Search Results (33)

Energy system optimization seeks to utilize multiple energy sources efficiently under technical, economic, and environmental constraints. The increasing integration of renewable energy and the need for sustainable operation have made the optimal planning and management of hybrid energy systems crucial. Classical optimization methods, including Linear Programming, Nonlinear Programming, and simulation-based models, often face limitations when addressing high-dimensional and nonlinear problems. This study introduces a deep learning–based surrogate modeling framework for sizing the components of hybrid renewable energy systems. Initially, Particle Swarm Optimization (PSO) is employed to determine the optimal component sizes for a large number of synthetically generated hourly solar irradiance and load profiles. These optimal solutions are then used as target labels. The associated annual time-series data are transformed into multi-channel Data Map (DMAP) images, which serve as inputs for convolutional neural networks (CNNs). After training, the CNN models are capable of directly estimating the required number of photovoltaic (PV) panels, inverter capacity, and battery units from the DMAP images, eliminating the need to perform the iterative PSO optimization during the prediction stage. Various convolutional neural network architectures, including ResNet, DenseNet121, RegNet, ConvNeXt, EfficientNet, SqueezeNet, MobileNet, and InceptionV3, were evaluated for this multi-output regression task. The results indicate that ResNet and DenseNet121 achieve the best performance, while ConvNeXt provides strong results with a modern architectural design. Among the evaluated models, DenseNet121 achieved coefficients of determination (R²) of 0.934, 0.988, and 0.947 for predicting the sizes of the PV array, inverter, and battery bank, respectively. These results correspond to an average prediction accuracy of approximately 90.6%. ResNet produced similar performance, with its highest R² value reaching 0.983 for inverter sizing. Lightweight networks such as SqueezeNet and MobileNet demonstrate notable effectiveness for resource-constrained systems, whereas InceptionV3 underperforms in leveraging its multi-scale architecture. These results demonstrate that, once the models have been trained, deep learning–based surrogate models can generate sizing decisions comparable to those obtained using PSO with only a fraction of the computational effort. As a result, they provide a fast and practical alternative to conventional iterative optimization methods for component sizing in smart grid and sustainable energy planning applications. Full article

Deep learning algorithms have become useful tools for predicting time-series displacements from historical displacements measured using interferometric synthetic aperture radar (InSAR) techniques. However, nearly all existing InSAR-related studies are based on data-driven deep learning algorithms, causing poor robustness, especially for long-term prediction with small-scale training samples. In this study, we propose a new algorithm, named physics-informed neural network with prior parameter inversion (PINNPI), for InSAR-based prediction of time-series displacements. PINNPI is a hybrid data-driven and knowledge-guided deep learning network, where two coupled deep neural networks are first constructed for network training and parameter inversion of prior knowledge. The outputs of these two deep neural networks are coupled by an automatic differentiation module. By minimizing a hybrid physics-informed and data-driven loss function, the proposed network simultaneously models time-series displacement and estimates prior parameters. Subsequently, time-series displacements are predicted based on the trained networks and inverted parameters. The incorporation of physical knowledge into PINNPI enhances the capability of long-term displacement prediction with respect to data-driven learning algorithms. In addition, PINNPI effectively improves the poor robustness of classical PINNs, when prior parameters are unknown. Simulations and two real-world tests suggest that the accuracy of displacement prediction by PINNPI is, on average, 85% and 88% higher than that of classical data-driven deep learning and PINN algorithms, respectively. This work offers a new insight for predicting InSAR-based displacements associated with anthropogenic and geophysical activities. Full article

Reliable fault detection and diagnosis (FDD) plays a key role in inverter-driven permanent magnet synchronous motor (PMSM) systems, especially in applications where operational continuity cannot be compromised. In this work, a hybrid deep learning framework is developed by combining one-dimensional convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and a multi-head self-attention mechanism. The model targets multi-class fault classification in a three-phase PMSM inverter system. Its effectiveness is evaluated on a publicly available experimental dataset consisting of 10,892 multi-sensor samples collected under nine operating conditions, including normal operation, open-circuit faults, short-circuit faults, and half-bridge overheating scenarios. To avoid temporal data leakage, a block-aware chronological splitting strategy is applied. Model hyperparameters are determined through a validation process involving 24 different configurations. The proposed CNN–BiLSTM–Attention model achieves a macro F1-score of 0.9681 ± 0.0195, accuracy of 0.9810 ± 0.0102, Matthews correlation coefficient (MCC) of 0.9757 ± 0.0130, and ROC-AUC of 0.9996 ± 0.0003 over five independent runs, achieving the highest accuracy and MCC among all evaluated models; although the Random Forest baseline attains a marginally higher macro F1 score (0.9747) by operating on temporally aggregated features without temporal modelling, the proposed model provides superior discrimination across the full confusion matrix structure alongside end-to-end temporal interpretability via SHAP. Model interpretability is provided through SHAP (SHapley Additive exPlanations) GradientExplainer analysis, revealing that temperature-related features dominate fault discrimination, particularly for over-heating conditions, while current imbalance features are critical for distinguishing open- and short-circuit faults. Full article

Permanent magnet synchronous motors (PMSMs) suffer performance degradation under parameter uncertainties and external load disturbances, reducing the effectiveness of conventional proportional-integral and field-oriented control (FOC) schemes. This paper presents an artificial intelligence (AI) enhanced hybrid controller that combines finite-control-set model predictive control (FCS-MPC) and active disturbance rejection control (ADRC). The FCS-MPC optimizes inverter switching states by minimizing a cost function through predicted current trajectories. Additionally, the ADRC employs an extended state observer to estimate and compensate for aggregated disturbances. A lightweight radial basis function neural network is utilized, whose centers and widths are initialized offline based on k-means clustering on representative data, while its output weights are updated online via a Lyapunov-based adaptive law. This network dynamically adjusts the MPC cost function weights and ADRC observer bandwidth according to real-time operating conditions, while enabling online identification of key motor parameters. MATLAB/Simulink R2024a simulations under step load torque conditions verify that the proposed method achieves a speed deviation within 3% of the rated value, an over 90% reduction in torque ripple compared to FOC, and a settling time of less than 5 ms. Although it incurs a moderate computational cost, the proposed controller exhibits improved tracking accuracy and enhanced robustness under simulated conditions. Consequently, the AI-enhanced MPC-ADRC strategy shows strong potential for high-performance applications, subject to future experimental validation. Full article

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring the safety, efficiency, and lifecycle management of electric vehicles (EVs). Although data-driven approaches have become the mainstream solution for SOH estimation, most existing studies rely heavily on laboratory datasets collected under controlled and idealized conditions. Such datasets fail to capture the stochastic characteristics of real-world vehicle operation, including fragmented charging behaviors, varying environmental conditions, and significant sensor noise. Moreover, single deep learning architectures often struggle to simultaneously model the long-term temporal evolution of battery degradation and the complex multivariate correlations among operational variables. To address these challenges, this study proposes a hybrid neural network framework termed Long Short-Term Memory (LSTM)-improved iTransformer, which integrates the temporal modeling capability of LSTM networks with the multivariate feature interaction ability of an improved inverted Transformer architecture. In addition, a high-fidelity dataset was constructed using operational data collected from ten real-world electric vehicles. To simulate a realistic cloud-deployment scenario, a strict cross-vehicle validation strategy was adopted, where data from seven vehicles were used for model training and data from three entirely unseen vehicles were reserved for testing. The experimental results demonstrate that the proposed framework significantly outperforms conventional baseline models. In the multi-vehicle experiment, the model achieved a root mean square error (RMSE) of 1.18% and a coefficient of determination (R²) of 0.97, indicating promising cross-vehicle prediction performance within the available fleet. Furthermore, in the single-vehicle robustness experiment with limited training data, the proposed model achieved the lowest prediction error with an RMSE of 0.0045% and an MAE of 0.0034%, demonstrating superior accuracy and robustness compared with baseline models. These results suggest that the proposed method is a promising solution for battery health monitoring based on real-world operational data. Full article

One of the main causes of failure in Insulated Gate Bipolar Transistor (IGBT) modules used in high-power conversion applications is thermal-stress-induced degradation. In this paper, an experimental testing setup for thermal stress and real-time degradation monitoring, as well as a deep neural network (DNN)-based lifetime prediction of IGBT modules under thermo-electrically stressed inverter operation, is proposed. A two-level SVPWM inverter is implemented to create a hybrid power cycling test platform that imposes well-defined junction-temperature swings representative of real-world operation by combining controlled electrical loading and active induction heating with water cooling. Throughout the aging process, on-state voltage and module temperature are constantly monitored to identify degradation precursors associated with thermo-mechanical fatigue. A physics-based Coffin–Manson lifetime model is fitted using failure datasets to characterize temperature-dependent lifetime behavior. An offline deep neural network (DNN) is trained on degradation trajectories derived from on-state collector–emitter voltage (V_ce,on) to predict remaining useful lifetime. This approach uses partial degradation histories for accurate early-life prediction. The proposed DNN model for competitive and computationally efficient lifetime prediction is validated experimentally on several IGBT modules under different thermal stresses, and its accuracy is compared with other prediction methods. Full article

Fault diagnosis in the power systems of Hybrid Electric Vehicles (HEVs) is crucial for ensuring vehicle safety and energy efficiency. This study proposes an innovative CNN-LSTM fusion model for diagnosing common faults in HEV power systems, such as battery degradation, inverter anomalies, and motor failures. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal dependency handling of Long Short-Term Memory (LSTM) networks. Through data preprocessing, model training, and validation, the approach achieves high-precision fault identification. Experimental results demonstrate an accuracy rate exceeding 95% on simulated datasets, outperforming traditional machine learning methods. This research provides a practical framework for HEV fault diagnosis and explores its potential in real-world applications. Full article

The accelerated integration of photovoltaic (PV) systems, particularly within Hybrid PV–Battery Storage Systems (PV-BSS), establishes a compelling need for advanced control strategies that are fundamental to achieving effective Energy Saving Management. However, conventional proportional–integral (PI) controllers demonstrate limited adaptability and necessitate tedious, manual parameter tuning, frequently resulting in suboptimal dynamic performance, especially under load transients. To specifically address these constraints within the domain of high-power electronics, this paper introduces a novel Artificial Neural Network (ANN)-based current controller tailored for the 1500 VDC Three-Level Hybrid Active Neutral Point Clamped (3L-HANPC) inverter, which is a widely accepted PV-BSS topology. The optimal Multi-Layer Perceptron (MLP) architecture was identified using a multi-criteria methodology, which strategically balanced Total Harmonic Distortion (THD) performance against training efficiency. Simulation results affirm that the proposed ANN controller achieves superior harmonic mitigation and demonstrates faster dynamic responses compared to the PI counterpart. Moreover, the controller fundamentally ensures stable operation while eliminating the necessity for complex PI parameter tuning. Its dependable performance across both trained and unseen operating points strongly validates its robust adaptability. This self-tuning ANN approach thus provides a viable pathway for enhancing the reliability of future hybrid energy storage systems. Full article

Accurate fault detection for Permanent Magnet Synchronous Motors (PMSMs) prevents costly failures and improves overall reliability. This paper presents a hybrid one-dimensional convolutional neural network (1DCNN)–bidirectional gated recurrent unit (BiGRU) deep learning model for PMSM fault detection. Inverter-driven short-circuit, open-circuit, and thermal faults, as well as stator faults, can cause electrical and thermal disturbances that affect PMSMs. Significant harmonic distortions, current and voltage peaks, and transient fluctuations are introduced by these faults. The proposed architecture utilizes handcrafted features, including statistical analysis, fast Fourier transform (FFT), and Discrete Wavelet Transform (DWT), extracted from the raw PMSM signals to efficiently capture these faults. 1DCNN effectively extracts local and high-frequency fault-related patterns that encode the effects of peaks and harmonic distortions, while the BiGRU of this enriched representation models complex temporal dependencies, including global asymmetries across phase currents and long-term fault evolution trends seen in stator faults and thermal faults. The proposed model reveals the highest metrics for inverter-driven and stator winding fault datasets compared to the other approaches, achieving an accuracy of 99.44% and 99.98%, respectively. As a result, the study with realistic and comprehensive datasets guarantees high accuracy and generalizability not only in the laboratory but also in industry. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

Due to the complementary operational features, photovoltaic (PV) and fuel cell (FC) systems are increasingly being integrated into hybrid microgrids. PV systems provide clean energy during the day, while FCs provide continuous power supply throughout the day and night; thus, FCs can address PV’s incapacity during the night. However, voltage instability, frequency deviation, and enhanced harmonic distortion can result from the intrinsic intermittency of solar energy, switching errors in power electronic equipment, and varying load demands. Thus, a fuzzy logic-based current-controlled voltage source inverter (CC-VSI) is proposed in this paper to overcome these issues and enhance power quality in PV-FC hybrid microgrids. As per IEEE 1547 regulations, the fuzzy controller dynamically modifies the inverter current to maintain steady voltage and frequency profiles. MATLAB/Simulink (R2022a) is used to model and simulate the system, and its performance is evaluated under various reactive load scenarios. To test the efficacy of the proposed control technique, various power quality metrics, viz., voltage profiles (sag and swell), frequency profile, and total harmonic distortions, are plotted when subjected to large reactive load variations. The simulation results that are obtained with the proposed fuzzy-based current control technique are compared with the conventional artificial neural networks-based controller to verify the effectiveness. From the comparison study, it is found that the proposed technique shows superior power quality performance over the conventional technique. This encourages the development of renewable energy-based sustainable hybrid microgrids worldwide. Full article

Accurate current sensing in rectangular conductors is challenged by mechanical deformations, including eccentricity (X/Y-axis shifts) and inclination (Z-axis tilt), which distort magnetic field distributions and induce measurement errors. To address this, we propose a bio-inspired error compensation strategy integrating an elliptically configured Hall sensor array with a hybrid Grey Wolf Optimizer (GWO)-enhanced backpropagation neural network. The eccentric displacement and tilt angle of the conductor are quantified via a three-dimensional magnetic field reconstruction and current inversion modeling. A dual-stage optimization framework is implemented: first, establishing a BP neural network for real-time conductor state estimations, and second, leveraging the GWO’s swarm intelligence to refine network weights and thresholds, thereby avoiding local optima and enhancing the robustness against asymmetric field patterns. The experimental validation under extreme mechanical deformations (X/Y-eccentricity: ±8 mm; Z-tilt: ±15°) demonstrates the strategy’s efficacy, achieving a 65.07%, 45.74%, and 76.15% error suppression for X-, Y-, and Z-axis deviations. The elliptical configuration reduces the installation footprint by 72.4% compared with conventional circular sensor arrays while maintaining a robust suppression of eccentricity- and tilt-induced errors, proving critical for space-constrained applications, such as electric vehicle powertrains and miniaturized industrial inverters. This work bridges bio-inspired algorithms and adaptive sensing hardware, offering a systematic solution to mechanical deformation-induced errors in high-density power systems. Full article

The early detection of faults in photovoltaic (PV) systems is crucial for ensuring efficiency, minimizing energy losses, and extending operational lifespan. This study evaluates and compares multiple machine-learning models for fault diagnosis in PV systems, analyzing their performance across different fault types and operational modes. A dataset comprising 2.2 million measurements from a laboratory-based PV model, covering seven fault categories—including inverter failures, partial shading, and sensor faults—is used for training and validation. Models are assessed under both Maximum Power Point Tracking (MPPT) and Limited Power Point Tracking (LPPT) conditions to determine their adaptability. The results indicate that the ensemble bagged tree classifier achieves the highest accuracy (92.2%) across all fault scenarios, while neural network-based models perform better under MPPT conditions. Additionally, the study highlights variations in model performance based on power mode, suggesting the potential for adaptive diagnostic approaches. The findings reinforce the feasibility of machine learning for predictive maintenance in PV systems, offering a cost-effective, sensor-free method for real-time fault detection. Future research should explore hybrid models that dynamically switch between classifiers based on system conditions, as well as validation using real-world PV installations. Full article

In this study, a new hybrid method based on the generalized finite difference method (GFDM) and radial basis function (RBF) neural network technologies is developed to solve the inverse problems of surface anomalous diffusion. Specifically, the GFDM is utilized to compute the time-fractional derivative model on the surface, whereas RBF neural networks are employed to invert the diffusion coefficient, source term coefficient, and the fractional order within the anomalous diffusion equation governing the surface. The results of four examples show that for the three parameters of diffusion coefficient, source term coefficient, and fractional order, the errors of inversion results are in the order of $10^{-2}$ under different conditions. Therefore, this method can obtain the required parameters quickly and accurately under different conditions. Full article