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Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid

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CSEE Journal of power and Energy Systems 2024年第3期10卷 1220-1230页

作者： Qiyue Li Yadong Zhu Jinjin Ding Weitao Li Wei Sun Lijian Ding Hefei University of Technology Engineering Technology research Center of Industrial Automation Hefei 230009China electric power research institute of State Grid Anhui electric power Co. Ltd.Hefei 230000China Hefei University of Technology Hefei 230009China IEEE CSEE

Real-time fault detection is important for operation of smart grid.It has become a trend of future development to design an anomaly detection system based on deep learning by using the powerful computing power of the cloud.However,delay of Internet transmission is large,which may make the delay time of detection and transmission go beyond the limits.However,the edge-based scheme may not be able to undertake all data detection tasks due to limited computing resources of edge devices.Therefore,we propose a cloud-edge collaborative smart grid fault detection system,next to which edge devices are placed,and equipped with a lightweight neural network with different precision for fault detection.In addition,a sub-optimal and realtime communication and computing resource allocation method is proposed based on deep reinforcement learning.This method greatly speeds up solution time,which can meet the requirements of data transmission delay,maximize the system throughput,and improve communication efficiency.Simulation results show the scheme is superior in transmission delay and improves real-time performance of the smart grid detection system.

关键词： Cloud-edge collaboration communication delay deep reinforcement learning fault detection smart grid

research on high energy efficiency and low bit-width floating-point type data for abnormal object detection of transmission lines

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Global Energy Interconnection 2024年第3期7卷 324-335页

作者： Chen Wang Guozheng Peng Rui Song Jun Zhang Li Yan China electric power research institute Beijing 100192P.R.China Information&Telecommunications Company State Grid Shandong Electric Power CompanyJinan 250001P.R.China

Achieving a balance between accuracy and efficiency in target detection applications is an important research topic.To detect abnormal targets on power transmission lines at the power edge,this paper proposes an effective method for reducing the data bit width of the network for floating-point quantization.By performing exponent prealignment and mantissa shifting operations,this method avoids the frequent alignment operations of standard floating-point data,thereby further reducing the exponent and mantissa bit width input into the training process.This enables training low-data-bit width models with low hardware-resource consumption while maintaining accuracy.Experimental tests were conducted on a dataset of real-world images of abnormal targets on transmission lines.The results indicate that while maintaining accuracy at a basic level,the proposed method can significantly reduce the data bit width compared with single-precision data.This suggests that the proposed method has a marked ability to enhance the real-time detection of abnormal targets in transmission circuits.Furthermore,a qualitative analysis indicated that the proposed quantization method is particularly suitable for hardware architectures that integrate storage and computation and exhibit good transferability.

关键词： power edge Data format Quantification Compute-in-memory

A Scenario-classification Hybrid-based Banding Method for power Transfer Limits of Critical Inter-corridors

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Journal of Modern power Systems and Clean Energy 2024年第2期12卷 547-560页

作者： Lidong Yi Maosheng Ding Jili Wang Gao Qiu Fei Xue Ji'ang Liu Yuxiong Huang Gengfeng Li Junyong Liu State Grid Ningxia electric power Co. Ltd.YinchuanChinaand M.Ding is also with State Grid Ningxia Electric Power Research InstituteYinchuanChina Northwest Branch State Grid Corporation of ChinaXi’anChina School of electrical Engineering Sichuan UniversityChengduChina State Grid Ningxia electric power research institute Yinchuan YinchuanChina School of electrical Engineering Xi’an Jiaotong UniversityXi’anChina IEEE

To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,computational complexities lead to over-conservative pre-settings of transfer limit,which further induce undesirable and non-technical congestion of power transfer.To conquer this barrier,a scenario-classification hybrid-based banding method is proposed.A cluster technique is adopted to separate similarities from historical and generated operating condition dataset.With a practical rule,transfer limits are approximated for each operating cluster.Then,toward an interpretable online transfer limit decision,costsensitive learning is applied to identify cluster affiliation to assign a transfer limit for a given operation.In this stage,critical variables that affect the transfer limit are also picked out via mean impact value.This enables us to construct low-complexity and dispatcher-friendly rules for fast determination of transfer limit.The numerical case studies on the IEEE 39-bus system and a real-world regional power system in China illustrate the effectiveness and conservativeness of the proposed method.

关键词： Transfer limit transfer capability power transfer inter-corridor cluster cost-sensitive learning banding power system operation

Inverse-time Backup Protection Based on Unified Characteristic Equation for Distribution Networks with High Proportion of Distributed Generations

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Journal of Modern power Systems and Clean Energy 2024年第1期12卷 202-212页

作者： Nana Chang Guobing Song Zhongxue Chang Yuping Zheng Xingang Yang the School of electrical Engineering Xi’an Jiaotong UniversityXi’anChina State Key Laboratory of Smart Grid Protection and Control NARI Group CorporationNanjingChina electric power research institute of State Grid Shanghai electric power Company ShanghaiChina

The setting work of backup protection using steady-state current is tedious,and mismatches occasionally occur due to the increased proportion of distributed generations(DGs)connected to the power grid.Thus,there is a practical need to study a backup protection technology that does not require step-by-step setting and can be adaptively coordinated.This paper proposes an action sequence adaptive to fault positions that uses only positive sequence fault component(PSFC)voltage.Considering the influence of DGs,the unified time dial setting can be obtained by selecting specific points.The protection performance is improved by using the adjacent upstream and downstream protections to meet the coordination time interval in the case of metallic faults at the near-and far-ends of the line.Finally,the expression and implementation scheme for inverse-time backup protection(ITBP)based on the unified characteristic equation is given.Simulation results show that this scheme can adapt to DG penetration scenarios and can realize the adaptive coordination of multi-level relays.

关键词： Inverse-time backup protection(ITBP) distributed generation(DG) positive sequence fault component(PSFC)voltage unified characteristic equation adaptive coordination

Finite Element Method for Transient electric Field by Using Indirect Laplace Transform with High Accuracy

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CSEE Journal of power and Energy Systems 2024年第1期10卷 401-411页

作者： Teng Wen Xiang Cui Xuebao Li Sijia Liu Zhibin Zhao State Key Laboratory of Alternate electrical power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China China electric power Planning&Engineering institute Beijing 100120China

This paper focuses on the finite element method in the complex frequency domain(CFD-FEM)for the transient electric field.First,the initial value and boundary value problem of the transient electric field under the electroquasistatic field in the complex frequency domain is given.In addition,the finite element equation and the constrained electric field equation on the boundary are derived.Secondly,the indirect algorithm of the numerical inverse Laplace transform is introduced.Based on it,the calculation procedures of the CFD-FEM are illustrated in detail.Thirdly,the step response,zero-state response under the positive periodic square waveform(PPSW)voltage,and the zero-input response by the CFD-FEM with direct algorithm and indirect algorithm are compared.Finally,the reason for the numerical oscillations of the zero-state response under the PPSW voltage is analyzed,and the method to reduce oscillations is proposed.The results show that the numerical accuracy of the indirect algorithm of the CFD-FEM is more than an order of magnitude higher than that of the direct algorithm when calculating the step response of the transient electric field.The proposed method can significantly reduce the numerical oscillations of the zero-state response under the PPSW voltage.The proposed method is helpful for the calculation of the transient electric field,especially in the case of frequency-dependent parameters.

关键词： Electroquasistatic field transient electric field finite element method complex frequency domain numerical Laplace transform

Detection of Nonlinear Behavior Induced by Hard Limiting in Voltage Source Converters in Wind Farms Based on Higher-order Spectral Analysis

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Journal of Modern power Systems and Clean Energy 2024年第2期12卷 630-645页

作者： Zetian Zheng Shaowei Huang Qiangsheng Bu Chen Shen Jun Yan State Key Laboratory of power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China State Grid Jiangsu electric power Co. Ltd.Research InstituteNanjingChina

In recent years, sub-synchronous oscillation accidents caused by wind power integration have received extensive attention. The recorded constant-amplitude waveforms can be induced by either linear or nonlinear oscillation mechanisms. Hence, the nonlinear behavior needs to be distinguished prior to choosing the analysis method. Since the 1960s, the higher-order statistics(HOS) theory has become a powerful tool for the detection of nonlinear behavior(DNB) in production quality control wherein it has mainly been applied to mechanical condition monitoring and fault diagnosis. This study focuses on the hard limiters of the voltage source converter(VSC) control systems in the wind farms and attempts to detect the nonlinear behavior caused by bi-or uni-lateral saturation hard limiting using the HOS analysis. First, the conventional describing function is extended to obtain the detailed frequency domain information on the bi-and uni-lateral saturation hard limiting. Furthermore, the bi-and tri-spectra are introduced as the HOS, which are extended into bi-and tri-coherence spectra to eliminate the effects of the linear parts on the harmonic characteristics of hard limiting in the VSC control system, respectively. The effectiveness of the HOS in the DNB and the classification of the hard-limiting types is proven, and its detailed derivation and estimation procedure is presented. Finally, the quadratic and cubic phase coupling in the signals is illustrated, and the performance of the proposed method is evaluated and discussed.

关键词： Bi-coherence spectrum detection of nonlinear behavior hard limiting higher-order statistics tri-coherence spectrum voltage source converter wind farms.

Improved Proximal Policy Optimization Algorithm for Sequential Security-constrained Optimal power Flow Based on Expert Knowledge and Safety Layer

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Journal of Modern power Systems and Clean Energy 2024年第3期12卷 742-753页

作者： Yanbo Chen Qintao Du Honghai Liu Liangcheng Cheng Muhammad Shahzad Younis State Key Laboratory of Alternate electrical power System with Renewable Energy Sources and School of electrical & Electronic Engineering North China Electric Power University School of Engineering Xining University Key Laboratory of Smart Operation of New Energy power System Ministry of Education Qinghai University China electric power research institute National University of Sciences and Technology

In recent years, reinforcement learning(RL) has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods. It has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing capabilities. However, existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration, which poses a risk of issuing instructions that threaten the safe operation of power system. Therefore, we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF) based on expert knowledge and safety layer to determine active power dispatch strategy, voltage optimization scheme of the units, and charging/discharging dispatch of energy storage systems. The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy. Additionally, to avoid line overload, we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem. Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.

关键词： Renewable energy sources Safety power systems Optimization power grids Energy storage Training

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

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Energy Engineering 2024年第6期121卷 1473-1493页

作者： Lin Ma Liyong Wang Shuang Zeng Yutong Zhao Chang Liu Heng Zhang Qiong Wu Hongbo Ren New Energy Technology Center State Grid Beijing Electric Power Research InstituteBeijing100075China College of Energy and Mechanical Engineering Shanghai University of Electric PowerShanghai200090China

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

关键词： Short-term household load forecasting long short-term memory network attention mechanism hybrid deep learning framework

Fully Decoupled Branch Energy Balancing Control Method for Modular Multilevel Matrix Converter Based on Sequence Circulating Components

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CSEE Journal of power and Energy Systems 2024年第1期10卷 235-247页

作者： Zexin Zhao Weijiang Chen Zhichang Yang Guoliang Zhao Bin Han Yunfei Xu Nianwen Xiang Shulai Wang China electric power research institute Beijing 100192China State Grid Corporation of China Beijing 100031China State Grid Smart Grid research institute Beijing 102209China State Key Laboratory of Advanced power Transmission Technology Global Energy Interconnection Research InstituteBeijing 102211China Hefei University of Technology Hefei 230009China

The modular multilevel matrix converter(M3C)is a potential frequency converter for low-frequency AC transmission.However,capacitor voltage control of high-voltage and largecapacity M3C is more difficult,especially for voltage balancing between branches.To solve this problem,this paper defines sequence circulating components and theoretically analyzes the influence mechanism of different sequence circulating components on branch capacitor voltage.A fully decoupled branch energy balancing control method based on four groups of sequence circulating components is proposed.This method can control capacitor voltages of nine branches in horizontal,vertical and diagonal directions.Considering influences of both circulating current and voltage,a cross decoupled control is designed to improve control precision.Simulation results are taken from a low-frequency transmission system based on PSCAD/EMTDC,and effectiveness and precision of the proposed branch energy balancing control method are verified in the case of nonuniform parameters and an unbalanced power system.

关键词： Branch energy balancing control decoupled control low-frequency AC transmission modular multilevel matrix converter(M3C) sequence circulating components