ANN for transmission system static security assessment

This paper presents computationally efficient artificial neural network technique for assessing the security of the power system against line outages. Performance index (PI), which accounts for various line limit violations per contingency, is defined. The basic purpose of ANN is to assess the severity of line outages in terms of PI, based on training examples from off-line analysis. The selection of input signals for ANN is influenced by the operating state of the system and the contingency in question which determines the extent of line power limit violations. In an attempt to attain perfection in PI prediction, suitable architecture and topology for the network is investigated. To expedite learning process, saturating linear coupled neuron model (sl-CONE) is also tried out. The effectiveness of proposed technique is demonstrated on 5-bus (7-line) and IEEE 14-bus (21-line) test systems. Computation efficiency of the method makes it potential candidate for inclusion in on-line comprehensive security analysis package.     

Cyber security assessment of a power plant

Critical infrastructures and systems are today exposed not only to traditional safety and availability problems, but also to new kinds of security threats. These are mainly due to the large number of new vulnerabilities and architectural weaknesses introduced by the extensive use of information and communication technologies (ICT) into such complex systems. In this paper we present the outcomes of an exhaustive ICT security assessment, targeting an operational power plant, which consisted also of the simulation of potential cyber attacks. The assessment shows that the plant is considerably vulnerable to malicious attacks. This situation cannot be ignored, because the potential outcomes of an induced plant malfunction can be severe.     

输电系统静态电压概率安全分析

介绍了一种基于随机潮流和静态电压稳定域的输电系统静态电压稳定概率模型。该模型能够计及节点注入功率和元件故障的不确定性。采用随机潮流确定输电系统关键割集中线路的潮流概率分布,利用半不变量法和Gram-Charlier级数,得到割集静态电压稳定域中线路潮流的线性组合随机变量的概率分布函数;根据超平面形式的静态电压稳定域计算出静态电压不稳定概率;通过对所得概率指标进行分析,能够找到对系统静态电压不稳定概率影响最大的预想事故,分析结果可以帮助运行人员进行预防控制决策;以10机39节点New England系统为例验证了所提算法的有效性。     

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.     

Neural network based transient stability assessment of electric power systems

In this paper a systematic procedure to design an intelligent neurocontroller to assess the dynamic security of interconnected power systems is presented. This approach focuses on the integration of modern control theory together with the adaptive networks to determine critical clearing time for power systems.     

电力系统网络安全体系研究

随着电力行业信息化的不断发展,信息安全面临日益严峻的考验。结合电力系统网络环境,分析了威胁电力系统网络安全的四个主要来源,依此给出了一套电力系统安全体系总体设计及解决方案,为电力系统网络安全体系建设提供了可参考的理论和实践依据。     

An intelligent power factor corrector for power system using artificial neural networks

An intelligent power factor correction approach based on artificial neural networks (ANN) is introduced. Four learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. The best test results obtained from the ANN compensators trained with the four learning algorithms were first achieved. The parameters belonging to each neural compensator obtained from an off-line training were then inserted into a microcontroller for on-line usage. The results have shown that the selected intelligent compensators developed in this work might overcome the problems occurred in the literature providing accurate, simple and low-cost solution for compensation.     

基于多输出衰减径向基函数神经网络的电网故障诊断

为提高电网故障诊断神经网络模型的构建速度,提出了一种基于多输出衰减径向基函数（Multi-output Decay Radial Basis Function, MDRBF）神经网络的故障诊断方法。DRBF神经网络不需训练即能以任意精度一致逼近任意连续多变量函数。介绍了单输出DRBF（Single-output DRBF, SDRBF）神经网络,分析了其存在的不足,即只能处理单输出变量问题,不能直接应用于电网故障诊断。在此基础上,根据电网元件的故障特点,提出了将SDRBF神经网络演变为多输出DRBF（Mu     

Calculation of weighting factors of static security indices used in contingency ranking of power systems based on fuzzy logic and analytical hierarchy process

Contingency screening and ranking is one of the most important issues for security assessment in the field of power system operation. The objective of contingency ranking is to quickly and accurately select a short list of critical contingencies from a large list of potential contingencies and rank them according to their severity. Then suitable preventive actions can be implemented considering these contingencies that are likely to affect the power system performance. In this paper a novel approach is presented for contingency ranking based on static security assessment. This method employs weighted performance index with the application of fuzzy logic based analytical hierarchy process in order to select appropriate weighting factors to be imposed. The proposed method is applied to IEEE 30 bus system and the results are presented.     

Application of analog and hybrid computation methods to fast power system security studies

In analog emulation, a non-linear system model is implemented as a set of reconfigurable analog circuits referred to as the emulator. This hardware is actuated, initialized, and allowed to settle to a steady-state during which time the user utilizes voltage and current measurement devices to observe the system’s transient response and constrained static solution. Because this method abandons the use of iterative numerical techniques, the length of time required to emulate a solution (this excludes the effects of actuation and data acquisition) is fully controllable and independent of the dimension of the system model. Analog computation has potential to perform many types of non-linear analyses significantly faster than is possible digitally; however, its popularity has traditionally been limited by a need for manual actuation and data acquisition. In this work, the authors examine the use of digital technology to automate actuation and data acquisition for emulation. They also discuss how intelligent design and control of the emulator may be utilized to minimize data collection and accelerate emulation-based power-flow analysis and system security studies.     

基于动态安全域的电力系统安全概率评估模型

针对电力系统安全性问题特性,提出一种基于动态安全域的安全概率评估模型。模型核心是不安全概率指标,并通过该指标来表征各电力元件以及整个电力系统的安全性状况。概率方法的引入,使得模型不仅计及系统中各种主要的不确定性因素,而且对系统的安全状况进行了定量的描述。动态安全域理论大大降低了安全概率评估的计算量,使得该模型具有实用性。评估结果可以用于指导运行人员在安全控制中制定正确的决策。用新英格兰10机39节点系统(New England 10-generator 39-bussystem)对该模型做了验证。     

基于人工神经网络的电力系统精细化安全运行规则

随着大规模可再生能源不断并网,对电网的实时调控能力提出了更高的要求。传统的基于在线关键断面自动发现以及基于连续潮流的在线极限传输容量计算方法,模型复杂、计算周期长,难以做到在线运行。从数据驱动的角度出发,首先将电网实时运行状态的潮流量抽象为该时刻电网的运行特征;然后对所有特征进行聚类和分布式特征选择;最后运用人工神经网络建立所选特征与关键断面极限传输容量之间的对应关系。算例分析表明,所提基于人工神经网络的电力系统精细化安全运行规则,在保证时间效率的前提下,能够在一定程度上提高关键断面极限传输容量的预测准确度。     

Classification and assessment of power system static security using decision tree and random forest classifiers

The power system static security classification and assessment is essential in order to identify the post‐contingency problems and take corrective measures and to protect the system from blackout. In this paper, application of two data mining classifiers have been proposed for the security classification and assessment of a multiclass security problem. To design the security problem, contingency analysis is carried out under N‐1 line outage, and static severity index (SSI) is computed, which is a function of the line overload and the voltage deviation using Newton–Raphson load flow method, considering the variable load and generating conditions. Corresponding to the computed values of SSI, the voltage, phase angle, Mega Volt Ampere line flow and so on, a 1 × 7 pattern vector is generated. The generated pattern vectors are used to design a multiclass security problem. The designed security pattern vectors are given as inputs to the decision tree (DT) and random forest (RF) model in order to classify the security status of the power system. The proposed classifiers are investigated on an IEEE 30‐bus test system. The classification accuracy of the DT and the RF are compared with state‐of‐the‐art classifier models, namely, multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM).The simulation results clearly indicate that the proposed DT and RF classifiers are more efficient, reliable, and out performs MLP, RBF, and SVM classifiers for the assessment of the security status of the power system. Hence, DT and RF classifiers are found to be suitable for online implementation. Copyright © 2015 John Wiley & Sons, Ltd.     

信息失效威胁下的电力信息物理系统安全评估与防御研究综述

电力CPS中信息失效可能导致严重物理后果,必须准确评估信息失效威胁下的电力CPS安全性,实现电力CPS安全防御.首先阐述电力CPS建模与信息物理交互机理研究思路.之后通过信息失效来源分析、失效跨空间传递过程分析、安全评估指标计算三个环节建立电力CPS安全评估研究框架.最后从信息网结构优化、路由优化、网络安全防御三方面分...     

Computational framework combining static and transient power system security evaluation using uncertainties

This paper presents a simplified but effective procedure to represent power system uncertainties that allow the development of a computational tool to tackle the power system probabilistic security problem from both the small signal stability (SSS) perspective, and the transient stability (TRS) analysis perspective. A set of examples using the New England test-system is presented and discussed. Among the advantages of the suggested method, the following points are evident: (i) the ability to discriminate between the three types of uncertainties (scenarios, fault events, and noise types) that permeate power systems and that are relevant to the system security; (ii) the capacity to use existing traditional tools from both small signal dynamic analysis and transient stability analysis to adapt them easily to the well-established concept of probabilistic adequacy assessment, without resorting to abstruse and hard-to-implement theoretical techniques; (iii) the enormous advantage of usual availability for the required statistical data (no hard-to-collect data are required); and (iv) proposal of a conceptual procedure that renders a highly combinatorial problem amenable to current state-of-the-art hardware resources, within acceptable limits of computational burden. Important practical results that one may wish to highlight are related to the effective representation of noise uncertainties through a straightforward combination of weighted histograms, and the successful performance of the new Apparent Stability Index – ASI.     

Study of a new method for power system transients classification based on wavelet entropy and neural network

The detection and classification of transient signals are widely applied in many fields of power system. The study of transient signal detection and classification is a sustaining focus of researchers as well as a difficult issue. There are still many problems needed to be solved in this area. Based on the wavelet transform (WT), the idea of entropy and weight coefficient is introduced, and the wavelet energy entropy (WEE) and wavelet entropy weight (WEW) are defined in this paper. The distribution picture of WEE and WEW along with scales are presented for the first time. PSCAD/EMTDC models for six types of transients, namely breaker switching, capacitor switching, short circuit fault, primary arc, lightning disturbance and lightning strike fault, are constructed. With WEE and WEW, the eigenvectors for the six transients are established and a model which uses the eigenvectors as the input of the BP (back-propagation) neural network is set up to realize the classification of these transients. The simulation has been executed based on a 500 kV transmission line model in China and the results show that feature extraction based on WEE and WEW can effectively discover the useful local features. With the help of neural network classifier, it has effective classifying result. This method is applicable in the power system.     

DSA Tools在电力系统小扰动稳定安全评估中的应用

为分析可再生能源并网中可能出现的电力系统小扰动稳定性问题,采用小扰动稳定性动态评估的方法,提出基于DSA Tools实现小扰动稳定性的综合评估。通过采用特征值分析、故障分析、单机无限大系统扫描和小扰动稳定性极限搜索等方法,对IEEE14节点系统和IEEE145节点系统的小扰动稳定性进行分析。研究结果表明IEEE14节点系统小扰动稳定性较好,IEEE145小扰动稳定性则较差。另外,研究表明DSA Tools软件和SSAT分析工具可应用于电力系统预警方案设计和系统保护框架策略的优化。     

中央制冷空调冷冻水系统模糊RBF控制研究

针对中央空调冷冻水系统回水温度快速准确调节问题,提出基于模糊径向基函数(radial basis function,RBF)网络的变流量回水温度智能控制方法。首先,对冷冻水系统旁通阀门的水量开度、泵组转速等输入量,按照模糊控制理论,进行模糊化与反模糊化处理,获得归一化的输入信息向量;然后,利用能够全局寻优的RBF网络进行温度预测,不断迭代预测产生理想的预测温度;最后,当期望温度与预测迭代的温度残差小于门限值时,停止迭代,输出并记录温度,完成冷冻水系统的非线性温度控制。仿真实验表明,相比于传统反向神经(back propagation,BP)网络控制,RBF控制方法迭代次数更少且精度更高,能够提高系统的整体性能。     

考虑电网静态安全的输电线路动态增容系统

针对电网调控的特点和需求,将动态增容技术从设备分析层面提升到电网分析层面,提出了动态增容在电网调控系统中应用的总体实施方案和功能模块设计。首先分析了调控系统中动态增容的应用原则,在此基础上提出了动态增容系统的总体架构、功能模块、接口设计和硬件结构;然后对系统的数据接入与处理、线路和输电断面动态限值计算、增容过程安全评估、电网增容预测分析等关键技术进行了阐述;最后介绍了该系统在南京地区的应用,基于电网实际运行数据对系统应用效果进行了分析。     

基于风险理论和模糊推理的电力系统暂态安全风险评估

在概率论的基础上将风险理论和模糊推理应用于电力系统暂态安全风险评估,利用特定的严重度函数将故障后系统的频率最大偏移、电压最大偏移、功角稳定裕度和故障切除时间裕度进行量化,得到量化的风险指标;将量化的频率风险指标和电压风险指标相结合,通过稳态模糊控制器计算故障的稳态指标,将量化的功角裕度指标与故障切除时间裕度指标相结合,通过暂态模糊控制器计算故障的暂态指标;通过两者的加权综合得到系统的综合暂态稳定指标.在此基础上开发了暂态安全风险评估软件,并以新英格兰39节点系统为例计算三相线路永久性接地故障各风险指标并排序,说明了该方法的有效性和合理性.