Energy Science & Engineering最新文献

Accurately estimating the state of health (SOH) of power batteries is beneficial for their maintenance, delaying aging, ensuring safety, and providing a basis for their secondary use to enhance resource utilization efficiency. However, existing data-driven methods rely heavily on laboratory data and lack adequate adaptability to real-world vehicle conditions. Moreover, traditional gradient boosting algorithms such as gradient boosting decision trees (GBDT) and LogitBoost encounter precision and generalization issues when faced with the complex operating conditions of real vehicles, thereby limiting their practical applications. To address these challenges, this paper proposes a method for estimating the SOH of power batteries in pure electric vehicles using an extreme gradient boosting (XGBoost) model optimized by the grid search cross-validation (GSCV) method, based on data from a vehicle manufacturer's monitoring platform. First, data are divided according to a “discharge + charge” pattern, and 16 capacity degradation feature factors from six categories are extracted from the discharge-charge segments as input variables for the XGBoost model, while partial charged capacity is extracted from the charge segments as the output label for the model. Subsequently, to overcome the XGBoost model's sensitivity to hyperparameters and its susceptibility to overfitting, the GSCV method is employed for parameter optimization of the XGBoost model, and the GSCV-XGBoost model is used to estimate partial charged capacity. Finally, an SOH correction method is applied to the output of the GSCV-XGBoost model to obtain the corrected SOH. Experimental results demonstrate that the SOH estimated by the GSCV-XGBoost model combined with the SOH correction method exhibits smaller errors and remains consistently below 2% compared to SOH corrected based on the Ampere-hour integral method. In estimating partial charged capacity, the GSCV-XGBoost model significantly outperforms the XGBoost model. Compared to the CBDT and linear regression (LR) models, the GSCV-XGBoost model achieves the highest goodness of fit (R²), with the smallest mean absolute error (MAE) and root mean squared error (RMSE). The research findings presented in this paper are expected to provide effective solutions for real-world vehicle power battery SOH monitoring.

As decentralized energy systems gain momentum, microgrids (MGs) have become a vital component of the modern power landscape. Yet, maintaining power quality (PQ) within these systems presents ongoing challenges due to the presence of nonlinear loads, variable renewable energy sources, and frequent switching operations. These factors contribute to PQ disturbances, such as harmonic distortion, voltage instability, and synchronization issues. Conventional mitigation methods often struggle to cope with such dynamic and complex environments. This review investigates the emerging role of artificial intelligence (AI) as a powerful tool for optimizing PQ in MGs. It presents a detailed overview of various AI-based methods, including machine learning (ML), metaheuristics, deep learning, fuzzy logic, and hybrid approaches and their implementation in areas like harmonic suppression, voltage and frequency regulation, islanding detection, renewable energy coordination, and predictive diagnostics. The study evaluates these techniques based on key performance indicators, such as precision, scalability, and suitability for real-time operation, while also addressing challenges related to data reliability, interpretability, and cybersecurity. The article concludes by highlighting future research directions, such as AI integration with Internet of Things (IoT), edge computing, and decentralized intelligence. Overall, the review illustrates how AI can play a pivotal role in transforming MG PQ optimization for the evolving smart grid era.

Prompt and accurate fault detection in extra high voltage transmission lines is required for guaranteeing the steadiness of power system. This study describes the performance of BiLSTM, GRU, and TCN as deep learning models for the detection and classification of faults in transmission lines through synthetic and real-time sequential datasets in 500 kV transmission line between Jamshoro and Karachi (NKI), in Sindh, Pakistan. Testing models' performance on simulated faults versus real fault events, the study concludes a major space and suggests insights for their practical applicability. The results show that deep learning models can reach vast level of accuracy in classifying different faults in transmission lines. This study forms the basis for exploiting modern fault detection practices in operating grids to improve their dependability and flexibility. The results revealed an accuracy of 98.31%, achieved by the BiLSTM, 94.27% for GRU and TCN as 99.8% through simulated data set, whereas using real-time fault data BiLSTM scored 62.05% accuracy, while GRU accuracy score achieved 96.43%, and TCN attained 100% accuracy. The results demonstrate that the deep learning models used in this study work well analyzing time series data by achieving high fault accuracy for fault classification in transmission lines. In general, the study was conducted to identify the best model in managing the fault over extra high voltage transmission lines under different conditions.

The proliferation of high-frequency wireless power transfer (WPT) technology in smart grid applications—particularly dynamic charging infrastructure, distributed device powering, and electrical fault diagnostics—has intensified concerns regarding leakage magnetic field effects on electromagnetic compatibility and operational integrity of critical grid components. Conventional electromagnetic shielding solutions suffer from the dual limitations of excessive spatial footprint and suboptimal material efficiency, proving inadequate for contemporary power systems requiring compact, resource-efficient electromagnetic protection. The study proposed a paradigm-shifting geometric optimization framework employing passive electromagnetic shielding to simultaneously enhance shielding performance and material utilization efficiency. Initially, through systematic finite element analysis (FEA) of four distinct configurations (disc, ring, concentric ring, and fan), the study establishes the concentric-ring topology as superior in achieving optimal balance between mass reduction and shielding efficiency. Parametric analysis reveals critical design interdependencies: shielding effectiveness (SE) demonstrates direct proportionality to ring width and inverse proportionality to inter-ring gap distance. An intelligent prediction model based on a deep belief–back propagation neural network (DBN-BP) was subsequently developed to generate customized parameter combinations, demonstrating either 113% SE or 71.4% material volume or 106% effectiveness at 43.36% material consumption. A practical solution for electromagnetic management in WPT-enabled power systems has been provided, and a physics-based machine learning research perspective for high-efficiency shielding design has been offered.

The HC method for hydropower is a commonly used rock mass quality classification technique in China's hydropower industry. Due to the anisotropic nature of the layered schist in the study area, and the varying angles between different tunnel layers and the tunnel axis, significant discrepancies arise between the HC method's classification results and actual rock mass classifications when these angles are parallel. This study employs uniaxial compression tests on schist to reveal its anisotropic characteristics under loading directions at 0°, 45°, and 90° angles relative to the bedding planes. The compressive strength exhibits a V-shaped variation with changes in angle between loading direction and schistosity plane, while the elasticity modulus shows a linear decrease as this angle varies. Numerical simulation experiments were conducted to monitor deformations of surrounding rock masses around tunnels. The findings indicate that as the angle between bedding orientation and tunnel axis decreases, both wall and roof deformations increase progressively. Under conditions of 0°, 30°, 45°, 60°, and 90° angles, the ratios of wall deformation values are approximately 1:3.73:4.74:5.44:7.7; whereas for roof deformation values, they are about 1:1.3:1.94:4.7:6.7. When applying traditional HC methods for classifying surrounding rock quality in parallel schist tunnels, a low agreement rate of only 13.33% was observed. However, by incorporating adjustments based on scoring criteria related to major structural plane orientations into numerical simulation results—specifically modifying weights assigned to structural planes—the agreement rate improved significantly to an impressive 100%. These research outcomes effectively enhance both accuracy and applicability in classifying layered rock masses, providing reliable foundations for tunneling construction practices.

The efficiency of a photovoltaic conversion chain depends heavily on the essential elements constituting the chain, in particular the photovoltaic generator, the maximum power point tracking technique used, the modulation system used to generate the control signals for static converter serving as an interface, and the static converter itself. However, despite the work already carried out to popularize the use of solar energy, the optimization of the efficiency of the energy produced in a photovoltaic chain still remains to be explored. To achieve the objective of improving the efficiency of energy produced and transferred, the method consists of using the maximum power point tracking technique based on fuzzy logic to have the input signal of the DCM modulator to generate control signal of the boost converter serving as an interface between the photovoltaic generator and the load. The results of simulations carried out in the MATLAB/Simulink environment present satisfactory results of the proposed solution facing the photovoltaic system using the P&O method associated with the DCM modulator. Faced with variations in irradiance and temperature, the proposed method presents a response time around 1 ms. These simulation results highlight the role that a modulation system can play in a photovoltaic chain in terms of improving the response time, efficiency and quality of the energy produced.

Accurate simulation and operation of photovoltaic (PV) systems depend on reliable extraction of model parameters from experimental data. These parameters are vital in assessing system efficiency under different environmental conditions. Due to the nonlinear characteristics of PV systems, robust optimization algorithms are necessary to ensure precise parameter estimation. This study introduces the Golden Jackal Optimization with dynamic Fitness Distance Balance (GJO-dFDB) algorithm in combination with the Berndt-Hall-Hall-Hausman (BHHH) method for estimating parameters of the three-diode PV model, which is widely observed as a benchmark for representing PV cell behavior. Integrating the fitness distance balance principle into the GJO framework strengthens its search capability by maintaining a dynamic balance between exploration and exploitation. This framework reduces the likelihood of premature convergence and improves adaptability across varying search landscapes. The performance of the proposed GJO-dFDB algorithm is compared with seven state-of-the-art optimization techniques on a commercial PV module under diverse operating conditions. The statistical results highlight its superiority, with average values of RMSE, MBE, R², RE, AE, and RT recorded as 3.675E−04, 5.789E−12, 0.9998, 3.340E−07, 1.483E−07, and 14.430, respectively. These findings confirm the GJO-dFDB algorithm's ability to achieve a trade-off between accuracy and computational efficiency in PV parameter estimation.

This study offers an in-depth examination of the physical characteristics of cubic AZnX₃ (A = Al, Ag; X = Cl, Br) halide perovskites, conducted through ab initio Density Functional Theory (DFT) calculations. The research indicates that substituting Al with Ag at position A and Cl with Br at position X results in a reduction of the compound's structural stability. The AlZnBr₃ compound demonstrates the greatest lattice parameter and primitive cell volume, whereas AlZnCl₃ presents the smallest values for both parameters. The evaluation of formation energy and Born stability criteria confirmed the compounds’ chemical and mechanical stability. The indirect band gaps for AlZnCl₃, AlZnBr₃, AgZnCl₃, and AgZnBr₃ were determined by the GGA-PBE functional to be 0.939 eV, 0.290 eV, 1.423 eV, and 0.111 eV, respectively. The adjusted band gaps using Meta-GGA are 1.433 eV, 0.781 eV, 1.966 eV, and 0.669 eV for the respective compounds. A comprehensive evaluation of the density of states further confirmed the semiconducting characteristics. A thorough analysis was conducted of the perovskites’ optical properties, which included the dielectric function, absorption coefficient, optical conductivity, reflectivity, refractive index, and extinction coefficient. The properties of the compounds, including their elastic constants, mechanical characteristics, and anisotropic behavior, were thoroughly investigated. AlZnCl₃ exhibits exceptional flexibility, workability, and strength. The distinct characteristics of all compounds are illustrated using three-dimensional contour maps. The thermodynamic analysis further validated the ability of these materials to sustain stability across varying temperature ranges. In summary, the results indicate that AZnX₃ perovskite compounds represent the best option for high-performance multijunction solar cells and optoelectronic devices.

This article proposes a composite fault-tolerant control strategy combining zero-mode control and half-wave reconfiguration to address single IGBT open-circuit faults in cascaded H-bridge converters. The composite strategy enables continuous operation without bypassing faulty modules by dynamically adjusting the working modes of faulty modules and reconstructing modulation waves for cascaded converters. The zero-mode control strategy, as the core of this study, adopts a digital logic-based architecture and utilizes the zero-mode equivalence of the two operating modes of the H-bridge module to dynamically switch modes according to the fault type. Through collaboration with the half-wave reconfiguration control strategy, precise compensation for missing positive or negative voltage levels caused by faulty modules is achieved within a specific interval, ensuring that the cascaded converter can maintain output characteristics even under single IGBT open-circuit faults and meet the requirements of grid-connected operation. The maximum power control strategy dynamically adjusts the output power of faulty and healthy modules to minimize power deviations between them, thereby optimizing energy distribution efficiency and extending the lifespan of the battery energy storage system. Experimental results validate the effectiveness of the strategy in addressing single IGBT faults in cascaded energy converters.

Smarter grids depend on Cyber-Physical Systems (CPS) to merge physical energy distribution networks with computational intelligence, because these systems optimize reliability and sustainability and power delivery efficiency. CPS in smart grids present both enhanced interconnectivity and complexity which creates substantial security challenges because they become exposed to complex cyber-attacks that harm operational processes and degrade data integrity criteria. The current intrusion detection systems in smart grid environments encounter multiple obstacles when they attempt to detect and counteract security threats effectively. This paper develops an innovative security solution by integrating Zero-DAGNet with POCO as a solution to combat smart grid security challenges. The Zero-DAGNet employs domain-adversarial learning techniques that operate inside a graph-based deep-learning structure for identifying complex network entity relationships. The designed structure helps the model adapt to unidentified attack patterns which resolves domain shift problems encountered during intrusion detection operations. The POCO brings forth an innovative optimization technique based on primate cognitive operations that optimizes network parameter settings efficiently. Through this well-merged structure, the model demonstrates enhanced flexibility and operational performance when operating in dynamic smart grid networks. Results from empirical tests confirm that the combination of Zero-DAGNet and POCO produces effective outcomes. On ICS and SWaT and CICIDS17 benchmark data sets, the proposed model demonstrates superior performance than traditional and deep-learning machine-learning algorithms. Using the ICS data set allows the framework to reach a 99.10% accuracy and a precision of 98.89% while producing a recall of 98.88%, which results in an F1-score of 99.08%. It shows improved performance compared to previous solutions. The Zero-DAGNet + POCO approach demonstrates its capability to deliver resilient and efficient intrusion detection solutions which strengthen the security features of smart grid networks.