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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.
AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
Copyright (c) 2026 Yaopu Zheng, Xiongfeng Lin, Shifeng Peng, Guangyu Deng, Yunsheng Huang, Jian Huang https://creativecommons.org/licenses/by-nc-sa/4.0
Capacity optimization of hybrid energy storage system considering battery loss
INTRODUCTION: Wind power instability in microgrids impairs system economy, and rational hybrid energy storage system (HESS) capacity allocation is critical for mitigation. OBJECTIVES: To address this, this study aims to optimize HESS configuration by minimizing battery loss and life cycle cost while ensuring stable operation. METHODS: The approach involves: constructing a battery equivalent running time model via the rain-flow counting method, establishing a dual-objective optimization model, decomposing HESS suppression power into K intrinsic mode function(IMF) components using MISOA-VMD, and deriving optimal energy storage configurations and critical mode point with the NSGA-II algorithm. RESULTS: Validation through comparative analysis of different schemes' minimum costs confirms the proposed scheme's superiority. CONCLUSION: This study provides a reliable HESS configuration method to enhance the economy of wind power-integrated microgrids.
Copyright (c) 2026 Hong Na, Jiacheng Wu, Guoping Lei, Jing An, Xue He https://creativecommons.org/licenses/by-nc-sa/4.0
Topology optimization and collaborative development planning of electro-hydrogen coupling based on multi-objective solution algorithms
INTRODUCTION: Renewable energy microgrids need planning methods to manage volatility and balance performance. OBJECTIVES: Develop a collaborative planning method for electro-hydrogen coupling systems, optimizing economy, environment and reliability. METHODS: Build a unified planning model for siting, capacity and topology. Solve using improved NSGA-II with adaptive operators and constraint handling. RESULTS: Based on 8760-hour data, the system increased energy self-sufficiency from 52% to >95%. With 28% extra investment, carbon emissions fell 54% and reliability rose 55%. Lower electrolyzer cost further cut emissions; carbon price at 300 CNY/ton improved scheme competitiveness. CONCLUSION: Electro-hydrogen coupling enhances microgrid performance. Multi-objective optimization finds the best trade-off. Falling costs and carbon policies will promote system application, aiding low-carbon transformation.
Copyright (c) 2026 Xiaowei Li, Guoxian Luo, Dandan Li, Peng Yan https://creativecommons.org/licenses/by-nc-sa/4.0
Research on distributed energy resources operation and control information model based on IEC 61850
INTRODUCTION: High-ratio distributed energy resources (DERs) impact distribution network operation and require effective scheduling. IEC 61850 7-420 defines a DER model, but massive small/medium DERs in low-voltage networks hinder station-area aggregated management. OBJECTIVES: Enable aggregated management, dynamic scheduling of small/medium DERs and ensure DER data security. METHODS: 1. Analyze station-area structure and information needs via cluster control 2.Establish a general information model of DER equipment. 3.Build a new communication security control logical node 4.Realize the source-side dynamic conversion of inverter data RESULTS: Key results: 1) IEC 61850-based DER scheduling model; 2) DER data security logical node; 3) Inverter data protocol conversion. CONCLUSION: The proposed model, node and method resolve core DER management issues, providing a feasible distribution network scheduling solution.
Copyright (c) 2026 Hechong Chen, Fan Yang, Zhichun Yang, Chao Wang, Yu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Research on MPPT control strategy for photovoltaic power generation based on improved incremental conductance method
INTRODUCTION: Maximum power point tracking (MPPT) is an important strategy for optimizing the power output of photovoltaic arrays. OBJECTIVES: The primary objective of this study is to develop an enhanced MPPT control method to address the inherent limitations of long tracking time and low tracking accuracy in the traditional conductance incremental method for MPPT. METHODS: An improved incremental conductance method is proposed, which introduces the fuzzy control algorithm to the incremental conductance method to dynamically adjusts the perturbation step size. RESULTS: The simulation results show that the improved method proposed shortens the MPPT time and improves the MPPT accuracy under start-up and sudden change operating condition comparing with other incremental conductance method. CONCLUSION: This study provides an effective and reference-worthy control method for further improving the efficiency of photovoltaic power generation.
Copyright (c) 2026 Changzheng Zhou, Wei An, Xiaoliang Zhu, Jun Li, Longwei Jiao https://creativecommons.org/licenses/by-nc-sa/4.0
Identification and operational optimization of virtual energy storage characteristics for customer-side resources
INTRODUCTION: This paper addresses the challenges of integrating diverse customer-side resources into virtual energy storage (VES) systems to enhance power system flexibility and renewable energy utilization. OBJECTIVES: The study aims to develop an integrated framework for the identification and optimal scheduling of VES systems, aggregating multi-type resources such as thermostatically controlled loads, electric vehicles, and distributed energy storage systems. METHODS: Quantitative models were established to characterize the operational features of each resource. A data-driven clustering algorithm was employed to aggregate heterogeneous resources into unified VES units. A bi-level optimization model was formulated to minimize the aggregator's operational cost while incorporating comfort penalty functions to maintain user satisfaction. RESULTS: Simulations across four scenarios (sunny, cloudy, high load, high EV penetration) demonstrated significant improvements: the peak-to-valley difference was reduced by 21.6%–28.4%, photovoltaic utilization exceeded 98%, and electricity purchase costs decreased by 4.8%–7.3%. CONCLUSION: The proposed framework provides an effective and scalable approach for VES scheduling, significantly enhancing renewable energy integration and operational flexibility in modern distributed power systems.
Copyright (c) 2026 Shiwang Yang, Jian Huang, Junshi Chen, Penghao Sun, Ziyi Zhan, Xinyi Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Anti-Islanding Protection Communication Based on IEC 61850 Standard
INTRODUCTION: As a high proportion of distributed power sources are widely connected to the distribution network, they change the topology of the traditional distribution network and the characteristics of electrical quantities, among others. OBJECTIVES: In order to prevent unplanned islanding from occurring in the distribution network, which affects the power supply quality and personal safety of the distribution network, the distribution network needs to be equipped with anti-islanding protection. METHODS: In this paper, the typical unplanned islanding scenarios of active distribution networks and the information interaction requirements for anti-islanding protection are analyzed. Based on the analysis of anti-islanding protection information interaction requirements, a SCADA fault detection information model is established based on IEC 61850 standard. A line topology description method based on IEC 61850 SCL is proposed to solve the problem of reduced reliability of anti-islanding protection detection due to line topology changes, inability to accurately determine the boundary of the island, and susceptibility to omission or misjudgement. Meanwhile, a subset of information models for anti-islanding protection has also been refined in conjunction with the functional requirements of typical active distribution network scenarios. RESULTS: Finally, the effectiveness of this paper's method is verified by examples, and the related results are effectively used in the smart power distribution management system CONCLUSION: Aiming at the aggregated management challenges of massive medium and small-capacity DERs in low-voltage distribution network station areas, this paper establishes an IEC 61850-compliant universal DER information model, constructs a communication security control logical node, and realizes source-side dynamic conversion of inverter data from Modbus to IEC 61850 protocol.
Copyright (c) 2026 Yubo Yuan, Juan Li, Yunlong Jiang, Sudi Xu, Haiou Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Refined 3D Gaussian Splatting Method for Distribution Network Modeling Towards AR-Oriented Operation and Maintenance
Aiming at the problems of missing slender structure modeling, complex background interference, and algorithm efficiency bottlenecks in the 3D reconstruction of distribution networks, this paper proposes an improved 3D Gaussian splatting-based algorithm for 3D reconstruction of distribution network scenes. First, through a semantics-guided density control strategy combined with adversarial background suppression, power equipment and vegetation artifacts are effectively separated. Second, a direction-sensitive covariance optimization method is designed to enhance the geometric continuity and detail fidelity of ultra-slender structures such as power lines and utility poles. Finally, a lightweight progressive splatting framework is constructed to achieve real-time rendering at ≥35 fps under 1080p resolution. Experiments show that the improved algorithm significantly increases the completeness rates of power lines, utility poles, and insulators to 90%, 95%, and 80%, respectively, reduces background artifacts to 2% in area, and shortens the reconstruction time to 0.4 hours. This study provides a high-precision modeling tool for digital twins of distribution networks and supports real-time applications of augmented reality (AR) technology in inspection navigation, fault localization, and remote collaboration.
Copyright (c) 2026 Conghan Liu, Lijuan Yan, Linjun Lu, Huazhang Tan https://creativecommons.org/licenses/by-nc-sa/4.0
Research on flexible load configuration of hydrogen production and storage and operation optimization of distribution network based on bi-level optimization
INTRODUCTION: High penetration of wind and photovoltaic generation causes voltage deviation, line congestion, and renewable curtailment in distribution networks. Hydrogen production and storage offer flexible regulation capabilities that can effectively improve system operational stability. OBJECTIVES: This study aims to construct a bi-level optimization framework for capacity allocation and day-ahead scheduling in a wind–PV–hydrogen coupled system. The goal is to enhance voltage quality, reduce thermal loading, and increase renewable energy utilization. METHODS: The upper level minimizes investment cost, network loss, and voltage deviation through a particle swarm optimization algorithm. The lower level solves a mixed-integer dynamic scheduling model in YALMIP considering grid trading cost, curtailment penalty, and peak-valley regulation benefits. RESULTS: Simulation results show that the minimum node voltage rises from 0.913 p.u to 0.952 p.u, while the maximum line loading decreases from 0.903 to 0.714 after introducing hydrogen flexibility. The system load curve becomes smoother and renewable consumption significantly improves. CONCLUSION: The proposed bi-level approach effectively enhances system flexibility and reduces operational constraints in distribution networks. The method offers practical support for coordinated planning of high-penetration renewable energy systems.
Copyright (c) 2026 Xuejie Wang, Changhao Song, Peng Ye, Tianyu Li, Zhiwei Xie, Shuo Yang, Jianming Qi, Yingxue Gao, Liang Dong https://creativecommons.org/licenses/by-nc-sa/4.0
Research on power generation prediction of photovoltaic power station based on improved neural network
The large-scale construction of photovoltaic power plants makes more and more green power connected and utilized, which also brings great impact on the carrying capacity of the power grid. Forecasting the power generation of photovoltaic power plants can better promote the stable regulation of the power grid and promote the consumption of new energy. In this paper, the influence of main environmental factors on photovoltaic power generation is analyzed by Pearson similarity, and a photovoltaic prediction model is established. The model is solved by combining adaptive genetic algorithm and improved Drosophila algorithm to optimize neural network. Finally, the historical sample data of photovoltaic power plants are used for simulation. By comparing the photovoltaic power generation prediction effects of single BP algorithm, GA-BP combined algorithm and GA-FOA-BP intelligent algorithm, it is proved that the method of combining improved Drosophila algorithm to optimize neural network and genetic algorithm to solve the prediction model can reduce the instability of photovoltaic output prediction to some extent and has good application value.
Copyright (c) 2026 Yuan Hu, Qin Xie, Zhao Liu, Mengjin Hu, Hongwei Li, Bo Yuan, Yaping Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Cause Diagnosis Method of Line Loss Anomaly Based on the Analysis of Electricity Fluctuation in the Substation Area
This paper presents a three-stage line loss anomaly diagnosis method based on power consumption fluctuation analysis in transformer districts. The proposed approach integrates causal forest modeling, SHAP value attribution, and TCN time series convolution networks to identify anomalies. By applying threshold-based analysis of power supply rate changes and consumption rate variations, it differentiates between grid-side anomalies, user-side anomalies, and composite anomalies. The causal forest model quantifies individual user processing effects, while the TCN network calculates load curve similarity to precisely detect hidden anomalies (such as minor power surges accompanied by load pattern deviations) that traditional methods might miss. SHAP values are used to visualize key feature contributions, enhancing diagnostic interpretability and field verification efficiency. Using real-world data from typical transformer districts, this study demonstrates the effectiveness and practicality of the proposed method, discusses its limitations, and outlines future research directions.
Copyright (c) 2026 Muxin Zhang, Zhijiang Ma, Rundan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
A Novel Power Load Identification Strategy Based on CEEMD-PCA-AE-Shapelet Dictionary Learning Algorithm
The development of the Energy Internet under the China 'dual-carbon' strategy has bring new challenges in load identification and anomaly detection, particularly in industrial applications. Traditional methods often struggle with the sparse, noisy nature of industrial load data, limiting their generalization ability and accuracy. This paper proposes a novel load identification method based on the CEEMD-PCA-AE-Shapelet dictionary learning algorithm, which addresses these challenges by combining an improved unsupervised anomaly detection model (CEEMD-PCA-AE) with the Shapelet dictionary learning algorithm. Firstly, CEEMD-PCA-AE enhances the model’s ability to handle non-linear and noisy data, significantly improving the generalization and accuracy of industrial load anomaly detection. Secondly, the Shapelet dictionary learning algorithm reduces computational complexity and improves model performance by incorporating dictionary learning into time-series classification. The proposed method outperforms traditional models, as demonstrated by numerical experiments, offering enhanced load identification accuracy, and improved detection of anomalies. This method has significant potential for optimizing the efficiency of energy management in industrial settings and can be applied globally, as shown by its effectiveness on real-world data from Liaoning Province, China.
Copyright (c) 2026 Junxiong Ge, Mingmin Yuan, Zhu Tang, Quancheng Pan, Congsu Jin, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
A novel dung beetle optimization algorithm of distribution network reconfiguration for power loss reduction and reliability improvement
In the evolution of energy systems, investigations into distribution networks have concentrated on enhancing reliability and optimizing performance. Distribution networks with distributed energy resources are expected to significantly enhance power accommodation capacity, but load flow distribution and network optimization remain key challenges. This study aims to develop an improved dung beetle optimization (IDBO) algorithm to minimize active power losses and node voltage deviations in distribution networks, while validating its superiority over existing algorithms across different network configurations. The IDBO algorithm is enhanced through three key strategies: (i) A variable spiral search strategy is employed to improve search efficiency and global exploration. (ii) A levy flight strategy is introduced to prevent algorithm premature convergence. (iii) A t-distribution adjustment strategy based on iteration count is adopted to strengthen local search capability. To verify whether the IDBO algorithm can achieve the optimal load flow distribution that meets the reliability requirements of distribution network operation, relevant validations have been conducted. Experimental results demonstrate that IDBO achieves faster convergence and superior performance on classical test functions. In practical applications to IEEE 33-bus and 69-bus distribution systems, it significantly reduces power losses and improves voltage profiles compared to other algorithms. The proposed IDBO algorithm provides an effective solution for distribution network reconfiguration, demonstrating enhanced optimization capability, improved convergence characteristics, and reliable performance across various operating conditions.
Copyright (c) 2026 Yanmin Wu, Ruiliang Guo, Lu Wang, Xiangqian Zhu, Zhenjie Wan https://creativecommons.org/licenses/by-nc-sa/4.0
Design of a Live-Line Inspection Robot System for Insulators in UHVDC Transmission Lines
To address the pain points of low-zero insulators in UHVDC transmission lines, which are prone to insulation flashover, and the low accuracy, reliance on manual labor, and weak anti-interference capabilities of traditional inspection methods, this study developed an integrated live-line inspection robot that combines mechanical climbing, precise inspection, intelligent control, and electromagnetic shielding. The robot system comprises a mechanical system (tracked movement mechanism, guiding and clamping mechanism), detection system (2500V high-voltage detection circuit, MEGA128 microcontroller control), control system (L298N motor drive, frequency-hopping spread spectrum wireless data transmission), and electromagnetic shielding system (multi-layer shielding shell, opto-isolation, multi-point common ground). An innovative "equipotential bypass" detection principle is proposed to eliminate leakage current interference, and an improved PSO-SVR fusion algorithm is introduced to optimize the detection data. Experimental verification showed that in laboratory tests, the robot exhibited excellent electromagnetic interference resistance (shielding effectiveness SE≥ 60 dB) and a resistance measurement error ≤±2.5%. In actual live-line testing of 500 kV/1000 kV lines, the detection error was ≤±3%, the detection cycle for a single string (28/32 pieces) was only 92~108s, the detection rate of degraded insulators was 100%, and it eliminated the need for power outages and manual tower climbing. This robot meets the "live-line, accurate, efficient, and interference-resistant" detection requirements of UHV lines, providing reliable technical support for the intelligent operation and maintenance of power grids and has broad engineering application prospects.
Copyright (c) 2026 Yunfeng Yan, Hao Sun, Fei Hao, Peng Chen, Tianlong Zhang, Zehua Yang https://creativecommons.org/licenses/by-nc-sa/4.0
Voltage Compensation-Type Current Limiter Based on Hybrid Distribution Transformer for Power Quality Enhancement
This paper presents a voltage compensation-type current limiter based on a hybrid distribution transformer (HDT) structure, which integrates a three-winding transformer, AC/DC converter, DC/AC converter, current-limiting unit, bypass switches, and a series transformer. The HDT operates under four distinct modes: steady-state operation, voltage recovery, active current limiting, and passive current limiting. The proposed system effectively compensates voltage sags and limits fault currents by dynamically switching between these modes. Simulation results demonstrate that the method can quickly restore load-side voltage during upstream voltage sags and efficiently limit fault currents during downstream faults. The proposed strategy ensures continuous and reliable operation of sensitive loads under fault conditions.
Copyright (c) 2026 Ruiayo Wang, Jianhua Wang, Han Yan, Weiming Chen, Yuanliang Fan https://creativecommons.org/licenses/by-nc-sa/4.0
Multi-State Reliability Modeling and Evaluation of Islanded Microgrids with Semi-Active Hybrid Energy Storage Systems
Accurate reliability assessment of islanded microgrids (iMGs) increasingly depends on intelligent system modeling and data-driven integration between physical assets and digital control frameworks. To address the limited adaptability of conventional reliability models, this paper proposes an ICT-integrated framework for reliability evaluation of iMGs equipped with semi-active lithium-ion battery-supercapacitor hybrid energy storage systems (HESS). The framework employs a 32-state Markov-based model to represent component-level degradation and failure dynamics, enabling state-aware reliability analytics through automatic state aggregation and transition probability learning. These states are further abstracted into representative operating modes that can be seamlessly interfaced with energy management systems for online evaluation. A minimum load curtailment strategy is embedded within a time-series Monte Carlo simulation environment to quantify the interactive impact between multi-state HESS behavior and overall system reliability. Standard indices-outage frequency, duration, and availability-are computed to characterize resilience under varying storage conditions. Comparative results verify that the proposed model substantially improves the reliability of iMGs by enabling degraded yet continuous operation during partial failures. The study provides a scalable and digitally implementable reliability framework for HESS-enabled microgrids, bridging the gap between detailed component modeling and real-time operational analytics.
Copyright (c) 2026 Wencong Wu, Haiqing Cai, Wei Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Assessing Lightweight YOLO Models for Vision-Based Fault Diagnosis in Wind Turbine and Cold Chain Inspection Systems
The reliability of wind energy systems depends on the timely detection of surface and structural defects in turbine blades. This paper compares six YOLO (You Only Look Once) architectures (v8n–v13n) for automatic fault detection in wind turbine inspection images. All models were trained under identical experimental settings and evaluated by precision, recall, mAP@0.50, mAP@[0.50:0.95], and inference latency. Results show that YOLOv12n achieved the highest performance (mAP@0.50 = 0.867, computed as the mean over three seeds using the Top-3 class protocol), while YOLOv10n delivered the lowest inference time of 0.7 ms. These findings support the suitability of lightweight YOLO variants for real-time fault inspection and predictive maintenance in wind energy systems and the same framework can be extended to visual quality inspection tasks in intelligent cold chain logistics systems.
Copyright (c) 2026 Siming Lin, Yongqiang Xiao, Yifang Gao, Rui Ni, Tianxiang Huang https://creativecommons.org/licenses/by-nc-sa/4.0
AC/DC Superimposed Breakdown Characteristics and Electric Field Simulation Analysis of Oil-Paper Insulation in Converter Transformers
This study investigates the electric field distribution and breakdown characteristics of oil-paper insulation in a 500 kV converter transformer under AC/DC superimposed voltages. The electric field distribution within the winding end insulation structure at the valve side was analyzed under various voltage conditions using finite element simulations. Experiments were conducted on insulating oil and oil-paper samples to measure their breakdown strengths under different AC/DC components. The results reveal that under DC voltage, the electric field concentrates inside the insulation paper, whereas under AC voltage, it is mainly distributed across the oil gap. Under AC/DC superimposed voltages, a decreasing AC ratio shifts the electric field from the oil gap to the insulation paper and increases the maximum electric field strength. As the DC component rises, the breakdown strength of both oil and oil-paper samples first decreases and then increases, with oil-paper insulation consistently exhibiting higher strength. These findings indicate that although oil-paper insulation can withstand higher electric fields and thus ensure transformer safety, low DC components may increase the risk of oil discharge or failure. Therefore, the design of next-generation high-voltage converter transformers must account for oil-paper insulation degradation under low DC ratios, and effective mitigation strategies—such as controlling the internal electric field or enhancing dielectric strength—are essential for ensuring reliability under AC/DC superimposed voltages.
Copyright (c) 2026 Xiaofan Huang, Jun Yan, Aiqiang Pan, Yufan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Wind Power Prediction Based on PSO-GSA-CS Synergistic Optimization of BP Neural Network
Accurate wind power prediction is essential for enhancing the dispatchability of wind power and the stability of power grid operation. However, traditional single-group intelligent algorithms for optimizing BP neural network modeling often suffer from large result fluctuations and poor stability. To address this, this paper proposes a wind power prediction model based on BP neural network optimized by a particle swarm–neighborhood gravitation–cuckoo collaborative optimization algorithm (PSGC). PSGC integrates the fast global convergence of particle swarm optimization (PSO), the Lévy flight characteristic of cuckoo search (CS), and the neighborhood gravitation mechanism of gravitational search algorithm (GSA) to maximize the advantages of each algorithm and overcome the limitations of single algorithms. This integration is applied to the initial optimization of the BP neural network’s weights and thresholds, followed by secondary optimization through the BP network algorithm, ultimately for wind power prediction. Results show that the PSGC-BP model performs well in prediction accuracy and stability. In the testing phase, the model’s root mean square error (RMSE) is 22.0127, mean absolute error (MAE) is 17.1045, and correlation coefficient (R) is 0.7903; in the prediction phase, RMSE is 45.2569, MAE is 27.9380, and R is 0.8408, with the smallest operating fluctuation, highest stability, and best comprehensive performance. This model provides a feasible method for wind power prediction, contributing to improved dispatchability of wind power and grid stability.
Copyright (c) 2026 Haijun Wang, Song Guo, Tao Jin, Xiaojiao Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on short-term photovoltaic power prediction based on LMD-IPSO-LSVM
With the rapid development of human society, resource shortages and environmental degradation have become increasingly pressing issues. To address these challenges, solar energy has garnered significant attention due to its high efficiency, safety, and pollution-free nature. This paper proposes a novel short-term photovoltaic power prediction framework based on an integrated LMD-IPSO-LSVM approach. The model's key innovation lies in its hierarchical decomposition-optimization architecture: First, Local Mean Decomposition (LMD) addresses the non-stationary and nonlinear characteristics of PV power data by decomposing original signals into physically meaningful Product Functions (PFs). Second, an Improved Particle Swarm Optimization (IPSO) algorithm featuring an adaptive inertia weight mechanism is developed to optimize LSVM hyperparameters for each PF component. This strategic integration enables the model to simultaneously capture complex temporal patterns while maintaining superior generalization capability. Experimental validation demonstrates that our IPSO achieves significantly faster convergence (46.3% improvement in convergence speed) and enhanced optimization precision compared to standard PSO, providing a solid foundation for accurate power forecasting. In order to evaluate the proposed methodology, comparative models including standalone LSVM and PSO-LSVM are also established and tested on the same dataset. Experimental results demonstrate that the proposed hybrid model called LMD-IPSO-LSVM achieves high prediction accuracy and better performance compared with other algorithms.
Copyright (c) 2026 Yanjuan Ma, Mai Jiang, Limin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Dynamic Spatial-Temporal Load Forecasting for Multi-Regional Power Systems via Graph Attention Networks
Accurate multi-region load forecasting (MRLF) can effectively ensure the stable operation of the power system and is essential for the economic dispatch of the power system. However, with the gradual deepening of the dynamic spatial dependence among multi-region loads and the unmeasured nature of loads, the challenge is posed to MRLF. To address this challenge, a graph attention learning-based load forecasting model for multi-region considering dynamic spatial correlations aggregation is presented in this paper. Firstly, the regional load series is decomposed into a trend component and a fluctuation component through the discrete wavelet transform. Secondly, a generative adversarial network with the concept of zero-sum game is proposed for adversarial training of spatio-temporal prediction models. Furthermore, reasonable future multi-regional load forecasting values are obtained through a aggregation module that aggregate the trend component and the fluctuation component. Finally, the multi-regional load data of the New York Independent System Operator (NYISO) is used as a case. Compared with the evaluation metrics of mainstream models, the model presented in this paper is effective and superior in MRLF.
Copyright (c) 2026 Junxiong Ge, Zhaojun Wang, Zhen Jiao, Lijun Liu, Xiao Li, Haimin Hong https://creativecommons.org/licenses/by-nc-sa/4.0
Optimization of operation and dispatching of integrated energy system based on Nash bargaining model
INTRODUCTION: With the growing energy demand, the Integrated Energy System (IES) has attracted wide attention for its high efficiency, economy, and environmental friendliness. Compared with traditional energy systems, IES realizes multi-energy complementarity through the coupling of electricity, heat, gas, and hydrogen. International research focuses on market mechanisms, renewable energy integration, and digital technologies, but issues such as wind-solar output uncertainty and fair benefit distribution in multi-party cooperation remain to be addressed. OBJECTIVES: This paper aims to construct an optimal operation and dispatching model for IES integrating renewable energy, gas-fired thermal power plants, and carbon capture power plants, handle wind-solar output uncertainty, and design a fair benefit distribution mechanism based on Nash bargaining theory to balance economy and environmental protection and maintain cooperation enthusiasm. Compared with traditional methods such as the Shapley value, which allocates benefits based on marginal contributions, the Nash bargaining approach emphasizes fair negotiation outcomes and better accommodates differences in participants' investments, risks, and bargaining power. METHODS: 1. Establish an IES mathematical model covering core equipment such as wind turbines, PV panels, P2G devices, hydrogen storage tanks, and CHP units. 2. Use KDE to fit wind-solar output marginal distribution, construct a joint probability model with Frank Copula function, and generate typical scenarios via K-means clustering. 3. Adopt a two-stage optimization: first, use mixed-integer linear programming to calculate the total alliance revenue; second, apply symmetric/asymmetric Nash bargaining models for benefit distribution. RESULTS: Based on Liaoning regional grid data, the tripartite alliance reduces system operation cost by approximately 22% and increases renewable energy consumption rate by 18% compared with independent operation. P2G and hydrogen storage realize time-shifting energy transfer, and CHP units adjust output to reduce costs. The Nash bargaining-based benefit distribution meets individual and collective rationality, with all parties’ revenues exceeding independent operation. CONCLUSION: The IES model integrating hydrogen storage, P2G, and carbon capture enhances multi-energy complementarity and time-shifting regulation capacity. The Nash bargaining mechanism ensures fair surplus distribution, providing theoretical and methodical references for multi-party IES collaborative operation.
Copyright (c) 2026 Yuwei Wang, Yu Shi, Rui Zhou, Yang Liu, Xin Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Transient Stability Evaluation Method of Power System Based on Improved Convolutional Neural Network
Transient stability analysis is a key link in the safe operation of power systems. However, traditional methods (such as time-domain simulation and direct methods) have problems such as low computational efficiency or limited applicability. Although artificial intelligence methods can enhance the evaluation speed, the existing shallow models have insufficient generalization ability in high-dimensional data classification and are mostly limited to binary stability determination, lacking quantitative evaluation. To this end, this paper proposes a transient stability evaluation method based on short-time disturbed trajectories and an improved convolutional neural network (CNN). Firstly, CNN is utilized to establish the mapping relationship between the short-term disturbance trajectory of the electrical quantity at the generator end and the transient stability of the system. Moreover, a sample matrix is constructed by considering the disturbance degree of the generator in the early stage of a fault to enhance feature robustness and reduce misjudgment and missed judgment. Secondly, optimize the network structure based on the inter-layer computing dimension of CNN to improve the accuracy of model evaluation. Furthermore, a composite model is constructed by combining the CNN feature extraction layer with the BP neural network. First, the samples are pre-classified, and then the transient stability margin is predicted to achieve quantitative evaluation. Finally, simulation results based on the IEEE 39-bus system demonstrate that the enhanced CNN model achieves 98.42% assessment accuracy, and maintains margin prediction errors below 3%. By enabling autonomous extraction of high-dimensional trajectory features, the proposed method overcomes the limitations of manual feature selection, offering novel insights for real-time security control in power systems.
Transient stability assessment in power systems
Transient stability margin quantification
Copyright (c) 2026 Huimin Peng, Mengyang Wu, Xiaojing Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
Stochastic Optimal Scheduling Analysis of Electric Vehicle Access to Microgrid Considering Multiple Uncertainties
The integration of new energy generation equipment into the microgrid can effectively reduce energy consumption and operational costs. However, due to the inherent spatial and temporal variability of sources such as wind and photovoltaic (PV) power, their integration poses challenges for grid stability. To address these issues, this paper proposes a stochastic optimal scheduling model for microgrids incorporating electric vehicle participation and multiple sources of uncertainty. Historical data on loads, wind power, and PV generation are first collected and modeled to capture their spatiotemporal uncertainties. Electric vehicles (EVs) are then integrated into the grid as bidirectional energy carriers, capable of both charging and discharging, thereby alleviating pressure on conventional energy storage systems. Since the charging and discharging processes contribute to battery degradation, participating EV users are provided with subsidies to compensate for associated costs. This mechanism reduces both the operational costs of the microgrid and the charging expenses for EV users, while enhancing power system stability. Simulation results demonstrate that, compared to the conventional distribution network operating independently, the proposed model significantly improves the overall benefits for both the microgrid and its users. Additionally, it achieves cost and carbon emission reductions, enhances system stability and economic performance, and offers a novel approach for optimal microgrid scheduling.
Copyright (c) 2026 Huang Yuxuan https://creativecommons.org/licenses/by-nc-sa/4.0
Construction and optimization technology of power grid dispatching knowledge graph based on multi-modal data fusion
The data in the field of power grid dispatching has multi-modal characteristics such as text, images, and time series. How to deeply integrate these heterogeneous data is a key challenge in building an intelligent knowledge graph. The research aims to construct and optimize a power grid dispatching knowledge graph based on multi-modal data fusion. To this end, a unified framework integrating text, images, and time series data is proposed. This framework first uses joint extraction technology to extract entity relationships from text; subsequently, an improved RESCAL model (fusing L2 regularization and data augmentation) is introduced for knowledge embedding to enhance generalization ability; for the multi-modal association problem, a cross-modal transformation network (CMTN) is designed to map different modal data to a shared semantic space to achieve precise retrieval. At the system level, the perceptual hashing algorithm is integrated for fast similarity matching, and a distributed storage architecture is adopted to ensure the efficient processing and dynamic update of massive multi-modal data. Experimental results show that the joint extraction technique achieves high accuracy and recall in entity recognition and relationship extraction tasks, with F1-scores of 0.82 and 0.86 on the PowerGraph and OmniCorpus datasets, respectively. The CMTN exhibits superior performance in cross-modal retrieval, with mean inter-modal similarities of 0.72 and 0.75 and Top-1 alignment accuracies of 0.85 and 0.88 on the two datasets. The constructed knowledge graph effectively supports intelligent power grid dispatching by accurately representing and managing complex multi-modal data. The research provides an effective solution for enhancing the representation, association, and update capabilities of grid dispatching knowledge through multi-modal data fusion technology. At the same time, it can focus on the lightweighting and real-time optimization of the model to promote the integration of this technology into online intelligent dispatching systems.
Copyright (c) 2026 Wei Li, Chao Hu, Siqi Shen, Zhangguo Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Distribution Network Target Framework Planning Algorithm Based on Fuzzy Optimization and Grey System Theory
INTRODUCTION: The global energy transition, driven by the rapid growth of distributed renewable energy, stochastic load profiles (e.g., EV charging spikes), and conflicting stakeholder objectives, has brought unprecedented complexities to distribution network planning. Traditional deterministic methods fail to handle qualitative fuzziness (e.g., subjective reliability thresholds) and quantitative uncertainty (e.g., sparse historical data), leading to inflexible and inefficient solutions. This study addresses these challenges by developing a hybrid planning framework. OBJECTIVES: This paper aims to solve the dual challenges of qualitative fuzziness and quantitative uncertainty in distribution network planning, providing a systematic solution to accommodate distributed renewable energy, handle load uncertainty, and balance conflicting stakeholder preferences through integrating fuzzy optimization theory and grey system theory. METHODS: The hybrid algorithm combines fuzzy optimization and grey system theory. Fuzzy optimization uses triangular fuzzy numbers for load growth rates ([3%, 5%, 8%]) and trapezoidal fuzzy intervals for voltage constraints ([−10%, −5%, 5%, 10%]) with membership functions (threshold λ≥0.8) to convert qualitative requirements into solvable constraints. Grey system theory applies the GM(1,1) model for load forecasting (achieving 4.2% MAPE with 15-month data) and grey relational analysis (GRA) for data-driven objective weighting to eliminate expert bias. An improved particle swarm optimization (IPSO) algorithm is used for optimization, validated in a 33-node network with 8.5 MW PV and 6 MW wind capacity. RESULTS: In the 33-node case study, compared to the deterministic genetic algorithm (D-GA), the hybrid algorithm reduces lifecycle costs by 19% (from $8.91M to $7.23M), increases renewable energy accommodation by 24% (from 9.8 MW to 12.3 MW), and improves the system average supply availability index (ASAI) from 99.92% to 99.95%. Under extreme uncertainties (±40% renewable output, ±30% load shifts), cost deviations remain within 6% and reliability metrics within 5%, demonstrating strong robustness. CONCLUSION: This research presents a robust hybrid framework that bridges fuzzy qualitative reasoning and grey data efficiency, effectively addressing both qualitative fuzziness and quantitative uncertainty in distribution network planning. It provides a science-based tool for resilient grid design, with potential for extension to multi-energy system integration and real-time optimization in future work.
Copyright (c) 2024 Jinsen Liu, Molin He, Jie Wang, Jie Lu https://creativecommons.org/licenses/by-nc-sa/4.0
Distributed photovoltaic power prediction considering spatiotemporal correlation and dual Attention-LSTM
Predicting the power output of photovoltaic clusters is crucial for optimizing regional solar power scheduling. To enhance the accuracy of distributed photovoltaic station power forecasts, a method incorporating spatiotemporal correlation and dual Attention-LSTM is introduced. The K-means algorithm is first employed to classify the distributed photovoltaic power station clusters in the area. The reference station for the target photovoltaic station is determined by calculating the Euclidean distance between the target station and the typical daily power profiles of other stations in the cluster. Simultaneously, pivotal weather features that influence photovoltaic output are ascertained through computation of the Pearson correlation coefficient. Subsequently, an Attention-LSTM-based power prediction and error correction model is constructed, utilizing both meteorological and power traits as input variables to finalize the photovoltaic power generation forecast. To validate the approach, a simulation analysis is conducted using empirical data from Arizona, USA. The experimental results indicate that the suggested method greatly improves the accuracy of predictions for distributed photovoltaic power.
Advanced Wireless Power Transmission Technology
Copyright (c) 2024 Yueyuan Zhang, Yifan Zhang, Dazhi Pan, Mingyu Sun, Huawei Mei, Wangbin Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Short-term photovoltaic power prediction based on dual decomposition with TCN-Informer-xLSTM
As renewable energy generation is increasingly integrated into power grids worldwide, the random nature of renewable energy output poses significant challenges to the stability of power systems. Therefore, it is essential to accurately predict the output of renewable energy sources. In this paper, a dual decomposition algorithm based on variational mode decomposition (VMD) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed to decompose the original photovoltaic power sequence and combine the entropy values of the subsequences to obtain the predicted sequences for the high frequency and low frequency components. Then, different prediction models are used for the high-frequency and low-frequency sequences to predict the photovoltaic outputs, where the Temporal Convolutional Networks (TCN)-Informer model is used for the high-frequency component and the xLSTM model is used for the low-frequency component, and finally, the RIME algorithm is applied to find the optimization of the hyperparameters. The results of simulation analysis show that the quadratic decomposition method proposed in this paper significantly improves the prediction accuracy of photovoltaic sequences and reduces the computational complexity
Copyright (c) 2024 Guancheng Jin, He Jiang, Mofan Wei, Rui Guo https://creativecommons.org/licenses/by-nc-sa/4.0
A reliability enhancement strategy of critical nodes in power system communication network based on quantitative calculation method and criticality index
INTRODUCTION: Power system communication networks are essential for smart grid operations, enabling real-time monitoring and control. Disruptions at critical communication nodes can jeopardize grid stability and lead to cascading failures, highlighting the need for accurate reliability assessment of these vital components. However, traditional methods often overlook the complex, dynamic, and interdependent nature of modern communication infrastructures. OBJECTIVES: This paper aims to develop a precise and scalable methodology for assessing and enhancing the reliability of critical nodes in smart grid communication networks. METHODS: The proposed approach integrates probabilistic failure modeling, graph-theoretic analysis, and heuristic optimization. Key techniques include a newly designed Criticality Index (CI) accounting for failure probabilities, repair dynamics, and topological relevance; a Monte Carlo simulation framework to assess network behavior under stochastic disturbances; and a genetic algorithm (GA) for optimizing node reinforcement strategies. RESULTS: Experiments conducted on the IEEE-118 bus system demonstrate that the GA-CI methodology improves the Network Robustness Index by 12.45%, consistently outperforming baseline methods with acceptable computational efficiency. CONCLUSION: The proposed framework provides a robust and interpretable solution for reinforcing critical communication infrastructure in smart grids. It holds potential for broader application in the reliability assessment of other complex networked systems.
Copyright (c) 2024 Xiaoyu Deng, Yabin Chen, Yu Sui, Hao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent substation communication network fault location method based on dynamic spatiotemporal graph association perception
INTRODUCTION: Accurately locating faults in intelligent substation communication networks is crucial for power grid safety. Existing methods fail to fully capture dynamic fault characteristic evolution and complex dependencies within network topologies OBJECTIVES: This paper aims to (1) model spatiotemporal fault features in communication networks, (2) enhance fault pattern capture through multi-view learning, and (3) improve fault location accuracy. METHODS: We propose a multi-view spatiotemporal dynamic graph network. First, a multi-view graph neural network models spatial dependencies via cross-view comparative learning using topological and attribute data. Second, a gated recurrent unit with dynamic time windows extracts temporal evolution trends, focusing on local fault patterns and short-term dependencies. RESULTS: Evaluations on a 220kV substation communication network show our method achieves higher fault location accuracy versus baselines, effectively capturing spatiotemporal fault characteristics. CONCLUSION: The proposed framework addresses dynamic fault evolution and topological dependencies, providing a robust solution for intelligent substation fault diagnosis.
Copyright (c) 2024 Ligang Ye, Wenzhang Li, Jing Zhao, Yuanyuan Liu https://creativecommons.org/licenses/by-nc-sa/4.0