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With the growing demand for battery-powered devices and electric vehicles, the need for improved battery performance and safety is paramount. A key determinant of battery health is the accurate monitoring of surface temperature (ST). Conventional ST estimation often depends on direct sensor measurements, which may not be cost-effective and can impact system reliability. This paper presents DeepTimeNet, a novel approach leveraging deep learning (DL) architectures for sensorless ST prediction in lithium-ion batteries. DeepTimeNet combines Convolutional Neural Networks (CNN), ResNet blocks, Inception modules, Bidirectional LSTM, and GRU layers to precisely model the time-dependent behaviour of batteries. A comprehensive evaluation against traditional models, across temperatures ranging from -20 °C to 25 °C and under various driving profiles, including US06 and Urban Dynamometer Driving Schedule (UDDS), is conducted. DeepTimeNet’s performance is quantified by metrics such as mean absolute error (MAE), surpassing that of models like Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Convolutional Neural Network-Long Short Term Memory Network (CNN-LSTM), and Long Short Term Memory Network (LSTM). The results demonstrate DeepTimeNet’s superior performance, with an RMSE of 0.0971, MSE of 0.0099, MAE of 0.0912, and MAXE of 0.3963, validating it as an advanced tool for enhancing the efficacy of battery management systems and underscoring its potential as a benchmark for future innovations.

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Typically, parasitic capacitances exist between the ground and the solar panel terminals in grid-connected PV systems. These parasitic capacitances provide a path for a leakage current, which leads to significant safety concerns, observable and seriously hazardous harmonic orders aligned with the injected grid current, and significant safety difficulties. In this research, a robust PWM controlling method that used competently in reducing the level of the leakage current and improving the power quality of a switched-capacitor Multilevel Inverter. This technique creates developed reference signals from the main signal to generate the switching scheme for the converter circuit. Additionally, the suggested control strategy only works with a small number of carrier signals, resulting in a quick system response and a simpler controller algorithm. Likewise, this controlling approach offers a stable way to maintain a constant output voltage in the suggested converter by adjusting the switching capacitors' voltages, which is not possible with traditional control techniques. MATLAB/Simulink is used to simulate the outcomes for both the suggested control approach and the traditional Phase Disposition (PD) PWM control method whereas the leakage current component reduces to 25 % compared to the captured component with the PDPWM. The simulation and the practical results based on the dSPACE-1103 hardware are quite similar.

The integration of Smart Grid technology and conceptual Industry 5.0 has paved the way for advanced energy management systems that enhance efficiency and revolutionized the parallel integration of power sources in a sustainable manner. However, this digitization has opened a new stream of the threat and opportunities of electricity theft posing a significant challenge to the security and reliability of Smart Grid networks. In this paper, we propose a secure and reliable theft detection technique using deep federated learning (FL) mechanism. The technique leverages the collaborative power of FL to train a Convolutional Gated Recurrent Unit (ConvGRU) model on distributed data sources without compromising data privacy. The training deep learning model backbone consists of a ConvGRU model that combines convolutional and gated recurrent units to capture spatial and temporal patterns in electricity consumption data. An improvised preprocessing mechanism and hyperparameter tuning is done to facilitate FL mechanism. The halving randomized search algorithm is used for hyperparameters tuning of the ConvGRU model. The impact of hyperparameters involved in the ConvGRU model such as number of layers, filters, kernel size, activation function, pooling, GRU layers, hidden state dimension, learning rate, and the dropout rate is elaborated. The proposed technique achieves promising results, with high accuracy, precision, recall, and F1 score, demonstrating its efficacy in detecting electricity theft in Smart Grid networks. Comparative analysis with existing techniques reveal the superior performance of the deep FL-based ConvGRU model. The findings highlight the potential of this approach in enhancing the security and efficiency of Smart Grid systems while preserving data privacy.

Abstract The rapid increase in nontechnical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes up a major part of NTL. It causes losses for the DSOs and also deteriorates the quality of electricity. The introduction of advanced metering infrastructure along with the upgradation of the traditional grids to the smart grids (SGs) has helped the electric utilities to collect the electricity consumption (EC) readings of consumers, which further empowers the machine learning (ML) algorithms to be exploited for efficient electricity theft detection (ETD). However, there are still some shortcomings, such as class imbalance, curse of dimensionality, and bypassing the automated tuning of hyperparameters in the existing ML‐based theft classification schemes that limit their performances. Therefore, it is essential to develop a novel approach to deal with these problems and efficiently detect electricity theft in SGs. Using the salp swarm algorithm (SSA), gate convolutional autoencoder (GCAE), and cost‐sensitive learning and long short‐term memory (CSLSTM), an effective ETD model named SSA–GCAE–CSLSTM is proposed in this work. Furthermore, a hybrid GCAE model is developed via the combination of gated recurrent unit and convolutional autoencoder. The proposed model comprises five submodules: (1) data preparation, (2) data balancing, (3) dimensionality reduction, (4) hyperparameters' optimization, and (5) electricity theft classification. The real‐time EC data provided by the state grid corporation of China are used for performance evaluations via extensive simulations. The proposed model is compared with two basic models, CSLSTM and GCAE–CSLSTM, along with seven benchmarks, support vector machine, decision tree, extra trees, random forest, adaptive boosting, extreme gradient boosting, and convolutional neural network. The results exhibit that SSA–GCAE–CSLSTM yields 99.45% precision, 95.93% F1 score, 92.25% accuracy, and 71.13% area under the receiver operating characteristic curve score, and surpasses the other models in terms of ETD.

In this paper, we suggest improving the performance of developed activation function-based Deep Learning Long Short-Term Memory (DLLSTM) structures by employing robust loss functions like Mean Absolute Error $(MAE)$ and Sum Squared Error $(SSE)$ to create new classification layers. The classification layer is the last layer in any DLLSTM neural network structure where the loss function resides. The LSTM is an improved recurrent neural network that fixes the problem of the vanishing gradient that goes away and other issues. Fast convergence and optimum performance depend on the loss function. Three loss functions (default $(Crossentropyex)$ , $(MAE)$ and $(SSE))$ that compute the error between the actual and desired output for two distinct applications were used to examine the effectiveness of the suggested DLLSTM classifier. The results show that one of the suggested classifiers’ specific loss functions $(SSE))$ works better than other loss functions and does a great job. The suggested functions $Softsign$ , Modified-Elliott, Root-sig, Bi-tanh1, Bi-tanh2, $Sech$ and $wave$ are more accurate than the tanh function.

Multilevel inverters (MLI) are finding widespread in various engineering and commercial applications owing to their immense performance. The cascaded H-bridge (CHB) inverter is the most potential MLI topology for renewable energy applications. The successful operation of the CHB-MLI depends on the integrity of the semiconductor devices and capacitors. Irrespective of its benefits the huge number of switches decreases the reliability of the inverter. Concerning reliability, this article proposes a fault-tolerant (FT) CHB MLI for solar photovoltaic applications. The proposed CHB MLI can withstand both the single and multiple open circuit faults in all the H-bridges of the CHB topology. The diagonally opposite switch pairs of CHB topology have similar fault features which lead to difficulty in finding the fault switches using the analytical fault diagnosis methods. Hence an artificial intelligence (AI) based fault diagnosis (FD) and FT operation of CHB MLI are interpreted. The proposed model offers complete FD and FT operation within one fundamental cycle which is advantageous relative to the existing methods. Compared to the existing methods, the proposed AI-based fault diagnosis strategy achieves a shorter diagnosis time and provides 96% classification accuracy between various fault conditions. Further, the simulation and HIL results demonstrated that the voltage magnitude and THD have been maintained at 8.24% before and after the fault state. In addition, the suggested FT structure ensures the constant output power over the post-fault operation for both single and multiple switch failure instances while improving the MLI resilience. The feasibility and performance of the proposed method have been investigated through related case studies using simulation and hardware-in-the-loop (HIL) tests on a single-phase fifteen-level CHB MLI.

Electric vehicles are projected to play an important role in the present and ensuing transportation as global environmental and energy issues become more serious. Looking into the growing popularity of electric vehicles, we need to pay even more attention to battery energy storage systems. Electric vehicles in the traditional sense rely on a battery to provide electricity. Influential requirements (acceleration and braking) will deteriorate the battery, or it may not be feasible to use it at all in some instances, due to the battery’s compactness. This paper proposes a Bidirectional DC/DC Converter topology and investigates its operation modes. The proposed converter can be used in hybrid electric vehicles. Hybrid electric vehicles use this converter type to connect a primary battery (ES1), an extra battery (ES2), and an adjustable voltage bus. Both step-up (i.e., Dual-source low-voltage powering mode) and step-down (i.e., energy-regenerating high-voltage dc-link mode) modes of operation are possible with the proposed converter, allowing bidirectional power flow. Furthermore, the model can handle the flow of electricity between any two low-voltage sources. For the proposed Bidirectional DC/DC Converter, two ways of power transmission, circuit design, and operation modes are analyzed. A MATLAB-Simulink model of the proposed, and the system is developed based on a Digital Signal Processor flow chart, and the results of the simulation as well as the performance of the system are discussed.

The static and dynamic economic dispatch problems are solved by creating an enhanced version of ant lion optimisation (ALO), namely a tournament selection-based ant lion optimisation (TALO) method. The proposed algorithm is presented to solve the combined economic and emission dispatch (CEED) problem with considering the generator constraints such as ramp rate limits, valvepoint effects, prohibited operating zones and transmission loss. The proposed algorithm’s efficiency was tested using a 5-unit generating system in MATLAB R2021a during a 24-hour time span. When compared to previous optimization methods, the suggested TALO reduces the costs of fuel and pollution by 9.01 and 4.7 percent, respectively. Furthermore, statistical analysis supports the suggested TALO optimization superiority over other methods. It is observed that the renewable energy output can be stabilized in the future by combining a hybrid dynamic economic and emission dispatch model with thermal power units, wind turbines, solar and energy storage devices to achieve the balance between operational costs and pollutant emissions.

The degradation of solar photovoltaic (PV) modules is caused by a number of factors that have an impact on their effectiveness, performance, and lifetime. One of the reasons contributing to the decline in solar PV performance is the aging issue. This study comprehensively examines the effects and difficulties associated with aging and degradation in solar PV applications. In light of this, this article examines and analyzes many aging factors, including temperature, humidity, dust, discoloration, cracks, and delamination. Additionally, the effects of aging factors on solar PV performance, including the lifetime, efficiency, material degradation, overheating, and mismatching, are critically investigated. Furthermore, the main drawbacks, issues, and challenges associated with solar PV aging are addressed to identify any unfulfilled research needs. Finally, this paper provides new directions for future research, best practices, and recommendations to overcome aging issues and achieve the sustainable management and operation of solar energy systems. For PV engineers, manufacturers, and industrialists, this review’s critical analysis, evaluation, and future research directions will be useful in paving the way for conducting additional research and development on aging issues to increase the lifespan and efficiency of solar PV.

Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions.

This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN). The goal of the proposed technique is to detect and classify Asynchronous Motor faults at their early stages, under both normal and abnormal operating conditions. The proposed technique uses a recurrent neural network in two phases to identify and label interturn insulation concerns, with the first phase being utilised to establish whether or not the motors are healthy. In the second step, it discovers and categorises potentially dangerous interturn errors. The SSO approach is used in the second phase of the recurrent neural network learning procedure, with the goal function of minimizing error in mind. The proposed CSSRN technique simplifies the system for detecting and categorizing the interturn insulation issue, resulting in increased system precision. In addition, the proposed model is implemented in the MATLAB/Simulink, where metrics such as accuracy, precision, recall, and specificity may be analysed. Similarly, existing methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), and Salp Swarm Algorithm Artificial Neural Network (SSAANN) are utilised to evaluate metrics such as Root mean squared error (RMSE), Mean bias error (MBE), Mean absolute percentage error (MAPE), consumption, and execution time for comparative analysis.

The increased need for renewable energy systems to generate power, store energy, and connect energy storage devices with applications has become a major challenge. Energy storage using batteries is most appropriate for energy sources like solar, wind, etc. A non-isolated three-port DC–DC-converter energy conversion unit is implemented feeding the brushless DCmotor drive. In this paper, a non-isolated three-port converter is designed and simulated for battery energy storage, interfaced with an output drive. Based on the requirements, the power extracted from the solar panel during the daytime is used to charge the batteries through the three-port converter. The proposed three-port converter is analyzed in terms of operating principles and power flow. An FPGA-based NI LabView PXI with SbRio interface is used to develop the suggested approach’s control hardware, and prototype model results are obtained to test the proposed three-port converter control system’s effectiveness and practicality. The overall efficiency of the converter’s output improves as a result. The success rate is 96.5 percent while charging an ESS, 98.1 percent when discharging an ESS, and 95.7 percent overall.

A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded in the load forecasting algorithm for online feature selection (FS). Using selected features, the performance of the forecaster was tested to signify the utility of the proposed methodology. For this, a day-ahead STLF using the M5P forecaster (a comprehensive forecasting approach using the regression tree concept) was implemented with FS and without FS (WoFS). The performance of the proposed forecaster (with FS and WoFS) was compared with the forecasters based on J48 and Bagging. The simulation was carried out in MATLAB and WEKA software. Through analyzing short-term load forecasts for the Australian electricity markets, evaluation of the proposed approach indicates that the input feature selected by the HFS approach consistently outperforms forecasters with larger feature sets.

The economic dispatch (ED) operation is based on determining the optimal output of all the distributed energy resources (DERs) of a microgrid to meet the load demand at the lowest cost. In this paper, a smart commercial building model is supplied by several distributed generations (DGs), for example, PV power plants, diesel generators, and energy storage system (ESS). In addition, the microgrid is connected to the main grid (grid-connected mode), which allows the system to trade power with the main grid. The aim of this paper is managing DERs that operate over a time horizon and satisfy several key constraints at the lowest possible cost. Therefore, an improved energy management (EM) operation is proposed to achieve better energy efficiency in the building. In the proposed EM operation, the ED problem is investigated according to several aspects, such as the impact of air temperature, thermal resistance (R) of the building envelope, and time horizon (day-ahead ED and five-minute-ahead ED). Finally, the results of the general algebraic modeling system (GAMS) are used to validate the accuracy and feasibility of the proposed EM operation.

Many studies have investigated resonator structures and winding methods. The aims of this paper are as follows. First, the paper proposes an optimized winding model for a bio-inspired joint for a wireless power transfer (WPT) system. The joint consists of a small spherical structure, which rotates inside a hemispherical structure. The transmitter coil (Tx) is wound on the hemisphere structure, and the receiver coil (Rx) is wound on the small sphere. The power is transferred while rotating Rx over a wide range of angular misalignment. In addition, the algorithm design of the proposed winding method is given to get an optimized model. Moreover, the circuit analysis of the WPT system is discussed. Second, the magnetic field density is investigated considering a safety issue, which is linked to human exposure to electromagnetic fields (EMFs). Moreover, EMF mitigation methods are proposed and discussed in detail. Finally, the simulation results are validated by experiments, which have confirmed that the proposed winding method allows the system to rotate up to 85 degrees and achieve an efficiency above 86%. The proposed winding method for the WPT system can be a good technique for some robotic applications or a future replacement of the human joint.