Artificial neural networks

A general neural network model is introduced. The authors begin with a discussion of models for both individual neurons and for networks of neurons. A common learning rule, i.e. backward error propagation, also known as backpropagation, is described briefly and applied to an example problem     

Artificial neural networks

Artificial neural networks are explained, and the different types are described. Three different tasks for which they are suitable are discussed. They are pattern classification and associative memory, self-organization and feature extraction, and optimization     

Artificial neural networks for solving the power flow problem in electric power systems

In this paper, the use of artificial neural networks (ANN) is proposed for solving the well known power flow (PF) problem of electric power systems (EPS). PF evaluates the steady state of EPS and is a fundamental tool for planning, operation and control of modern power systems. The mathematical model of the PF comprises a set of non-linear algebraic equations conventionally solved with the Newton-Raphson method or its decoupled versions. In order to take advantage of the superior speed of ANN over conventional PF methods, multilayer perceptrons neural networks trained with the second order Levenberg–Marquardt method have been used for computing voltages magnitudes and angles of the PF problem. The proposed ANN methodology has been successfully tested using the IEEE-30 bus system.     

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.     

Robust decentralized neural networks based LFC in a deregulated power system

In this paper, a decentralized radial basis function neural network (RBFNN) based controller for load frequency control (LFC) in a deregulated power system is presented using the generalized model for LFC scheme according to the possible contracts. To achieve decentralization, the connections between each control area with the rest of system and effects of possible contracted scenarios are treated as a set of input disturbance signals. The idea of mixed H₂/H_∞ control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the RBFNN based controller is to take large modeling uncertainties into account, cover physical constraints on control action and minimize the effects of area load disturbances. This newly developed design strategy combines the advantage of the neural networks and mixed H₂/H_∞ control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the proposed method is demonstrated on a three-area restructured power system. The results of the proposed controllers are compared with the mixed H₂/H_∞ controllers for three scenarios of the possible contracts under large load demands and disturbances. The resulting controller is shown to minimize the effects of area load disturbances and maintain robust performance in the presence of plant parameter changes and system nonlinearities.     

电力系统负荷频率控制LFC的小波神经网络辨识

建立了非线性的电力系统负荷频率控制LFC模型,利用递归NARMA模型的小波网络的实现方法对LFC模型进行了辨识,利用Akaike's的最终预测误差准则FPE和信息准则AIC,进行了隐层节点数目和反馈阶次的计算,理论和仿真表明辨识模型可取得较好效果.     

电力系统负荷频率控制LFC的小波神经网络辨识

建立了非线性的电力系统负荷频率控制LFC模型,利用递归NARMA模型的小波网络的实现方法对LFC模型进行了辨识,利用Akaike's的最终预测误差准则FPE和信息准则AIC,进行了隐层节点数目和反馈阶次的计算,理论和仿真表明辨识模型可取得较好效果。     

基于神经网络的油田l电力网无功功率管理技术

油田电力网在传输无功功率时产生了巨大的员耗。针对这一问题,本文应用一扩展Ｈｏｐｆｉｅｌｄ神经网络－耦合梯度网络,建立了油田电力网无功功率管理的全局优化的数学模型。仿真结果表明,通过这一网络模型的优化计算,可以获得可观的经济效益。     

Current state of neural networks applications in power system monitoring and control

For over two decades Neural Network (NN) has been applied to power system monitoring and control. Conventional controllers suffer from certain limitations which NN as an Artificial Intelligence (AI) technique is able to overcome. Therefore, many researchers prefer to use NN technique in the monitoring and control of power systems. This paper reviews published recently schemes for control and monitoring based on NN. The performance of various NN controllers is compared with one another as well as to the performance of other types of controllers. This review further reveals that the design of a proper NN control can maintain first-swing stability, damp oscillation, ensure voltage stability and the reliable supply of electric power.     

Special considerations in power system restoration

Power system restoration issues are considered. The authors discuss a number of particular problems that require special attention in the development of system restoration plans. The areas analyzed include excessive alarms during restoration, switching during restoration, underground transmission system concerns, and telecommunication capabilities and limitations     

New approaches in power system restoration

Power system restoration issues are considered. The authors explore some of the possibilities that are opening up for more power system restoration strategies and practices. The areas analyzed include system-wide coordination and overall organization of restoration, expert systems for power system restoration, and operator training simulators in power system restoration     

基于小波神经网络的电力负荷预测方法

分析了小波神经网络的特点，研究了在电力负荷预测中小波神经网络存在的优缺点及适用范围。通过对小波神经网络和BP神经网络的结构和算法进行理论分析，并对实际电力负荷预测算例进行对比研究，指出小波神经网络本身适合对波动性的信号进行预测，而且在神经网络节点数目相同的情况下，小波神经网络比BP神经网络具有更高的预测精度，因此采用小波神经网络有利于减少隐节点数目。还指出由于当前的连续小波神经网络主要使用传统BP神经网络的随机初始化方法和基于梯度的训练算法，因此存在收敛性差的缺点。     

改进模糊控制的光伏发电系统最大功率点跟踪

研究了光伏发电系统最大功率点跟踪的问题,由于其存在着随机性,且往往不够充分与准确,容易导致系统稳态剧烈震荡或无法准确跟踪。鉴于传统人工总结模糊控制规则难度高,提出了模糊神经网络控制算法,将T-S模糊推理方法与神经网络理论相结合,选择混合法作为训练方法,网格法作为生成算法,由实测数据自动生成模糊控制规则,将其嵌入到模糊控制器当中,从而实现了MPPT控制功能。仿真结果表明,采用该方法生成的模糊规则实用准确,系统稳态性能与动态性能均十分优越。实验证明人工神经网络法与模糊控制技术相结合,实现光伏发电MPPT高效准确。     

Artificial neural network based fault diagnostic system for electric power distribution feeders

This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for distribution feeders. The principal functions of this diagnostic system are: (i) detection of fault occurrence, (ii) identification of faulted sections, and (iii) classification of faults into types, e.g. HIFs (high impedance faults) or LIFs (low impedance faults). This has been achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies HIFs, which are relatively difficult to identify with other methods. Test results are generated using the Manitoba Hydro 24 kV distribution feeder. These results amply demonstrate the capacibility of the FDS in terms of accuracy and speed with respect to detection, localization, and classification of distribution feeder faults.     

Damping enhancement of multimachine power system using adaptive generator control system with neural networks

The authors proposed a nonlinear adaptive generator control system with neutral networks for improving damping of power systems, and showed its effectiveness in a one-machine infinite bus test power system in a previous paper. The proposed neurocontrol system adaptively generates appropriate supplementary control signals to the conventional controllers such as the automatic voltage regulator and speed governor so as to enhance transient stability and damping of the power system. In this paper, the applicability of the proposed neurocontrol system to multimachine power systems is discussed. Digital time simulations are carried out for a 4-machine test power system, where one or several synchronous generators is equipped with the neurocontrol system. As a result, also in the multimachine power system, the proposed adaptive neurocontrol systems improve the system damping effectively and they work adaptively against the wide changes of the operating conditions and the network configuration.     

应用人工神经网络进行电力系统动态安全评价的新方法

本文提出了一种应用人工神经网络进行电力系统动态安全评价的新方法。把故障前系统的稳态运行参数作为特征量,摇摆过程中发电机转子间的最大相对摇摆角作为系统稳定性的量度,并证明了它们之间呈连续映射关系。随后表明了用三层前向网络实现这类映射的可行性。为减少神经网络的训练时间,本文提出了网络“分解—集结”方法和映射与分类相结合的思想。6机22节点系统的试验结果表明了本文提出方法的有效性。     

基于反馈型神经网络的光伏系统发电功率预测

分析了光伏系统的发电特性以及影响光伏发电的因素,建立了反馈型神经网络光伏系统发电功率预测模型.该模型采用Elman神经网络结构,利用其强大计算能力、映射能力和稳定性,将光伏发电的历史数据和天气情况一同作为样本,对模型进行训练和发电功率预测.仿真结果表明,该方法建立的预测模型具有较高的精度,为解决光伏系统发电功率预测提供了一种可行路径.     

专家系统在电力系统结构恢复中的应用

电力系统的恢复是一个多变量、多目标、多步骤、非线性的优化问题，以往预先大量的潮流和稳定计算而制定的系统恢复计划很难跟上系统的变化。而依靠智能性的专家系统在很大程度上可以解决这个难题。为了提高专家系统的搜索效率，在使用传统的深度优先搜索策略的基础上，结合启发式算法、关键路径算法，以及Petri网，Tabu search等优化算法短时间内给出一个最合适的方案。该专家系统由人工智能语言AMZI Prolog和C 混合编程实现，既考虑到传统智能语言的优越性，又兼顾到使用流行的面向对象语言实现友好的人机接口。     

Bulk power system restoration training techniques

The authors focus industry attention on various tools and techniques used to develop and implement effective bulk power system restoration training programs. The need to continue developing improved training tools is highlighted. Six short note reports are presented. The first two reports focus on the preparation which should take place as a precursor to the development of a formal system restoration training program; this includes identification of the operating issues which should be addressed in a system restoration plan/training program and an analysis of how operators should recognize and respond to these conditions. Three reports describe the wide variety of training methods currently used in existing system restoration training programs; the methods described range from simple classroom lectures to the use of advanced power system simulators. The last short note provides a synopsis of the emergency preparedness training programs used in other industries