运用人工神经网络进行电力系统运行参数的在线估计

尝试运用3层前向人工神经网络对电力系统的运行参数进行在线估计，神经网络的输入特征 为电力系统中某一点的可量测运行参数，神经网络的输出特征为电力系统中另一点的运行参 数。仿真结果表明，利用神经网络进行电力系统运行参数的在线估计具有较好的精度和良好 的泛化能力，在今后的电网控制领域将有一定的应用价值。     

基于混合决策树——人工神经网络的电力系统动态安全评价

本文建议用一种新型的决策树（ＤＴ）－人工神经网络（ＡＮＮ）混合结构形式，把简单的ＡＮＮ装于ＤＴ的叶子上来拟合电力系统动态安全域，并且提出了一种改进的神经网络训练学习方法。同时，依据近似安全域的知识改进了样本的选取方法。验证表明，在采取了这三种新的方法之后所得的结果同传统的决策树和神经网络相比，不仅可使训练速度提高近一个数量级，而且在边界上具有很高的精度。     

电力系统动态安全分析的ANN动态安全域

采用人工神经网络(ANN)多维空间的强拟合特性，能够较精确地拟合动态安全域，减少了临界点以及稳定点和非稳定点的误判，克服了超平面理论近似拟合电力系统动态安全域误差较大的不足。同时，对于某个具体的故障，采用临界切除时间ter建立ANN稳定裕度拟合器，确定电力系统中运行点的稳定裕度。通过PSASP中的3机9节点模型验证了此理论的正确性，而且准确性优于采用超平面理论拟合的动态安全域。     

应用人工神经网络进行电力系统暂态稳定性分析的新方法

提出了一种利用人工神经网络进行电力系统暂态稳定分析的方法。该神经网络取故障后系统 暂态量为特征量，采用改进BP算法进行训练。将样本空间进行模式分类，并对不同类样本作 不同处理。以实际系统为例，将选用暂态特征与选稳态特征进行比较，验证了选用暂态特征 的准确性和有效性。     

一种新的电力系统动态安全评价模式识别方法

本提出一种以高阶多项式逼近系统运行状态与稳定性指标间的映射进行电力系统动态安全评价的模式识别方法。这种方法的优点是，进行训练几乎无任何困难，且速度很快，易适用于多事故情况。应用加权最小二乘法求多项式系数可保证在稳定与不稳定边界附近识别结果的精度，应用递推最小二乘法可以提高学习的灵活性和效率。另外，本所提出的利用导数信息扩充样本的方法可以提高样本获取的效率。     

电力系统动态安全分析的ANN动态安全域

采用人工神经网络（ANN）多维空间的强拟合特性，能够较精确地拟合动态安全域，减少了临界点以及稳定点和非稳定点的误判，克服了超平面理论近似拟合电力系统动态安全域误差较大的不足。同时，对于某个具体的故障，采用临界切除时间tcr建立ANN稳定裕度拟合器，确定电力系统中运行点的稳定裕度。通过PSASP中的3机9节点模型验证了此理论的正确性，而且准确性优于采用超平面理论拟合的动态安全域。     

人工神经网络技术在电力系统中的应用

电力系统是由发电、输电、变电、配电和用电等多个部分组成的复杂的非线性动态系统。应用传统方法分析电力系统常常存在诸多缺陷,而人工神经网络技术由于具有不可替代的优势,文中介绍了人工神经网络在电力系统故障诊断、智能控制、继电保护、优化运算以及负荷预测等方面的应用情况。     

前馈神经网络用于电力系统动态安全评价的研究

本文作者曾提出一种应用单隐层前馈神经网络进行电力系统动态安全评价的方法．该神经网络取故障前发电机的功率作为输人，故障后某一适当时段为发电机转子间的最大相对摇摆角作为输出．通过论证其输入、输出间呈连续映射关系，从而表明了神经网络结构的合理性．同时指出了关于样本集的获取、隐层神经元的个数及神经网络的训练等有待进一步研究的问题．本文将针对这些问题进行比较系统的研究和探讨，提出相应的原则和方法，并给出两个示例系统的研究结果．     

人工神经网络及其电力系统应用综述

本文介绍了人工神经网络的发展及其特点，并将常规的数值分析方法与人工神经网络方法做以比较，在介绍了得到公认的几种网络及其应用方向之后，本文评述了目前人工神经网络电力系统应用的现状，最后是发展的趋势及存在的问题。     

电力系统在线动态安全评价的一种快速数值积分方法

本文提出了一种基于特殊二阶微分方程数值积分的预估──校正法，并利用稀疏向量法和补偿法进行电力系统动态安全评价的快速方法。计算量分析表明，对每一预想事故进行暂态稳定分析所需的积分运算时间不超过一次ＰＱ解耦潮流计算中迭代时间的两倍．６３机和４５机系统的计算结果表明，所提方法准确、可靠，对每一预想事故进行暂态稳定分析的平均时间少于一次ＰＱ解耦潮流计算，具备在线实现动态安全评价的能力．     

A new method for system optimization by neural networks

This paper proposes a new method of optimizing the operation of a power transmission system, based on a structured neural network. Because the structured neural network is built by prewiring, the network construction time is very short. Simulations of transmission loss of reduction have been carried out. It is shown that the optimization performance of the proposed method is satisfactory in practice. © 1997 Scripta Technica, Inc. Electr Eng Jpn. 118 (4): 27–34, 1997     

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.     

Power system dynamic load modeling using artificial neural networks

The dynamic characteristics of power system loads are critical to obtaining quality operating point-prediction and stability calculations. The composition of components at a load bus makes the aggregated behavior too complicated to be expressed by a simple form. Armed with the theorems recently developed on the approximation capability of artificial neural networks, the authors devise a load model to describe the complex dynamic behavior of loads. Real field data are used to train and test this model. The results verify that this model can emulate load dynamics well and should therefore be suitable as a representation of load for stability analysis     

Improved techniques for power system voltage stability assessmentusing energy methods

An improved method of assessing power system voltage stability using energy techniques is presented. The concept of an energy function providing a localized measure of voltage security in a particular portion of the system is developed. A crucial factor in the use of the energy function method is the ability to rapidly determine the appropriate low-voltage solution to use in the energy measure calculation. Also, an improved method of locating power alternative solutions with low associated energy measures is presented. Techniques are demonstrated on the IEEE 118 bus system and a 415-bus system     

A solution method of unit commitment by artificial neural networks

The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging     

基于模糊小波网络的电力系统短期负荷预测方法

提出一种基于模糊小波网络的短期负荷预测模型.模糊小波网络结合了小波变换良好的时频局域化性质、模糊推理和神经网络的学习能力,因此函数逼近能力大大提高.模糊小波网络由一组模糊推理规则和若干小波子网络组成,其中模糊规则的结论部分与某一特定尺度的小波子网络相对应.在学习过程中通过同时调整小波基函数的平移因子和隶属度函数的形状,使得模糊小波网络的精度和泛化能力大大提高.实例计算表明,这种模型是切实可行的.     

基于模糊小波网络的电力系统短期负荷预测方法

提出一种基于模糊小波网络的短期负荷预测模型。模糊小波网络结合了小波变换良好的时频局域化性质、模糊推理和神经网络的学习能力,因此函数逼近能力大大提高。模糊小波网络由一组模糊推理规则和若干小波子网络组成,其中模糊规则的结论部分与某一特定尺度的小波子网络相对应。在学习过程中通过同时调整小波基函数的平移因子和隶属度函数的形状,使得模糊小波网络的精度和泛化能力大大提高。实例计算表明,这种模型是切实可行的。     

A new method for fast dynamic simulation of power systems

A new simultaneous-solution approach named the sub-tree reduction method is proposed for fast dynamic simulation of large-scale interconnected power system. In this method, a power system is represented by a component tree; the correction factors in Newton iteration of system simulation are calculated by a tree-traversing procedure called forward reduction and backward evaluation. This method can remarkably reduce computation burden of the Newton-type simultaneous-solution approach with greater programming flexibility. Case studies on two power systems verify the correctness and efficiency of this method.     

人工神经网络在传动领域中的应用

从人工神经网络的统一描述及优越特性出发，论述其适用于传动领域的主要特征，概述了其在传动领域中的应用现状，并综述了作者的相关工作。     

Artificial neural networks in power system restoration

Power system restoration (PSR) has been a subject of study for many years. Many techniques were proposed to solve the limitations of the predetermined restoration guidelines and procedures used by a majority of system operators to restore a system following the occurrence of a wide area disturbance. This paper discusses limitations encountered in some currently used PSR techniques and a proposed improvement based on artificial neural networks (ANNs). The proposed scheme is tested on a 162-bus transmission system and compared with a breadth-search restoration scheme. The results indicate that the use of ANN in power system restoration is a feasible option that should be considered for real-time applications.