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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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MPGA-NET: A MULTI-PATH GUIDED ATTENTION NETWORK FOR DESERT SEISMIC NOISE ATTENUATION

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International Journal of Innovative Computing, Information and control 2025年第3期21卷 667-682页

作者： Zhong, Tie Song, Yaling Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education China School of Electrical Engineering Northeast Electric Power University No 169 Changchun Road Jilin 132012 China

Desert seismic exploration is a key technology for detecting natural resources. However, due to the unique surface conditions and complex environmental factors in desert regions, seismic data are often contaminated by strong noise, which poses serious challenges for subsequent data processing. Furthermore, background noise not only exhibits high intensity and a wide frequency band but also overlaps with effective seismic signal in the frequency spectrum, rendering it challenging for traditional signal processing techniques to separate noise from signals. In recent years, denoising models based on convolutional neural network (CNN) have shown significant potential for denoising tasks due to their strong feature extraction and learning capabilities. However, existing models have limitations in feature extraction and representation, which may affect denoising performance. To address this shortcoming, this paper proposes a new Multi-Path Guided Attention Network (MPGA-Net). Specifically, MPGA-Net integrates three paths: the primary core path is responsible for multi-scale feature extraction;the guided enhancement path enhances feature representation;and the attention assisted path focuses on key information. Experimental results show that MPGA-Net not only effectively suppresses noise but also recovers weak reflected signals masked by noise, achieving denoising performance that is significantly superior to both traditional methods and existing CNN approaches. © 2025, ICIC International.

关键词： Convolutional neural networks

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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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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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Study on Insulation Characteristics of Moisture in the Buffer Layer of 66 kV AC XLPE Cables 19th

Study on Insulation Characteristics of Moisture in the Buffe...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Cao, Kangyangyong Xing, Hongquan Huang, Dawei Wei, Zhenhao Liu, Shili State Grid Changchun Power Supply Company Jilin China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9789819613861

To address the frequent failure issues caused by defects in the buffer layer of high-voltage cross-linked polyethylene (XLPE) cables, research on buffer layer has become a hot topic at present. This paper first introduces the input impedance model of the 66 kV cable buffer layer and the corrugated aluminum sheath. The water content can change its resistance and capacitance, thereby affecting the resistivity and dielectric constant. Secondly, the paper introduces the intrinsic principles of frequency domain dielectric spectroscopy technology, where changes in water content alter the relaxation time of the Debye model, thereby changing the dielectric loss factor. Then, by conducting resistance and capacitance measurement experiments on a 6 cm long cable buffer layer, the paper derives the resistivity and dielectric constant variation curves. Through dielectric loss factor measurement experiments, it derives the dielectric loss factor curve, demonstrating the correlation between water content and the dielectric loss factor. The paper concludes that the dielectric loss factor curve is more sensitive to insulation conditions at low frequencies. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Dielectric losses

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Study on the Effects of Moisture on Dielectric Loss and Capacitance Parameters of 66 kV AC XLPE Cable Insulation 19th

Study on the Effects of Moisture on Dielectric Loss and Capa...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Cao, Kangyangyong Xing, Hongquan Huang, Dawei Zong, Guanbo Liu, Shili State Grid Changchun Power Supply Company Jilin China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9789819613861

Long-term operation or external forces may cause damage to the outer sheath of the cable, allowing moisture to enter the cable body and affecting the insulation and dielectric performance of the main XLPE insulation of the cable. For 66 kV cables, there are no clear values provided for the changes in the dielectric loss tangent and capacitance after water ingress into the main insulation. Using a self-built experimental platform, dielectric spectroscopy curves were plotted for the main insulation under 1 kV voltage, showing the variation of the dielectric loss tangent with water content and frequency. The unit capacitance changes of the main insulation due to moisture were also measured. The study indicates that at 0.01 Hz, when the water content in the main insulation increases from 0.86% to 1.29%, the dielectric loss tangent value changes by several times. Additionally, this value is not affected by the location of the moisture—whether it is accumulated, evenly distributed, or symmetrically distributed, the dielectric loss tangent remains unchanged. When the volumetric water content increases by 0.48%, the unit capacitance value increases by 0.8%. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Dielectric losses

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

关键词： Protective relaying Distribution networks Reliability theory controllability Regulation Topology power system reliability Voltage control Protection Load flow

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