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An online distributed optimisation model solving the time-coupled conundrum by the Lyapunov drift plus penalty method

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IET GENERATION TRANSMISSION & DISTRIBUTION 2025年第1期19卷

作者： An, Molin Lu, Tianguang Han, Xueshan Ding, Zhaohao Shandong Univ Sch Elect Engn 17923 Jingshi Rd Jinan Peoples R China North China Elect Power Univ Sch Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China

With the increasing proportion of distributed energy resources, optimisation in the active distribution network becomes highly complex and challenging. To protect user privacy and perform efficient calculations, this paper proposes an online distributed optimisation between the user and the operator. To overcome the constraint non-linearity, a new linear power flow model is adopted, which can be updated online. In addition, to solve the time-coupled conundrum of energy storage, the Lyapunov drift plus penalty method is employed to transform the long-time scale optimisation problem into an instantaneous problem. Furthermore, a novel proportional integral derivative (PID)-Lagrange algorithm is proposed to improve the efficiency, where the distributed optimisation algorithm is considered as a control process, and a PID controller is used to control it. The proposed PID-Lagrange algorithm has an explicit parameter tuning strategy and significantly improves the efficiency by 54.23% in the case study.

关键词： power distribution control power engineering computing

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A Survey of Open-Source Tools for Transmission and Distribution Systems Research

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IEEE ACCESS 2025年 13卷 4966-4983页

作者： Appiah-Kubi, Jennifer Anderson, Alexander A. Reeve, Hayden M. Pacific Northwest Natl Lab Elect Infrastruct & Bldg Div Richland WA 99352 USA

This work presents a review of open-source electric power transmission and distribution systems analysis tools suitable for use by industry professionals and academic researchers. Due to the high complexity of the electric grid, there exist numerous tools and extensive research pertaining to nearly every aspect of the design, operation, and control of transmission and distribution networks. In addition to the commercial tools, a wide range of free, open-source tools, models, and data usable by the scientific community for related research have been developed by different organizations, including both international and US universities and national laboratories. However, due to the absence of a catalog of available tools, models and data, researchers often lack a knowledge of existing capabilities and may develop duplicative software and tools. Increasing awareness of these available resources seeks to accelerate their broader use, leading to more efficient and standardized grid analysis. This review paper (which is part of a larger survey effort that studied over 400 tools in the transmission, distribution, buildings, and electric vehicles space) outlines selected open-source resources that have been developed in power transmission and distribution systems research. It is anticipated that this work can serve as a guide for industry and academic researchers alike, ensuring that research efforts are well-channeled.

关键词： power systems Linux Distributed power generation power system stability Load modeling Capacity planning Software tools Software Reviews Planning Open-source software power distribution planning power engineering computing power system simulation power transmission

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Grids as production computing environments: the engineering aspects of NASA's Information power Grid

Grids as production computing environments: the engineering ...

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International Symposium on High Performance Distributed computing

作者： W.E. Johnston D. Gannon B. Nitzberg Numerical Aerospace Simulation Division NASA Ames Research Center Moffett Field CA USA

Information power Grid (IPG) is the name of NASA's project to build a fully distributed computing and data management environment-a Grid. The IPG project has near, medium, and long-term goals that represent a continuum of engineering, development, and research topics. The overall goal is to provide the NASA scientific and engineering communities a substantial increase in their ability to solve problems that depend on use of large-scale and/or dispersed resources: aggregated computing, diverse data archives, laboratory instruments and engineering test facilities, and human collaborators. The approach involves infrastructure and services than can locate, aggregate, integrate, and manage resources from across the NASA enterprise. An important aspect of IPG is to produce a common view of these resources, and at the same time provide for distributed management and local control. In addition to addressing the overall goal of enhanced science and engineering, there is a potential important side effect. With a large collection of resources that have common use interfaces and a common management approach, the potential exists for a considerable pool of computing capability that could relatively easily, for example, be called on in extraordinary situations such as crisis response.

关键词： Production Grid computing power engineering and energy Resource management NASA Data engineering power engineering computing power grids Distributed computing Energy management

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Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

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Chinese Journal of Electronics 2021年第1期30卷 26-35页

作者： JIA Jianfang WANG Keke PANG Xiaoqiong SHI Yuanhao WEN Jie ZENG Jianchao School of Electrical and Control Engineering North University of China School of Data Science and Technology North University of China

The prediction of Remaining useful life(RUL) and the estimation of State of health(SOH) are extremely important issues for operating performance of Lithium-ion(Li-ion) batteries in the Battery management system(BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network(WNN) with Unscented particle filter(UPF)model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform(DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.

关键词： low-frequency degradation trend data WNN-UPF model Kalman filters high-frequency fluctuation components secondary cells RUL prediction results prediction starting point short-term SOH high-frequency fluctuation data remaining life assessment multiscale prediction approach wavelet neural nets particle filtering (numerical methods) Unscented particle filter model power engineering computing WNN-UPF combined model battery management systems capacity degradation data Lithium-ion batteries discrete wavelet transforms long-term RUL Battery management system Li-ion batteries

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Induction Motor Fault Diagnosis Based on Transfer Principal Component Analysis

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Chinese Journal of Electronics 2021年第1期30卷 18-25页

作者： YAN Ruqiang SHEN Fei ZHOU Mengjie School of Mechanical Engineering Xi'an Jiaotong University School of Instrument Science and Engineering Southeast University

This paper presents a transfer learning-based approach for induction motor fault diagnosis,where the Transfer principal component analysis(TPCA)is proposed to improve diagnostic performance of the induction motors under various working conditions. TPCA is developed to minimize the distribution difference between training and testing data by mapping cross-domain data into a shared latent space in which domain difference can be reduced. The trained model can achieve a good performance in testing data by using the learned features consisting of common latent principal components. Experimental results show that the proposed approach outperforms traditional machine learning techniques and can diagnose induction motor fault under various working conditions effectively.

关键词： latent principal components transfer learning-based approach cross-domain data distribution difference power engineering computing domain difference principal component analysis learning (artificial intelligence) TPCA induction motors induction motor fault diagnosis Transfer principal component analysis fault diagnosis

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EFFECT OF LOAD CHARACTERISTICS AND VOLTAGE-REGULATOR SPEED-STABILIZING SIGNAL ON power SYSTEM DYNAMIC STABILITY

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PROCEEDINGS OF THE INSTITUTION OF ELECTRICAL ENGINEERS-LONDON 1977年第7期124卷 613-618页

作者： RAO, ND TRIPATHY, SC UNIV CALGARY DEPT ELECT ENGN CALGARY T2N 1N4 ALBERTA CANADA

It has been recognised that the dynamic-stability margin of synchronous generators can be improved by the damping effect of the speed-stabilising signal in the excitation control system. The dynamic-stability analysis is extended to account for the effect of loads modelled as a nonlinear function of voltage. In order to make the analysis realistic, a conventional load-frequency control system is also considered to be regulating the power-system frequency and tie-line power. The load voltage is considered to be undergoing changes in the dynamic period in response to speed signal in the excitation system which establishes a coupling betweenQVand p.f. control loops. The damping effect on frequency and tie-line power oscillations is studied by varying the load characteristics and excitation stabiliser parameters.

关键词： damping Voltage control load characteristics synchronous generators load (electric) stability load characteristics effect power engineering computing Control of electric power systems excitation stabiliser parameters voltage regulator speed stabilising signal dynamic stability digital simulation Velocity, acceleration and rotation control velocity control digital simulation load voltage a.c. machines power system control voltage control speed control parameters

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Temperature prediction of the molten salt collector tube using BP neural network

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IET RENEWABLE power GENERATION 2016年第2期10卷 212-220页

作者： Ren, Ting Liu, Shi Yan, Gaocheng Mu, Huaiping North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102206 Peoples R China

The collector tubes in a receiver play a vital role in the solar power tower system, and directly influence the cost of the power generation. Fast forecast of the temperatures of the collector tubes from the limited number of the temperature measurement data is important. Different from the common computational fluid dynamics prediction method, in this study a back-propagation neural network method is developed to fast acquire the temperature of the receiver, such as the peak temperatures of the inner and outer surfaces, and the outlet mean temperature and the outlet highest temperature of the molten salt. The numerical simulations are implemented to validate the feasibility and effectiveness of the proposed method. Moreover, in the proposed method the temperatures of the tube wall and the molten salt can be fast forecasted without the thermal physical parameters of materials, the boundary conditions or the initial conditions, and the solution of the complicated governing equations.

关键词： solar absorber-convertors temperature measurement backpropagation neural nets power generation economics computational fluid dynamics pipes solar power stations power engineering computing molten salt collector tube temperature prediction BP neural network solar power tower system power generation cost temperature measurement computational fluid dynamics prediction method backpropagation neural network method receiver outlet mean temperature outlet highest temperature numerical simulation tube wall thermal physical parameters boundary conditions

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Sound-quality diagnosis method of permanent magnet synchronous motor for electric vehicles based on critical band analysis

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IET ELECTRIC power APPLICATIONS 2019年第10期13卷 1613-1621页

作者： Ma, Conggan An, Yuansheng Liu, Lantao Degano, Michele Ning, Xingjiang Zhang, Jijian Gao, Peng Lu, Haifeng Harbin Inst Technol Weihai Sch Automot Engn Weihai Peoples R China Univ Nottingham Dept Elect & Elect Engn Nottingham England First Auto Work Volkswagen Automot Ltd Co Tech Dev Dept Changchun Jilin Peoples R China Beijing Hikey Technol Dev Ltd Liabil Co Tech Dev Dept Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

As it has been widely and wrongly believed that noises at any frequency of permanent magnet synchronous motor (PMSM) are positively correlated with subjective annoyance, reducing sound pressure level or sound power level of PMSM is mostly regarded as the only optimisation goal of sound quality (SQ). This study presents a sensitive critical band (SCB) diagnostic method for SQ of PMSM for electric vehicles (EV). First, a new acquisition method of near-field noises without sound attenuation of PMSM was proposed and a neural network model for SQ evaluation was established to determine the specific operating condition with the worst SQ. Second, the band-pass filter of CB (BPFCB) and band-stop filter of CB (BSFCB) were designed to diagnose positive SCBs or negative SCBs in which noises were positively or negatively correlated with the subjective annoyance, respectively. Finally, the major excitation sources of abnormal noises in SCBs were diagnosed by using the analytical calculation. An 8-pole-48-slot PMSM was considered as a case study to testify to the effectiveness and practicability of the proposed method. It may provide a new route for the precise SQ optimisation of PMSM.

关键词： electric vehicles band-stop filters permanent magnet motors band-pass filters synchronous motors neural nets optimisation power engineering computing neural network model band-pass filter 8-pole-48-slot PMSM sound-quality diagnosis method permanent magnet synchronous motor electric vehicles critical band analysis sound quality sensitive critical band diagnostic method sound attenuation sound pressure level reduction SQ optimisation evaluation PMSM sound power level reduction negative SCB diagnostic method positive SCB diagnostic method EV near-field noise acquisition method BPFCB BSFCB band-stop filter

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Prediction method of line loss rate in low-voltage distribution network based on multi-dimensional information matrix and dimensional attention mechanism-long-and short-term time-series network

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第20期16卷 4187-4203页

作者： Zhang, Zhanlong Yang, Yu Zhao, Hui Xiao, Rui Chongqing Univ Sch Elect Engn Chongqing 400044 Peoples R China

Line loss accounts for most of the energy loss in low-voltage distribution network. Accurate prediction of line loss rate (LLR) is of great significance to eliminate abnormal transmission line faults in time and ensure power supply safety. The existing prediction methods for LLR seldom consider the seasonal trend data that affect it, and there is a hysteresis effect in predicting non-stationary line loss sequences. To solve the above problems, this paper proposes a prediction method based on multi-dimensional information matrix and dimensional attention mechanism-long-and short-term time-series network (DAM-LSTNet). Firstly, the maximum information coefficient (MIC) method is used to screen the distribution network characteristics and seasonal trend parameters. Secondly, the variational mode decomposition (VMD) method optimized by the genetic algorithm is used to decompose the historical line loss data and form a multi-dimensional information matrix with the screened characteristic parameters of the network. Finally, the multi-dimensional information matrix is put into the LSTNet network with dimensional attention mechanism to predict the LLR. The example analysis shows that compared with the existing methods, it has the advantages of sufficient consideration of prediction parameters, adaptive changes of prediction weights, weak hysteresis effect and high prediction accuracy.

关键词： nonstationary line loss sequences VMD energy loss LLR low-voltage distribution network long-and short-term time-series network MIC genetic algorithm hysteresis effect prediction method line loss rate DAM-LSTNet network power distribution faults genetic algorithms transmission line faults multidimensional information matrix recurrent neural nets maximum information coefficient method time series dimensional attention mechanism variational mode decomposition method power supply safety power engineering computing

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Service restoration for resilient distribution system with coordination of battery swapping stations and distributed generations

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IET RENEWABLE power GENERATION 2023年第4期17卷 814-828页

作者： Guo, Yina Liao, Kai Yang, Jianwei He, Zhengyou Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

Due to sufficient energy storage resources and central dispatch, the battery swapping station (BSS) offers a promising solution for the service restoration and resilience enhancement of the distribution system (DS). This paper proposes a joint service restoration strategy for the DS by coordinating BSSs and distributed generations (DGs) with distribution network reconfiguration. Firstly, the spatio-temporal distribution of battery swapping demand for electric vehicles (EVs) is analyzed. On this basis, the participation of the BSS in the DS service restoration is investigated, and the model of restoration capacity for the BSS is established, which can describe the power supply ability of the BSS to support the DS restoration. Secondly, a mathematical model for the resilient DS restoration with coordination between BSSs and DGs is established, aiming to minimize the total system cost by optimally allocating BSSs and DGs during the restoration stage. The second-order cone programming (SOCP) relaxation is also adopted to transform the proposed restoration model into a mixed-integer convex formulation, which can be tractably solved. Finally, numerical simulations are conducted based on the modified IEEE 33-bus distribution test system. The results demonstrate the effectiveness of the proposed strategy for the service restoration and resilience enhancement of the DS.

关键词： Distribution networks battery powered vehicles Optimisation techniques power system management, operation and economics resilient distribution system joint service restoration strategy total system cost BSS resilient DS restoration convex programming distribution network reconfiguration distribution networks Transportation power system restoration sufficient energy storage resources restoration model battery swapping station distributed power generation DS service restoration restoration capacity integer programming restoration stage power engineering computing electric vehicle charging modified IEEE 33-bus distribution test system Distributed power generation resilience enhancement

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