基于粒子群优化的BP神经网络在火电厂大气环境评价中的应用

利用粒子群优化算法对传统的BP神经网络算法改进,建立了基于粒子群优化BP神经网络的评价模型,并将其应用到火电厂大气环境评价研究中。结合粒子群优化算法的全局寻优能力和BP神经网络算法的局部搜索优势,有效防止了网络陷入局部极小值,同时能保证评价结果的准确性。火电厂实例验证结果表明:利用粒子群优化的BP神经网络模型进行火电厂环境评价不仅计算简便,而且评价结果具有较高的可靠性。     

A parallel genetic algorithm approach to solving the unitcommitment problem: implementation on the transputer networks

Through a constraint handling technique, this paper proposes a parallel genetic algorithm (GA) approach to solving the thermal unit commitment (UC) problem. The developed algorithm is implemented on an eight-processor transputer network, processors of which are arranged in master-slave and dual-direction ring structures, respectively. The proposed approach has been tested on a 38-unit thermal power system over a 24-hour period. Speed-up and efficiency for each topology with different number of processor are compared to those of the sequential GA approach. The proposed topology of dual-direction ring is shown to be well amenable to parallel implementation of the GA for the UC problem     

Using integer programming to refine Lagrangian-based unitcommitment solutions

The authors develop a technique for refining the unit commitment obtained from solving the Lagrangian. Their model is a computer program with nonlinear constraints. It can be solved to optimality using the branch-and-bound technique. Numerical results indicate a significant improvement in the quality of the solution obtained     

Application of artificial neural networks in network calculations

Contents We describe radial basis functions as a special form of artificial neural networks and test whether they are applicable to power systems. In the application to distance protection, the dividing and filtering capability are in the centre of interest. In modelling of equivalent circuits for external networks, primarily the static case is important. In the dynamic case the research is started by using the E′ model and the Park equations for generator modelling. Received: 30 April 2001/Accepted: 22 June 2001     

Application of artificial neural networks in voltage stabilityassessment

Voltage stability problems have been one of the major concerns for electric utilities as a result of system heavy loading. This paper reports on an investigation on the application of ANNs in voltage stability assessment. A multilayer feedforward artificial neural network (ANN) with error backpropagation learning is proposed for calculation of voltage stability margins (VSM). Based on the energy method, a direct mapping relation between power system loading conditions and the VSMs is set up via the ANN. A systematic method for selecting the ANN's input variables was developed using sensitivity analysis. The effects of ANN's training pattern sensitivity problems were also studied by dividing system operating conditions into several loading levels based on sensitivity analysis. Extensive testing of the proposed ANN-based approach indicate its viability for power system voltage stability assessment. Simulation results on five test systems are reported in the paper     

RBF神经网络在谐波检测中的应用

有源电力滤波器补偿性能与所采用的谐波检测方式有很大的依赖关系,现有的检测方法存在精度不高、对电网频率变化比较敏感、自适应能力不强的缺点.本文提出基于RBF神经网络的谐波检测方法,具有较高的运算速度、较高的检测谐波精度,以及较强的自适应能力.     

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。     

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.     

An application of artificial neural networks to maintenance scheduling covering of thermal units over several consecutive years

Thermal units must be maintained periodically as prescribed by the electric utility industry law. As time to execute maintenance works increases with thermal unit capacity, maintenance scheduling has a great influence on the reliability and economy of a power system. In the recent amendment in the law, three inspection rankings have been introduced and scheduling over several consecutive years becomes mandatory, thus making maintenance scheduling extremely difficult. Reflecting a recent stringent supply capability, the emphasis is laid on security rather than a minimum operating cost, having been the primary objective in determining the schedules. Therefore, this study aims to level the spinning reserve at each period under study in the maintenance scheduling while taking into consideration all the amendments in the law. Although rigorous methods such as integer programming and branch and bound method can solve small scale problems, large size problems are beyond these techniques due to an exponential explosion in the number of possible combinations. The prime objective of this paper is to investigate the capability of the Hopfield neural network (HNN) in solving the newly formulated maintenance scheduling problem. The scheduling problem has been mapped on the HNN with slight problem relaxations to facilitate the implementation. A small scheduling problem that determines the maintenance schedules of 3 generators over 3 years (divided to 78 periods) has been solved by the neural network simulator. It has been made clear from simulation results that the proposed approach is very promising in handling a complicated combinatorial optimization problem.     

Application of artificial neural networks for optimization ofelectrode contour

In this paper artificial neural networks (NN) with supervised learning are proposed for HV electrode optimization. To demonstrate the effectiveness of artificial NN in electric field problems, a simple cylindrical electrode system is designed first where the stresses can be computed analytically. It is found that once trained, the NN can give results with mean absolute error of ~1% when compared with analytically obtained results. In the next section of the paper, a multilayer feedforward NN with a back-propagation algorithm is designed for electrode contour optimization. The NN is first trained with the results of electric field computations for some predetermined contours of an axisymmetric electrode arrangement. Then the trained NN is used to give an optimized electrode contour in such a way that a desired stress distribution is obtained on the electrode surface. The results from the present study show that the trained NN can give optimized electrode contours to get a desired stress distribution on the electrode surface very efficiently and accurately     

Application of neural networks to the management of voltage constraints in the Spanish market

The security criteria of a power system require that branch power flows and bus voltages are within their limits, not only in normal operating conditions but also when any credible contingency occurs. In the Spanish electricity market, voltage constraints are solved by connecting a set of off-line generators located in the areas where they occur. Thus, for a market participant it is necessary to predict approximately when its generating units are connected in order to prepare the annual budget and/or decide the time and location of new plants. The authors have presented in a former paper a methodology based on decision trees to estimate the daily load pattern of units, which have not been cleared in the daily energy market and can be connected to alleviate voltage constraints. In this paper, considering a set of potential explanatory variables, a different methodology based on neural networks is proposed to forecast if a non-connected unit will be committed by the System Operator to remove voltage violations. The performance of neural networks is illustrated with a study case. In addition, a thorough comparison with the methodology based on decision trees is carried out.     

Multi-layer neural networks applied to distance relaying

This paper demonstrates the use of Artificial Neural Networks (ANNs) as pattern classifiers for a distance relay operation of transmission lines. Two different types of ANN architectures, concerning the input data, are taken into account. One approach utilises the first five post-fault samples as inputs. The other one employs the magnitudes of the three-phase voltage and current phasors (including the zero sequence) as inputs. A comparison of how well the schemes performed is carried out. An improvement concerning the use of ANNs for protection purposes is found.     

忆阻神经网络在智能电网监控系统中的应用

随着能源危机的到来,智能电网技术成为世界各国所关注的重点。而与此同时,智能发电、输电、变配电、用电以及智能调度等各个环节所取得的技术性突破也为智能电网的大规模实现提供了可能。然而,智能电网结构复杂,电气设备分布广泛,应用需求多,这些特点都给电网运行的安全性和可靠性提出了挑战。针对智能电网的安全运行问题,提出了将忆阻器与人工神经网络相结合,构建出基于忆阻神经网络的智能电网运行状态监控系统,从而可以利用忆阻器所具有的记忆功能,节省人工神经网络的权值传输时间,提高神经网络的数据训练效率,保证了监控系统的及时性和有效性。     

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     

Feed-forward neural networks

One critical aspect neural network designers face today is choosing an appropriate network size for a given application. Network size involves in the case of layered neural network architectures, the number of layers in a network, the number of nodes per layer, and the number of connections. Roughly speaking, a neural network implements a nonlinear mapping of u=G(x). The mapping function G is established during a training phase where the network learns to correctly associate input patterns x to output patterns u. Given a set of training examples (x, u), there is probably an infinite number of different size networks that can learn to map input patterns x into output patterns u. The question is, which network size is more appropriate for a given problem? Unfortunately, the answer to this question is not always obvious. Many researchers agree that the quality of a solution found by a neural network depends strongly on the network size used. In general, network size affects network complexity, and learning time. It also affects the generalization capabilities of the network; that is, its ability-to produce accurate results on patterns outside its training set     

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     

A genetic-based algorithm for optimal estimation of input–output curve parameters of thermal power plants

This paper presents a new and accurate method for estimating the input–output curve parameters of thermal power plants. These parameters are very important for performing the economic dispatch calculations. The higher the accuracy of the estimated coefficients, the more accurate the results obtained from the economic dispatch calculations. Different models of thermal input–output curve fitting of the thermal units are considered. The problem is formulated as an estimation problem. The goal is to minimize the estimation error. The proposed genetic-based method is used to perform this job. Practical study cases are considered to test the performance of the method. Results obtained are evaluated.     

Application of BP neural networks in non-linearity correction of optical tweezers

The back-propagation (BP) neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer system. Considering the low convergence rate of the BP algorithm, the Levenberg-Marquardt (LM) algorithm is used to improve the BP network. The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics. The result shows that with the nonlinear correction using BP networks, the range of force measurement of an optical tweezer system is enlarged by 30% and the precision is also improved compared with the polynomial fitting method. It is demonstrated that nonlinear correction by the neural network method effectively improves the performance of optical tweezers without adding or changing the measuring system. __________ Translated from Optics and Precision Engineering, 2008, 16(1): 6–10 [译自: 光学精密工程]     

BP神经网络在车辆类型识别系统中的应用

介绍了基于BP及其改进算法的多层神经网络的学习训练方法,阐述了噪声测量法识别车辆类型的系统原理和方法。实践证明,基于BP神经网络的噪声测量方法能够快速、准确、有效地识别汽车车辆类型。     

Application of fuzzy neural networks and artificial intelligence for load forecasting

An integrated evolving fuzzy neural network and simulated annealing (AIFNN) for load forecasting method is presented in this paper. First we used fuzzy hyper-rectangular composite neural networks (FHRCNNs) for the initial load forecasting. Then we used evolutionary programming (EP) and simulated annealing (SA) to find the optimal solution of the parameters of FHRCNNs (including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). We knew that the EP has a good capability for searching for globe optimal value, but a poor capability for searching for the local optimal value. And, the SA only had a good capability for searching for a local optimal value. Therefore, we combined both methods to obtain both advantages, and so improve the shortcoming of the traditional ANN training where the weights and biases are always trapped into a local optimum. Finally, we use the AIFNN to see if we could improve the solution quality, and if we actually could reduce the error of load forecasting. The proposed AIFNN load forecasting scheme was tested using data obtained from a sample study including 1 year, 1 month and 24 h time periods. The result demonstrated the accuracy of the proposed load forecasting scheme.