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Constraint Learning-based Optimal Power Dispatch for Active distribution networks with Extremely Imbalanced Data

CSEE Journal of Power and Energy Systems 2024年第1期10卷 51-65页

作者： Yonghua Song Ge Chen Hongcai Zhang State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907 USA

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable *** to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their *** tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible ***,since ADNs are mostly operated in a normal status,only very few historical samples are ***,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network *** address this issue,we propose an enhanced constraint learning ***,it leverages constraint learning to train a neural network as surrogate of ADN's ***,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical *** incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural ***,we establish a trust region to constrain and thereafter enhance reliability of the *** confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

关键词： Deep learning demand response distribution networks imbalanced data optimal power flow

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Location of Asymmetric Ground Fault Using Virtual Injected Current Ratio and Two-stage Recovery Strategy in distribution networks

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 151-161页

作者： Haiting Shan Luliang Zhang Q.H.Wu Mengshi Li School of Electric Power Engineering South China University of Technology(SCUT)Guangzhou 510641China

Sparse measurements challenge fault location in distribution *** paper proposes a method for asymmetric ground fault location in distribution networks with limited measurements.A virtual injected current vector is formulated to estimate the fault line,which can be reconstructed from voltage sags measured at a few buses using compressive sensing(CS).The relationship between the virtual injected current ratio(VICR)and fault position is deduced from circuit analysis to pinpoint the ***,a two-stage recovery strategy is proposed for improving reconstruction accuracy of the current vector,where two different sensing matrixes are utilized to improve the *** proposed method is validated in IEEE 34 node test *** results show asymmetric ground fault type,resistance,fault position and access of distributed generators(DGs)do not significantly influence performance of our *** addition,it works effectively under various scenarios of noisy measurement and line parameter *** on 134 node test feeders prove the proposed method is also suitable for systems with more complex structure.

关键词： distribution networks fault location reconstruction accuracy two-stage recovery virtual injected current ratio

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Outage Detection for distribution networks Using Limited Number of Power Flow Measurements

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Journal of Modern Power Systems and Clean Energy 2020年第2期8卷 315-324页

作者： Basel Alnajjab Ananth Narayan Samudrala Chen Chen Rick S.Blum Soummya Kar Emma M.Stewart the Department of Electrical and Computer Engineering Lehigh UniversityBethlehemPA18015USA the School of Electrical Engineering Xi'an Jiaotong UniversityXi'an 710049 the Department of Electrical and Computer Engineering Carnegie Mellon UniversityPittsburgh.PA15213USA Lawrence Livermore National Laboratory LivermoreCA94550USA

Accurate topology estimation is crucial for effectively operating modern distribution *** outages in a distribution network change the network topology by disconnecting some parts of the network from the main *** this paper,an outage detection(or topology estimation)algorithm for radial distribution networks is *** algorithm utilizes noisy power flow measurements collected from a subset of lines in the network,and statistical information characterizing errors in forecasting load ***,a sensor placement scheme is *** sensor placement provides critical sensing for the outage detection algorithm so that any number of possible outages in the network can be *** performance of the proposed outage detection algorithm using the proposed sensor placement is demonstrated through several numerical results on the IEEE 123-node test feeder.

关键词： Outage detection distribution networks maximum likelihood(ML)detection sensor placement

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Frequency Response of PV Inverters Toward High Renewable Penetrated distribution networks

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 465-475页

作者： Feifei Bai Yi Cui Ruifeng Yan Tapan Kumar Saha Huajie Gu Daniel Eghbal the School of Information Technology and Electrical Engineering the University of QueenslandBrisbaneQLD 4072Australia the School of Engineering and Built Environment at Griffith University Gold CoastQLD 4222Australia the Australian Energy Market Operator(AEMO) BrisbaneQLD 4000Australia Energy Queensland(Energex) BrisbaneQLD 4006Australia

Substantial usage of electronic-based renewable energy resources has completely changed the dynamic behaviours and response time of power networks,which are now fundamentally different from traditional power networks dominated by Synchronous Generators(SGs).This paper evaluates the dynamic response of small-scale Photovoltaic(PV)inverters,which dominate the distribution networks and influence the dynamics of the entire power ***,some critical events which occurred in Australia have shown that the dynamic responses of small-scale inverters do not always follow the inverter ***,these uncertainties make PV inverters’response unpredictable and have the potential to threaten the security of power *** detailed investigation of the dynamic response characteristics of small-scale PV inverters to grid disturbances is lacking in the current *** paper presents new findings from experimental testing under extensive network disturbance ***,a datadriven method is proposed to accurately describe the dynamics of solar PV subjected to various frequency *** results provide beneficial insight to the network operators in predicting power system response to extreme disturbances and avoiding potential grid instability issues,which will assist in achieving 100%penetration of power electronics-based renewable energy resources in the future.

关键词： Deep learning distribution networks dynamic response distributed PV load modelling PV inverter

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Data-driven Reactive Power Optimization for distribution networks Using Capsule networks

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1274-1287页

作者： Wenlong Liao Jiejing Chen Qi Liu Ruijin Zhu Like Song Zhe Yang AAU Energy Aalborg UniversityAalborgDenmark School of Softwareand Microelectronics Peking UniversityBeijingChina Key Laboratory of Smart Gridof Ministry of Education Tianjin UniversityTianjinChina School of Electrical Engineering Tibet Agriculture and Animal Husbandry UniversityLinzhiChina Maintenance Branch of State Grid Jibei Electric Power Co. Ltd.BeijingChina

The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel datadriven approach is proposed for reactive power optimization of distribution networks using capsule networks(CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines(e.g.,convolutional neural network, multi-layer perceptron, and casebased reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.

关键词： Data-driven reactive power optimization distribution networks deep learning capsule networks

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Data-driven Inverter-based Volt/VAr Control for Partially Observable distribution networks

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 548-560页

作者： Tong Xu Wenchuan Wu Yiwen Hong Junjie Yu Fazhong Zhang Southwest Electric Power Design Institute Co. Ltd.of China Power Engineering Consulting GroupChengduSichuan 610021China Tsinghua University Beijing 100084China Department of Electrical Engineering State Key Laboratory of Power SystemsTsinghua UniversityBeijing 100084China Guangdong Power Grid Zhongshan Power Supply Bureau Zhongshan 528400China

For active distribution networks(ADNs)integrated with massive inverter-based energy resources,it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ***,current models of ADNs usually involve significant errors or even unknown ***,ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant *** provide a practical Volt/Var control(VVC)strategy for such networks,a data-driven VVC method is proposed in this ***,the system response policy,approximating the relationship between the control variables and states of monitoring nodes,is estimated by a recursive regression closed-form ***,based on real-time measurements and the newly updated system response policy,a VVC strategy with convergence guarantee is *** the recursive regression solution is embedded in the control stage,a data-driven closedloop VVC framework is *** effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads,where not only the rapid and self-adaptive voltage regulation is realized,but also systemwide optimization is achieved.

关键词： Data-driven distribution networks partial observation Volt/VAr control

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Evaluation of the situational awareness effects for smart distribution networks under the novel design of indicator framework and hybrid weighting method

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Frontiers in Energy 2021年第1期15卷 143-158页

作者： Leijiao GE Yuanliang LI Suxuan LI Jiebei ZHU Jun YAN School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Concordia Institute for Information Systems Engineering Concordia UniversityMontreal 300072Canada

As a key application of smart grid technologies,the smart distribution network(SDN)is expected to have a high diversity of equipment and complexity of operation *** awareness(SA),which aims to provide a critical visibility of the SDN,will enable a significant assurance for stable SDN ***,the lack of systematic evaluation through the three stages of perception,comprehensive,and prediction may prevent the SA technique from effectively achieving the performance necessary to monitor and respond to events in *** analyze the feasibility and effectiveness of the SA technique for the SDN,a comprehensive evaluation framework with specific performance indicators and systematic weighting methods is proposed in this ***,to implement the indicator framework while addressing the key issues of human expert scoring ambiguity and the lack of data in specific SDN areas,an improved interval-based analytic hierarchy process-based subjective weighting and a multi-objective programming method-based objective weighting are developed to evaluate the SDN SA *** addition,a case study in a real distribution network of Tianjin,China is conducted whose outcomes verify the practicality and effectiveness of the proposed SA technique for SDN operating security.

关键词： distribution networks operation and maintenance expert systems

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U-shaped energy loss curves utilization for distributed generation optimization in distribution networks

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第11期14卷 887-898页

作者： Reza EBRAHIMI Mehdi EHSAN Hassan NOURI Department of Electrical Engineering Science and Research BranchIslamic Azad University Centre of Excellence in Power System Management and Control (CEPSMC) Sharif University of Technology Department of Engineering Design and Mathematics University of the West of England (UWE Bristol)

We propose novel techniques to find the optimal location,size,and power factor of distributed generation(DG) to achieve the maximum loss reduction for distribution *** the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG *** on the network's total load demand,four DG sizes are *** are used to form energy loss curves for each bus and then for determining the optimal DG *** study shows that by defining the energy loss minimization as the objective function,the time-varying load demand significantly affects the sizing of DG resources in distribution networks,whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other *** devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method(EIM) and recently published *** showed that the proposed technique can provide an optimal solution with less computation.

关键词： Distributed generation(DG) distribution networks Loss reduction DG optimization Energy loss curves

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Hybrid classifier for fault location in active distribution networks

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Protection and Control of Modern Power Systems 2020年第1期5卷 194-202页

作者： Sadegh Jamali Alireza Bahmanyar Siavash Ranjbar Centre of Excellence for Power System Automation and Operation School of Electrical EngineeringIran University of Science and TechnologyP.O.Box:16846-13114TehranIran

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation(DG)and *** method uses the voltage and current data from the measurement points at the main substation,and the connection points of DG and *** data is used in a single feedforward artificial neural network(ANN)to estimate the distances to fault from all the measuring points.A k-nearest neighbors(KNN)classifier then interprets the ANN outputs and estimates a single fault *** results validate the accuracy of the fault location method under different fault conditions including fault types,fault points,and fault *** performance is also validated for non-synchronized measurements and measurement errors.

关键词： Artificial neural networks Distributed generation distribution networks Fault location K-nearest neighbors

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Optimal Implementation of Photovoltaic and Battery Energy Storage in distribution networks

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Computers, Materials & Continua 2021年第11期69卷 1463-1481页

作者： Hussein Abdel-Mawgoud Salah Kamel Hegazy Rezk Tahir Khurshaid Sang-Bong Rhee Department of Electrical Engineering Faculty of EngineeringAswan University81542 AswanEgypt College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz University11911 Al-KharjSaudi Arabia Electrical Engineering Department Faculty of EngineeringMinia University61517 MiniaEgypt Department of Electrical Engineering Yeungnam UniversityGyeongsan38541Korea

Recently,implementation of Battery Energy Storage(BES)with photovoltaic(PV)array in distribution networks is becoming very popular in overall the *** PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural *** can be able to generate constant output power during 24-hours by installing BES with ***,this paper presents a new application of a recent metaheuristic algorithm,called Slime Mould Algorithm(SMA),to determine the best size,and location of photovoltaic alone or with battery energy storage in the radial distribution system(RDS).This algorithm is modeled from the behavior of SMA in *** the optimization process,the total active power loss during 24-hours is used as an objective function considering the equality and inequality *** addition,the presented function is based on the probabilistic for PV output and different types of system *** candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor(PLSF).IEEE 69-bus RDS with different types of loads is used as a test *** effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms.

关键词： Slime mould algorithm optimization distribution networks renewable energy uncertainty

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