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9th Purple Mountain Form on Smart Grid Protection and control, PMF 2024

作者： Ji, Xingquan Zhang, Bowen Zhang, Yumin Sun, Pengkai Zhang, Xiaofeng Liu, Wei dong Shandong University of Science and Technology Qingdao266590 China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan250061 China Information and Communication Company State Grid Shandong Electric Power Company Jinan250013 China Qingdao Yihe Electric Group Co. Ltd. Qingdao266510 China

ISBN: (纸本)9789819616701

A prediction method of distributed photovoltaic accommodation capability is proposed based on ridge regression. First, the factors influencing the distributed photovoltaic accommodation capacity are analyzed, and the concept of the contribution degree of distributed photovoltaic accommodation is introduced. The gray relation analysis method is used to evaluate the correlation between different factors and distributed photovoltaic accommodation, and a model of factors affecting distributed photovoltaic accommodation is constructed. Then, a prediction model of distributed photovoltaic accommodation capability is established based on ridge regression. The factors with high photovoltaic accommodation contribution degrees are incorporated in the prediction model. The mapping relationship between the driving factors of distributed photovoltaic accommodation and the prediction model is derived, then the development strategies for future photovoltaic accommodation capacity are formulated. Finally, simulation is performed on the SPSSPRO platform, and the results show that the proposed method can predict the distributed photovoltaic accommodation capability accurately and provide strategic suggestions for improving future photovoltaic accommodation capacity. © State Grid Electric Power 2025.

关键词： Photovoltaics

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The Commonality of Economic dispatch Models Based on Scenario/Interval 7

The Commonality of Economic Dispatch Models Based on Scenari...

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7th IEEE Conference on Energy Internet and Energy system Integration, EI2 2023

作者： Wang, Yishen Wei, Renjie Sun, Fengbin Zhang, Ruihan Yao, Tiechui Ren, Lan State Grid Smart Grid Research Institute Co Ltd Future Science Park Beijing China State Grid Smart Grid Research Institute Co Ltd Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China

ISBN: (纸本)9798350345094

With the increasing penetration of renewable energy, how to maximize the acceptance of renewable energy on the basis of satisfying the economy security and reliability of the power system is an urgent problem to be solved. In this area, mathematical optimization methods such as robust optimization and stochastic optimization are generally used to construct models based on different uncertainty descriptions. Previous studies have shown that robust optimization has high computational efficiency but conservative results, while stochastic optimization has low computational efficiency but accurate results. Therefore, different studies will choose suitable methods based on the characteristics of the models to be constructed. However, existing studies have not thoroughly discussed whether there is a connection between the two methods. This paper first proposes an economic dispatch model based on scenario considering the admissible region of net load, and introduces a new affine strategy to transform this model into a deterministic form. Secondly, based on further exploration of the characteristics of economic dispatch models based on scenario/interval, the paper concludes that the two models are common when they are sufficiently precise. Finally, the correctness of the conclusion is verified on the IEEE-RTS system. © 2023 IEEE.

关键词： Computational efficiency

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Target Recognition Inference Based on Knowledge Graph and Graph Neural Network

Target Recognition Inference Based on Knowledge Graph and Gr...

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2024 China Automation Congress, CAC 2024

作者： Li, Wei Xu, Hongfeng Li, Yuan Beijing Institute of Technology Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9798350368604

In complex environments with vast and diverse information sources, target recognition serves as a crucial precursor to situational analysis, providing the foundation for further decision-making and analysis. Traditional algorithms for target recognition often rely on human experience and rules, necessitating expert knowledge and subjective judgments, which introduces uncertainties. This paper introduces a method for target recognition inference, leveraging Situation Knowledge Graph Convolutional Neural Network (SKGCN). By employing active and passive radar sensor data in complex scenarios, the algorithm processes inputs within a self-constructed situational knowledge graph. It derives vector representations of target entities through aggregation operations among neighboring nodes and extracts perceptual data vectors using the embedding layer. The algorithm calculates the alignment between sensor data and target entities, facilitating target recognition and inference in complex scenarios. Simulation results confirm the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Knowledge graph

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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A Bayesian Structural Time Series Approach for Forecasting Photovoltaic Power Generation

A Bayesian Structural Time Series Approach for Forecasting P...

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International Conference on Power system Technology (POWERCON)

作者： Chenghao Sun Xiaodong Chu Hua Ye Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan P.R. China

As a renewable energy source, photovoltaic (PV) power generation is becoming increasingly essential in power systems. Accurate forecasting of PV power output is crucial for effective regulation and control of these systems. This paper explores the applicability of the Bayesian structural time series model for PV power generation forecasting using causal inference techniques. The research examines various factors that influence PV power generation and assesses their causal relationships. The developed forecasting model for PV power generation integrates the Bayesian structural time series approach. The accuracy of the proposed model is validated through case studies, demonstrating the successful incorporation of causal inference in PV power generation prediction. This study lays the foundation for future research on utilizing causal inference to evaluate the impacts of specific system changes.

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Fault Diagnosis of Rolling Bearings Under Variable Speed Conditions Based on Order Analysis

Fault Diagnosis of Rolling Bearings Under Variable Speed Con...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Liu, Guiyi Zhang, Chao Wu, Le Liu, Tongtong Ouyang, Bing School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031702341

To address the issue of bearing fault diagnosis without speed signal, a phase-ratio analysis method based on speed tracking is proposed. Firstly, the Short-Time Fourier Transform (STFT) is employed to obtain a time-frequency map. Then, the rotation frequency is calculated based on the parameters of the bearings, as well as the speed range. Subsequently, the speed matching tracking method is utilized to track the rotation frequency from the time-frequency map, followed by linear fitting and resampling to approximate the speed signal. Finally, the obtained signal is angularly sampled and subjected to FFT spectral analysis to obtain the order ratio spectrum. Based on the features of the order ratio spectrum, bearing faults are diagnosed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Roller bearings

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Short-Term Load Forecastiong Based on Residual Graph Convolutional Neural Network

Short-Term Load Forecastiong Based on Residual Graph Convolu...

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International Conference on Power system Technology (POWERCON)

作者： Mengqing Bian Xiaodong Chu Hua Ye Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan P.R. China

As various industries experiencing post-pandemic economic recovery, power systems face increased challenges. Short-term power load forecasting for regional power grids can assist in anticipating load fluctuations and optimizing power system management planning. This paper tackles the gradient vanishing issue in deep graph convolutional neural networks (GCNs) by incorporating a residual module to create a residual graph convolutional neural network (ResGCN). Data from a regional power grid in the United States is utilized for validation purposes. Experimental results show that the graph convolutional neural network with the residual module generates predictions closer to the actual values, consequently enhancing prediction accuracy and confirming the efficacy of the proposed ResGCN.

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Dehazing Network Based on U-Net Structure and Residual Block 41

Dehazing Network Based on U-Net Structure and Residual Block

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第41届中国控制会议

作者： Wenbo Zhang Haoze Li Han Sun Yongqiang Bai School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of deep learning technology,the dehazing method based on convolutional neural network has also developed ***,it still faces some problems such as incomplete dehazing and difficulty in detail *** at the problems,we propose a dehazing network based on U-Net structure and residual *** the down sampling process,we introduce the residual block of attention mechanism,which effectively improves the feature extraction and expression ability of the *** the middle part of the network,the smooth dilation convolution residual block is used to expand the receptive field of the network and improve the restoration effect of small *** the same time,we introduce a feature fusion mechanism based on attention mechanism,which can reduce the loss of information and weight the down-sampling features into intermediate *** the process of up sampling,the method of adding elements instead of feature stitching is used to reduce the number of parameters and realize the function of jump ***,dense convolution residual blocks are added to the jump connection between input and output to further improve the *** experimental results show that the PSNR of this method can reach 32.893,which is better than the existing methods.

关键词： Image Dehazing Residual Network Attention mechanism

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A Case Study of Renewable Energy Resources Assessment Results in Argentina 4

A Case Study of Renewable Energy Resources Assessment Result...

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4th IEEE China International Electrical and Energy Conference, CIEEC 2021

作者： Tang, Qiong Wu, Jiawei Xiao, Jinyu Zhou, Feng Wu, Xiaoqing Beijing China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Jinan China

ISBN: (纸本)9781728171494

With the increasing development of renewable energy resources in South America, to analyze "the location", "the quantity"and "the quality"of wind and solar energy resources will, not only benefit for one country on efficient resource utilization, but also promote the realization of large- scale international resource utilization and complementation. Argentina, as a major country in South America, researches on its renewable energy development is of great significance. This paper completed the renewable resources assessment through modeling and estimating the theoretical reserves, technical potential installed capacity and development cost of wind and solar energy in Argentina. Then, a more precise quantitative evaluation of development potential is given through the calculated development cost and capacity factor of technical potential installed capacity. Finally, after compared the existing power stations with the assessment results, suggestions on how to development renewable energy resources in Argentina are given. © 2021 IEEE.

关键词： Solar energy

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Source-Load-Storage Coordinated Optimal Scheduling Model Considering Admissible Region of Net Load

Source-Load-Storage Coordinated Optimal Scheduling Model Con...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Fengbin Sun Mingqiang Wang Caishuai Liang Xia Zhang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China Luyuan Electric Group Co. LTD Jinan China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

With the increasing penetration of renewable energy, the urgent issue of maximizing the integration of renewable energy while meeting economy security and reliability requirements needs to be addressed. Based on the theory of admissible region, integrating models of renewable energy, energy storage systems, and demand response, this paper proposes a source-load-storage coordinated optimal scheduling model considering admissible region of net load. The model uses the generation-load transfer factors based on decoupled linearized power flow to construct power flow constraints, enabling coordinated optimization of active power, reactive power, and voltage in the power system. Uncertainty constraints in the model are transformed into deterministic constraints based on affine strategy, and the entire model is transformed into a bilinear programming model using duality theory and power circle linearization, facilitating solution using mature commercial solvers. Finally, the effectiveness and superiority of the model are verified on the IEEE-RTS system.

关键词： Renewable energy sources Analytical models Reactive power Uncertainty Biological system modeling Computational modeling Optimal scheduling

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