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In this paper, a microscopic oil displacement visualization experiment based on the glass etching model to simulate the tight oil reservoir of underground rocks is carried out. At present, water flooding technology is widely used in the development of oil and gas fields, and the remaining oil content is still very high after water flooding. It is the key to improving oil recovery to identify and study the remaining oil form distribution after water flooding. The experiment result shows there are five types of residual oil after water flooding: columnar residual oil, membranous residual oil, oil droplet residual oil, blind terminal residual oil and cluster residual oil. A convolution neural network is suitable for complex image characteristics with good robustness. In recent years, it has made a breakthrough in a set of small and efficient neural networks with SqueezeNet, Google Inception and the flattened network method put forward. In order to solve the problems of low automation, low efficiency and high error rate in the traditional remaining oil form recognition algorithm after water flooding in tight oil reservoirs, an image recognition algorithm based on the MobileNets convolutional neural network model was proposed in this paper to achieve accurate recognition of the remaining oil form. Based on traditional image processing methods which, respectively, extracted the whole picture of the different types of remaining oil in the image block, it uses the MobileNets network structure to classify different types of image block and realizes the layered depth convolution neural network system. The experiment result shows that the model can accurately identify the remaining oil forms, and the overall recognition accuracy is up to 83.8% after the convergence of the network model, which infinitely identifies the remaining oil forms in the morphological library, proving the strong generalization and robustness of the model. [ABSTRACT FROM AUTHOR]

A fault section in Korean distribution networks is generally determined as a section between a switch with a fault indicator (FI) and a switch without an FI. However, the existing method cannot be applied to distribution networks with distributed generations (DGs) due to false FIs that are generated by fault currents flowing from the load side of a fault location. To identify the false FIs and make the existing method applicable, this paper proposes a method to determine the fault section by utilizing an artificial neural network (ANN) model for validating FIs, which is difficult to determine using mathematical equations. The proposed ANN model is built by training the relationship between the measured A, B, C, and N phase fault currents acquired by numerous simulations on a sample distribution system, and guarantees 100% FI validations for the test data. The proposed method can accurately distinguish genuine and false Fis by utilizing the ability of the ANN model, thereby enabling the conventional FI-based method to be applied to DG-connected distribution networks without any changes to the equipment and communication infrastructure. To verify the performance of the proposed method, various case studies considering real fault conditions are conducted under a Korean distribution network using MATLAB. [ABSTRACT FROM AUTHOR]

Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%. [ABSTRACT FROM AUTHOR]

Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model's accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models. [ABSTRACT FROM AUTHOR]

Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator defect diagnosis model based on acoustic–electric feature fusion and a multi-scale perception multi-input of stacked auto-encoder (MMSAE) network is proposed in this paper. Initially, during the withstanding voltage experiment, the electromagnetic wave spectrometer and ultrasonic detector were used to collect and process the data of six types of composite insulator samples with artificial defects. The electromagnetic wave spectrum, ultrasonic power spectral density, and n-S map were then obtained. Then, the network architecture of MMSAE was built by integrating a stacked auto-encoder and multi-scale perception module; the feature extraction and fusion methods of the electromagnetic wave spectrum and ultrasonic signal were investigated. The proposed method was used to diagnose test samples, and the diagnostic results were compared to those obtained using a single input source and the artificial neural network (ANN) method. The results demonstrate that the detection accuracy of acoustic–electric feature fusion is greater than that of a single feature; the accuracy of the proposed method is 99.17%, which is significantly higher than the accuracy of the conventional ANN method. Finally, composite insulator defect diagnosis software based on PYQT5 and Keras was developed. Ten 500 kV aging composite insulators were used to validate the effectiveness of the proposed method and design software. [ABSTRACT FROM AUTHOR]

Identifying statistical trends for a wide range of practical power system applications, including sustainable energy forecasting, demand response, energy decomposition, and state estimation, is regarded as a significant task given the rapid expansion of power system measurements in terms of scale and complexity. In the last decade, deep learning has arisen as a new kind of artificial intelligence technique that expresses power grid datasets via an extensive hypothesis space, resulting in an outstanding performance in comparison with the majority of recent algorithms. This paper investigates the theoretical benefits of deep data representation in the study of power networks. We examine deep learning techniques described and deployed in a variety of supervised, unsupervised, and reinforcement learning scenarios. We explore different scenarios in which discriminative deep frameworks, such as Stacked Autoencoder networks and Convolution Networks, and generative deep architectures, including Deep Belief Networks and Variational Autoencoders, solve problems. This study's empirical and theoretical evaluation of deep learning encourages long-term studies on improving this modern category of methods to accomplish substantial advancements in the future of electrical systems. [ABSTRACT FROM AUTHOR]

Physics-informed neural network (PINN) architectures are recent developments that can act as surrogate models for fluid dynamics in order to reduce computational costs. PINNs make use of deep neural networks, where the Navier-Stokes equation and freestream boundary conditions are used as losses of the neural network; hence, no simulation or experimental data in the training of the PINN is required. Here, the formulation of PINN for fluid dynamics is demonstrated and critical factors influencing the PINN design are discussed through a low Reynolds number flow over a cylinder. The PINN architecture showed the greatest improvement to the accuracy of results from the increase in the number of layers, followed by the increase in the number of points in the point cloud. Increasing the number of nodes per hidden layer brings about the smallest improvement in performance. In general, PINN is much more efficient than computational fluid dynamics (CFD) in terms of memory resource usage, with PINN requiring 5–10 times less memory. The tradeoff for this advantage is that it requires longer computational time, with PINN requiring approximately 3 times more than that of CFD. In essence, this paper demonstrates the direct formulation of PINN without the need for data, alongside hyperparameter design and comparison of computational requirements. [ABSTRACT FROM AUTHOR]

Recently, biomass has become an increasingly widely used energy resource. The problem with the use of biomass is its variable composition. The most important property that determines the energy content and thus the performance of fuels such as biomass is the heating value (HHV). This paper focuses on selecting the optimal number of input variables using linear regression (LR) and the multivariate adaptive regression splines approach (MARS) to create an artificial neural network model for predicting the heating value of selected biomass. The MARS model selected the input data better than the LR model. The best modeling results were obtained for a network with three input neurons and nine neurons in the hidden layer. This was confirmed by a high correlation coefficient of 0.98. The obtained results show that artificial neural network (ANN) models are effective in predicting the calorific value of woody and field biomass, and can be considered a worthy simulation model for use in selecting biomass feedstocks and their blends for renewable fuel applications. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLABTM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections. [ABSTRACT FROM AUTHOR]

Conservation through voltage reduction (CVR) aims to reduce the peak load and energy savings in electric power systems and is being deployed at various utilities. The effectiveness of the CVR program depends on the load characteristics, i.e., the sensitivity of the load to voltage variation, and voltage regulation device settings. In the current literature, there is a lack of discussion on the CVR factor calculation using different measurements, and there is a lack of method for active power loss estimation using substation measurements. This paper provides insights into CVR factor calculation based on the measurements captured at the substation and those at the load location. This paper also proposes a new method based on curve fitting and artificial neural network to estimate the active power loss using input active power, input reactive power and input voltage at the substation. The CVR comparison study conducted in this paper helps in understanding the factors affecting CVR factor and may provide guidance in CVR implementation and impact assessment. The proposed loss estimation method sheds light on the impacts of CVR in terms of load and loss reduction. The results based on simulation studies using the IEEE 13-bus and 34-bus systems are reported in this paper, noting that the proposed methods are applicable to larger systems, as long as the required measurements at the substation are available. Future research includes testing and refining the methods using large IEEE and utility distribution systems and considering the stochastic nature of the CVR factor with changing load and voltage regulator control schemes. [ABSTRACT FROM AUTHOR]

Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R2, MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA–Elman model is better than the Elman neural network model and the BP neural network model. [ABSTRACT FROM AUTHOR]

This paper proposes a fault diagnosing system for the Ice-Storage Air-Conditioning System (ISACS) to supervise the operation conditions of the brine chillers. Combining the Radial Basis Function Network (RBFN) and Robust Quality Design (RQD), an Enhanced RBFN (ERBFN) is proposed to pursue fast and accurate fault diagnosis. The RQD method is used to adjust the parameters in the RBFN training stage to improve the searching ability, and good performance with a close spike tracking capability can be seen. The efficiency of the brine chiller in the ISACS was considered as the quality characteristics, the values measured by all instruments were considered as control factors, and noise factors were abnormal variable control factors in the system. ERBFN can improve the efficiency of the ISACS and prevent the equipment from being damaged without warning. ERBFN is used for fault diagnosis to ensure the ISACS performance is normal. Experimental results are provided to show the effectiveness of the proposed method. The new artificial neural network algorithm proposed in this paper was successfully applied to the fault diagnosis of ISACS. It not only provides a reference for enterprises but can also be applied to studies on other topics in the future. [ABSTRACT FROM AUTHOR]

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments. [ABSTRACT FROM AUTHOR]

In IoT applications, energy supply, especially wireless power transfer (WPT), has attracted the most attention in the relevant literature. In this paper, we present a new approach to laser irradiance solar cell panels and predicting energy transfer efficiency. From the previous experimental datasets, it has been discovered that in the laser charging (LC) process, temperature has a great impact on the efficiency, which is highly correlated with the laser intensity. Then, based on artificial neural network (ANN), we set the above temperature and laser intensity as inputs, and the efficiency as output through back propagation (BP) algorithm, and use neural network and BP to train and modify the network parameters to approach the real efficiency value. We also propose the genetic algorithm (GA) to optimize the learning rate of the BP, which achieved slightly superior results. The results of the experiment indicate that the prediction method reaches a high accuracy of about 99.4%. The research results in this paper provide an improved solution for the LC application, particularly the energy supply of IoT devices or small electronic devices through WPT. [ABSTRACT FROM AUTHOR]

To detect the aging of power cables in the TOKAMAK power supply systems, this paper proposed a deep neural network diagnosis model and algorithm for power cable aging, based on logistic regression according to the characteristics of different high-order harmonics generated by different aging parts of the power cable. The experimental results showed that the model has high diagnostic accuracy, and the average error is only 2.35%. The method proposed in this paper has certain application potential in the CFETR power cable auxiliary monitoring system. [ABSTRACT FROM AUTHOR]

Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize the utilization of power-generating plants and schedule them both reliably and economically. In this paper, a novel and hybrid forecasting method is proposed, combining a long short-term memory network (LSTM) and neural prophet (NP) through an artificial neural network. The paper aims to predict electric load for different time horizons with improved accuracy as well as consistency. The proposed model uses historical load data, weather data, and statistical features obtained from the historical data. Multiple case studies have been conducted with two different real-time data sets on three different types of load forecasting. The hybrid model is later compared with a few established methods of load forecasting found in the literature with different performance metrics: mean average percentage error (MAPE), root mean square error (RMSE), sum of square error (SSE), and regression coefficient (R). Moreover, a guideline with various attributes is provided for different types of load forecasting considering the applications of the proposed model. The results and comparisons from our test cases showed that the proposed hybrid model improved the forecasting accuracy for three different types of load forecasting over other forecasting techniques. [ABSTRACT FROM AUTHOR]

Hydropower is the most prevalent source of renewable energy production worldwide. As the global demand for robust and ecologically sustainable energy production increases, developing and enhancing the current energy production processes is essential. In the past decade, machine learning has contributed significantly to various fields, and hydropower is no exception. All three horizons of hydropower models could benefit from machine learning: short-term, medium-term, and long-term. Currently, dynamic programming is used in the majority of hydropower scheduling models. In this paper, we review the present state of the hydropower scheduling problem as well as the development of machine learning as a type of optimization problem and prediction tool. To the best of our knowledge, this is the first survey article that provides a comprehensive overview of machine learning and artificial intelligence applications in the hydroelectric power industry for scheduling, optimization, and prediction. [ABSTRACT FROM AUTHOR]

In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid's stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers' privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers' activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems' speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives. [ABSTRACT FROM AUTHOR]

This article deals with fault detection and the classification of incipient and intermittent open-transistor faults in grid-connected three-level T-type inverters. Normally, open-transistor detection algorithms are developed for permanent faults. Nevertheless, the difficulty to detect incipient and intermittent faults is much greater, and appropriate methods are required. This requirement is due to the fact that over time, its repetition may lead to permanent failures that may lead to irreversible degradation. Therefore, the early detection of these failures is very important to ensure the reliability of the system and avoid unscheduled stops. For diagnosing these incipient and intermittent faults, a novel method based on a Walsh transform combined with a multilayer perceptron (MLP)-based classifier is proposed in this paper. This non-classical approach of using the Walsh transform not only allows accurate detections but is also very fast. This last characteristic is very important in these applications due to their practical implementation. The proposed method includes two main steps. First, the acquired AC currents are used by the control system and processed using the Walsh transform. This results in detailed information used to potentially identify open-transistor faults. Then, such information is processed using the MLP to finally determine whether a fault is present or not. Several experiments are conducted with different types of incipient transistor faults to create a relevant dataset. [ABSTRACT FROM AUTHOR]

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series. [ABSTRACT FROM AUTHOR]

Renewable energy penetration increases Smart Grid (SG) instability. A power balance between consumption and production can mitigate this instability. For this, intelligent and optimizing techniques can be used to properly combine and manage storage devices like Electric Vehicle Batteries (EVBs) with Demand-Side Management (DSM) strategies. The EVB helps distribution networks with auxiliary services, backup power, reliability, demand response, peak shaving, lower renewable power production's climate unpredictability, etc. In this paper, a new energy management system based on Artificial Neural Networks (ANNs) is developed to maximize the performance of islanded SG-connected EVBs. The proposed ANN controller can operate at specified periods based on the demand curve and EVB charge level to implement a peak load shaving (PLS) DSM strategy. The intelligent controller's inputs include the time of day and the EVB's State of Charge (SOC). After the controller detects a peak demand, it alerts the EVB to start delivering power. This decrease in peak demand enhances the load factor and benefits both SG investors and end users. In this study, the adopted SG includes five parallel Distribution Generators (DGs) powered by renewable resources, which are three solar Photovoltaics (PVs) and two Wind Turbines (WTs). Sharing power among these DGs ensures the SG's stability and efficiency. To fulfill demand problem-free, this study dynamically alters the power flow toward equity in power sharing using virtual impedance-based adaptive primary control level. This study proposes a decentralized robust hierarchical secondary control system employing Genetic Algorithm (GA)-optimized Proportional-Integral (PI) controller parameters with fine-grained online tuning using ANNs to restore frequency and voltage deviations. The proposed system is evidenced to be effective through MATLAB simulations and real-time data analysis on the ThingSpeak platform using internet energy technology. Our presented model not only benefits users by enhancing their utility but also reduces energy costs with robust implementation of a control structure by restoring any frequency and voltage deviations by distributing power equally among DGs regardless of demand condition variations. [ABSTRACT FROM AUTHOR]

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values. [ABSTRACT FROM AUTHOR]

Daily electricity peak load forecasting is important for electricity generation capacity planning. Accurate forecasting leads to saving on excessive electricity generating capacity, while maintaining the stability of the power system. The main challenging tasks in this research field include improving forecasting accuracy and reducing computational time. This paper proposes a hybrid model involving variational mode decomposition (VMD), empirical mode decomposition (EMD), fast Fourier transform (FFT), stepwise regression, similar days selection (SD) method, and artificial neural network (ANN) for daily electricity peak load forecasting. Stepwise regression and similar days selection method are used for input variable selection. VMD and FFT are applied for data decomposition and seasonality capturing, while EMD is employed for determining an appropriate decomposition level for VMD. The hybrid model is constructed to effectively forecast special holidays, which have different patterns from other normal weekdays and weekends. The performance of the hybrid model is tested with real electricity peak load data provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Experimental results show that the hybrid model gives the best performance while saving computation time by solving the problems in input variable selection, data decomposition, and imbalance data of normal and special days in the training process. [ABSTRACT FROM AUTHOR]

The synchronous generators (SGs) supplying non-linear loads have harmonically distorted terminal voltages. Hence, these distorted terminal voltages adversely affect the performance parameters of the supplied loads such as the power factor, current distortion, losses, and efficiency. To mitigate the harmonic voltages and currents, passive and active filters are generally employed. However, passive filters cause resonance problems, while active filters can cause high costs. On the other hand, in several recent studies to reduce the SG's terminal voltage harmonic distortion, which depends on the constructional design under the no-loading condition, the conventional DC excitation current has been modulated with AC harmonic components. These field current modulation methods have high computational complexity, and require extra hardware for their implementation. In the present paper, firstly, for the reduction of the terminal voltage harmonic distortion of the SG under non-linear loading conditions, the validity of the field current modulation technique is investigated. The numerical results show that by using the field current modulation method, under rated loading conditions, the total harmonic distortion of the terminal voltage can be reduced from 18% to 11%. Secondly, to provide a computational efficient and low-cost tool for optimal field current modulation, which minimizes the terminal voltage harmonic distortion, an Artificial Neural Network (ANN)-based model is proposed. Finally, with the integration of ANSYS Maxwell, ANSYS Simplorer, and MATLAB/Simulink software, the implementation of the developed model is demonstrated for the construction of the optimally modulated field current. [ABSTRACT FROM AUTHOR]

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration. [ABSTRACT FROM AUTHOR]

The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weather parameters that can be used by manufacturing companies in evaluating the profitability of siting renewable energy systems in the region. Crucial weather parameters such as daily air temperature, relative humidity, atmospheric pressure, wind speed, and rainfall were obtained from NASA for a period of 19 years (viz. 2004–2022), resulting in the collection of 6664 high-resolution data points. These data were used to build diverse regressive neural networks with varying hyperparameters to find the best network arrangement. In summary, a low mean-squared error of 7 × 10−3 and high regression correlations of 96% were obtained during the training. [ABSTRACT FROM AUTHOR]

Wind turbines are expected to provide on the order of 50% of the electricity worldwide in the near future, and it is therefore fundamental to reduce the costs associated with this form of energy conversion, which regard maintenance as the first item of expenditure. SCADA-based condition monitoring for anomaly detection is commonly presented as a convenient solution for fault diagnosis on turbine components. However, its suitability is generally proven by empirical analyses which are limited in time and based on a circumscribed number of turbines. To cope with this lack of validation, this paper performs a controlled experiment to evaluate the suitability of SCADA-based condition monitoring for fault diagnosis in a fleet of eight turbines monitored for over 11 years. For the controlled experiment, a weakly supervised method was used to model the normal behavior of the turbine component. Such a model is instantiated as a convolutional neural network. The method, instantiated as a threshold-based method, proved to be suitable for diagnosis, i.e. the identification of all drivetrain failures with a considerable advance time. On the other hand, the wide variability between the time the alarm is raised and the fault is observed suggests its limited suitability for prognosis. [ABSTRACT FROM AUTHOR]

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method. [ABSTRACT FROM AUTHOR]

Fluctuations in the critical current along the length of high-temperature superconducting (HTS) tapes manufactured in the form of coated conductors is a common manufacturing phenomenon. These fluctuations originate in the generation of weak points through the length of HTS tapes that may cause quenching later. By means of the propagation of quenches in HTS tapes, the reliability, stability, and the performance of the device and the system that contain HTS tapes could be seriously degraded. In this study, an artificial intelligence technique based on artificial neural networks (ANN) was proposed to estimate the probability of quenches in HTS tapes caused by weak points. For this purpose, six different HTS tapes were considered with different widths, total thicknesses, and thicknesses of sub-layers. Then, for each one of these tapes, different operating conditions were considered, where the operating temperature changed from 40 K to 80 K, in 1 K steps. Under each operating temperature, different operating currents were considered from 50% to 100% of tape critical current. All of these resulted in more than 5000 different data points. Then, for each of these data points, analytical modelling was performed to provide initial inputs and outputs for the ANN model. It should be noted that the performed simulations were conducted based on an analytical method that was experimentally validated in the literature. After that, a sensitivity analysis was conducted to select the hyperparameters and structure of the ANN-based model. The last step was to take advantage of the trained model, as a function in the MATLAB software package to estimate the probability of quenches in different case studies. The significant feature of the proposed model is the capability for estimating the probability of quenches under different operating temperatures and currents for different types of HTS tapes. [ABSTRACT FROM AUTHOR]

The Special Issue is completed with two review articles: the first one [[12]] presents a classification of STLF state-of-the-art methods based on four categories, and the second one [[13]] compares criteria used to tune and assess STLF methods with estimated costs caused by the forecasting errors. The 11 research papers published in the Special Issue and in the book attempt to solve some of the STLF limitations, mainly by proposing new methods that improve the forecasting results with respect to those published in previous works. Consequently, STLF methods aimed at DR actions should explore new explanatory variables such as the electricity price forecasts for the next hours or DR event signals triggered by operators. As a result, adapting successful forecasting methods from those fields, the proposal of new ANN architectures and the development of new approaches (beyond current hybrid models) will be crucial to achieving significant progress in STLF. [Extracted from the article]

The wide use of communication layers in DC microgrids to transmit voltage and current measurements of each distributed generator unit (DGU) increases the possibility of exposure to cyber-attacks. Cyber-attackers can manipulate the measured data to distort the control system of microgrids, which may lead to a shutdown. This paper proposes distributed mitigation layers for the false data injection attacks (FDIA) on voltages and currents of DGUs in meshed DC microgrids. The proposed control strategy is based on integrating two layers for cyber-attack detection and mitigation to immune the primary and the secondary control loops of each DGU. The first layer is assigned to mitigate FDIAs on the voltage measurements needed for the voltage regulation task of the primary control loop. The second layer is devoted to the mitigation of FDIAs on the DGU current measurements, which are crucial for the secondary control level to guarantee the proper current sharing of each DGU. Artificial neural networks (ANNs) are employed to support these layers by estimating the authenticated measurements. Different simulation and experimental case studies are provided to demonstrate the proposed mitigation layers' effectiveness in detecting and mitigating cyber-attacks on voltage and current measurements. The simulation and experimental results are provided to evaluate the dynamic performance of the suggested control approach and to ensure the accurate operation of DC microgrids despite the existence of cyber-attacks on the measurements employed in the control strategy. Moreover, the control strategy succeeds to keep the maximum voltage error and the maximum error in current sharing within tolerance. [ABSTRACT FROM AUTHOR]

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification. [ABSTRACT FROM AUTHOR]

A comprehensive review of modelling techniques for the flow-induced vibration (FIV) of bluff bodies is presented. This phenomenology involves bidirectional fluid–structure interaction (FSI) coupled with non-linear dynamics. In addition to experimental investigations of this phenomenon in wind tunnels and water channels, a number of modelling methodologies have become important in the study of various aspects of the FIV response of bluff bodies. This paper reviews three different approaches for the modelling of FIV phenomenology. Firstly, we consider the mathematical (semi-analytical) modelling of various types of FIV responses: namely, vortex-induced vibration (VIV), galloping, and combined VIV-galloping. Secondly, the conventional numerical modelling of FIV phenomenology involving various computational fluid dynamics (CFD) methodologies is described, namely: direct numerical simulation (DNS), large-eddy simulation (LES), detached-eddy simulation (DES), and Reynolds-averaged Navier–Stokes (RANS) modelling. Emergent machine learning (ML) approaches based on the data-driven methods to model FIV phenomenology are also reviewed (e.g., reduced-order modelling and application of deep neural networks). Following on from this survey of different modelling approaches to address the FIV problem, the application of these approaches to a fluid energy harvesting problem is described in order to highlight these various modelling techniques for the prediction of FIV phenomenon for this problem. Finally, the critical challenges and future directions for conventional and data-driven approaches are discussed. So, in summary, we review the key prevailing trends in the modelling and prediction of the full spectrum of FIV phenomena (e.g., VIV, galloping, VIV-galloping), provide a discussion of the current state of the field, present the current capabilities and limitations and recommend future work to address these limitations (knowledge gaps). [ABSTRACT FROM AUTHOR]

The market share of electric vehicles (EVs) has grown exponentially in recent years to reduce air pollution and greenhouse gas emissions. The principal part of an EV is the energy storage system, which is usually the batteries. Thus, the accurate estimation of the remaining useful life (RUL) of the batteries, for an optimal health management and a decision-making policy, still remains a challenge for automakers. In this paper, the problem of battery RUL prediction is studied from a new perspective. Unlike other estimation strategies existing in the literature, the proposed technique uses an intelligent prediction of the lifespan of lithium–iron–phosphate (LFP) batteries via a modified version of neural networks. It uses a data-driven life estimation approach and optimization method and does not require any prior comprehension and initialization of any parameters of the battery model. To validate and verify the proposed technique, we use LFP battery data sets, and the experimental results showed that the proposed methodology can well learn the characteristic relationship of battery discharge capacities as well as its state of health (SOH), where the battery life cycle changes as the battery ages with time and cycles. [ABSTRACT FROM AUTHOR]

The reliability of capacitors is one of the most important issues in power electronics. The health status of capacitors can be evaluated through the comparison of estimated C/ESR values with their original values. In this paper, a two-input artificial neural network (ANN) is proposed for C and ESR estimation of DC-link capacitors in Maglev choppers; combined with the existing voltage and current sensors that are used for protection and control, it provides a promising solution for the health condition monitoring of the Maglev chopper in the Maglev train. Compared with prior-art research, the actual capacitor degradation progress where both C and ESR degrade is considered in training. Moreover, ANN's advantage of fitting nonlinear and complex relationships is explored by building an aggressive mapping between the voltage ripple at 5 kHz to C/ESR at 120 Hz, which cannot be described or analyzed by linear circuit equations. Thus, cross validation must be implemented to avoid an occasional poor fitting, and ensure the stability of ANN. Experimental results show that the proposed ANN outperforms the support vector regression in both ESR and C estimation. While C estimation suffered from over-fitting and instability, ESR estimation by the ANN is accurate and stable with a low average prediction error within 1%, showing great potential for condition monitoring of DC-link capacitors in Maglev choppers. [ABSTRACT FROM AUTHOR]

The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used. [ABSTRACT FROM AUTHOR]

Energy and power demands for military operations continue to rise as autonomous air, land, and sea platforms are developed and deployed with increasingly energetic weapon systems. The primary limiting capability hindering full integration of such systems is the need to effectively and efficiently manage, generate, and transmit energy across the battlefield. Energy efficiency is primarily dictated by the number of dissimilar energy conversion processes in the system. After combustion, a Compression Ignition (CI) engine must periodically continue to inject fuel to produce mechanical energy, simultaneously generating thermal, acoustic, and fluid energy (in the form of unburnt hydrocarbons, engine coolant, and engine oil). In this paper, we present multiple sets of Shallow Artificial Neural Networks (SANNs), Convolutional Neural Network (CNNs), and K-th Nearest Neighbor (KNN) classifiers, capable of approximating the in-cylinder conditions and informing future optimization and control efforts. The neural networks provide outstanding predictive capabilities of the variables of interest and improve understanding of the energy and power management of a CI engine, leading to improved awareness, efficiency, and resilience at the device and system level. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors. [ABSTRACT FROM AUTHOR]

To reduce carbon dioxide emissions, wind power generation is receiving more attention. The conversion of wind energy into electricity requires frequent use of insulated-gate bipolar transistors (IGBTs). Therefore, it is important to improve their reliability. This study proposed a method to predict the junction temperature of IGBTs, which helps to improve their reliability. Limited by the bad working environment, the physical temperature measurement method proposed by previous research is difficult to apply. Therefore, a junction temperature prediction method based on an extreme learning machine optimized by an improved honey badger algorithm was proposed in this study. First, the data of junction temperature were obtained by the electro-heat coupling model method. Then, the accuracy of the proposed method was verified with the data. The results show that the average absolute error of the proposed method is 0.0303 °C, which is 10.62%, 11.14%, 91.67%, and 95.54% lower than that of the extreme learning machine optimized by a honey badger algorithm, extreme learning machine optimized by a seagull optimization algorithm, extreme learning machine, and back propagation neural network model. Therefore, compared with other models, the proposed method in this paper has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the "best parameters" (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency. [ABSTRACT FROM AUTHOR]

Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error of 2.84 percent for one-step-ahead wind power forecasts. The study illustrates the plausibility of the development of neuromorphic devices aligned with the specific demands of the wind energy sector. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still a major challenge to accurately predict power load due to social and natural factors, such as temperature, humidity, holidays and weekends, etc. Therefore, it is very important for the efficient feature selection and extraction of input data to improve the accuracy of STLF. In this paper, a novel hybrid model based on empirical mode decomposition (EMD), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), a self-attention mechanism (SAM), and a long short-term memory network (LSTM) is proposed to fully decompose the input data and mine the in-depth features to improve the accuracy of load forecasting. Firstly, the original load sequence was decomposed into a number of sub-series by the EMD, and the Pearson correlation coefficient method (PCC) was applied for analyzing the correlation between the sub-series with the original load data. Secondly, to achieve the relationships between load series and external factors during an hour scale and the correlations among these data points, a strategy based on the 1D-CNN and TCN is proposed to comprehensively refine the feature extraction. The SAM was introduced to further enhance the key feature information. Finally, the feature matrix was fed into the long short-term memory (LSTM) for STLF. According to experimental results employing the North American New England Control Area (ISO-NE-CA) dataset, the proposed model is more accurate than 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM models. The proposed model outperforms the 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM by 21.88%, 51.62%, 36.44%, 42.75%, 16.67% and 40.48%, respectively, in terms of the mean absolute percentage error. [ABSTRACT FROM AUTHOR]

Green energy sources are implemented for the generation of power due to their substantial advantages. Wind generation is the best among renewable options for power generation. Generally, the wind system is directly connected with the power network for supplying power. In direct connection, there is an issue of managing power quality (PQ) concerns such as voltage sag, swells, flickers, harmonics, etc. In order to enhance the PQ in a power network with a wind energy conversion system (WECS), peripheral compensation is needed. In this paper, we highlight a novel control technique to improve the PQ in WECS by adopting an Artificial Neural Network (ANN)-based Distribution Static Compensator (DSTATCOM). In our proposed approach, an online learning-based ANN Back Propagation (BP) model is used to generate the gate pulses of the DSTATCOM, which mitigate the harmonics at the grid side. It is modelled using the MATLAB platform and the total harmonic distortion (THD) of the system is compared with and without DSTATCOM. The harmonics at the source side decreased to less than 5% and are within the IEEE limits. The results obtained reveal that the proposed online learning-based ANN-BP is superior in nature. [ABSTRACT FROM AUTHOR]

The Unified Power Quality Conditioner (UPQC) is a technology that has successfully addressed power quality issues. In this paper, a photovoltaic system with battery storage powered Unified Power Quality Conditioner is presented. Total harmonic distortion of the grid current during extreme voltage sag and swell conditions is more than 5% when UPQC is controlled with synchronous reference frame theory (SRF) and instantaneous reactive power theory (PQ) control. The shunt active filter of the UPQC is controlled by the artificial neural network to overcome the above problem. The proposed artificial neural network controller helps to simplify the control complexity and mitigate power quality issues effectively. This study aims to use a neural network to control a shunt active filter of the UPQC to maximise the supply of active power loads and grid and also used to mitigate the harmonic problem due to non-linear loads in the grid. The performance of the model is tested under various case scenarios, including non-linear load conditions, unbalanced load conditions, and voltage sag and voltage swell conditions. The simulations were performed in MATLAB/Simulink software. The results showed excellent performance of the proposed approach and were compared with PQ and SRF control. The percent total harmonic distortion (%THD) of the grid current was measured and discussed for all cases. The results show that the %THD is within the acceptable limits of IEEE-519 (less than 5%) in all test case scenarios by the proposed controller. [ABSTRACT FROM AUTHOR]

The safety of power transmission systems in wind turbines is crucial to the wind turbine's stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies. [ABSTRACT FROM AUTHOR]

Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU's "Green Deal", financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital's facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors' applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries generated from a simulated healthcare facility. ANFIS and backpropagation-based trained models outperformed all other models since they both deal well with complex nonlinear problems. LSTM also performed adequately. The models trained with metaheuristic algorithms demonstrated poor performance. [ABSTRACT FROM AUTHOR]

One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network for object tracking. The ImageNet Large-Scale Visual Recognition Challenge 2017 dataset was used to train the network in the pre-training phase. Then, in the experimental phase, the object tracking benchmark dataset was used to quantitatively evaluate the network. The experimental results revealed that the proposed tracking algorithm produced more competitive results compared to other tracking algorithms. [ABSTRACT FROM AUTHOR]