Search Results (1,226)

This article investigates the zonotope-based state estimation for boost converter system with Markov jump process. DC-DC boost converters are pivotal in modern power electronics, enabling renewable energy integration, electric vehicle charging, and microgrid operations by elevating low input voltages from sources like photovoltaics to stable high outputs. However, their nonlinear dynamics and sensitivity to uncertainties/disturbances degrade control precision, driving research into robust state estimation. To address these challenges, the boost converter is modeled as a Markov jump system to characterize stochastic switching, with time delays, disturbances, and noises integrated for a generalized discrete-time model. An adaptive event-triggered mechanism is adopted to administrate the data transmission to conserve communication resources. A zonotopic set-membership estimation design is proposed, which involves designing an observer for the augmented system to ensure $H_{\infty }$ performance and developing an algorithm to construct zonotopes that enclose all system states. Finally, numerical simulations are performed to verify the effectiveness of the proposed approach. Full article

Low-voltage series arc faults pose a significant threat to power system safety due to their random, nonlinear, and non-stationary characteristics. Traditional detection methods often suffer from low sensitivity and poor robustness under complex load conditions. To address these challenges, this paper proposes a novel detection framework based on Random Forest (RF) feature selection, Adaptive Boosting (Adaboost) classification, and SHapley Additive exPlanations (SHAP) interpretability. First, RF is employed to rank and select the most discriminative features from arc fault current signals. Then, the selected features are input into an Adaboost classifier to enhance the detection accuracy and generalization capability. Finally, SHAP values are introduced to quantify the contribution of each feature, improving the transparency and interpretability of the model. Experimental results on a self-built arc fault dataset demonstrate that the proposed method achieves an accuracy of 97.1%, outperforming five widely used traditional classifiers. The integration of SHAP further reveals the physical relevance of key features, providing valuable insights for practical applications. This study confirms that the proposed RF-Adaboost-SHAP framework offers both high accuracy and interpretability, making it suitable for real-time arc fault detection in complex load scenarios. Full article

Microgrids have emerged as a crucial solution for addressing environmental concerns, such as reducing greenhouse gas emissions and enhancing energy sustainability. By incorporating renewable energy sources like solar and wind, microgrids improve energy efficiency and offer a cleaner alternative to conventional power grids. Among various microgrid architectures, DC microgrids are gaining significant attention due to their higher efficiency, reduced reactive power losses, and direct compatibility with renewable energy sources and energy storage systems. However, DC microgrids face stability challenges, particularly due to the presence of constant power loads (CPLs), which exhibit negative incremental impedance characteristics. These loads can destabilize the system, leading to oscillations and performance degradation. This paper explores various control strategies designed to enhance the stability and dynamic response of DC microgrids, with a particular focus on interleaved boost converters (IBCs) interfaced with CPLs. Traditional control methods, including proportional–integral (PI) and sliding mode control (SMC), have shown limitations in handling dynamic variations and disturbances. To overcome these challenges, this paper proposes a novel RST-based control strategy for IBCs, offering improved stability, adaptability, and disturbance rejection. The efficacy of the RST controller is validated through extensive simulations tests, demonstrating competitive performance in maintaining DC bus voltage regulation and current distribution. Key performance indicators demonstrate competitive performance, including settling times below 40 ms for voltage transients, overshoot limited to ±2%, minimal voltage deviation from the reference, and precise current sharing between interleaved phases. The findings contribute to advancing the stability and efficiency of DC microgrids, facilitating their broader adoption in modern energy systems. Full article

In this study, data on the characteristics of overhead power lines of high voltage was used in a classification task to predict power supply outages by means of a supervised machine learning technique. In order to choose the most optimal features for power outage prediction, an Exploratory Data Analysis on power line parameters was carried out, including statistical and correlational methods. For the given task, five classifiers were considered as machine learning algorithms: Support Vector Machine, Logistic Regression, Random Forest, and two gradient-boosting algorithms over decisive trees LightGBM Classifier and CatBoost Classifier. To automate the process of data conversion and eliminate the possibility of data leakage, Pipeline and Column Transformers (builder of heterogeneous features) were applied; data for the models was prepared using One-Hot Encoding and standardization techniques. The data were divided into training and validation samples through cross-validation with stratified separation. The hyperparameters of the classifiers were adjusted using optimization methods: randomized and exhaustive search over specified parameter values. The results of the study demonstrated the potential for predicting power failures on 110 kV overhead power lines based on data on their parameters, as can be seen from the derived quality metrics of tuned classifiers. The best quality of outage prediction was achieved by the Logistic Regression model with quality metrics ROC AUC equal to 0.78 and AUC-PR equal to 0.68. In the final phase of the research, an analysis of the influence of power line parameters on the failure probability was made using the embedded method for determining the feature importance of various models, including estimating the vector of regression coefficients. It allowed for the evaluation of the numerical impact of power line parameters on power supply outages. Full article

This paper presents an enhanced operation strategy for a recently proposed converter called Modified Non-Inverting Step-Down/Up (MNI-SDU) DC-DC converter intended for battery voltage regulation. Unlike the conventional approach, where both switching stages share a single duty cycle, the proposed method controls asynchronously the two duty cycles through a fixed time offset to optimize performance. A methodology is developed to define suitable duty cycle ranges that ensure proper converter operation according to input/output voltage specifications, while simultaneously reducing the current and voltage ripples and electrical stress in the capacitor and semiconductors. Furthermore, a model-based control strategy is proposed, taking into account the enhanced operational characteristics. Consequently, a PI-PI current-mode controller is designed using loop shaping techniques to maintain the output voltage regulated at the desired level. The proposed approach is analyzed mathematically and validated through experimental results. The findings demonstrate that optimizing through asynchronous duty-cycle control with a fixed time offset improves ripple, stress values, and overall efficiency, while maintaining robust output voltage regulation, making this method well-suited for applications requiring compact and reliable power conversion. Full article

This study presents the design and implementation of a crop environmental monitoring system powered by a plant–soil bioelectrochemical energy source. The system integrates a Cu–Zn electrode power unit, a boost converter, a supercapacitor-based energy management module, and a wireless sensing node for real-time monitoring of environmental parameters. Unlike conventional plant microbial fuel cells (PMFCs), the output current originates partly from the galvanic effect of Cu–Zn electrodes and is further regulated by rhizosphere conditions and microbial activity. Under the optimal external load (900 Ω), the system achieved a maximum output power of 0.477 mW, corresponding to a power density of 0.304 mW·cm⁻². Stability tests showed that with the boost converter and supercapacitor, the system maintained a stable operating voltage sufficient to power the sensing node. Soil moisture strongly influenced performance, with higher water content increasing power by about 35%. Theoretical calculations indicated that Zn corrosion alone would limit the anode lifetime to ~66 days; however, stable output during the experimental period suggests contributions from plant–microbe interactions. Overall, this work demonstrates a feasible self-powered crop monitoring system and provides new evidence for the potential of plant–soil bioelectrochemical power sources in low-power applications. Full article

The health of electricity meters directly affects measurement accuracy and the interests of users. Traditional evaluation methods for electricity meters are limited by static error detection and manual calibration, and are unable to capture dynamic operating conditions or the complex influence of the power environment. To address this issue, this paper proposes an enhanced Cox proportional hazard (CoxPH) model based on Transformer for evaluating the health of electricity meters through a data-driven approach. This model integrates the data collected by the terminal (such as three-phase voltage, current, power, etc.) and operation and maintenance records. After data preprocessing, key covariates were extracted, including the average values of three-phase voltage and current fluctuations, current polarity reversal, and measurement error. The Transformer-based Cox proportional hazard (Trans CoxPH) model overcomes the linear assumption of the traditional CoxPH model by utilizing the self-attention and multi-head attention mechanisms of Transformer, and is able to capture the nonlinear relationships and time dependencies in time-series power data. Experimental results show that the performance of the Trans CoxPH model is superior to the traditional CoxPH model, temporal convolutional network-based Cox proportional hazard (TCN-CoxPH) model, extreme gradient boosting-based Cox proportional hazard (XGBoost CoxPH) model, and DeepSurvival long short-term memory (DeepSurvival LSTM) model. On the validation set, its concordance index (C-index) reaches 0.7827 with a Brier score of only 0.0501, significantly improving prediction accuracy and generalization ability. This model can effectively identify complex patterns and provides a reliable tool for the intelligent operation and maintenance of a power metering system. Full article

This work focuses on the synthesis and the comprehensive characterization of polydianiline (PDANI) polymer, obtained via oxidative polymerization of dianiline, a low-toxicity and more environmentally friendly starting monomer for polyaniline (PANI) formation. Despite the structural similarity to PANI, PDANI remains underexplored, especially regarding the effect of different synthesis conditions. Here, we investigate how chloride, sulfate, and camphor sulfonate dopants, combined with green solvents such as water and DMSO, modulate the final properties of PDANI in the emeraldine salt configuration. The produced materials were extensively characterized using a multi-technique approach. FTIR, Raman, EPR, and UV-Vis spectroscopies provided insights into chemical structure, molecular order, and polaron population. Electrical conductivity was disclosed via current-voltage (I-V) measurements, while cyclic voltammetry (CV) and coulovoltammetry (QV) were employed to evaluate redox activity and charge reversibility. The resulting PDANI displays structural and functional features comparable to those of PANI synthesized under similar conditions. Notably, the nature of the dopant and acidic medium was found to crucially govern the oxidation level, molecular organization, and electrochemical performance, boosting PDANI as a tunable and sustainable alternative for applications ranging from electronics to energy storage. Full article

Power converters play an important role in incorporating renewable energy sources into power systems. Among different converter designs, Buck and Boost converters are popular, as they use fewer components and deliver cost savings and high efficiency. However, Boost converters are known as non–minimum phase systems, imposing harder constraints for designing a robust converter. Developing an efficient controller for these topologies can be difficult since they exhibit nonlinearity and distortion in high frequency modes. The Lyapunov-based Adaptive Backstepping Control (ABSC) technology is used to regulate suitable outputs for these structures. This approach is an updated version of the technique that uses the stability Lyapunov function to produce increased stability and resistance to fluctuations in real-world circumstances. However, in real-time situations, disturbances with larger ranges such as supply voltage changes, parameter variations, and noise may have a negative impact on the operation of this strategy. To increase the controller’s flexibility under more difficult working settings, the most appropriate first gains must be established. To solve these concerns, the ABSC’s performance is optimized using the Reinforcement Learning (RL) adaptive technique. RL has several advantages, including lower susceptibility to error, more trustworthy findings obtained from data gathering from the environment, perfect model behavior within a certain context, and better frequency matching in real-time applications. Random exploration, on the other hand, can have disastrous effects and produce unexpected results in real-world situations. As a result, we choose the Deep Deterministic Policy Gradient (DDPG) approach, which uses a deterministic action function rather than a stochastic one. Its key advantages include effective handling of continuous action spaces, improved sample efficiency through off-policy learning, and faster convergence via its actor–critic architecture that balances value estimation and policy optimization. Furthermore, this technique uses the Grey Wolf Optimization (GWO) algorithm to improve the initial set of gains, resulting in more reliable outcomes and quicker dynamics. The GWO technique is notable for its disciplined and nature-inspired approach, which leads to faster decision-making and greater accuracy than other optimization methods. This method considers the system as a black box without its exact mathematical modeling, leading to lower complexity and computational burden. The effectiveness of this strategy is tested in both modeling and experimental scenarios utilizing the Hardware-In-Loop (HIL) framework, with considerable results and decreased error sensitivity. Full article

The increasing penetration of electrified vehicles is accelerating the evolution of on-board and off-board charging systems, which must deliver higher efficiency, power density, safety, and bidirectionality under increasingly demanding constraints. This article presents a system-level review of state-of-the-art charging architectures, with a focus on galvanically isolated power conversion stages, wide-bandgap-based switching devices, battery pack design, and real-world implementation trends. The analysis spans the full energy path—from grid interface to battery terminals—highlighting key aspects such as AC/DC front-end topologies (Boost, Totem-Pole, Vienna, T-Type), high-frequency isolated DC/DC converters (LLC, PSFB, DAB), transformer modeling and optimization, and the functional integration of the Battery Management System (BMS). Attention is also given to electrochemical cell characteristics, pack architecture, and their impact on OBC design constraints, including voltage range, ripple sensitivity, and control bandwidth. Commercial solutions are examined across Tier 1–3 suppliers, illustrating how technical enablers such as SiC/GaN semiconductors, planar magnetics, and high-resolution BMS coordination are shaping production-grade OBCs. A system perspective is maintained throughout, emphasizing co-design approaches across hardware, firmware, and vehicle-level integration. The review concludes with a discussion of emerging trends in multi-functional power stages, V2G-enabled interfaces, predictive control, and platform-level convergence, positioning the on-board charger as a key node in the energy and information architecture of future electric vehicles. Full article

Ultra-high-voltage (UHV) transmission lines are prone to galloping and oscillations under ice and wind loads, posing risks to system reliability and safety. Accurate aerodynamic coefficients are essential for evaluating these effects, but conventional wind tunnel and CFD methods are costly and inefficient for practical applications. To address these challenges, this study develops a surrogate model for rapid and accurate prediction of aerodynamic coefficients for six-bundle conductors. Initially, a CFD model to calculate the aerodynamic coefficients of six-bundle conductors was proposed and validated against wind tunnel experimental results. Subsequently, Latin hypercube sampling (LHS) was employed to generate datasets covering wind speed, icing shape, icing thickness, and wind attack angle. High-throughput numerical simulations established a comprehensive aerodynamic database used to train and validate multiple tree-based surrogate models, including decision tree (DT), random forest (RF), extremely randomized trees (ERTs), gradient boosted decision tree (GBDT), and extreme gradient boosting (XGBoost). Comparative analysis revealed that the XGBoost-based model achieved the highest prediction accuracy, with an R² of 0.855 and superior generalization performance. Feature importance analysis further highlighted wind speed and icing shape as the dominant influencing factors. The results confirmed the XGBoost surrogate as the most effective among the tested models, providing a fast and reliable tool for aerodynamic prediction, vibration risk assessment, and structural optimization in UHV transmission systems. Full article

Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. Full article

This paper presents a novel partial-power DC-DC converter architecture specifically designed for Photovoltaic (PV) energy systems. Unlike traditional full-power converters, the proposed topology processes only a fraction of the total power, resulting in improved overall efficiency, reduced component stress, and lower system cost. The converter is integrated into a PV-based energy system and regulated by a dual-loop control strategy consisting of a Proportional-Integral (PI) voltage controller and an inner Sliding-Mode Controller (SMC) for current regulation. This control scheme ensures robust tracking performance under dynamic variations in irradiance, load, and reference voltage. The paper provides a comprehensive mathematical model and control formulation, emphasizing the robustness and fast transient response offered by SMC. Simulation results obtained in MATLAB-Simulink, along with real-time implementation on the OPAL-RT hardware-in-the-loop (HIL) platform, confirm the effectiveness of the proposed design. The system achieves stable voltage regulation with low ripple and accurate current tracking. Compared to conventional boost configurations, the proposed converter demonstrates superior performance, particularly under moderate voltage conversion conditions. The system achieves high efficiency levels, validated through both analytical estimation and real-time hardware-in-the-loop (HIL) implementation. Its high efficiency, scalability, and real-time control feasibility make it a promising solution for next-generation PV systems, battery interfacing, and DC-microgrid applications. Full article

As low-voltage distribution networks incorporate increasingly diverse loads, series arc faults exhibit weak characteristics that are easily masked by load currents, leading to high misjudgment rates in traditional detection methods. This paper proposes a series arc fault detection method based on an improved Light Gradient Boosting Machine (LightGBM) model. First, a test platform containing 12 household loads was built to collect arc data from both individual and composite loads. Composite loads refer to composite load conditions where multiple devices are running simultaneously and arcing occurs on some loads. To address the challenge of feature extraction, Variational Mode Decomposition (VMD) is employed to isolate the fundamental frequency component. To enhance high-frequency arc characteristics, singular value decomposition (SVD) is then applied. A multidimensional statistical feature set—comprising peak-to-peak value, kurtosis, and other indicators—is constructed. Finally, the LightGBM algorithm is used to identify arc faults based on these features. To overcome the LightGBM model’s limited ability to focus on hard-to-classify samples, a dynamic weighted hybrid loss function is developed. Experiments demonstrate that the proposed method achieves 98.9% accuracy across 223,615 sample groups. When deployed on STM32H723VGT6 hardware, the average fault alarm time is 83.8 ms, meeting requirements. Full article

This study presents a machine learning-enhanced optimization framework for proton exchange membrane fuel cell (PEMFC), designed to address critical challenges in dynamic load adaptation and thermal management for automotive applications. A high-fidelity model of a 65-cell stack (45 V, 133.5 A, 6 kW) is developed in MATLAB/Simulink, integrating four core subsystems: PID-controlled fuel delivery, humidity-regulated air supply, an electrochemical-thermal stack model (incorporating Nernst voltage and activation, ohmic, and concentration losses), and a 97.2–efficient SiC MOSFET-based DC/DC boost converter. The framework employs the NSGA-II algorithm to optimize key operational parameters—membrane hydration (λ = 12–14), cathode stoichiometry (λO₂ = 1.5–3.0), and cooling flow rate (0.5–2.0 L/min)—to balance efficiency, voltage stability, and dynamic performance. The optimized model achieves a 38% reduction in model-data discrepancies (RMSE < 5.3%) compared to experimental data from the Toyota Mirai, and demonstrates a 22% improvement in dynamic response, recovering from 0 to 100% load steps within 50 ms with a voltage deviation of less than 0.15 V. Peak performance includes 77.5% oxygen utilization at 250 L/min air flow (1.1236 V/cell) and 99.89% hydrogen utilization at a nominal voltage of 48.3 V, yielding a peak power of 8112 W at 55% stack efficiency. Furthermore, fuzzy-PID control of fuel ramping (50–85 L/min in 3.5 s) and thermal management (ΔT < 1.5 °C via 1.0–1.5 L/min cooling) reduces computational overhead by 29% in the resulting digital twin platform. The framework demonstrates compliance with ISO 14687-2 and SAE J2574 standards, offering a scalable and efficient solution for next-generation fuel cell electric vehicle (FCEV) aligned with global decarbonization targets, including the EU’s 2035 CO₂ neutrality mandate. Full article