Search

The power transformer is a vital element in power system assets. When failure occurs in transformers, it leads to more revenue loss for the electrical utilities. Power transformer failures may occur due to high stresses on the insulating systems (oil and paper). These stresses categorize as electrical, thermal, and mechanical stresses. The main contribution of the current work is to increase the contribution of essential test parameters, which contribute to determining the insulating transformer state. In this work, principal component analysis (PCA) is not used as a prediction or classification tool. Still, it preserves only the necessary information and marginalizes or reduces the role of parameters with fewer contributions. PCA is a statistical method to compress an extensive data set of variables to a smaller number of orthogonal factors unrelated to interpreting the correlation matrix. One hundred forty-seven data samples were collected from the Saudi Electricity Company and used in this study. The data include the dissolved gas analysis (DGA) test, the breakdown voltage (BDV), moisture content (MC), Acidity (AC), interfacial tension (IFT), oil color (OC), dissipation factor (DF) or tan □, furan content (2-FAL). Based on PCA, some test variables can be selected as indicators for the insulating paper state from more test variables groups, reducing the cost of all tests. [ABSTRACT FROM AUTHOR]

In this paper, maintenance process for on-load tap changer (OLTC) would be enhanced using nanofluid that developed by adding nanoparticles (titanium dioxide, TiO2) to the insulating oil. The occurrence of the internal arcs inside OLTC resulted in the formation of carbon atoms on the inner bonding points during its operation. Over time, these points became unusable since the contact resistance value was high. To increase the efficiency of the maintenance process, infrared radiation was used to excite the additive TiO2 atoms to attract carbon atoms. Electrical tests were performed before and after the proposed maintenance process according to IEC standards. The results showed significant differences in the maintenance process with and without nanoparticles. A prediction model to identify the OLTC state was constructed based on support vector machine classifier. The results revealed the ability of the predicted model to predict the OLTC state with accuracy 100%. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal grounding grid design using artificial intelligence techniques based on the empirical formula for grounding resistance (Rg), touch and step voltages (Et and Es), which are addressed in IEEE Std. 80-2013/Cor. 1-2015. The objective function is formulated based on the grid conductors' material cost and the installation cost. Particle swarm optimization (PSO) and genetic algorithm (GA) are individually utilized to search for and confirm the global best solution of minimizing the grounding system cost with considering all operation constraints including the safety criteria. A modified objective function is constructed based on self-adaptive penalization utilized to account for constraint violations during the optimization process. The selection strategy for modifying the local best positions of particles and the global best position of the swarm is proposed to enhance the ability of PSO for fast convergence. Results proved that either PSO or GA settles on the global best solution, successfully. To perform the space domain investigation, the optimal grounding grid design is implemented using a finite element method, where the COMSOL Multiphysics program is selected to verify the settled optimal global solution. Using COMSOL, the grounding resistance and safety criteria are evaluated over the grid diagonally. The COMSOL results ensure the operating constraints satisfaction of the optimal grounding grid design. The performance evaluation reveals that the grounding potential rise limit, the available grid area, and the number of vertical rods greatly affect the optimization of the grounding system design. The results also illustrate a great significant effect of the fault current and upper surface resistivity on the optimal grid design. [ABSTRACT FROM AUTHOR]

In this study, newly structured nanoparticles are synthesised and incorporated in the transformer oil for enhancing the dielectric properties. A new nanocrystalline series of zinc oxide (ZnO) and ZnO partially substituted by lanthanum oxide is presented for the first time, where the precursor technique is utilised to create the nano‐powders. Different partial substitutions facilitate to manipulate nanocrystalline sizes, and therefore the effect on nanofluid dielectric characteristics is attained. Another factor during the laboratory nanoparticles preparation is considered that the precursor is annealed at different temperatures. The nanocrystalline phase compositions are experimentally examined and evaluated using X‐ray diffraction and transmission electron microscope. Accordingly, single‐phase and multi‐phase nanocrystalline powders are ascertained. The transformer oil incorporated with nanoparticles is prepared and its dielectric strength is measured using an oil breakdown voltage tester. The measured dielectric strength of transformer oil with and without nanoparticles is statistically evaluated using Weibull distribution. The most appropriate nanofluid and concentration are estimated experimentally. Toward interpreting the nanofluid dielectric enhancement, their electric field distribution is numerically evaluated at different nanoparticle concentrations using COMSOL multiphysics. This study supports the analysis and optimisation of transformer oil dielectric performance. [ABSTRACT FROM AUTHOR]

In this paper, the dielectric characteristics of the power transformer oil are enhanced by dispersing newly synthesized nanoparticles. Two nanocrystalline powders, barium titanate (BaTiO3) and nickel ferrite (NiFe2O4), are synthesized via the tartaric acid precursor method. The change in the crystal structure, crystalline size, and microstructure of the synthesized nanoparticles as a function of annealing temperatures are characterized using X-ray diffraction and transmission electron microscope. These nanoparticles are mixed with the transformer oil considering different concentrations such as 25, 50, and 75 mg/l where the sonicator is used to disperse the nanoparticles in the oil samples. The dielectric breakdown strength is tested based on IEC standard. The results confirm the efficiency of the NiFe2O4 than BaTiO3 for enhancing the dielectric characteristics of the power transformer oils. [ABSTRACT FROM AUTHOR]

The insulating oil performance could be enhanced in high-voltage apparatus using barriers. The importance of the barrier in increasing the dielectric strength of the insulating oils in order to reduce the oil failure stresses had not been sufficiently studied. In this paper, the effects of the barrier variables on the insulation performance of the transformer oil for point-plate and plate-plate gaps were demonstrated. These variables are: gap space (d), the barrier location relative to the high-voltage electrode (a / d) %, barrier diameter (D), barrier thickness (e), electrode configurations (EC), the presence of contaminating particles, the weight of the contaminating particles (W) and the temperature of the insulating oil (T). The statistical t test was used to explain whether the effect of these parameters was significant or not. Furthermore, the above-mentioned variables were used as training variables to construct the prediction model of oil breakdown voltage considering barrier effect based on the artificial neural networks (ANN). The ANN model was developed based on the results from experimental works. Therefore, 784 samples were used as training data set and other 25 samples were taken as testing and validating samples. The results explained that the prediction ANN model had a high ability to expect the breakdown voltage for other different experiment cases. The average errors of the training and testing samples were 1.6%, and 2.66%, respectively. Therefore, the prediction accuracy could be considered as 98.4% for training and 97.34% for testing. [ABSTRACT FROM AUTHOR]

Most presented dissolved gas analysis (DGA) techniques were interested in determining the fault types (FTs), but few articles discussed the corresponding severity of these faults. Here, the thermodynamic theory is utilised to evaluate the fault severity based on the energy associated with each FT. Therefore, energy weighted DGA is proposed, where the individual gas concentration is multiplied by a relative factor that relates to the enthalpy change of reaction. A fuzzy logic system is built based on the IEC code rules, the transformer condition code that is reported in IEEE Standard C57.104-2008, and the thermodynamic theory. For enhancing the network fault diagnosis of the power transformers all over the distribution network, the proposed fuzzy logic approach is employed for its integration in accordance with the distributed agents of the distribution substations. This smart system facilitates evaluating decisions of the distributed agents as well as providing a higher decision level if needed. That is achieved by sending the important information about transformers attained by the proposed fuzzy approach such as the FT, its severity, the total dissolved combustion gases condition, the recommended action, in addition to the period of incoming action to the primary substation. [ABSTRACT FROM AUTHOR]

This paper focuses on a Smart Fault Diagnostic Approach (SFDA) based on the integration among the output results of recognized dissolved gas analysis (DGA) techniques. These techniques are Dornenburg method, Electro-technical Commission standard (IEC) Code, the Central Electricity Generating Board (CEGB) Code based on Rogers' four ratios, Rogers method given in IEEE-C57 standard, and the Duval triangle. The artificial neural networks (ANN) model is constructed to monitor the transformer fault conditions and trained for each technique individually. The fault decision of each ANN model supplies the proposed integrated SFDA. The integration between these DGA approaches not only improves the fault condition monitoring of the transformers but also overcomes the individual weakness and the differences between the above methods. Toward a better diagnostic scheme, a new SFDA is developed based on the integration of the most three appropriate DGA methods. Further gas concentrations have been considered as raw data (California State University Sacramento (CSUS) as an example) to enhance the proposed SFDA performance. Comparison of each DGA concept with respect to the proposed one is reported, where the results provide evidences of the efficacy of the proposed SFDA. [ABSTRACT FROM PUBLISHER]

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

The aging of power transformers causes several defects and damages in the insulating system, especially in the insulating paper. The degradation of the insulating paper generates dissolved gases in the insulating oil, which are measured by gas chromatography and used as an indicator of the insulation status. The state of the insulating paper can be identified based on the degree of polymerization (DP) measurement. In some cases, when the measurement of DP is difficult, estimating DP can be accomplished through gathering information about some of the testing parameters, such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldhyde (FA)). The statistical tools (correlation and multiple linear regression), based on 131 transformer samples, can be used to build a relation linking DP and one or more of the previous parameters to identify the insulating paper status and the percentage of remaining life of the transformer. The results indicated that it is difficult to build a mathematical model to relate between the DP and the testing variables, except with FA, where the trend of DP with FA is more obvious than with other variables. The empirical formula to compute DP based on the FA concentration was developed and gave promising results to compute DP and the remaining life of the power transformers. [ABSTRACT FROM AUTHOR]

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained. [ABSTRACT FROM AUTHOR]

The article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation. [ABSTRACT FROM AUTHOR]

The prediction of fault type in transformers at an early stage is an important aspect for power system reliability. Checking the transformer status begins with the dissolved gas analysis (DGA) test. Rogers' four-ratio and IEC 60599 standard methods are commonly DGA-based techniques that used to detect the transformer faults. These methods are very easy to implement, but their diagnostic accuracies are very poor. The target of this work is to build a new PSO-FS optimization platform identifying the optimal limits of gas ratios and corresponding rules for each fault type to enhance the diagnostic accuracies of Rogers' four-ratio and IEC 60599 code methods. In the proposed platform, PSO optimization technique specifies the gas ratio limits and corresponding rules where the fuzzy system identifies the fault types. Based on the results of the optimization process, the new codes that used to diagnose the fault types were developed. The performance of the proposed approach investigated using 481 collected datasets. The overall accuracies of the Rogers' four-ratio and IEC code models enhanced from 47.19 and 55.09 to 85.65 and 85.03, respectively. However, when datasets due to mixtures of faults (samples with undetermined or misidentified faults) are removed from the calculations, the accuracies of the PSO-FS methods are not significantly better than when using existing DGA methods. [ABSTRACT FROM AUTHOR]

Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA's Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output. [ABSTRACT FROM AUTHOR]

This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives. [ABSTRACT FROM AUTHOR]

According to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). Recent advances in the accuracy of neural network have effective solutions to solving the smart grid stability prediction issues, but it remains necessary to develop high performance neural networks that give higher accuracy. In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. This neural network is applied to a dataset aggregated from simulations of grid stability, executed on a four-node network with star topology, and engaged in two classes of grid stability–stable and unstable. Keras framework is used to train the proposed neural network, and a hyperparameter tuning method is utilized to achieve high accuracy. Receiver operating characteristic (ROC) curves and confusion matrices are experimentally utilized to evaluate the performance of the proposed neural network. The neural network provides high performance, with a testing loss rate of 0.0619, and a testing accuracy of 97.36%. The weighted average recall, precision, and F1-score for the proposed neural network are 98.02%, 98.03%, and 98.02%, respectively, while the area under the ROC curves (AUCs) is 100%. This neural network with the utilized dataset indeed provides an accurate and quick approach of predicting grid stability to analyze DSGC systems. [ABSTRACT FROM AUTHOR]

This paper discusses the efficient implementation of a new hybrid approach to forecasting short-term PV power production for four different PV plants in Algeria. The developed model incorporates a time-varying filter-empirical mode decomposition (TVFEMD) and an extreme learning machine (ELM) as an essence regression. The TVF-EMD technique is used to deal with the fluctuation of PV power data by splitting it into a series of more stable and constant subseries. The specified set of features (intrinsic mode functions (IMFs)) is utilized for training and improving our forecasting extreme learning machine model. The adjusted ELM model is used to evaluate prediction e1ciency. The suggested TVF-EMD-ELM model is assessed and verified in various Algerian locations with varying climate conditions. In all examined regions, the TVF-EMD-ELM model generates less than 4% error in terms of normalized root mean square error (nRMSE). [ABSTRACT FROM AUTHOR]

In this manuscript, a compact in size yet geometrically simple Ultra-Wideband (UWB) antenna is demonstrated. The flexible-by-nature substrate ROGERS 5880, having a thickness of 0.254 mm, is utilized to design the proposed work. The antenna configuration is an excerpt of a traditional rectangular monopole antenna resonating at 5 GHz. Initially, a pair of triangular slots are employed to extend the impedance bandwidth of the antenna. In addition, a semi-circular-shaped, short-ended stub is connected at the upper edges of the patch to further increase the operational bandwidth. After optimization, the proposed antenna offers UWB ranging from 2.73–9.68 GHz, covering almost the entire spectrum allocated globally for UWB applications. Further, the antenna offers a compact size of 15 × 20 mm2 that can easily be integrated into small, flexible electronics. The flexibility analysis is done by bending the antenna on both the x and y axes. The antenna offers performance stability in terms of return loss, radiation pattern, and gain for both conformal and non-conformal conditions. Furthermore, the strong comparison between simulated and measured results for both rigid and bent cases of the antenna, along with the performance comparison with the state-of-the-art, makes it a potential candidate for present and future compact-sized flexible devices. [ABSTRACT FROM AUTHOR]

Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers' different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy. [ABSTRACT FROM AUTHOR]

Insulator monitoring using leakage current characteristics is essential for predicting an insulator's health. To evaluate the risk of flashover on the porcelain insulator using leakage current, experimental investigation of leakage current indices was carried out. In the first stage of the experiment, the effect of contamination, insoluble deposit density, wetting rate, and uneven distribution pollution were determined on the porcelain insulator under test. Then, based on the laboratory test results, leakage current information in time and frequency characteristics was extracted and employed as assessment indicators for the insulator's health. Six indicators, namely, peak current indicator, phase shift indicator, slope indicator, crest factor indicator, total harmonic distortion indicator, and odd harmonics indicator, are introduced in this work. The obtained results indicated that the proposed indicators had a significant role in evaluating the insulator's health. To evaluate the insulator's health levels based on the extracted indicator values, this work presents the naïve Bayes technique for the classification and prediction of the insulator's health. Finally, the confusion matrix for the experimental and prediction results for each indicator was established to determine the appropriateness of each indicator in determining the insulator's health status. [ABSTRACT FROM AUTHOR]

This study aims to develop an accurate dynamic cutting force model in the milling process. In the proposed model, the estimated cutting force tackles the effect of the self-excited vibration that causes machining instability during the cutting process. In particular, the square root of the residual cutting force between the prediction and the actual cutting force is considered as an objective function for optimizing the cutting force coefficients using the equilibrium optimizer (EO) approach instead of the trial-and-error approach. The results confirm that the proposed model can provide higher prediction accuracy when the EO is applied. In addition, the proposed EO has a minimum integral square error (ISE) of around 1.12, while the genetic algorithm (GA) has an ISE of around 1.14 and the trial-and-error method has an ISE of around 2.4. Moreover, the proposed method can help to investigate the cutting stability and to suspend the chatter phenomenon by selecting an optimal set of cutting parameters. [ABSTRACT FROM AUTHOR]

Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive. [ABSTRACT FROM AUTHOR]

Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers' ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults' diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon's rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases. [ABSTRACT FROM AUTHOR]

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions. [ABSTRACT FROM AUTHOR]

Many authors have recently examined the relationship between symmetry and generalized convexity. Generalized convexity and symmetry have become a new area of study in the field of inequalities as a result of this close relationship. In this article, we introduce the idea of preinvex fuzzy-interval-valued functions (preinvex F∙I-V∙F) on coordinates in a rectangle drawn on a plane and show that these functions have Hermite–Hadamard-type inclusions. We also develop Hermite–Hadamard-type inclusions for the combination of two coordinated preinvex functions with interval values. The weighted Hermite–Hadamard-type inclusions for products of coordinated convex interval-valued functions discussed in a recent publication by Khan et al. in 2022 served as the inspiration for our conclusions. Our proven results expand and generalize several previous findings made in the body of literature. Additionally, we offer appropriate examples to corroborate our theoretical main findings. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Fuzzy-MPDTC control applied to a fuel cell battery electric vehicle whose traction is ensured using a permanent magnet synchronous motor (PMSM). On the traction side, model predictive direct torque control (MPDTC) is used to control PMSM torque, and guarantee minimum torque and current ripples while ensuring satisfactory speed tracking. On the sources side, an energy management strategy (EMS) based on fuzzy logic is proposed, it aims to distribute power over energy sources rationally and satisfy the load power demand. To assess these techniques, a driving cycle under different operating modes, namely cruising, acceleration, idling and regenerative braking is proposed. Real-time simulation is developed using the RT LAB platform and the obtained results match those obtained in numerical simulation using MATLAB/Simulink. The results show a good performance of the whole system, where the proposed MPDTC minimized the torque and flux ripples with 54.54% and 77%, respectively, compared to the conventional DTC and reduced the THD of the PMSM current with 53.37%. Furthermore, the proposed EMS based on fuzzy logic shows good performance and keeps the battery SOC within safe limits under the proposed speed profile and international NYCC driving cycle. These aforementioned results confirm the robustness and effectiveness of the proposed control techniques. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-024-59024-4.Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-59024-4, published online 08 April 2024The original version of this Article contained an error in the spelling of the author Messaouda Khennane Benbitour which was incorrectly given as Messaouda Khennane Benbiotur.The original Article has been corrected.By Layachi Zaghba; Abdelhalim Borni; Messaouda Khennane Benbitour; Amor Fezzani; Abdullah Alwabli; Mohit Bajaj; Shir Ahmad Dost Mohammadi and Sherif S. M. GhoneimReported by Author; Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches. [ABSTRACT FROM AUTHOR]

Radio communication systems are very widely present in our current smart lifestyles. It consists of two ends, which can carry the transmitter's information to the receptor. Before installing any radio communication system, it is necessary to analyze the link resources. Hence, this analysis allows the determination of the received radio communication strength to prove if it is sufficient for the link to work correctly and assure a high quality of service. For this reason, new services and technologies are integrated. The objective of the present work is to improve the performance of the radio communication link of 4G systems. The study is based on real measurements using the drive test. The data collected by the drive test are analyzed to increase the performance of the radio communication. Based on this data analysis, recommendations and suggestions are issued for improving the radio communication link. The obtained results indicate a significant amelioration in the performance of the radio communication link. [ABSTRACT FROM AUTHOR]

In the wake of the United Nation's Sustainable Development Goals—zero hunger and affordable modern/clean energy for all—many developing countries have taken serious steps in recent years to increase clean energy access for the rural population. The government of Pakistan has similarly made numerous efforts to promote the use of clean energy sources in the rural areas of the country. Therefore, this study examines rural households' energy choices for cooking and lighting in Pakistan. In doing so, a comprehensive dataset is collected from three different districts of Pakistan between 2020 and 2021, and multivariate probit (MVP) model and Chi-square tests are employed. The Chi-square results indicate that the age, education level, and occupation of the household-head; household size and income; distance to market and wood source; and biogas system ownership are the significant factors affecting cooking choices. The MVP results show that an increase in education level, school-going children, access to credit facilities, and gender (female) are the key positive factors, whereas an increase in the distance to nearest market/road, household size, and age are the factors that negatively affect the likelihood of using clean energy sources for lighting. While comparing the propensity to use modern/clean energy fuels across the three districts, infrastructural development and literacy rate were found to be crucial factors. [ABSTRACT FROM AUTHOR]

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator's strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation. [ABSTRACT FROM AUTHOR]

Power lines or underground power cables generate electromagnetic interaction with other objects near to them. This study evaluates the magnetic field emitted by underground extra high voltage cables. The presented work aims to show a numerical simulation of the magnetic field of a buried 400 kV underground power line, which is used as a novel prototype in several countries at a short distance. The underground power cable study, in the presence of the current harmonics at different positions, with time variation by finite element resolution, using Comsol Multiphysics with Matlab software in two dimensions. The simulation results illustrate the magnetic flux density variation-in terms of amplitude and distribution as a function of different actual harmonics rates. The underground cable performance and magnetic field have affected by the harmonics effects. The maximum magnetic induction levels generated by significant harmonics are superior to the limits recommended by the international standard norms. In this paper, shielding has been used as an appropriate remedy to attenuate the magnetic field. [ABSTRACT FROM AUTHOR]

The effect of isothermal conditions on the trapping/detrapping process of charges in e-beam irradiated thermally aged XLPE insulation in scanning electron microscopy (SEM) has been investigated. Different isothermal conditions ranging from room temperature to 120 °C are applied on both unaged and aged XLPE samples (2 mm thick) by a suitable arrangement associated with SEM. For each applied test temperature, leakage, and influence currents have been measured simultaneously during and after e-beam irradiation. Experimental results show a big difference between the fresh and aged material regarding trapping and detrapping behavior. It has been pointed out that in the unaged material deep traps govern the process, whereas the shallow traps take part in the aged one. Almost all obtained results reveal that the trapped charge decreases and then increases as the temperature increases for the unaged sample. A deflection temperature corresponding to a minimum is observed at 50 °C. However, for the aged material, the maximum trapped charge decreases continuously with increasing temperature, and the material seems to trap fewer charges under e-beam irradiation at high temperature. Furthermore, thermal aging leads to the occurrence of detrapping process at high temperatures even under e-beam irradiation, which explains the decrease with time evolution of trapped charge during this period. The recorded leakage current increases with increasing temperature for both cases with pronounced values for aged material. The effect of temperature and thermal aging on electrostatic influence factor (K) and total secondary electron emission yield (σ) were also studied. [ABSTRACT FROM AUTHOR]

This paper focuses on robustness and sensitivity analysis for sensor fault diagnosis of a voltage source converter based microgrid model. It uses robust control parameters such as minimum sensitivity parameter (H −) , maximum robustness parameter (H ∞) , and compromised both (H − / H ∞) , being incorporated in the sliding mode observer theory using the game theoretic saddle point estimation achieved through convex optimization of constrained LMIs. The approach used works in a way that the mentioned robust control parameters are embedded in Hamilton–Jacobi–Isaacs-Equation (HJIE) and are also used to determine the inequality version of HJIE, which is, in terms of the Lyapunov function, faults/disturbances and augmented state/output estimation error as its variables. The stability analysis is also presented by negative definiteness of the same inequality version of HJIE, and additionally, it also gives linear matrix inequalities (LMIs), which are optimized using iterative convex optimization algorithms to give optimal sliding mode observer gains enhanced with robustness to maximal preset values of disturbances and sensitivity to minimal preset values of faults. The enhanced sliding mode observer is used to estimate states, faults, and disturbances using sliding mode observer theory. The optimality of sliding mode observer gains for sensitivity of the observer to minimal faults and robustness to maximal disturbance is a game theoretic saddle point estimation achieved through convex optimization of LMIs. The paper includes results for state estimation errors, faults' estimation/reconstruction, fault estimation errors, and fault-tolerant-control performance for current and potential transformer faults. The considered faults and disturbances in current and potential transformers are sinusoidal nature composite of magnitude/phase/harmonics at the same time. [ABSTRACT FROM AUTHOR]

In-depth understanding of the pollution problems such as dry bands and the polymeric aging process requires better determination of electric field strength and its distribution over the polymeric surface. To determine the electric field distribution over the insulator surface, this research proposes utilizing a novel approach model based on nonlinear electrical characteristics derived from experimental results for polluted polymer insulators. A case study was carried out for a typical 11 kV polymeric insulator to underline the merits of this new modeling approach. The developments of the proposed pollution model and the subsequent computational works are described in detail. The study is divided into two main stages; laboratory measurements and computer simulations. In the first stage, layer conductance tests were carried out to develop nonlinear field-dependent conductivity for the pollution modeling. In the second part, equipotential and electric field distributions along the leakage were computed using the finite element method (FEM). Comparative field studies showed that the simulation using the proposed dynamic pollution model results in more detailed and realistic field profiles around insulators. This may be useful to predict the formation of dry bands and the initiation of electrical discharges on the polymeric surface. [ABSTRACT FROM AUTHOR]

The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies' co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot's response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters' uncertainty compared with other techniques. [ABSTRACT FROM AUTHOR]

Recently, most transportation systems have used an integrated electrical machine in their traction scheme, resulting in a hybrid electrified vehicle. As a result, an energy source is required to provide the necessary electric power to this traction portion. However, this cannot be efficient without a reliable recharging method and a practical solution. This study discusses the wireless recharge solutions and tests the system's effectiveness under various external and internal conditions. Moreover, the Maxwell tool is used in this research to provide a complete examination of the coils' position, size, number, and magnetic flux evolution when the coils are translated. In addition, the mutual inductance for each of these positions is computed to determine the ideal conditions for employing the wireless recharge tool for every charging application. A thorough mathematical analysis is also presented, and the findings clearly demonstrate the relationship between the magnet flux and the various external conditions employed in this investigation. [ABSTRACT FROM AUTHOR]

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter "λ" and penalty margin parameter "C" were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers' accuracy was evaluated and compared with several DGA techniques in the literature. [ABSTRACT FROM AUTHOR]