converters. This technology is based on rectifying AC power into DC and then DC into AC with an inverter circuit. The power quality concerns of both conversion stages are tackled by selecting high switching frequency PWM control and harmonics mitigation filters. Also, using a bulky DC-link capacitor is one of the big sources of low system reliability, so this approach increases the conversion losses, circuit, and control complications. The frequency step-down conversion is very attractive with direct AC-AC converters as it has a simple control and circuit structure, but these converters face poor power quality challenges once the output frequency is decreased with respect to an input. In these converters, the total harmonic distortion (THD) of the output voltage becomes very poor once the output frequency is reduced. The problem of high THD of the output is addressed in the power converting circuits implemented with line frequency multi-winding transformers. The required number of output winding and switching devices (diodes and thyristors) increases once the value of the output frequency is decreased. This will increase the overall volume, cost, and losses. The use of a bulky and costly line frequency transformer may be eliminated if AC voltage controllers have non-inverted and inverted voltage buck capabilities, such existing topologies either have complex control schemes or require a large number of operating devices. Therefore, in this research article, a new transformerless frequency step-down converter employing fewer devices is proposed. This approach is realized with a high-frequency controlled rectifier for the required voltage stabilization and a low-frequency inverter bridge for frequency control. Its validation is supported by the results attained from Simulink and practical-based prototypes.

High-voltage direct-current (HVDC) power transmission is becoming increasingly important due to steadily rising need for bulk power delivery and interconnected power transmission and distribution systems. DC grids are vulnerable to dc faults, which lead to a rapid rise in dc fault currents. The dc faults must be cleared within the timeframe of milliseconds to avoid the collapse of the HVDC system. In the event of primary protection (PP) failure, back-up protection (BP) must be applied to clear the fault. In this paper, a novel algorithm based on a Naïve Bayes classifier is proposed to determine threshold levels and operational time frames for primary and back-up protection in multi-terminal voltage source converter-based HVDC. Local voltage and currents are measured to detect and identify the kind of fault. A four-terminal HVDC transmission system is developed in PSCAD/EMTDC and is subjected to line-line faults at different locations and time, to assess the designed protection schemes. Results show that a relaying algorithm effectively detects the fault and expedite the primary protection operation. On malfunctioning of PP, BP is accelerated in a short delay of 0.2 ms. Furthermore, the relaying algorithm provides faster protection compared with techniques available in the literature. The resulting reduced fault clearance time truncates the maximum fault current and, inevitably, leads to reduced power ratings required for dc grid equipment.

Wireless multimedia sensor network has been renewed to the distributed model by incorporating the single board computer (i.e., camera nodes and mobile beacons). The redesigned camera nodes, such as unmanned aerial vehicle and mobile beacon nodes are limited in terms of memory, computation power, bandwidth, and node power while the real-time processing requires the multimedia contents transmission over the wireless interface in the presence of all these constraints. To tackle this problem, advanced video compression schemes have been adopted by which bit rate overhead over the network is greatly improved. H.264/moving picture experts group (MPEG)-4 is a state-of-the-art video compression standard developed jointly by International Telecommunication Union Telecommunication Standardization Sector video coding experts group and ISO/Joint Technical Committee of International Electrotechnical Commission MPEG. The H.264 provides a number of tunable parameters (e.g., quantization parameter (QP) and number of reference frames (NoRF)) for tailoring the encoding process as per requirements. In this paper, we propose a framework consisting of a joint parameter cost function to find the best trade-off between QP and NoRF with the policy of controlling the bit-rate (BR) while maintaining reasonably good video quality without compromising on processing delay (PD) and nodal power consumption (NPC). The framework has been proposed by transforming the multi-objective problem into a convex optimization problem. It is shown that an optimal trade-off between these conflicting objectives can be achieved by choosing appropriate values for QP and NoRF. Results for optimal values of QP and NoRF with corresponding trade-off between BR, quality of multimedia contents, low PD and low NPC are presented.

A novel coplanar waveguide-fed rectenna with high efficiency is proposed and implemented in this paper for 2.45-GHz Bluetooth/ wireless local area network applications. The antenna has compact dimensions of 18 mm × 30 mm, which is simulated and manufactured using a low-cost FR4 substrate with a thickness of 1.6 mm. A tuning stub technique with rectangular slots is used for better impedance matching and enhancing the impedance bandwidth of the antenna with a peak gain of 5.6 dB. The proposed novel antenna for RF energy harvesting applications exhibits dipolelike radiation pattern in H-plane and omnidirectional pattern in E-plane with improved radiation efficiency. Single-stage Cockcroft-Walton rectifier with L-shaped impedance-matching network is designed in advance design system and fabricated on FR4 substrate. The dc output of the rectenna is measured as 3.24 V with a load resistance of 5 kΩ. A simulated peak conversion efficiency of 75.5% is attained, whereas the measured one is observed to be 68% with an input signal power of 5 dBm at 2.45 GHz.

The objective of this paper is to enhance the control performance of voltage source converter (VSC) stations within multi-terminal high voltage direct current (M-HVdc) grids under dynamic conditions. This paper presents the application of particle swarm optimization (PSO) to tune the control parameters of VSC stations. VSCs are non-linear components of the M-HVdc grids. Conventional techniques use approximated linear models to tune the control parameters which do not produce optimal results. Thus, PSO is employed to optimally tune the control parameters of VSC stations. The algorithm of the proposed objective function applies simultaneous optimization of inner and outer control layers. Dynamic simulations of a four-terminal HVdc system are developed in PSCAD/EMTDC to extol the merits of the presented optimization technique. Results show the steady state and dynamic control performances, both for classically and PSO-based tuned parameters, during load demand change from ac grids, wind power change, and eventual permanent VSC disconnection.

Electromagnetic spectrum is too cluttered to add additional broadband channels of high data rate and bandwidth. Free space optical (FSO) communication is one of the most dominant optical wireless communication systems, which provides huge and licensed free spectrum, non-interfering link, and high data rate. Despite of having above-mentioned advantages, the FSO links are very sensitive to bad weather conditions (i.e., fog, snow, dust, and their combination). Therefore, there is a dire requirement to develop a system that can overcome issues of individual communication system and adjust the current demand of new broadband channels. Adaptive FSO-radio frequency (RF) communication system is proposed to tackle the individual channel issues. A novel throughput maximization algorithm (TMA) is developed for the adaptive hybrid FSO-RF channel. Performance of the proposed algorithm for adaptive hybrid communication system is analyzed considering the regular and right-regular low density parity check (LDPC) code under various weather conditions. Simulation results show that the TMA performs well under all weather conditions and gives a performance gain of up to 2.25 dB considering the right-regular LDPC code. From the presented results, it is also seen that the suggested communication system optimizes mapping schemes, bit proportions for each channel, and puncturing ratios adaptively for different degree variable nodes in worst weather conditions.

One of the technical challenges that needs to be addressed for the future of the multi-terminal high voltage direct current (M-HVDC) grid is DC fault isolation. In this regard, HVDC circuit breakers (DCCBs), particularly hybrid circuit breakers (H-DCCBs), are paramount. The H-DCCB, proposed by the ABB, has the potential to ensure a reliable and safer grid operation, mainly due to its millisecond-level current interruption capability and lower on-state losses as compared to electromechanical and solid-state based DCCBs. This paper aims to study and evaluate the operational parameters, e.g., electrical, and thermal stresses on the IGBT valves and energy absorbed by the surge arrestors within H-DCCB during different DC fault scenarios. A comprehensive set of modeling requirements matching with operational conditions are developed. A meshed four-terminal HVDC test bench consisting of twelve H-DCCBs is designed in PSCAD/EMTDC to study the impacts of the M-HVDC grid on the operational parameters of H-DCCB. Thus, the system under study is tested for different current interruption scenarios under a (i) low impedance fault current and (ii) high impedance fault current. Both grid-level and self-level protection strategies are implemented for each type of DC fault.

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts.

The multiple uncertainties in a microgrid, such as limited photovoltaic generations, ups and downs in the market price, and controlling different loads, are challenging points in managing campus energy with multiple microgrid systems and are a hot topic of research in the current era. Microgrids deployed at multiple campuses can be successfully operated with an exemplary energy management system (EMS) to address these challenges, offering several solutions to minimize the greenhouse gas (GHG) emissions, maintenance costs, and peak load demands of the microgrid infrastructure. This literature survey presents a comparative analysis of multiple campus microgrids’ energy management at different universities in different locations, and it also studies different approaches to managing their peak demand and achieving the maximum output power for campus microgrids. In this paper, the analysis is also focused on managing and addressing the uncertain nature of renewable energies, considering the storage technologies implemented on various campuses. A comparative analysis was also considered for the energy management of campus microgrids, which were investigated with multiple optimization techniques, simulation tools, and different types of energy storage technologies. Finally, the challenges for future research are highlighted, considering campus microgrids’ importance globally. Moreover, this paper is expected to open innovative paths in the future for new researchers working in the domain of campus microgrids.

This paper deals with the power dispatch and direct voltage control in multiterminal high voltage direct current (MT-HVDC) systems. Generalized voltage droop (GVD) control is adopted for voltage source converters (VSC)s of a MT-HVDC system. A mechanism has been designed based on the power ratio within the GVD controlled stations to achieve flexible autonomous coordination control among VSC-HVDC stations, without need for communication. In this paper, several alternatives are considered to guarantee fault ride through of onshore converter stations. The performance of the proposed control strategy is analyzed with time-domain dynamic simulations, in an EMDTC/PSCAD platform, and experimentally validated. Results demonstrate the robust performance and capabilities of the proposed control strategy during changes in the power demand of the ac grids, unexpected change in wind power generation, and eventual permanent VSC-HVDC station disconnection.

In this research work, a resilient finite-time consensus-based distributed secondary control protocol is presented for droop-based distributed generating (DG) units of an islanded AC microgrid (MG). Through a multi-agent control structure, the DG units of the microgrid adjust their active power outputs so that they reach an agreed-upon value in a finite time. Concurrently, all the DG units are forced to operate with their frequencies regulated to the reference MG frequency in a finite time, despite time-varying load perturbations. Each DG unit is provided with a hierarchical control architecture, where the primary control is achieved using the droop control method, while the secondary control is established through the proposed distributed control protocol. The communication between DG units takes place over a sparse communication network. The proposed control protocol is robust to both small and sufficiently large communication latencies and it supports the plug-and-play feature of DG units. Different time-domain-based numerical simulations are carried out on a small as well as large microgrid testbenches in Matlab/Simulink and demonstrate the correctness and effectiveness of the proposed distributed control protocol. A comparative study is also presented with the existing distributed control protocol, and it is found that the proposed strategy is superior in its performance.

The article portrays an adaptive control paradigm for the swift response of a solid-oxide fuel cell (SOFC) in a grid-connected microgrid. The control scheme is based on an adaptive feedback-linearization-embedded fully recurrent NeuroFuzzy Laguerre wavelet control (FBL-FRNF-Lag-WC) framework. The nonlinear functions of feedback linearization (FBL) are estimated using a fully recurrent NeuroFuzzy Laguerre wavelet control (FRNF-Lag-WC) architecture with a recurrent Gaussian membership function in the antecedent part and a recurrent Laguerre wavelet in the consequent part, respectively. The performance of the proposed control scheme is validated for various stability, quality, and reliability factors obtained through a simulation testbed implemented in MATLAB/Simulink. The proposed scheme is compared against adaptive NeuroFuzzy, PID, and adaptive PID (aPID) control schemes using different performance parameters for a grid-connected load over 24 h.

Energy-saving strategies cannot be implemented without having detailed and regular power consumption data of a facility. The installation of an energy monitoring and data logging system can help in planning energy efficiency improvement policies by providing daily, monthly, and yearly energy consumption reports and graphs. The purpose of this study was to demonstrate the impact of an energy monitoring and management system on the improvement of energy efficiency in the industrial sector of developing countries. This study introduced an energy monitoring and data logging system installed in an automobile factory in Pakistan. Energy consumption data, which also included power quality data, were collected with the help of energy analyzers and transmitted to a centralized supervisory control and data acquisition (SCADA) software for data logging and monitoring purposes. This system was developed by combining Modbus with industrial Ethernet to communicate real-time energy consumption data of the factory to multiple local and remote locations. Monitoring and logging the real-time energy consumption data helped the user to find the significant energy losses inside the factory and to implement various energy conservation policies inside the facility, resulting in energy efficiency improvement. The energy consumption results indicate that the proposed system can help achieve an approximately 8% improvement in energy efficiency.

Electromyography (EMG) is a measure of electrical activity generated by the contraction of muscles. Non-invasive surface EMG (sEMG)-based pattern recognition methods have shown the potential for upper limb prosthesis control. However, it is still insufficient for natural control. Recent advancements in deep learning have shown tremendous progress in biosignal processing. Multiple architectures have been proposed yielding high accuracies (>95%) for offline analysis, yet the delay caused due to optimization of the system remains a challenge for its real-time application. From this arises a need for optimized deep learning architecture based on fine-tuned hyper-parameters. Although the chance of achieving convergence is random, however, it is important to observe that the performance gain made is significant enough to justify extra computation. In this study, the convolutional neural network (CNN) was implemented to decode hand gestures from the sEMG data recorded from 18 subjects to investigate the effect of hyper-parameters on each hand gesture. Results showed that the learning rate set to either 0.0001 or 0.001 with 80-100 epochs significantly outperformed (p < 0.05) other considerations. In addition, it was observed that regardless of network configuration some motions (close hand, flex hand, extend the hand and fine grip) performed better (83.7% ± 13.5%, 71.2% ± 20.2%, 82.6% ± 13.9% and 74.6% ± 15%, respectively) throughout the course of study. So, a robust and stable myoelectric control can be designed on the basis of the best performing hand motions. With improved recognition and uniform gain in performance, the deep learning-based approach has the potential to be a more robust alternative to traditional machine learning algorithms.