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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.
AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability
Copyright (c) 2026 Yuwei Wang, Yu Shi, Rui Zhou, Yang Liu, Xin Wang 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
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
Deep Learning and GIM Standard-Based Intelligent 3D Modeling Algorithm for Substations with Distributed Collaborative Design System
INTRODUCTION: Contemporary substation design methodologies encounter fundamental limitations in achieving optimal geometric precision and collaborative efficiency, particularly when addressing the integration of heterogeneous data sources while maintaining strict adherence to Geographic Information Modeling (GIM) standards. Existing computational approaches demonstrate significant deficiencies characterized by prolonged processing durations and constrained accuracy levels, thereby necessitating the development of innovative solutions that leverage cutting-edge artificial intelligence techniques to overcome these systematic challenges. OBJECTIVES: This investigation aims to develop and validate a comprehensive intelligent 3D modeling algorithm specifically designed for electrical substation applications that seamlessly integrates advanced deep learning methodologies with rigorous GIM standard compliance and sophisticated distributed collaborative design functionalities, while simultaneously achieving substantial improvements in geometric accuracy and computational efficiency compared to conventional design paradigms. METHODS: The proposed algorithmic framework employs sophisticated hierarchical neural network architectures that incorporate multi-scale convolutional feature extraction mechanisms and adversarial generative training protocols. The comprehensive system architecture integrates four critical components: intelligent data acquisition and preprocessing modules, advanced deep learning computational engines, automated GIM standard compliance verification systems, and distributed collaborative design platforms. Experimental validation was conducted using an extensive dataset encompassing 12,847 technical engineering drawings, 1,156 high-resolution point cloud segments, and 3,428 photogrammetric image collections, with comprehensive field testing involving up to 32 concurrent collaborative users across diverse operational scenarios. RESULTS: The developed intelligent modeling algorithm achieved exceptional geometric accuracy of 96.8% compared to 87.3% demonstrated by traditional methodologies, representing a substantial 9.5 percentage point improvement in modeling precision. Computational efficiency demonstrated remarkable optimization with processing time reduced by 94%, decreasing from the conventional range of 180-240 minutes to an unprecedented 12.4 minutes per complete substation model. Extensive field validation trials confirmed seamless collaborative scalability with negligible performance degradation under multi-user operational conditions, while maintaining GIM standard compliance exceeding 99.2% across all tested configurations and operational scenarios. CONCLUSION: The developed intelligent 3D modeling system establishes a new technological paradigm for substation design applications, delivering exceptional improvements in both geometric accuracy and computational efficiency while maintaining stringent GIM compliance requirements. The framework's robust integration capabilities enable seamless deployment within existing power system management infrastructures without necessitating extensive modifications to established operational workflows, thereby providing a comprehensive foundation for next-generation collaborative engineering design platforms in critical infrastructure applications.
Copyright (c) 2024 Zhongsheng Kan, Yu Xin, Delong Meng, Jie Chen, Xiaoming Ju https://creativecommons.org/licenses/by-nc-sa/4.0
Phase Robust Optimization of PV-Energy Storage Microgrid Based on Deep Reinforcement Learning and Mixed Integer Constraint Model
The pronounced dependence of photovoltaic (PV) generation on meteorological conditions, coupled with substantial fluctuations in load demand, renders conventional deterministic optimization approaches inadequate. Addressing the need for robust multi-phase decision-making across temporal domains (e.g., day-ahead scheduling and real-time adjustment) and the coordinated optimization of continuous variables (such as energy storage charge/discharge rates) and discrete variables (such as unit commitment states), this research proposes a phased robust optimization strategy for PV-storage microgrids. This strategy integrates Deep Reinforcement Learning (DRL) with a Mixed-Integer Constrained Model (M-ICM). The methodology explicitly accounts for the coupling effects between irradiance intensity, temporal sequence efficiency, and the state-of-charge of energy storage systems. This ensures that the microgrid control system provides sufficient resilience mechanisms for dynamic energy allocation in practical applications, facilitating global optimization of microgrid energy utilization. The simulation results show significant improvements over conventional methods, which includes reduction in time-to-peak under dynamic balancing conditions, maintenance of lower output current-to-power ratios, and enhanced convergence speed of the neural network model.
Copyright (c) 2024 Wei Li, Zhihang Qin https://creativecommons.org/licenses/by-nc-sa/4.0
Fine-Tuning the Qwen2.5-VL Model for Intelligent Applications in the Electrical Domain
This study explores the fine-tuning application of the Qwen2.5-VL multi modal large model in the electrical domain. The electrical industry faces numerous challenges in maintaining and managing complex electrical systems. Traditional methods often rely on manual inspection and analysis. With the rapid advancement of artificial intelligence (AI) technologies, there is a growing need to explore how these tools can be applied to improve efficiency and accuracy in the electrical domain. Qwen2.5-VL is a state-of-the-art visual language model. We adopted the LoRA (Low Rank Adaptive) method to fine tune the model, which enables efficient parameter updates in low resource environments while maintaining high performance. This study analyzes the data characteristics and task requirements in the electrical domain, designs fine-tuning strategies with a focus on image-based applications, including data preprocessing, model fine-tuning, and training parameter optimization. The experimental re-sults show that the fine tuned model has achieved significant performance im-provements in tasks such as electrical equipment fault detection, image recogni-tion, and text classification. This study provides new ideas and methods for the application of artificial intelligence in the electrical domain, which is of great significance for promoting the development of electrical intelligence.
Copyright (c) 2024 Yao Song, Chunli Lv, Kun Zhu, Xiaobin Qiu https://creativecommons.org/licenses/by-nc-sa/4.0
Demand Response Control Strategy of Photovoltaic Grid-connected Inverter Based on Improved Linear Active Disturbance Rejection Dynamic Decoupling
In power systems, because the Auto Disturbances Rejection Controller (ADRC) performs relatively reliable stability and safety, it finds extensive application in the design of power inverters. However, its performance is constrained by inherent response lag and limited harmonic suppression capabilities. To address the evolving demands of modern power systems, Linear Active Disturbance Rejection Control (LADRC) has emerged as an upgraded alternative to conventional control strategies through its enhanced adaptability in practical applications. We propose a frequency-domain-equivalent-based LADRC control strategy, where systematic parameter calibration is implemented through transfer function methodology. Subsequent fine-tuning of critical parameters, which includes proportional gain, derivative gain, observer's natural frequency, control gain and so on, are made fine adjustments to optimize circuit performance. Experimental validation confirms the efficacy of first-order LADRC in improving voltage-current conversion characteristics of LLC-operated transformers. Furthermore, an adaptive control framework for third-order LADRC is established to achieve coupled parameter optimization. This research innovatively incorporates Particle Swarm Optimization (PSO) into the LADRC parameter configuration process, enhancing adjustment efficiency and facilitating optimal solutions. Simulation results verify that the enhanced LADRC effectively mitigates phase delay and improves dynamic response characteristics. The modified PSO algorithm exhibits technical superiority with 11.7% precision enhancement. The response speed has obvious technical advantages, and the coefficient of variation is significantly lower than that of the comparison algorithm, which verifies the advantages of the improved PSO algorithm in robustness. The proposed control strategy successfully compensates for ADRC limitations, achieving an optimal balance between control precision and steady-state performance through gain regulation in repetitive control systems. This advancement will provide critical technical support for addressing control challenges in renewable energy integration.
Active Disturbance Rejection Dynamic Decoupling
Copyright (c) 2024 Chao Zhang, Yangrui Zhang, Yongliang Jia, Peng Tao, Guinan Han, Junpeng Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
A fault diagnosis and location method for power grid simulators based on voltage threshold and MCNN
To accommodate the testing requirements of high-power wind turbines, this paper designs a power grid simulator topology and investigates fault diagnosis and localization methods by integrating mathematical models and neural networks. To address the drawback of lengthy computation times associated with intelligent diagnostic methods, this paper employs a threshold-based approach using voltage mathematical models to achieve rapid preliminary diagnostics. To address the positioning challenges brought about by symmetrical structures, a multi-layer convolutional neural network (MCNN) model is utilized to achieve accurate positioning. To tackle the issue of insufficient fault samples, a sliding window technique and frequency domain transformation methods are applied to expand the sample set, enabling the diagnosis and localization of 36 types of faults. This paper builds an inverter-side model of the power grid simulator using Simulink to verify the proposed method. And the diagnostic accuracy rate reaches 100%, and the overall localization accuracy exceeds 96%.
Copyright (c) 2024 Jinyue Su, Yingwen Yang, YiLin Yu, Yueshuo Li, Shiwei Zhao, Zhidong Wang, Ling Yang, Fengqiang Deng https://creativecommons.org/licenses/by-nc-sa/4.0
On-Site Protection Scheme for Microgrid with High Penetration Rate of Grid-Forming Inverter Interfaced Distributed Energy Resources
INTRODUCTION: Renewable energy represented by grid forming inverter interfaced distributed energy resources is increasingly connected to microgrids that can operate independently and have various modes, resulting in traditional relay protection schemes that cannot meet the requirements of protection selectivity and speed. OBJECTIVES: To this end, the fault characteristics of the grid forming distributed energy resources are analyzed. On this basis, the current, voltage and phase angle characteristics between them under the grid-connected/islanded operation mode of the microgrids are analyzed. METHODS: Aiming at the problem that the traditional protection loses selectivity, the phase angle difference between the pre-fault voltage and the post-fault current is used to judge the fault direction, and the internal fault acceleration factor of the integrated phase angle difference and the measured impedance is used to improve the action equation of the inverse time overcurrent protection. Aiming at the problem that the fault current is too small and the inverse-time overcurrent protection cannot be started when the two-phase short-circuit fault occurs in the islanded state, a low-voltage protection criterion based on the comprehensive phase angle difference characteristics is set up, and a microgrid protection scheme combining improved inverse-time over-current protection and low-voltage protection is obtained. RESULTS: The model is built by PSCAD, the theoretical analysis and simulation results show that the scheme can operate quickly and reliably in the grid-connected/islanded operation state of the microgrid. CONCLUSION: The results verify the effectiveness of the proposed protection scheme.
Inverter Interfaced Distributed Energy Resources
Voltage and Current Phase Angle Difference
Copyright (c) 2024 Yubo Yuan, Juan Li, Yunlong Jiang, Sudi Xu, Haiou Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Application of Offshore Wind Power Digital Twin Technology in Remote Operation and Maintenance
INTRODUCTION: Offshore wind (OsW) energy has emerged as a key factor in the global transition towards high-energy-return renewable energy, with stable winds and minimal land take. However, the Maintenance and Operation (O&M) of OsW wind farms are very challenging due to their hostile marine conditions, huge running costs, and poor accessibility. OBJECTIVES: To address these issues, the proposed method offers a Digital Twin (DT) strategy designed to enhance the remote operation and maintenance (O&M) of OsW wind power equipment. From the use of high-resolution global OSW wind turbine observations from Sentinel-1, combined with domain-specific feature engineering and data preprocessing, including outlier removal and normalization, the approach provides robust input for modelling and analysis. METHODS: One of the most important aspects of the system is the integration of real-time sensor feeds through Arduino devices, secure data exchange through OPC UA, and middleware processing through Node-RED. The sensor-based data architecture feeds into a Unity 3D-based digital twin environment, which continuously synchronizes virtual models with the physical conditions of the turbines. RESULTS: Besides, fault classification is handled with a Categorical Network (CatNet), where attention mechanisms and convolutional layers are used to detect abnormalities such as gearbox faults, generator faults and yaw misalignment. Interactive dashboards, 3D visualization, and predictive analytics are supported within the framework, enabling operators to monitor, diagnose, and control offshore turbines remotely. CONCLUSION: Ultimately, this approach significantly reduces unplanned downtime, enhances safety, and maximizes power output through intelligent, data-driven decision-making.
Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems
Copyright (c) 2024 Huatao Si, Xiaotao Chen, Qingyu Men https://creativecommons.org/licenses/by-nc-sa/4.0
Data-Driven Decision-Making Method of Intelligent Supervision and Command Platform in Offshore Wind Power Operation and Maintenance
INTRODUCTION: Innovations in offshore wind farm operation and maintenance require intelligent monitoring platforms that can leverage high-resolution SCADA data to enhance predictive precision and operational effectiveness. With the use of deep learning algorithms, specifically LSTM, this work achieves improved forecasting and anomaly detection accuracy on a real wind turbine dataset recorded in Turkey in 2018. OBJECTIVES: The proposed approach involves extensive data preprocessing, including cleaning, synchronization, and normalization, followed by advanced feature extraction using signal processing transforms such as the Fast Fourier Transform and wavelet transforms. METHODS: Different predictive models, including Linear Regression, Random Forest Regression, Support Vector Regression, Gradient Boosting Machines, and LSTM, were trained and tested within a Python setting. The LSTM model achieved a remarkable improvement, with a Mean Absolute Error of 78.6 kW, compared to traditional machine learning methods such as RF Regression, SV Regression, and Gradient Boosting Machines. The enhanced accuracy results from the LSTM's ability to derive intricate temporal patterns and nonlinear relationships inherent in sequential turbine operational data. RESULTS: The results affirm the potential of deep learning approaches in reshaping offshore wind turbine management and highlight the importance of tailored temporal modeling for resolving the specific challenges of renewable energy systems. CONCLUSION: The system not only accurately predicts power production but also performs anomaly detection and optimises maintenance scheduling, resulting in enhanced reliability and energy production for offshore wind farms. By integrating these data-oriented approaches with an intelligent command and supervision system, the strategy facilitates proactive decision-making and real-time operation control.
Copyright (c) 2024 Jia Kun Wang, Yi Liu, Suowei Song https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the relationship between the digital transformation of new energy enterprises in the context of electricity reform
Copyright (c) 2026 Chunxi Wang, Tong Shi https://creativecommons.org/licenses/by-nc-sa/4.0
Towards Real-Time Spatial Distance Monitoring of Power Transmission Lines Using LiDAR Point Clouds and Visual Imaging
INTRODUCTION: Efficient monitoring of power transmission lines is paramount to grid safety, clearance violation prevention, and uninterrupted supply of electricity. Classic inspection approaches like ground surveys by manual methods and visual inspections by drones are time-consuming, costly, and susceptible to human error. OBJECTIVES: Current LiDAR-based approaches are limited in automation, with extensive post-processing based on manual intervention. Additionally, most existing models are not scalable and fail under changing environmental conditions because of a lack of generalization. In this research, a spatial monitoring platform that combines LiDAR point clouds with high-resolution imagery through RandLA-Net is presented for semantic segmentation and hazard detection. METHODS: Combining geometric information (LiDAR) and visual features (images) with an optimized RandLA-Net architecture allows for accurate, real-time infrastructure features and hazard detection in dense or cluttered scenarios. RESULTS: The system presented here attained a semantic segmentation accuracy of 99.1% and a mean Intersection over Union (mIoU) of 93.2%. Spatial distance estimation had a low Mean Absolute Error (MAE) of 0.16 meters and Root Mean Square Error (RMSE) of 0.23 meters. The rate of safety violations detected never exceeded 4% among all object pairs. Compared to alternative techniques the proposed approach offers higher segmentation accuracy and more comprehensive hazard detection. CONCLUSION: It uniquely combines LiDAR and image data with advanced algorithms for precise, real-time distance measurement and monitoring. This study provides a cost-effective, scalable, and real-time-enabled monitoring solution, lessening reliance on human inspections and hugely enhancing hazard detection accuracy for power transmission infrastructure.
Copyright (c) 2024 Li Zhendong, Wang Feiran, Han Geng, Guo Xinyang, Shi Zhaoyang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Mechanism of Power Quality in Renewable Energy Marketing Based on BMA Algorithm
INTRODUCTION: Based on the BMA (Bayesian Model Average) algorithm, this paper deeply studies the mechanism of power quality in renewable energy marketing. By collecting a large amount of renewable energy market data and utilising the BMA algorithm for model building and data analysis, this paper aims to reveal the internal relationship between power quality and renewable energy marketing and explore its impact on actual market operations. OBJECTIVES: In the context of data analysis, this paper selects multidimensional information, including sales data, user feedback data, and power quality monitoring data, from the renewable energy market over recent years. By integrating and analysing these data with the BMA algorithm, we find that power quality has a significant positive impact on the marketing of renewable energy. METHODS: Specifically, when power quality is improved by 10%, the sales of renewable energy products are expected to increase by approximately 6% on average, and customer satisfaction will rise by 7% accordingly. This data shows that improving power quality can effectively promote the expansion of the renewable energy market and enhance user loyalty. Our research, leveraging the BMA algorithm, reveals that improving power quality serves as a dual catalyst for promoting renewable energy. RESULTS: Firstly, it directly bolsters consumer trust and purchase intention towards renewable energy products. Secondly, it indirectly enhances the market competitiveness of these products by optimising their performance and reducing failure rates. These insights provide renewable energy enterprises with a pivotal foundation for optimising their marketing strategies. CONCLUSION: In conclusion, power quality emerges as a crucial factor in the marketing of renewable energy. By prioritising its improvement, enterprises can simultaneously enhance sales, customer satisfaction, and product competitiveness, thereby underscoring the importance of power quality as a core component of their marketing strategy.
Copyright (c) 2024 Qinghua Fan https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Optimization of Combustion Process and NOx Emission Control of Power Plant Boiler Based on Deep Learning and Multi-objective Optimization
This study proposes an intelligent optimization and nitrogen oxide (NOx) emission control method for power plant boiler combustion processes by integrating deep learning and multi-objective optimization. While traditional empirical tuning and single-objective algorithms struggle with dynamic, multi-variable combustion environments and lack real-time adaptability and synergistic optimization of efficiency and emissions, this research addresses these gaps by establishing a rolling optimization model that considers load and emissions. By analyzing the relationship between boiler combustion efficiency and nitrogen oxides generation, a rolling optimization model considering load and emission is established. The study analyzes and predicts the operation data and optimizes the combustion strategy in real time by a dynamic multi-objective optimization evolutionary algorithm. Performance evaluation shows that the model achieves high prediction accuracy, with an average absolute error of 2.36×10-5 kW for boiler load, and outperforms existing models in key metrics such as ignition success rate (98.7%) and load adjustment accuracy (3.4 MW). The approach significantly improves combustion efficiency and tightens NOx control, reducing energy waste and improving power plant energy efficiency. These advances demonstrate their effectiveness in improving combustion efficiency, enhancing nitrogen oxide control, and reducing energy waste, providing a powerful solution for operating smart power plants that integrates real-time adaptability and multi-objective synergy, outperforming traditional methods.
Copyright (c) 2024 Xiaowei Chen, Lei Yan https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data
Introduction: The rapid expansion of energy networks has significantly increased energy consumption, resulting in higher electricity costs. Abnormal energy usage in buildings and industries, often caused by system malfunctions, leads to substantial energy waste. Detecting such anomalies is essential for cost control and efficient energy management. Objectives: This study aims to develop a deep learning-based method to detect anomalies in high-dimensional energy metering data, overcoming the limitations of existing techniques that struggle with data complexity and lack effective contextual analysis. Methods: High-dimensional metering data from a city energy provider is processed using a Convolutional Autoencoder (CAE) to extract deep features and reduce dimensionality. These features are then fed into a Cascaded Long Short-Term Memory (CLSTM) network, which identifies anomalous patterns in the data. Results: The cascaded CLSTM model effectively detects anomalies in the energy consumption data by accurately predicting deviations from normal patterns. Conclusion: The proposed CAE-CLSTM approach enhances anomaly detection in complex energy datasets, enabling more effective monitoring and reducing unnecessary energy waste and costs.
Copyright (c) 2024 Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Coal Energy Production Efficiency Improvement Strategy with Integrated Genetic Algorithm and Its Economic Impact Assessment
INTRODUCTION: Global energy systems heavily rely on coal energy generation, particularly in emerging nations. OBJECTIVES: Strategies that maximize the efficiency of coal energy generation while limiting environmental harm are essential to addressing these issues. With an emphasis on increasing productivity and minimizing environmental effects, this study suggests an integrated strategy for optimizing coal energy production processes using Genetic Algorithms (GA). METHODS: Key factors, including GDP growth rate, pollution abatement investment, coal intensity, and clean technology efficiency, are all optimized using GA in the suggested approach. Finding the best combination of these factors to maximize coal production efficiency while reducing CO2 emissions and other pollutants is made possible by GA-based optimization. A Social Cost-Benefit Analysis (SCBA) and environmental impact appraisal are also included to assess various scenarios' economic and environmental consequences. The findings show that, particularly in situations with slower GDP growth, more pollution abatement expenditures and cleaner technology adoption result in notable emissions reductions and increased overall efficiency. RESULTS: The results show how crucial it is to balance environmental sustainability and economic prosperity. The study offers insightful information to industry executives and regulators, highlighting GA's potential to maximize the efficiency of coal energy generation. CONCLUSION: Scenario A provided the best economic advantages, with a greater GDP growth rate and higher environmental costs.
Copyright (c) 2024 Fangmin Chen, Zheng Ma https://creativecommons.org/licenses/by-nc-sa/4.0