Virtual State Estimation Calculator Model for Three Phase Power System Network

The main objective of this research work is to develop a simple state estimation calculator in LabView for three phase power system network. LabView based state estimation calculator has been chosen as the main platform because it is a user friendly and easy to apply in power systems. This research work is intended to simultaneously acclimate the power system engineers with the utilization of LabView with electrical power systems. This proposed work will discuss about the configuration and the improvement of the intelligent instructional VI （virtual instrument） modules in power systems for state estimation solutions. In the proposed model state estimation has been carried out and model has been developed such that it can accommodate the latest versions of state estimation algorithm.

Impact of power system network topology errors on real-time locational marginal price

This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch.The main goal of this paper is to assess the economic impact of this misconfigured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.

Transient Stability Analysis of Power System Based on an Improved Neural Network

A new type of ANN (Artificial Neural Network) structure is introduced, and a nonlinear transformation of the original features is proposed so as to improve the learning covergence of the neural network. This kind of improved ANN is then used to analyse the transient stability of two real power systems. The results show that this method possesses better effectiveness and high convergence speed.

Accuracy Improvement in CCT Estimation of Power Systems by iRprop-RAN Hybrid Neural Network

This paper proposes a new Initial CCT (Critical Clearing Time) estimation method using a hybrid neural network composed of iRprop (Improving the Resilient back PROPation Algorithm) and RAN (Resource Allocation Network). In transient stability study, CCT evaluation is very important but time consuming due to the fact it needs many iteration of time domain simulations gradually increasing the fault clearing time. The key to reduce the required computing time in this process is to find accurate initial estimation of CCT by a certain handy method before going to the iterative stage. As one of the strongest candidates of this handy method is the utilization of the pattern recognition ability of neural networks, which enable us to jump to a close estimation of the real CCT without any heavy computing burden. This paper proposes a new hybrid neural network which is a combination of the well-known iRprop and RAN. In the proposed method, the outputs of the hidden units of RAN are modified by multiplying the contribution factors calculated by an additional iRprop network. Numerical studies are done using two different test systems for the purpose of confirming the validity of the proposal. The result of the proposed method is the best. Properly evaluating the contribution of each input to the hidden units, the estimation error obtained by the proposed method is improved further than the original RAN based estimation.

Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems

The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System

In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other

Reactive Power Reserve Improvement Using Power Systems Inherent Structural Characteristics

This paper considers the use of the inherent structural characteristics of power system networks for improving the reactive power reserve margins for both topologically weak and strong networks. The inherent structural characteristics of the network are derived from the Schur complement of the partitioned Y-admittance matrix using circuit theory representations. Results show that topologically strong networks, operating close to the upper voltage limit could be made to increase their loadability margin by locating reactive power compensators close to generator sources, whereas topologically weak (ill conditioned) networks could be made to operate within the feasible operating limits by locating reactive power compensators on buses farther from generator sources.

System Vulnerability Analysis Using Graph Pathfinding Strategies in Partitioned Networks

In this paper, a new method has been introduced to find the most vulnerable lines in the system dynamically in an interconnected power system to help with the security and load flow analysis in these networks. Using the localization of power networks, the power grid can be divided into several divisions of sub-networks in which, the connection of the elements is stronger than the elements outside of that division. By using our proposed method, the probable important lines in the network can be identified to do the placement of the protection apparatus and planning for the extra extensions in the system. In this paper, we have studied the pathfinding strategies in most vulnerable line detection in a partitioned network. The method has been tested on IEEE39-bus system which is partitioned using hierarchical spectral clustering to show the feasibility of the proposed method.

考虑次生故障差异化影响下韧性主动提升的输电系统网架重构策略

近年来,我国能源电力加速转型,极端自然灾害频发,国际形势不稳定,使得常规安全风险与非常规安全风险交织,大停电风险有增加的趋势。稳健可靠的网架重构方案对防止恢复中再次发生系统崩溃具有重要意义,针对当前研究中未有效计及恢复中次生故障的不足,该文提出一种考虑次生故障差异化影响下系统韧性主动提升的网架重构策略。首先,讨论在网架重构恢复方案中考虑次生故障影响的必要性;然后,基于分割多目标风险分析方法提出一种系统韧性指标,旨在差异化处理次生故障并突出高风险故障对恢复过程的影响;进一步建立考虑韧性主动提升的网架重构全局优化模型,以增强系统对高风险故障的抵御能力。为提高模型的求解效率,提出一种基于窗口滚动机制的求解策略,可给出兼顾快速性与稳健性的网架重构方案。最后,采用新英格兰10机39节点系统和IEEE 118节点系统验证所提出方法的有效性。

高比例新能源接入的主动配电网规划综述

新能源以分布式电源的形式接入配电网,给系统带来了不可控性、随机性和波动性问题。借助现代电力电子、信息通信及自动控制等技术,灵活可控的主动配电网成为发展趋势,其中主动配电网规划是近年来研究的热点之一。综合国内外在这一领域的研究成果,对主动配电网规划相关研究内容及其方法进行总结、分析及展望。对主动配电网基本结构进行了描述,介绍了配电网组成元素的特点;根据其控制变量的不同,对主动配电网规划模型进行了归类,总结了模型中的优化目标;针对常用模型求解算法及其优缺点进行分析、总结;通过对关键性问题的讨论,分析了未来主动配电网的发展趋势。

新型配电网中的软件定义技术理念及特征

配电网是支撑新型电力系统建设的重要环节,分布式能源、互动负荷与新型装备的大量接入推动了系统形态的巨大变革。文中立足于配电网在柔性化、数字化、智能化方面发展基础,提出新型配电网的软件定义技术构想,旨在从系统层面解决海量、分散灵活资源的协调利用问题,支撑整体系统运行服务水平提升。基于上述观点,文中从软件定义技术的发展基础入手,分析了其技术思想起源与在配电网中的应用条件,建立了配电网软件定义技术的三层式架构,分析了软件定义灵活性的来源和表现形态,梳理了配电网软件定义技术面临的挑战性问题,初步探讨了在其构建与应用中可能涉及的关键技术。最后,对配电网软件定义技术的未来发展前景进行了总结与展望。

Smart Street Lighting Control and Monitoring System for Electrical Power Saving by Using VANET

The huge amount of electrical power of many countries is consumed in lighting the streets. However, vehicles pass with very low rate in specific periods of time and parts of the streets are not occupied by vehicles over time. In this paper, we propose a system that automatically switches off the light for the parts of the streets having no vehicles and turns on the light for these parts once there are some vehicles that are going to come. Logically, this system may save a large amount of the electrical power. In addition, it may increase the lifetime of the lamps and reduce the pollutions. This system automatically controls and monitors the light of the streets. It can light only the parts that have vehicles and help on the maintenance of the lighting equipments. Vehicular Ad-Hoc Networks (VANET) make it possible to propose such system. VANET enables the possibility to know the presence of vehicles, their locations, their directions and their speeds in real time. These quantities are what are needed to develop this system. An advantage of using VANET is that there is no need to use specific network and equipments to design the system, but VANET infrastructure will be used. This decreases the cost and speed up the deployment of such system. This paper focuses on the proposal of different possible architectures of this system. Results show that the saved energy may reach up to 65% and an increase of the lifetime of the lamps of 53%.

基于深度卷积神经网络的电力系统故障预测

通过对深度卷积神经网络的深入研究,提出基于深度卷积神经网络的电力系统故障预测方法,保障系统安全运行。采用广域测量系统测量每个支路与节点,将获得的功率与关键特征值分别作为深度卷积神经网络模型输入、输出,训练这2个数据,并使用深度卷积神经网络AlexNet分析输入数据与输出数据的映射关系,建立基于深度卷积神经网络的电力系统故障预测模型,通过特征值分组、振荡模式筛选、数据预处理、模型训练和模型评估,实现电力系统运行状态评估,完成电力系统故障预测。实验结果说明:该方法的关键特征值计算结果与实际结果基本一致,可靠性高;使用正则化可提升模型泛化效果,防止模型过拟合;与其余方法的准确率和误报率指标相比,所提方法的准确率高达99.52%,误报率为1.16%,综合评价指标较高,评估效果优势显著。

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.

Unbalance Level Regulating Algorithm in Power Distribution Networks

The paper dwells on the unified power quality indexes characterizing the phenomenon of voltage unbalance in three-phase systems. Voltage unbalance is one of the commonest occurrences in the town mains of 0.38 kV voltage. The phenomenon describes as inequality of vector magnitude of phase voltage and shearing angle between them. Causes and consequences of the voltage unbalance in distribution networks have been considered. The algorithm, which allows switching one-phase load, has been developed as one of the methods of reducing the unbalance level. The algorithm is written in the function block diagram programming language. For determining the duration and magnitude of the unbalance level it is proposed to introduce the forecasting algorithm. The necessary data for forecasting are accumulated in the course of the algorithm based on the Function Block Diagram. The algorithm example is given for transforming substation of the urban electrical power supply system. The results of the economic efficiency assessment of the algorithm implementation are shown in conclusion. The use of automatic switching of the one-phase load for explored substation allows reducing energy losses (active electric energy by 7.63%;reactive energy by 8.37%). It also allows improving supply quality to a consumer. For explored substation the average zero-sequence unbalance factor has dropped from 3.59% to 2.13%, and the negative-sequence unbalance factor has dropped from 0.61% to 0.36%.

基于5G的电力系统配网自动化网络风险评估研究

在当今数字化时代,电力系统配网自动化的重要性日益突显。电力系统的配网自动化不仅能够提高电力系统的效率和可靠性,还可以降低运营成本和维护成本。然而,随着电力系统自动化程度的提升,网络安全和风险评估也成了关注焦点。文章致力于研究基于5G的电力系统配网自动化网络风险评估方法。文章介绍基于5G的电力系统配网自动化网络风险评估,概述自动化技术在配电网中的应用和5G与电力系统自动化的结合,详细探讨电力系统配网自动化网络风险评估模型的构建和案例研究与实证分析。

新型电力系统数据跨域流通泛安全边界防护技术

新型电力系统建设涉及多业务系统、多部门、多方主体间进行海量、异构数据的交互和共享,电力数据的内外部网络环境与安全形势日趋复杂化,数据流通的脆弱性风险加剧。首先,分析新型电力系统下数据流的类型与特性,概括电力数据流通安全防护面临的新形势;其次,基于专用数据处理器(DPU)的高性能流量编排和多功能安全网关能力,构建面向电力数据跨域流通安全增强的泛安全边界,凭借数据面可编程技术沟通网络安全与数据安全双维度安全能力,提出基于DPU的数据跨域流通协同防护技术应用方案;最后,阐释DPU在不同电力通信网络层次的部署方式、价值与关键技术,分析现阶段DPU在电力领域应用存在的挑战。

不同控制方法下混合电源对配电网系统电能质量优化分析

目前分布式电源主要有风、光、储等形式,由于风电与光伏发电的波动性和不确定性,使得配电网系统的电能质量下降,如电压不稳定、谐波量过大等。根据风、光、储三种电源的自身特性,选择相应的控制模型,减小配电网系统的谐波量,提高电能质量。风力发电机发电功率随风能的变化而变化,ip-iq控制方法 能准确捕捉风能变化时风力发电机产生的电压高次谐波及负序电流等参量;光伏发电主要是由于电荷的定向移动而形成稳定的电极,实现太阳能电池板发电,电流电压双环控制方法能够灵敏地反映光伏发电时电荷的移动情况,更好地检测光伏发电的实时动态;功率控制方法 则能够及时地反映蓄电池充放电及负载用电情况。通过上述控制方法,以simulink为平台,组建分布式电源系统,分别对配电网系统处于电压稳定、三相电流不平衡以及电压突变、三相电流不平衡两种情况下进行系统仿真,对配电网系统、风力发电系统、光伏发电系统及负载端的总谐波量进行比较。结果表明,根据不同电源特性实施不同的控制方法 ,可以有效降低整体系统的谐波量,提高配电网系统的电能质量。同时还可有效抑制风、光发电系统自身的谐波量,使得风、光发电系统低压穿越能力得以提高,降低弃风弃光现象的产生。