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Fluctuation Classification and Feature Factor Extraction to Forecast Very Short-Term Photovoltaic Output powers

CSEE Journal of power and energy systems 2025年第2期11卷 661-670页

作者： Mao Yang Xiaoxuan Shen Dawei Huang Xin Su Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China

In recent years, a spurt of photovoltaic power generation has brought certain impact on stability of the power system, which puts forward higher requirements on accuracy of photovoltaic power prediction. Therefore, this paper proposes a hybrid power prediction model based on fluctuation classification and feature factor extraction. First, based on fluctuation characteristics of photovoltaic power, fluctuation classification is applied to forecast power before the day, and weather is divided into complex fluctuation types and simple types. Then, parallel factor algorithm is used to reduce prediction model redundancy, which can reduce high-dimensional numerical weather prediction feature matrix to extract relevant features. Finally, the Long Short-Term Memory (LSTM) deep learning model is used to forecast very short-term photovoltaic power. The proposed hybrid model is compared with other methods, and photovoltaic data from several sites are selected for comparison and validation in this paper. simulation results show that very short-term prediction method of photovoltaic power proposed in this paper can significantly improve prediction accuracy.

关键词： Deep neural networks fluctuation classification PARAFAC photovoltaic generation very short-term forecasting

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Reactive power Optimization Model of Active Distribution Network with New energy and Electric Vehicles

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energy Engineering 2025年第3期122卷 985-1003页

作者： Chenxu Wang Jing Bian Rui Yuan Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control&Renewable Energy TechnologyMinistry of EducationJilin132011China

Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is ***,the k-medoids clustering algorithm is used to divide the reduced power scene into ***,the discrete variables and continuous variables are optimized in the same period of ***,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging *** to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution *** simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.

关键词： Active distribution network new energy electric vehicles dynamic reactive power optimization kmedoids clustering hybrid optimization algorithm

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GD-YOLO:A Network with Gather and Distribution Mechanism for Infrared Image Detection of Electrical Equipment

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Computers, Materials & Continua 2025年第4期83卷 897-915页

作者： Junpeng Wu Xingfan Jiang Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China School of Electrical Engineering Northeast Electric Power UniversityJilin132012China

As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical *** response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution *** combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel ***,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution ***,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over *** GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,***,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.

关键词： Infrared image detection aggregation and distribution mechanism sample imbalance strategy lightweight structure

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Investigating Black-Box Model for Wind power Forecasting Using Local Interpretable Model-Agnostic Explanations Algorithm

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CSEE Journal of power and energy systems 2025年第1期11卷 227-242页

作者： Mao Yang Chuanyu Xu Yuying Bai Miaomiao Ma Xin Su Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Daqing Power Industry Bureau Daqing 163000China

Wind power forecasting(WPF)is important for safe,stable,and reliable integration of new energy technologies into power *** learning(ML)algorithms have recently attracted increasing attention in the field of ***,opaque decisions and lack of trustworthiness of black-box models for WPF could cause scheduling *** study develops a method for identifying risky models in practical applications and avoiding the ***,a local interpretable model-agnostic explanations algorithm is introduced and improved for WPF model *** that basis,a novel index is presented to quantify the level at which neural networks or other black-box models can trust features involved in ***,by revealing the operational mechanism for local samples,human interpretability of the black-box model is examined under different accuracies,time horizons,and *** interpretability provides a basis for several technical routes for WPF from the viewpoint of the forecasting ***,further improvements in accuracy of WPF are explored by evaluating possibilities of using interpretable ML models that use multi-horizons global trust modeling and multi-seasons interpretable feature selection *** results from a wind farm in China show that error can be robustly reduced.

关键词： Black-box model correlation analysis feature trust index local interpretability local interpretable modelagnostic explanations(LIME) wind power forecasting

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Abnormal Data Identification and Reconstruction Based on Wind Speed Characteristics

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CSEE Journal of power and energy systems 2025年第2期11卷 612-622页

作者： Mao Yang Tian Peng Wei Zhang Xin Su Chao Han Fulin Fan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China Department of Electronic and Electrical Engineering University of StrathclYdeGlasgow G11XWUnited Kingdom

High availability of wind power data is the basis for wind power research, but there are a large number of abnormal data in actual collected data, which seriously affects analysis of wind power law and reduces prediction accuracy. Measured power data of wind farm are analyzed, influence of wind speed fluctuation characteristics on wind power is discussed, and abnormal points are identified for data of different wind types. The Cluster-Based Local Outlier Factor (CLOF) algorithm based on K-means is used to identify outlier abnormal points, and conditional constraints based on physical background are used to identify accumulation abnormal points. Reconstructed data segment is divided according to fluctuation of wind speed. The Bidirectional Gate Recurrent Unit (BiGRU) model with wind speed as input reconstructs fluctuation segment data, and bi-directional weighted random forest model reconstructs stationary segment data. Based on analysis of measured data of a wind farm, results show the method can effectively identify various abnormal data, and complete high-quality reconstruction of data, thereby improving accuracy of wind power prediction.

关键词： Abnormal data bidirectional weighted random forest BiGRU CLOF reconstruction wind power prediction wind speed characteristics

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Bipolar Direct AC/AC Conversion-based Dual-bus Parallel Supply system and Its Flexible Regulation Characteristics

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Protection and control of modern power systems 2025年第3期10卷 114-124页

作者： Wang, Yibo Liu, Yu Liu, Chuang Cai, Guowei Zhang, Xinyi Liu, Kaipu Wang, Yuan Liu, Chen Du, Zhanhui Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Jilin132012 China

In the context of increasing demand for flexibility and controllability in distribution networks, this paper proposes a dual-bus parallel supply (DBPS) system based on bipolar direct AC/AC conversion. First, the topology of the DBPS system, which is composed of both conventional and flexible bus ports, is described. Then, through analyzing the principles of the DBPS system, the voltage flexible regulation range is obtained, and its superiority over a topological reciprocity system is achieved. Then, a control strategy for the DBPS system is proposed based on the theory of power flow regulation of distribution ring networks and the principles of flexible bus regulation of the DBPS system. Finally, simulation analyses on flexible control scenarios in different networking modes of the DBPS system verify the correctness and validity of the proposed theory. © 2019 power system Protection and control Press.

关键词： Electric power system control

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Stackelberg Game for Bilateral Transactions between energy Storage and Wind Farms Considering the Day-Ahead Electricity Market

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energy Engineering 2025年第5期122卷 1645-1668页

作者： Xingxu Zhu Guiqing Zhao Gangui Yan JunhuiLi Hongda Dong Chenggang Li Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012China Electric Power Research Institute of State Grid Jilin Electric Power Co. Ltd.Jilin130021China

The participation of wind farms in the former energymarket faces challenges such as power fluctuations and energy storage construction *** this end,this paper proposes a joint energy storage operation scheme for multiple wind farms based on a leasingmodel,which assistswind farms in bidding for participation in the former energy market through leasing services,thereby enhancing energy storage efficiency and maximizing economic *** this paper,based on theWeibull probability distribution to portray the uncertainty of wind power,and considering the lifetime capacity loss caused by charging and discharging of energy storage,we construct a bilateral transaction model aiming at maximizing the multi-objective revenue of wind farms and shared energy *** trading strategy is designed based on the Stackelberg game framework and solved jointly by the improved genetic algorithm and interior *** exploring the effects of different lease price intervals on the overall systemperformance,and analyzing the systemstate undermultiple charging and discharging *** results show that a reasonable lease price range can significantly improve the energy storage system utilization and wind *** new ideas to enhance the economic benefits of wind farms and promote the application of shared energy storage,and promotes the wide application of shared energy storage systems.

关键词： energy storage wind power uncertainty energy market stackelberg game leasing price

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Multi-stage image deblurring method based on deep convolutional neural networks

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Journal of Electronic Imaging 2025年第2期34卷

作者： Zhao, Liquan Lin, Yongpei Jia, Yanfei Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Jilin China Beihua University College of Electrical and Information Engineering Jilin China

Due to factors such as relative motion between the imaging device and the target object or out-of-focus optical system, some images may become blurred, severely affecting subsequent image processing tasks. We propose an image deblurring method based on deep convolutional neural networks to recover clear images from blurred ones. The network consists of three structurally similar sub-networks. Each sub-network comprises the multi-scale feature extraction module, single-scale feature extraction module, fast encoding-decoding module, encoding-decoding feature fusion module, and fusion strategy we proposed. The network can extract multi-scale features from input images of different scales, fusing coarse and fine-scale features and ultimately enhancing the clarity of the blurred images. In addition, we introduce the content loss function and frequency reconstruction loss function into the conventional loss function to better measure the quality of deblurred images. Experiments were conducted on the GoPro and HIDE datasets. The HIDE dataset is divided into distant and close-up scenes, allowing for a better evaluation of the generalization ability of deblurring methods in different scenarios. Compared with the existing methods, the experimental results demonstrate that the proposed approach achieves the highest peak signal-to-noise ratio and structural similarity index measure values, resulting in clearer and more natural deblurred images. Furthermore, the proposed algorithm exhibits faster image processing speed and smaller model size. The model performs better in terms of real-time capability. © 2025 SPIE and IS&T.

关键词： image deblurring image processing multi-stage neural network

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Analysis of the impact of substation grounding network on the resistive coupling of the surrounding gas pipeline system and the newly built grounding system under high frequency lightning currents

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Dianli Xitong Baohu yu Kongzhi/power system Protection and control 2025年第9期53卷 166-175页

作者： Ma, Chenglian Yao, Xinyue Song, Mengqing Liu, Chunming Yang, Mao Sun, Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China Beijing102206 China

Based on the analysis of the power frequency grounding network, this paper focuses on the upgrading of the old grounding networks in some substations and the safety protection of the surrounding pipeline systems in the power grid, taking a 500 kV substation’s new grounding network renovation project and a 220 kV substation’s grounding network pipeline stray current interference protection project as examples. Based on the four-layer soil model calculated using ETAP power Station and CDEGS, combined with the CDEGS grounding network simulation software, actual models of the new and old grounding networks and surrounding metal pipelines are built. The resistive coupling effects on the grounding networks and its surrounding structures under power frequency and high-frequency currents are compared and analyzed. This paper specifically examines the similarities and differences between the effects of lightning currents and power frequency short-circuit currents on substation grounding networks and surrounding pipeline systems, identifying weak points in the discharge and dispersion process of high-frequency lightning currents. The results show that high-frequency lightning currents cause a sharp increase in the ground potential at the current injection point, with a rapid stepwise decaying of current amplitude, resulting in minimal impact on the ground potential at the edge of the grounding network. When metal pipelines exist around the substation, the sudden rise in transient voltage can easily cause partial discharge in the pipeline insulation layer, leading to a sudden drop in the potential of the gas pipeline insulation layer and resulting in breakdown. © 2025 power system Protection and control Press. All rights reserved.

关键词： Short circuit currents

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GENERATIVE ADVERSARIAL NETWORKS FOR REMOTE SENSING IMAGE DEHAZING WITH COLOR FEATURE RESTORATION

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International Journal of Innovative Computing, Information and control 2025年第2期21卷 323-338页

作者： Zhao, Liquan Qin, Yuqing Jia, Yanfei Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University No. 169 Changchun Road Jilin132012 China College of Electric and Information Engineering Beihua University No. 3999 Beijing Road Jilin132013 China

Remote sensing images are often affected by atmospheric factors such as haze during the acquisition process, resulting in blurring and low contrast in the collected remote sensing images. This problem impacts the image quality, thereby affecting the analysis of remote sensing images. To mitigate the impact of haze on remote sensing images, a generative adversarial network is proposed. It comprises a generative network and an adversarial network. Firstly, a novel feature extraction module is designed to enhance the capability of extracting useful information from remote sensing images. It enables the network to focus more on regions with dense haze, allowing it to extract more important information while filtering out irrelevant details. Secondly, a residual attention module is designed which can allocate different weights based on varying haze density in the feature map. This module readjusts features outputted by the encoder, facilitating better image restoration. Thirdly, a multi-scale module is also incorporated to extract feature information across various image scales. Lastly, a color feature extraction module is designed to extract color features. The novel feature extraction module, residual attention module, multi-scale module, and color feature extraction module are utilized for constructing the generative network. Besides, an adversarial network is also designed to indirectly enhance the dehazing capability of the generative network. Synthetic and real datasets are used to test six different methods for dehazing remote sensing images, respectively. The proposed method achieves higher PSNR, SSIM, and lower MSE on the synthetic remote sensing dataset. On the other hand, it achieves lower PIQE, BRISQUE, and higher MetaIQA on the real remote sensing dataset. The proposed method has best performance in dehazing remote sensing images than other methods. © 2025, Int. J. Innov. Comput. Inf. control. All rights reserved.

关键词： Generative adversarial networks

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