EAI Endorsed Transactions on Energy Web is an open access, peer-reviewed scholarly journal focused on cross-section topics related to IT and Energy. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a continuous frequency. INDEXING: Scopus (CiteScore: 1.7), Compendex, DOAJ, ProQuest, EBSCO, DBLP eISSN: 2032-944X
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
Retraction Notice: Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data
We, the publisher, have retracted the following article: Wang et al. (2024). Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data. https://doi.org/10.4108/ew.6097 The article has been retracted due to misconduct during the peer review process. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 Keming Wang, Chengli Wang, Wenbing Jin, Liuming Qi https://creativecommons.org/licenses/by-nc-sa/4.0
Retraction Notice: Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
We, the Publisher, have retracted the following article: Devikanniga et al (2020). Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm. https://doi.org/10.4108/eai.13-7-2018.164177 The article has been retracted due to misconduct during the peer review process. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 D. Devikanniga, Arulmurugan Ramu, Anandakumar Haldorai https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Accurate Matching Model of New Energy Supply and Demand Empowered by Blockchain
Under the guidance of the "dual carbon" goals, green electricity has become the core carrier of the new energy transition. However, issues such as mismatched supply and demand of green electricity, information asymmetry, and lack of trust have seriously restricted the efficient development of green electricity marketization. Blockchain technology, with its core features of distributed ledger, immutability, and smart contracts, provides technical support for addressing the pain points in green electricity supply - demand matching. This paper constructs an accurate matching model for green electricity supply and demand based on blockchain, clarifies the model's technical architecture, participating entities, and operation process, designs a full - process collaboration mechanism, and verifies the model's effectiveness through simulation experiments. The results show that this model can increase the accuracy of green electricity supply - demand matching to over 92%, which is 15 - 20 percentage points higher than the traditional model; the transaction cycle is shortened to less than 7 days, with an efficiency improvement of over 60%; at the same time, it reduces the trust verification cost by more than 30%, significantly strengthens the trust relationship between supply and demand parties, optimizes the allocation of green electricity resources, and provides a technical path and practical reference for the market - oriented collaborative development of green electricity.
Copyright (c) 2026 Chuang Li, Junshi Zhang, Lei Zhou, Lili Li, Jiangtao Li, Guomin Li https://creativecommons.org/licenses/by-nc-sa/4.0
Dynamic Error Compensation Method for Binocular Ranging of Transmission Lines Integrating IMU Data
This paper proposes a dynamic error compensation method that integrates inertial measurement unit (IMU) data. The initial distance is obtained based on the binocular vision triangulation ranging principle. By analyzing the dynamic error sources such as camera movement, cable vibration and environmental interference, the IMU multi-sensor is integrated to collect the system's motion, attitude and environmental parameters in real time. The IMU data is integrated and processed to calculate the compensation amount of multi-source errors. With the help of the PID controller, the proportional-integral-derivative operation and linear combination of the total compensation amount are carried out to achieve effective compensation of dynamic errors. Experiments show that this method can significantly improve the ranging stability under different working conditions, and the coefficient of variation is always lower than 0.2.
Copyright (c) 2026 Zhimeng Zhang, Jie Liu, Cuiying Sun, Xiaoyu Yi, Yixin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
An Early Warning Method for Abnormal Fluttering Behavior in Transmission Lines Based on Deep Binocular Vision
Under adverse weather conditions, ice accumulation and strong winds cause low-frequency and high-amplitude swaying of transmission lines, increasing the risk of wire fatigue, hardware damage, and tower collapse, posing a threat to the safety of the power grid. The existing monitoring methods suffer from inaccurate 3D spatial data acquisition, leading to errors in edge detection, difficulty in suppressing background interference, insufficient feature extraction, and reduced accuracy in detecting abnormal oscillations. Therefore, this article proposes a warning method based on deep binocular vision, which captures three-dimensional data with a stereo camera and converts it into two-dimensional images. Mathematical morphological operators are used to determine the edge of the transmission line, solving the problem of inaccurate edge detection. The morphological edge results are input into the ResNet LSTM network to suppress background noise and extract features. Finally, compare the detection results with the actual values to achieve multi-level alarms. The experiment shows that this method is efficient and accurate, with a detection efficiency of 0.958-0.968. Improved the reliability of early warning.
Partial Discharge Detection and Identification in High-Voltage Cable Systems Based on Heterogeneous Sensor Fusion and Enhanced Deep Learning
High-voltage cable systems are critical components of power transmission networks, where partial discharge (PD) can cause insulation degradation and severe operational failures. Accurate detection and reliable identification of PD are therefore essential, yet conventional methods are often vulnerable to electromagnetic interference and exhibit limited recognition performance. This paper proposes a PD detection and identification framework for high-voltage cable systems based on heterogeneous sensor fusion and enhanced deep learning. A high-speed optical electric-field sensor is employed to localize potential insulation defects, while an acoustic pressure wave sensor is used to confirm PD occurrence and intensity. An improved adaptive-threshold discrete wavelet transform is applied for signal denoising, and an optimized Gramian Angular Field transformation converts one-dimensional signals into two-dimensional feature representations. A residual convolutional neural network incorporating an efficient channel attention mechanism is then developed for PD pattern identification. Experiments involving corona, void, and surface discharges demonstrate that the proposed system achieves a 100% PD detection rate and a recognition accuracy of 96.0% under laboratory conditions. Field tests on 220 kV tunnel-laid cables further verify that both detection and identification accuracies reach 100%, with superior robustness and environmental adaptability compared with conventional approaches.
Copyright (c) 2026 Ling Gao, Lixing Zhang, Qi Hu, Lining Jia, Xue Zhao, Penglong Liu, Bozhi Liu, Shunjin Shi https://creativecommons.org/licenses/by-nc-sa/4.0
Photovoltaic Power Adaptive Hybrid Forecasting Model Integrated with Multi-Dimensional Error Compensation
INTRODUCTION: With the deepening of China's "Dual Carbon" goals, the scale of photovoltaic (PV) installations continues to expand, placing stringent requirements on the accuracy, scenario adaptability, and robustness of day-ahead power prediction for PV plants in grid frequency regulation, peak shaving, and electricity market trading. However, existing single prediction models have obvious limitations: linear models struggle to capture nonlinear power disturbances under cloudy days with sudden irradiance changes and extreme weather; nonlinear models are prone to overfitting during stable, high-irradiance sunny periods, leading to redundant accuracy; and most methods lack sufficient robustness against meteorological fluctuations and data noise, resulting in large prediction errors that seriously affect the economy and security of grid operation. OBJECTIVES: Aiming at the problems in PV plant prediction where a single model finds it difficult to balance linear patterns and nonlinear disturbances, and prediction accuracy is greatly affected by meteorological fluctuations and data noise, this paper proposes a hybrid prediction model based on Linear Regression and Gradient Boosting Tree, along with a multi-dimensional error compensation mechanism. METHODS: 1. Otptimize feature engineering design, selecting time features, historical power lag terms, and meteorological interaction features as inputs, unifying their scales via Z-score standardization to simplify model complexity and construct a high-quality training set. 2. Design a dynamic weight allocation strategy based on irradiance intensity grading and intraday time periods, integrating the precise fitting advantage of Linear Regression during high-irradiance periods with the nonlinear fluctuation capture capability of Gradient Boosting Tree to establish a hybrid prediction model. 3. Use hourly operational data from a specific PV plant from May 2024 to March 2025 as a sample, performing validation combining an error compensation mechanism composed of sliding window error correction, extreme weather compensation, and residual feedback. RESULT: The test set RMSE of the hybrid model decreased by 18.2% and 22.5% compared to the single Linear Regression and Gradient Boosting Tree models respectively, with the error deviation rate under extreme weather controlled within 12%. CONCLUSION: These results verify the effectiveness and practicality of the proposed hybrid prediction model and error compensation method.
Copyright (c) 2026 Shuye Liu, Bingkuan Gong, Cuiyu Cui, Kun Zang https://creativecommons.org/licenses/by-nc-sa/4.0
Time-Sensitive Service Scheduling Method for LEO Broadband Satellite-Based Power Emergency Networks
INTRODUCTION: The frequent occurrence of extreme natural disasters poses severe threats to power grids, necessitating robust emergency communication. Low Earth Orbit (LEO) broadband satellite networks offer a promising solution but suffer from high dynamics and bit error rates, leading to jitter and disorder in time-sensitive services. OBJECTIVES: To address these challenges, this paper proposes a time-sensitive service scheduling method for LEO satellites in power emergency networks. The primary goal is to facilitate a transition from "traffic-driven" to "service-driven" paradigms, ensuring the prioritized and orderly delivery of critical control instructions. METHODS: An improved QUIC scheduling framework is introduced, incorporating the Acknowledgment Fast Return and Distinguish Loss (AFR-DL) mechanism to optimize congestion control. Furthermore, the Multiplex Data Arrive In Order (MDAIO) algorithm is proposed, which combines throughput prediction with dynamic error correction and service priority strategies to manage multipath transmission. RESULTS: Simulation results demonstrate that the AFR-DL algorithm improves throughput compared to OLIA, while the MDAIO algorithm significantly outperforms traditional schedulers like Lowest-RTT and DPSAF. The proposed method effectively reduces data disorder and transmission delay for high-priority services. CONCLUSION: The proposed method significantly enhances throughput and reliability, effectively meeting the high real-time requirements of power emergency communication. This work provides a viable solution for guaranteeing the stability of time-sensitive services in harsh LEO satellite environments.
Copyright (c) 2026 Xing Yang, Linhui Yang https://creativecommons.org/licenses/by-nc-sa/4.0
Lightweight Real-Time power quality disturbance recognition using Time-Frequency fusion with Cross-Attention mechanism
INTRODUCTION: Accurate power quality disturbances (PQDs) classification is critical for maintaining grid stability and reliability in modern power systems. However, existing deep learning methods predominantly rely on single-domain feature extraction, limiting their discriminative capability for complex composite disturbances under noisy conditions. This study addresses these limitations by proposing a dual-pathway architecture that synergistically integrates time-domain and frequency-domain representations through cross-attention fusion. OBJECTIVES: This work aims to develop a robust PQDs classification framework capable of accurately identifying 24 disturbance classes, including complex composite types, while maintaining high noise immunity and computational efficiency for real-time monitoring applications. METHODS: A dual-pathway deep learning architecture is proposed, comprising parallel CNN-BiLSTM branches for time-domain temporal modeling and FFT-based frequency-domain spectral analysis. A cross-attention mechanism dynamically fuses complementary features from both pathways. The model is trained and evaluated on a comprehensive dataset containing 24 PQDs classes under multiple noise levels. RESULTS: The proposed model achieves 99.73% accuracy on the validation set and maintains 98.94% accuracy under 30dB noise conditions. Ablation studies confirm the dual-pathway structure improves accuracy by 6.51 percentage points over single-branch variants, while the cross-attention mechanism contributes an additional 2.08 percentage points. The model converges within 43 epochs with inference latency of 251μs per sample, satisfying real-time requirements. CONCLUSION: The proposed dual-pathway cross-attention architecture demonstrates superior performance in PQDs classification, effectively balancing accuracy, noise robustness, and computational efficiency. This approach provides a viable solution for intelligent power quality monitoring in practical smart grid applications.
Copyright (c) 2026 Fei Yu, Haoyang Xue, Jiaming Qi, Xiaoqian Yue, Xinrui Pei https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Electromagnetic-Thermal-Mechanical Multiphysics Coupling Simulation Analysis of Converter Transformers
Addressing the limitations of conventional single-physics analysis methods in accurately revealing the internal electro-thermal-mechanical interaction mechanisms, this study established a fully coupled electro-magnetic-thermal-mechanical model for converter transformer windings. This model comprehensively accounts for the effects of harmonic currents and DC bias, employing bidirectional coupling calculations to elucidate the complete propagation process from electromagnetic losses to temperature distribution and structural stresses. Simulation results indicate that harmonic losses constitute the primary factor driving localised hotspot temperature rise. The resulting non-uniform thermal expansion, coupled with electromagnetic forces, generates significant stress concentrations in regions such as the winding ends.
Copyright (c) 2026 Zhichuang Li, Tan Li, Yongfan Guo, Shenghui Wang https://creativecommons.org/licenses/by-nc-sa/4.0
A Novel Hybrid Collaborative Forecasting Method for Offshore Wind Power Based on Intelligent Optimization
INTRODUCTION: With the rapid growth of offshore wind power integration and increasing wind power penetration, accurate power forecasting has become essential for maintaining grid stability and supporting economic dispatch. OBJECTIVES: This paper aims to develop a high-accuracy offshore wind power forecasting framework that can effectively handle noisy, non-stationary data and reduce the impact of outliers on prediction performance. METHODS: A two-stage data cleaning procedure is first constructed by combining density-based spatial clustering of applications with noise (DBSCAN) and polynomial regression to accurately identify and correct anomalous power data. The cleaned series is then decomposed using a sequential scheme that applies complete ensemble empirical mode decomposition followed by particle-swarm-optimized variational mode decomposition, producing multiple intrinsic mode components. Each component is fed into a hybrid temporal convolutional-gated recurrent unit (TCN-GRU) network, whose hyperparameters are globally tuned using an intelligent optimization algorithm, and the component-wise forecasts are aggregated to obtain the final power prediction. RESULTS: Simulation studies based on measured data from an offshore wind farm show that the proposed method significantly reduces forecasting errors compared with conventional forecasting models and single-stage decomposition approaches. CONCLUSION: The results demonstrate that the proposed adaptive optimization-based composite collaborative framework effectively improves both the accuracy and robustness of offshore wind power forecasting.
Copyright (c) 2026 Ruanming Huang, Chen Qian, Tianli Song, Yumeng Jiang, Hongyu Wen, Bing Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Data-driven Predictive Individual Pitch Control for Floating Offshore Wind Turbines
A method of data-driven predictive individual pitch control is proposed for floating offshore wind turbine. It can reduce platform motion and asymmetric aerodynamic loads effectively. The method is designed, which includes model predictive controller with feedforward compensation, to generate the voltage control signal component required by the pitch mechanism. Firstly, the models of floating offshore wind turbines and wind turbine loads are presented. Then, a model predictive controller utilizes a data-driven methodology grounded in subspace identification for the nonlinear wind turbines. The feedforward compensation with observing past system responses and control experience reduces the external turbulence and enhances the accuracy of the control signal. The simulations indicate that the proposed strategy performs better in terms of reducing fatigue loads and improving the system's stability and reliability.
Copyright (c) 2026 Xiaosuo Luo, Sergey Gorbachev https://creativecommons.org/licenses/by-nc-sa/4.0
Design and Implementation of a Low-Frequency Current Comparator Bridge Based on Quantized Hall Resistance
Quantumized Hall resistance (QHR) provides a traceable absolute reference for resistance measurement. However, the traditional transfer system based on super-conducting current comparator (CCC) relies on cryogenic liquid helium environment, which is complicated to operate and expensive. In this paper, a wide-range QHR transfer bridge scheme based on room temperature low-frequency current comparator (LFCC) is proposed and implemented. The system works under low-frequency AC excitation (<10 Hz), uses high-precision active/slave current sources to drive precision proportional windings, and achieves flux balance through detection windings and resonant amplifier feedback loops, thereby achieving high-precision resistance value transfer. The LFCC core magnetic circuit model, detection sensitivity model and closed-loop control model are established, and the proportional transfer relationship and error source are derived in detail. An experimental device including multi-layer magnetic shielding proportional windings, low-noise current sources, high-Q resonant detection amplifiers and digital control systems is designed and constructed. Experimental results show that under room temperature conditions, the LFCC bridge can achieve a proportional transfer uncertainty (k=2) better than 10⁻⁸ in the 1kΩ range. This solution gets rid of the dependence on liquid helium and has a relatively simple structure. It provides an effective technical approach for wide-range and high-precision resistance calibration in new power systems and has significant engineering application value.
Copyright (c) 2026 Jingfen Bai, Zongrong Li, Jing Meng, Xiaomeng Duan, Ailing Geng https://creativecommons.org/licenses/by-nc-sa/4.0
Short-Term Power Load Forecasting for Industrial Parks Using CNN-BiLSTM Network with Kernel Density Estimation-based Interval Prediction Method
Predicting electrical power loads in industrial parks is challenging due to the uncertainty and variance in model construction. This paper proposes an interval forecasting approach based on a residual-form CNN-BiLSTM, which effectively captures spatial patterns and bidirectional temporal dependencies in time series data. To handle uncertainty, a kernel density estimation (KDE)-based method is employed to generate probabilistic prediction intervals. Experiments using 18 days of real industrial data validate the model’s performance. Results show superior robustness and accuracy compared with LSTM, BiLSTM, and GRU. The proposed model achieves perfect prediction interval coverage probability (PICP = 1.0), narrow normalized interval width (PINAW = 0.0828), and minimal CWC = 0.0828, while baseline models exhibit low coverage or excessively wide intervals. These findings confirm that the method provides both reliable and sharp uncertainty quantification, making it suitable for practical energy forecasting applications.
Copyright (c) 2026 Rui Hua, Tao Han, Yue Ran, Qiongxiao Wu https://creativecommons.org/licenses/by-nc-sa/4.0
Analysis of the Impact of Active Power Recovery Rate of Wind Farms on Power System Transient Stability
INTRODUCTION: The post‑fault active‑power recovery of DFIG wind farms strongly influences transient‑stability assessment, yet conventional equivalent models fail to capture variations in recovery rate during LVRT. This paper proposes an equivalent modeling method that embeds optimized recovery characteristics. First, the post‑fault recovery behavior is analyzed. A single‑machine equivalent with an optimized recovery rate and a multi‑machine equivalent representing piecewise recovery are then developed. Differences from a detailed wind‑farm model are quantified using error indices and curve similarity, and impacts on simulation credibility are assessed. Results show the proposed models better reproduce recovery dynamics and improve transient‑stability accuracy. OBJECTIVES: characterize post‑fault active‑power recovery in wind farms, develop accurate equivalents for the restoration stage, and reveal how integration models affect power‑system transient stability. METHODS: Single‑machine and multi‑machine wind‑farm equivalencing with optimized active‑power recovery (and turbine time constants), plus the transient energy‑function method. RESULTS: Compared with conventional methods reported in the literature, the proposed wind farm equivalencing methods reduce the relative error of model equivalencing from 18.84% to 9.49% and 3.70%, respectively. In addition, the relative error in transient stability analysis is reduced from 22.68% to 9.19% and 2.35%, respectively. CONCLUSION AND SIGNIFICANCE: To establish a high-accuracy equivalent model of the wind farm, thereby providing a reliable modeling basis for transient stability analysis of power systems with wind power integration.
AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
Copyright (c) 2026 Hongxuan Zhang, Peng Zhou, Jianxin Zhang, Qing Gao, Tuo Jiang, Huanhuan Yang, Yanzhe Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Optimal scheduling of renewable power distribution systems combining deep learning and particle swarm optimization
Introduction: To address the optimal scheduling problem of renewable power distribution systems, this paper proposes an integrated framework combining deep learning-based load forecasting with particle swarm optimization. Objectives: The first objective is accurate multivariate load forecasting (cooling, heating, and electric loads). The second objective is minimizing network loss by optimally scheduling electric vehicle (EV) charging locations and times. Methods: For load forecasting, a hybrid RCNN-SVR model is constructed. The convolutional neural network (CNN) acts as a feature extractor to implicitly capture representative patterns from input data, while support vector regression (SVR) produces the final load predictions. Missing and outlier data are pre-processed. For optimal scheduling, a dual-layer particle swarm optimization (PSO) algorithm is developed. The inner layer enforces system constraints, and the outer layer minimizes network loss. EV charging load is simulated using the Monte Carlo method, and two cases (variable vs. fixed charging addresses) are optimized. Results: Experimental results demonstrate that the proposed RCNN-SVR model achieves high prediction accuracy, with mean absolute percentage error as low as 2.41% for winter electric loads. The dual-layer PSO reduces peak system load from 11.2×10³ kW to 10.5×10³ kW and decreases network loss by 2.13%, effectively smoothing grid fluctuations. Conclusion: The RCNN-SVR model significantly improves multivariate load prediction accuracy compared to separate forecasting methods. The dual-layer PSO successfully converts disorderly EV charging into orderly scheduling, reducing peak load and network loss. Together, they provide a practical solution for renewable distribution system scheduling.
Copyright (c) 2026 Qiuyong Yang, Bin Chen, Yanlu Huang, Xudong Hu https://creativecommons.org/licenses/by-nc-sa/4.0
Reinforcement Learning-enhanced Policy-aware Modeling of Smart Grid Efficiency under Carbon Constraints: Integration of SBM DEA and Dynamic Policy Response Simulation
Artificial intelligence-based sensing, forecasting, and decision optimization are being rapidly integrated into smart grid operations. Although reinforcement learning has created new opportunities for dispatch optimization and low-carbon transition under carbon constraints, most existing studies focus primarily on short-term economic or operational objectives and rarely incorporate system-level carbon reduction efficiency benchmarks into the learning process. To address this gap, this study proposes an integrated SBM-DEA and reinforcement learning framework for policy-aware smart-grid dispatch, in which carbon reduction efficiency scores are transformed from static evaluation results into dynamic learning signals for dispatch optimization. Using panel data from 30 provinces in China over the period 2011–2022, this study develops an indicator system covering capital input, labor input, electricity service output, and electricity-related carbon dioxide emissions. An SBM-DEA model with undesirable outputs is employed to measure the carbon reduction efficiency of smart grids. The estimated efficiency scores are then embedded into both the state representation and reward function of a reinforcement learning framework, where the agent learns dispatch policies that balance economic performance, carbon constraints, and efficiency improvement. A dynamic policy response simulation environment is further constructed, incorporating a hybrid energy storage system comprising battery storage and pumped hydro storage. The results show that the carbon reduction efficiency of smart grids in China exhibits stage-specific fluctuations, with annual average values ranging from 0.505 to 0.568 and pronounced interprovincial disparities. In the simulation experiments, the reinforcement learning agent trained with efficiency-based penalties achieves 7.3% lower operational costs and 8.5% higher average efficiency compared to an economic-only agent. The trained policies also exhibit clear policy-responsive behavior: when carbon prices rise, hybrid storage utilization increases and coal-fired generation declines. The main innovation of this study is that it integrates historical efficiency benchmarking with reinforcement learning-based dispatch optimization, providing a policy-aware and efficiency-guided decision-support framework for carbon-constrained smart grids.
Copyright (c) 2026 Gemei Shi https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Active Equalization of Energy Storage Lithium Batteries under a Modular Layered Architecture for Smart Grid Applications
INTRODUCTION: In smart grid applications, energy storage systems (ESS) are critical for balancing power supply and demand, but they often suffer from performance degradation due to State of Charge (SOC) inconsistencies in series-configured lithium battery packs. These disparities can compromise grid stability and battery lifespan. OBJECTIVES: This study proposes an active equalization method based on a novel modular layered architecture for ESS in smart grids. The core innovation lies in the synergistic combination of a hierarchical bidirectional Buck-Boost topology and a multivariable fusion fuzzy logic control strategy, aiming to enhance battery consistency, efficiency, and reliability for grid support. METHODS: A hierarchical BUCK-BOOST-based circuit is designed to enable bidirectional energy transfer, incorporating a multivariable fuzzy controller for real-time regulation of balancing currents. This approach facilitates cooperative equalization within and between battery groups, optimizing energy flow. RESULTS: Simulations based on an eight-cell model in Matlab/Simulink demonstrate that the proposed hierarchical topology reduces equalization time by 11.53% compared to the conventional single-layer topology. Furthermore, with the proposed multivariable fusion fuzzy logic control algorithm, the equalization time is further reduced by 26%, significantly improving both the equalization speed and adaptability to dynamic grid conditions. CONCLUSION: The proposed strategy effectively mitigates battery inconsistencies, enhancing the overall performance and safety of energy storage systems in practical applications. It provides a reliable technical approach for battery management in smart grids.
Energy storage lithium battery, State of charge (SOC), Active equalization, Hierarchical equalization circuit, Fuzzy control
Copyright (c) 2026 Qingdong Luo, Xiyuan Wan, Qiangwei Liu, Jingjing Lou, Zhikang Jin, Chaoqun Jin, Pengfei Zhen https://creativecommons.org/licenses/by-nc-sa/4.0
Reactive Power Optimization of Distribution Network Considering DG Based on Improved Ant Lion Algorithm
Large-scale and high-proportion access of distributed generation to distribution network breaks the original power flow structure of distribution network, which affects the power quality and safe operation of distribution network system. Voltage stability can be maintained through reactive power optimization, and the system operation economy can be improved. In this paper, the influence of distributed generation access on the stability of distribution network is analyzed. On this basis, the mathematical model of reactive power optimization is established with the comprehensive consideration of active power loss and node voltage deviation of distribution network with distributed generation access, and the improved ant lion algorithm with dynamic weight coefficient is used to solve the model. Finally, the simulation analysis is carried out in IEEE33-bus system, and the improved ant lion algorithm and standard ant lion algorithm are used to solve the reactive power optimization of distribution network. The comparison of the optimization results of the two algorithms proves the feasibility and superiority of the improved ant lion algorithm in solving the reactive power optimization problem.
Copyright (c) 2026 Yuan Hu, Jing Yang, Yuehua Cui, Xingjun Tian, Pan He, Peng Wu, Fusheng Zhang, Jianpeng Bian https://creativecommons.org/licenses/by-nc-sa/4.0
Malfunction Diagnosis of Microgrid Devices Based on Optimized Graph Neural Network
Malfunction diagnosis based on deep learning ensures the stable operation of microgrid. However, the microgrid system with new energy access appears to be increasingly complex and changeable, and its frequently changing topology brings challenges to the training of traditional neural network models. A method for malfunction diagnosis of microgrid devic-es based on optimized Graph Neural Network is proposed in the paper. Firstly, data reconstruction is completed through the mapping of nodes, edges and graphs in order to standardize changeable data. Secondly, abnormal data is processed to improve the quality of database. In that way, device misoperation caused by extreme data loss can be avoided, thus im-proving the training efficiency. Then, aiming at fully capturing the characteristics of time and space dimensions of data at different latitudes, the graph-convolution network under Multi-head Attention mechanism is adopted in the process. Finally, a microgrid simulation model is built to prove that the proposed scheme has the advantages of good robustness, strong adaptability, high reliability and accurate diagnosis.
Copyright (c) 2026 Qiyue Huang, Qi Song, Hao Lei https://creativecommons.org/licenses/by-nc-sa/4.0
Uncertainty Pattern Recognition of Multi-Type Renewable Power Plants and the Coupling Mechanism with Multi-Level Voltage Sensitivity
With high penetrations of renewable power plants, increased output volatility and uncertainty exacerbate local voltage-stability risks. Targeting heterogeneous renewable facilities, this paper proposes an integrated analysis framework that couples deep feature disentanglement, joint forecasting, and multi-level voltage sensitivity. First, using event logs as the driver, we combine variational mode decomposition (VMD) with a residual convolutional autoencoder (Res-CAE) to extract multi-band disturbance modes from power time series. An adversarial mechanism is then employed to disentangle meteorological disturbances from device-driven behaviors, yielding canonical uncertainty labels dominated by high-/mid-frequency components. Next, a spatio-temporal attention–driven multi-task sequence model is designed to jointly forecast active/reactive power and nodal voltage, providing stable inputs for subsequent sensitivity assessment. Finally, dynamic voltage sensitivity is estimated via sliding-window regression, and the disturbance–response chain and its propagation scope are quantified across local, dynamic, and system-level tiers. Event-driven simulations on the SoCal 28-Bus benchmark indicate that, under high- and mid-frequency dominated scenarios, dynamic voltage sensitivity increases by approximately 58.3% relative to low-volatility regimes, revealing a nonlinear transition by which renewable uncertainty is amplified through multi-frequency modes toward critical nodes. The proposed paradigm offers a transferable pathway for modeling and predicting complex source–network coupling in renewable-dominated power systems and provides data support for proactive voltage-control strategy design.
Copyright (c) 2026 Xiaoyu Luo, Li Xiong, Lili Lv, Zhi Zhang, Yulong Li, Mingzhao Meng, Lei Zhuo, Xin Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
Capacity Optimization for Minimizing the Carbon Footprint of Power Systems
The carbon footprint of electricity supply is strongly influenced by power system capacity configuration and operational flexibility under uncertainty. This paper proposes a carbon-footprint-oriented capacity optimization framework for integrated power systems comprising thermal power, wind power, photovoltaic generation, and energy storage. A dispatch model is developed to represent coordinated system operation under uncertain load and renewable generation, which are modeled using information gap decision theory and Monte Carlo simulation. The carbon footprint of electricity supply is evaluated on a life-cycle basis as carbon emissions per unit of delivered electricity, together with electricity cost and power fluctuation rate as performance indicators. A multi-objective capacity optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), and a practical optimal solution is selected from the Pareto front using the technique for order preference by similarity to an ideal solution (TOPSIS). Case studies show that the optimized capacity configuration reduces the carbon footprint of electricity supply by approximately 28%, while electricity cost and power fluctuation rate decrease by about 7% and 27%, respectively. Meanwhile, renewable energy utilization increases from 71.4% to 86.8%, and the renewable energy share rises from 42.0% to 55.6%, demonstrating that coordinated deployment of renewable energy and energy storage is essential for achieving carbon-efficient electricity supply under uncertainty.
Copyright (c) 2026 Xinzhi Wang, Bo Chen, Yunpei Cheng https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Key Technologies of AI-Based Source-Load Coordinated Regulation at the Edge of Distribution Networks
INTRODUCTION: The integration of a high proportion of renewable energy into distribution networks introduces significant challenges, including increased source-load volatility, uncertainty, and operational difficulties. OBJECTIVES: This study aims to address these challenges by proposing an artificial intelligence-based framework for source-load coordination at the grid edge. METHODS: The proposed framework integrates deep reinforcement learning, federated learning, and edge computing technologies. It employs multi-timescale optimization and distributed cooperative control methods to enhance the distribution network's capability to accommodate renewable energy. RESULTS: Experimental results on the IEEE 33-node system demonstrate that the proposed framework can reduce the wind and solar curtailment rate by 65.5% and decrease operational costs by 12.7% compared to traditional methods, while simultaneously satisfying all distribution network security constraints. CONCLUSION: This research provides a theoretical foundation and a technical pathway for building a new data-driven intelligent control system for distribution networks.
Copyright (c) 2026 Jie Chen, Haodong Ren, Jiaxu Zhou, Binyu Liu, Guangru Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
A Medium and Long-term Forecasting Method for Electric Load Curves Integrating Dynamic Variable Selection and Sparse Attention Mechanism
Accurate forecasting of electric load curves is critical for stable power system operation, efficient dispatch, and scientific planning, with medium- and long-term forecasting providing key support for grid expansion, energy allocation, and electricity market decisions. Existing methods struggle with insufficient use of load-related multi-variable data and inefficient extraction of long-term temporal features from load measurements, limiting forecasting accuracy and efficiency. To address these issues, this paper proposes a medium- and long-term electric load curve forecasting model integrating dynamic variable selection and a sparse attention mechanism, with a focus on load measurement correlation. The model takes static variables and temporal variables related to load changes as inputs. Static variables include industry type and location, and temporal variables cover historical load and meteorological data. It uses a gated feedforward network to adaptively weight variables based on their correlation with actual load measurements and filters redundant information. Meanwhile, it adopts a dual-layer encoding structure with sparse attention to prioritize key features from long load measurement sequences, reducing computational complexity while enhancing capture of long-term dependencies between historical load measurements and future loads. Experiments on three datasets show the model outperforms baselines including Informer, Autoformer and TFT. The proposed model reduces average relative error by 14.3%–55.1% and improves computational efficiency, providing reliable technical support for power system medium- and long-term planning by closely linking load measurements to forecasting performance.
Copyright (c) 2026 Bingqian Chen, Jinlin Liao, Shiyuan Ni, Sudan Lai https://creativecommons.org/licenses/by-nc-sa/4.0
Multidimensional Uncertainty Integration based on Spatiotemporal Hybrid Copula: A Resilience-Oriented Modeling Paradigm for IES and Virtual Power Plant
Integrated Energy Systems (IES) and Virtual Power Plants (VPPs) are pivotal for achieving the "dual-carbon" goals, yet they face significant challenges from multidimensional spatiotemporal uncertainties. Traditional uncertainty modeling methods, relying on static correlation assumptions, are inadequate for dynamically capturing the complex fluctuations in source-load-market parameters, especially the aggregation uncertainties inherent in VPPs. This paper proposes a novel resilience-oriented modeling paradigm centered on a spatiotemporal hybrid Copula (ST-HC) framework. The primary innovation lies in its dynamic correlation capture mechanism, which integrates time-decay and spatial-distance factors to accurately characterize nonlinear, time-varying dependencies among cross-regional uncertainties. Furthermore,a comprehensive five-dimensional evaluation system is established, incorporating metrics for storage flexibility, degradation, market volatility, risk preference, and system efficiency—including a novel data security risk entropy indicator. Validated through case studies, the proposed approach demonstrates superior performance over conventional static models, enhancingplanning accuracy and reducing total costs by approximately 9.98%, while effectively quantifying the economic-low-carbon trade-offs under different risk strategies for IES-VPP synergy.
Copyright (c) 2026 Junxiong Ge, Lijun Liu, Zhaojun Wang, Xiao Li, Hongxia Guo, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
Controllable Load Regulation Model of Distribution Network Source Load Energy Storage Based on Machine Learning and Flexible Interconnection Optimization Framework
With the integration of high-penetration renewable energy sources, distribution networks are confronted with increasing challenges such as source-load uncertainty and delayed regulation response. Conventional approaches like PID control struggle to accommodate millisecond-level fluctuations, while existing Model Predictive Control (MPC) approaches suffer from high computational complexity and difficulties in online updates. Relating to these problems, this study proposes a Machine Learning based Flexible Interconnection Optimization (ML-FIO) framework, which achieves breakthroughs through three technological innovations: First, a predictive-control closed-loop coupling integrating Long Short-Term Memory (LSTM) and Hierarchical Deep Q-Network (HDQN) reduces the dynamic response time of Nondominated Sorting Genetic Algorithm II (NSGA-II) to 1.89 seconds (a 60.2% improvement compared to PID control). Second, a flexible interconnection strategy based on dynamic impedance matching reduces the line loss rate to 3.01% during midday peak hours (a 47.6% reduction compared to droop control). Finally, a multi-timescale weight allocation mechanism maintains the root mean square (RMS) voltage deviation at critical nodes within 0.015 pu. Simulation results indicate that the proposed framework maintains robustness in over 80% of mutation scenarios and exhibits convergence in non-convex problems, providing an engineering-ready solution for Source-Load-Storage Coordination (SLSC) in modern power systems and establishing a rigorous mathematical foundation for the application of intelligent algorithms in power systems.
Machine learning based flexible interconnection optimization
Copyright (c) 2026 Ziqing Qiu, Qiuyue Huang, Guanpeng Fu, Yan Chen, Zhengui Yang https://creativecommons.org/licenses/by-nc-sa/4.0
Construction and Optimization of Knowledge Base for Commissioning Plans of New Power System Devices
To address the challenge of efficiently commissioning new equipment amid grid expansion, this study proposes an intelligent start-up plan generation method based on knowledge graphs. The approach first employs a bidirectional Long Short-Term Memory-Conditional Random Field model to extract entities such as equipment and operations from historical plans. This extraction process enables the construction of a structured knowledge base. Subsequently, it enhances plan standardization and reliability through multi-source data fusion and credibility analysis optimization logic. The results show that the named entity recognition model used in the study achieves an F1 score of 0.99, with an entity recognition accuracy of 92.5% for knowledge related to new power system devices commissioning plans. Testing of the proposed knowledge base reveals a knowledge coverage rate of 93.8%, which is 10.4% higher than that of traditional methods. The efficiency of plan generation significantly improves, with a rule compliance rate reaching 97%. The study provides a feasible pathway for transforming grid startup procedures from reliance on manual experience to data-driven intelligence.
Copyright (c) 2026 Yaopu Zheng, Xiongfeng Lin, Shifeng Peng, Guangyu Deng, Yunsheng Huang, Jian Huang https://creativecommons.org/licenses/by-nc-sa/4.0
Control Method for Overvoltage During Power Supply Loss in Medium-Voltage Flexible DC Sections Based on Guided Strategy Adaptive Particle Swarm Optimization
To address the challenge of overvoltage control during power loss in medium-voltage flexible DC sections under complex and variable operating conditions, a control method based on guided-strategy adaptive particle swarm optimization (GS-APSO) is proposed to ensure voltage stability. This method analyzes the overvoltage formation mechanism during faults according to the topology of the medium-voltage flexible DC section. Building on this analysis, an adaptive nearest-level modulation (ANLM) strategy with voltage-limiting functionality is employed. The strategy adjusts the DC voltage based on the changes in the average submodule capacitor voltage, constraining the voltage near the target value. Furthermore, to enhance the overvoltage suppression performance, the guided-strategy adaptive particle swarm algorithm optimizes the voltage-limiting coefficient, deriving the coefficient that minimizes the overvoltage decay ratio, thereby improving the overvoltage loss control in the medium-voltage flexible DC section. The experimental results demonstrate that under different unbalanced power conditions, the proposed method achieves overvoltage control within 1.5 s, enabling rapid voltage recovery and stabilization. Even under significant power fluctuations, it effectively restricts the peak voltage variation range. After control, the overvoltage decay ratio remains below 0.015 on average.
Copyright (c) 2026 Asleng, Jiadong Zhao, Peng Wang, Jun Guo, Ran Lu, Yang Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Calibration Framework for Intelligent Meter Error Prediction Based on Machine Learning
INTRODUCTION: Accurate measurement in intelligent metering systems is crucial for reliable energy monitoring, billing, and smart grid management. However, intelligent power meters often experience measurement inaccuracies due to environmental disturbances, hardware aging, and insufficient calibration mechanisms. These issues may lead to systematic errors and unreliable data in energy management systems. Therefore, improving the reliability and accuracy of intelligent metering devices through advanced data-driven techniques has become an important research focus. OBJECTIVES: The main objective of this study is to develop an Adaptive Calibration Framework for Intelligent Meter Error Prediction using Machine Learning. The framework aims to detect abnormal data, predict measurement errors, and improve the accuracy and reliability of intelligent metering systems operating under diverse environmental conditions. METHODS: The proposed framework integrates dynamic error detection, outlier filtering, and predictive modeling. Data collected from intelligent meters are first normalized using Min–Max scaling to ensure consistent training. Robust Principal Component Analysis (RPCA) with an improved distance function and adaptive thresholding based on the box plot method is used to detect and remove anomalous data. Subsequently, an Adaptive Cockroach Swarm Optimized Weighted Isolation Forest (ACSO-WIForest) model is employed to predict measurement errors by learning complex relationships between environmental stress factors and device performance. RESULTS: The experimental evaluation of the proposed ACSO-WIForest model using datasets from intelligent meters in harsh environmental conditions shows its effectiveness. The model demonstrates strong predictive performance with a Mean Absolute Error (MAE) of 0.00156, Root Mean Square Error (RMSE) of 0.0040, and a low Mean Absolute Percentage Error (MAPE) of 0.221, indicating high prediction precision. With a coefficient of determination (R²) of 0.998, the model achieves excellent fit and reliability, significantly outperforming existing methods in intelligent meter error prediction. CONCLUSION: The results indicate that machine learning–based adaptive calibration can significantly improve the accuracy, reliability, and self-correcting capability of intelligent metering systems. This approach provides a practical solution for enhancing trustworthy energy monitoring and management infrastructures
Copyright (c) 2026 Chunguang Wang https://creativecommons.org/licenses/by-nc-sa/4.0
A deep reinforcement learning-based adaptive multivariate state estimation fault diagnosis method for a photovoltaic storage grid-connected system under a high proportion of new energy integration
With the increasing penetration of photovoltaic and other renewable energy sources in power systems, the operational characteristics of photovoltaic storage grid-connected systems have become increasingly complex. The stochastic nature of renewable energy output and the alternating charging/discharging states of energy storage systems due to diurnal cycles lead to significant time-varying operational conditions, under which the performance of conventional multivariate state estimation methods for fault diagnosis markedly deteriorates. To address this, targeting high-penetration renewable energy integration scenarios, this paper proposes an adaptive state estimation-based fault diagnosis method for photovoltaic-storage grid-connected systems using deep reinforcement learning. Within the deep reinforcement learning framework, an agent is deployed to adaptively match and update modeling samples according to the real-time system state, thereby enhancing the adaptability of state estimation and the fault diagnosis accuracy. Test results under multivariate typical operational scenarios demonstrate that, compared to existing advanced fault diagnosis methods, the proposed approach achieves an average improvement of 10% in fault detection rate and reduces the missed detection rate by 3%, validating its superior adaptability and diagnostic performance in systems with complex time-varying characteristics. This research provides a new technical pathway for ensuring the safe and stable operation of photovoltaic-storage grid-connected systems under high-penetration renewable energy integration, contributing to enhanced intelligent operation and maintenance capabilities and improved fault response.
high proportion of new energy integration
photovoltaic storage grid connected system
Copyright (c) 2026 Yiguo Guo, Zhiwei Wang, Fumu Lu, Yimu Fu, Zhiqing Wang, Jinghui Meng https://creativecommons.org/licenses/by-nc-sa/4.0