An adaptive approach in distance protection using an artificial neural network

All relay settings are a compromise. Adaptive relaying accepts that relays that protect a power system may need to change their characteristics to suit the prevailing power system conditions. This philosophy has a wide range of applications covering many protective schemes. Here we consider a two-terminal transmission line, confirm that fault resistance and the location of faults can produce erroneous relay function and finally suggest ways to ensure the generation of the correct signal for relay operation. Retaining the microprocessor based framework, we show how artificial neural networks (ANNs) can be used effectively to achieve adaptive relaying for the above-mentioned problem. Adaptive relaying covers a large number of applications and the characteristics of relays vary widely, so the philosophy of adaptive relaying must vary accordingly. A modified multilayered perceptron model employs an additional node in the input layer. This additional input facilitates changes in the relay characteristic. The desired change in the quadrilateral relay characteristic is achieved by making appropriate changes in the thresholds and weights of the hidden layer neurons. A multiparameter adaptive scheme assumes that the additional input of the phase angle is available. Simulation results using ANNs for the different applications of adaptive relaying mentioned above are presented and discussed.     

Daily electric load forecasting using artificial neural network

A multilayered-type neural network is attractive for daily electric load forecasting because the neural network can acquire a nonlinear relationship among the electric load data and their factors (weather, temperature, etc.) automatically. This paper discusses first some essential issues to be considered in neural network applications. One is difficulty of obtaining sufficient effective training data, another is the influence of abnormal learning data, and one more is the inevitable outerpolation. For these issues, the following three methods are developed in order to forecast more accurately: (1) a structure of the neural networks for insufficient training data; (2) detection and diminishing the influence of abnormal data; (3) employment of interpolation network and outerpolation network with additional data for outerpolation. Furthermore, to increase the sensitivity between electric loads and factors, (4) removal of base load is developed. Those methods work effectively to decrease the average absolute errors of peak-load forecasting and 24-hour load forecasting to 1.78 percent and 2.73 percent, respectively.     

基于快速人工神经网络的短期负荷预测

根据负荷的不确定性和非线性的特点 ,采用了ANN和AFS理论进行STLF ,分两个步骤 :在ANN中引入了平滑因子和遗忘因子 ,来加快收敛速度并解决ANN的遗忘问题 ;在AFS中对基本负荷预测值进行修正 ,引进不平均的隶属函数来体现负荷变化对温度的敏感性。实践表明该模型具有速度快、预测精度高等优点     

基于快速人工神经网络的短期负荷预测

根据负荷的不确定性和非线性的特点,采用了ANN和AFS理论进行STLF,分两个步骤：在ANN中引入了平滑因子和遗忘因子,来加快收敛速度并解决ANN的遗忘问题;在AFS中对基本负荷预测值进行修正,引进不平均的隶属函数来体现负荷变化对温度的敏感性。实践表明该模型具有速度快、预测精度高等优点。     

基于SoPC的人工神经网络的硬件实现方法

提出了一种基于SoPC的神经网络的硬件实现方法,该方法以FPGA器件为硬件载体,NIOSⅡ软核处理器为CPU,Avalon片内总线为数据交换架构。研究了多层前馈神经网络在FPGA上的实现方法,描述了神经网络模块与Avalon片内总线的接口技术。整个系统在Altera的EP2C8Q208C8器件上实现,结果表明,该方法的应用不仅提高了人工神经网络的运算速度,还提高了整个系统的灵活性。     

An artificial neural network approach to transformer faultdiagnosis

This paper presents an artificial neural network (ANN) approach to the diagnosis and detection of faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach     

An adaptive neural network approach to one-week ahead loadforecasting

A neural network approach is proposed for one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called Adaline. An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. A load decomposition is made for the load sequence using digital filters with different passband frequencies. After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized least mean square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4% are presented from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts     

Results of Egyptian unified grid hourly load forecasting using an artificial neural network with expert system interface

This paper presents the hourly load forecasting results of the Egyptian unified grid (EUG). The technique is based on a generalized model combining the features of ANN and an expert system. The above methodology makes the technique robust, updatable and provides for operator intervention when necessary. This property makes it especially suitable for the EUG where the load patterns are influenced mostly because of social activities, and weather contributes very little to load forecast. For example, many social occasions depend on religious preferences which cannot be decided well in advance.This technique has been tested with one year data of EUG during 1993. The results clearly demonstrate the advantage of the above methodology over statistical based techniques. The average absolute forecast errors for the proposed methodology is 2.63% with a standard deviation of 2.62% whereas, the conventional multiple regression method scores an average absolute error of 4.69% with a standard deviation of 4.03%.     

战略资产管理及其在电力企业中的应用

介绍了 UMS公司用以评估国电华东公司资产管理水平的战略资产管理 (SAM)流程框架 ,分析了SAM各组成部分间的相互关系及其所体现的先进管理思想。对电力企业采纳 SAM改进资产管理工作提出一些建议 ,指出电力企业应在行业改革的情况下 ,借鉴 SAM管理理念 ,结合企业自身的实际 ,适时应用 SAM流程框架 ,提高企业的核心竞争能力     

基于ANN的变电站故障诊断系统及其容错性

介绍了基于人工神经网络 (ANN)方法的变电站故障诊断的系统 ,并对其容错性进行了研究 ,该系统充分利用人工神经网络所具有的强大的学习能力及高度的容错性等特点 ,实现对变电站故障元件的诊断。仿真结果表明 ,本系统不仅能在输入信息正确的条件下准确地诊断出故障元件 ,而且在输入信息不完整或少部分信息错误的情况下 ,仍能给出满意的诊断结果。     

基于ANN的变电站故障诊断系统及其容错性

介绍了基于人工神经网络(ANN)方法的变电站故障诊断的系统,并对其容错性进行了研究,该系统充分利用人工神经网络所具有的强大的学习能力及高度的容错性等特点,实现对变电站故障元件的诊断.仿真结果表明,本系统不仅能在输入信息正确的条件下准确地诊断出故障元件,而且在输入信息不完整或少部分信息错误的情况下,仍能给出满意的诊断结果.     

An adaptive neural network approach for the estimation of power system frequency

A new approach to the estimation of power system frequency using an adaptive neural network is presented in this paper. This approach uses a linear adaptive neuron or an adaptive linear combiner called “Adaline” to identify the parameters of a discrete signal model of the power system voltage. Here, the learning parameters are adjusted to force the error between the actual and the computed signal samples to satisfy a stable difference error equation, rather than to minimize an error function. The proposed algorithm shows a high degree of robustness and estimation accuracy over a wide range of frequency changes. The technique is shown to be capable of tracking power system conditions and is immune to the effects of harmonics and random noise.     

Cellular neural network for fast adaptive equalization

The use of an analogue neural network in the adaptive equalization of time-varying communication channels is proposed. the network is used to compute the coefficients of a linear transversal filter. the settling time decreases as the filter order increases and as the signal-to-noise ratio decreases. Owing to the real-time processing capabilities, the network can be useful when it is of interest to track fast variations, as in radio links. the special properties of the tap input correlation matrix result in a cellular network architecture which greatly simplifies the VLSI implementation. Simulation results are presented which point out very satisfactory performance.     

应用人工神经网络预测电力负荷

介绍了在批量处理时间序列情况下，BP神经网络辨识预测电力负荷的方法和步骤。网络成批训练，是权重矢量和偏导数矢量都同时与所有训练矢量的变化成正比地改变。由于采用附加动量项和自适应率等措施，克服了BP规则的局限性，加快了训练速度，增强了网络的泛化能力。在此基础上对某地区实际电力负荷进行了预测，取得了满意的结果。     

智能用电网络的设计与初步实现

发展智能用电是建设智能电网的重要环节之一。本文综合以往智能用电、智能家居与自动需求响应的认识,提出了"智能用电网络",给出其定义。智能用电网络是将用户侧的各种电器通过能量信息网关互连而形成的网络。它旨在建立基于物联网、互联网与智能电网技术的联系海量电力用户的大型用电网,不仅可以帮助用户节能,给电力用户提供智能友好的个性化、差异化服务,还可以引导电力用户灵活互动地参与协同需求响应,帮助电网实现优化运行。智能用电网络的硬件部分由智能插座、智能红外控制器、能量信息网关、云端服务器、移动终端等部分组成;软件部分基于Android平台进行开发。目前,该网络已在清华大学紫荆公寓投入试运行,运行结果表明本文方法的可行性与有效性。     

基于优化神经网络的电网谐波检测方法

为了检测电力系统中的谐波,本文提出了一种基于优化神经网络的电网谐波测量方法.该方法应用一个结构和训练算法都优化了的多层前馈神经网络(MLFNN)对电网中的谐波进行检测,即首先考虑到神经网络的权值记忆负担主要来自谐波幅值和相角的变化,因此先对相角进行确定;再用基于神经网络理论方法对幅值进行检测,并使每一个输出神经元都对应于自己的隐层;然后利用多层前馈神经网络对当前及上一时刻的采样值进行分析,实现了对电网谐波的检测.实验仿真结果证明了该方法的有效性.     

A novel neural network controller and its efficient DSP implementation for vector-controlled induction motor drives

An artificial neural network controller is experimentally implemented on the Texas Instruments TMS320C30 digital signal processor (DSP). The controller emulates indirect field-oriented control for an induction motor, generating direct and quadrature current command signals in the stationary frame. In this way, the neural network performs the critical functions of slip estimation and matrix rotation internally. There are five input signals to the neural network controller, namely, a shaft speed signal, the synchronous frame present and delayed values of the quadrature axis stator current, as well as two neural network output signals fed back after a delay of one sample period. The proposed three-layer neural network controller contains only 17 neurons in an attempt to minimize computational requirements of the digital signal processor. This allows DSP resources to be used for other control purposes and system functions. For experimental investigation, a sampling period of 1 ms is employed. Operating at 33.3 MHz (16.7 MIPS), the digital signal processor is able to perform all neural network calculations in a total time of only 280 /spl mu/s or only 4700 machine instructions. Torque pulsations are initially observed, but are reduced by iterative re-training of the neural network using experimental data. The resulting motor speed step response (for several forward and reverse step commands) quickly tracks the expected response, with negligible error under steady-state conditions.     

弹性自适应人工鱼群-BP神经网络模型及在短期电价预测中的应用

为了提高短期电价预测精度,分析了人工鱼群算法及其缺点,提出了一种弹性自适应人工鱼群算法(RAAFSA).应用RAAFSA算法训练BP神经网络,实现了BP神经网络参数优化,形成弹性自适应人工鱼群-BP神经网络混合算法(RAAFSA-BP),对贵州电网进行短期电价预测.仿真表明,弹性自适应人工鱼群优化的BP神经网络算法收敛速度快于BP神经网络算法和人工鱼群-BP神经网络算法,该混合算法克服了BP神经网络和人工鱼群算法易陷于局部极值、搜索质量差和精度不高的缺点,改善了BP神经网络的泛化能力,输出稳定性好,预报精度显著提高,各日预测电价的平均百分比误差可控制在2%以内,平均绝对误差最大值为1.762$/MWh.该混合算法可有效用于电力市场短期电价预测.     

Daily peak electric load forecasting using an artificial neural network and an improvement method for reducing the forecasting errors

This paper proposes a forecasting method for shortterm peak electric loads using a 3-layer neural network of locally active units. Each unit in the hidden layer of the neural network is activated only by input vectors in a bounded domain of vector space. This characteristic enables additional learning. Furthermore, it is supposed to provide the network structure with information that helps to improve forecasting accuracy. The neural network is applied to daily peak load forecasting simulations in summer. The results show that the proposed method is superior to a conventional neural network with the backpropagation algorithm. To make the best use of the neural network, an error-oriented method of parameter modification is also examined.     

An artificial neural network based digital differential protectionscheme for synchronous generator stator winding protection

This paper describes a new artificial neural network (ANN) based digital differential protection scheme for generator stator winding protection. The scheme includes two feedforward neural networks (FNNs). One ANN is used for fault detection and the other is used for internal fault classification. This design uses current samples from the line-side and the neutral-end in addition to samples from the field current. Fundamental and/or second harmonic present in the field current during a fault help the ANN, used for fault detection, to differentiate between generator states (normal, external fault and internal fault states). Results showing the performance of the protection scheme are presented and indicate that it is fast and reliable