Fast real power contingency ranking using a counterpropagationnetwork

This paper proposes a fast real power contingency ranking approach which is based on a pattern recognition technique using a forward-only counterpropagation neural network (CPN). The power system operating state is described by a set of variables which compose the pattern. The corresponding performance indices of various contingencies can then be recognised by a properly trained counterpropagation network. A feature selection method is also employed for reducing the dimensionality of the input patterns. When compared with a full AC load flow the proposed method is more superior and has good pattern recognition ability     

Fast voltage contingency screening using radial basis function neural network

Power system security is one of the vital concerns in competitive electricity markets due to the delineation of the system controller and the generation owner. This paper presents an approach based on radial basis function neural network (RBFN) to rank the contingencies expected to cause steady state bus voltage violations. Euclidean distance-based clustering technique has been employed to select the number of hidden (RBF) units and unit centers for the RBF neural network. A feature selection technique based on the class separability index and correlation coefficient has been employed to identify the inputs for the RBF network. The effectiveness of the proposed approach has been demonstrated on IEEE 30-bus system and a practical 75-bus Indian system for voltage contingency screening/ranking at different loading conditions.     

基于多粒度特征选择和模型融合的复合电能质量扰动 分类特征优化

现代电力系统因其“双高”特性造成电能质量扰动模式愈加复杂,对复合扰动的准确分类提出了挑战。传统电能质量扰动分类方法在特征提取阶段所提取的特征由人为确定,难以判断所提取的特征对分类问题是否有效,加之多重复合扰动特征相互耦合导致扰动特征的可分性确定困难。为此,提出一种基于粒度的计算方法进行特征选择的模型。在提取的扰动特征集的基础上,通过构建多粒度空间反映特征分布差异性,进而挖掘各粒度下的最优特征子集以确定有效和冗余的分类特征,达到优化分类效果的目的。在此基础上,通过集成分类模型融合不同粒度空间最优扰动特征集所训练的同质弱分类器模型,提出一种新的电能质量扰动多粒度集成分类方法。该方法克服了现有方法在进行多粒度分类时通过寻找最优单粒度空间特征而导致的其他粒度空间信息丢失的问题。实验表明,多粒度特征选择算法可提取对分类有效的扰动特征,集成分类模型可进一步改善模型的分类性能。     

A feed-forward artificial neural network with enhanced feature selection for power system transient stability assessment

This paper describes an approach where an artificial neural network is used to predict the stability status of the power system. This efficient and robust approach combines the advantages of the time–domain integration schemes and artificial neural network for on-line transient stability assessment of the power system. The transient stability index has been obtained by the extended equal area criterion method and is used as an output of the neural network. Two feature selection techniques have been used to identify the input variables best suitable for training. The proposed technique predicts the transient stability index correctly, without any false alarm. In addition, the transient stability index as an output of the neural network helps to implement possible control actions. The results obtained demonstrate the potential for neural network to be a part of any on-line dynamic security assessment tool.     

Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines

This paper presents a new approach for the classification of the power system disturbances using support vector machines (SVMs). The proposed approach is carried out at three serial stages. Firstly, the features to be form the SVM classifier are obtained by using the wavelet transform and a few different feature extraction techniques. Secondly, the features exposing the best classification accuracy of these features are selected by a feature selection technique called as sequential forward selection. Thirdly, the best appropriate input vector for SVM classifier is rummaged. The input vector is started with the first best feature and incrementally added the chosen features. After the addition of each feature, the performance of the SVM is evaluated. The kernel and penalty parameters of the SVM are determined by cross-validation. The parameter set that gives the smallest misclassification error is retained. Finally, both the noisy and noiseless signals are applied to the classifier given above stages. Experimental results indicate that the proposed classifier is robust and has more high classification accuracy with regard to the other approaches in the literature for this problem.     

Short-term wind power forecasting using model structure selection and data fusion techniques

This paper proposes a novel strategy to forecast the short-term wind power using model structure selection in combined with data fusion technique. The available inputs are usually treated as an integrated one for the predictive modeling, which ignores the fusion of the different types of inputs. This results the relationship between the multi-inputs and desired outputs are not effectively reflected in forecasting procedure. Moreover, the performances of the various types of data fusion methods are susceptible to the wind power distribution and critical factors such as smoothness, overlapping, the intrinsic amount of information evaluation and the number of neighbor points. These outlined factors can result the lower generality ability of the proposed methods due to the insufficient optimization of the model structure. So this paper presents short-term wind power forecasting method using model structure selection technique in combined with four representative fusions of dimensionality reduction methods to optimize the model structure, promote the computational efficiency and improve the forecasting accuracy. Experimental evaluation based on the real data from the wind farm in Jiangsu province is given to verify the effectiveness of the proposed method by comparing the traditional techniques.     

A portfolio selection model using genetic relation algorithm and genetic network programming

In this paper, new evolutionary computation methods named genetic relation algorithm (GRA) and genetic network programming (GNP) have been applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up the most efficient portfolio, the proposed model considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. Then, the selected portfolio is further optimized by the stock trading model of GNP. In a sense, the proposed model is an integrated intelligent model. A comprehensive analysis of the results is provided, and it is clarified that the proposed model can obtain much higher profits than other traditional methods. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于随机Petri网模型的地区电网事故链监控研究

地区电网事故一般是由多重并发故障的复杂序列引起的。如果能在事故前建立系统事故链模型,分析出造成系统事故的复杂事件序列,将有利于对电力系统事故进行监控。该文提出了基于随机Petri网(SPN)建立地区电网事故链模型的新方法,设计了一种快速动态搜索算法,根据负荷和结构的变化快速求取地区电网事故链,并根据定量分析确定需要重点监控的危险事故链,在此基础上设计了针对地区电网事故影响因素的预控算法。最后对某地区电网进行了实例分析,结果表明该方法具有有效性和正确性。     

基于随机Petri网模型的地区电网事故链监控研究

地区电网事故一般是由多重并发故障的复杂序列引起的.如果能在事故前建立系统事故链模型,分析出造成系统事故的复杂事件序列,将有利于对电力系统事故进行监控.该文提出了基于随机Petri网(SPN)建立地区电网事故链模型的新方法,设计了一种快速动态搜索算法,根据负荷和结构的变化快速求取地区电网事故链,并根据定量分析确定需要重点监控的危险事故链,在此基础上设计了针对地区电网事故影响因素的预控算法.最后对某地区电网进行了实例分析,结果表明该方法具有有效性和正确性.     

On-line evaluation of loadability limit for pool model with TCSC using back propagation neural network

This paper presents an approach for on-line evaluation of loadability limit for pool model with Thyristor Controlled Series Compensator (TCSC) using Back Propagation Neural Network (BPNN). The optimal location, setting of TCSC and loadability limit for various load patterns in off-line are determined using Differential Evolution (DE) algorithm. This approach uses AC load flow equations with constraints on real and reactive power generations, transmission line flows, magnitude of bus voltages and TCSC setting. The input parameters to BPNN are real and reactive power loads at all buses. Data for training the BPNN is generated through Optimal Power Flow (OPF) solution using DE and the trained BPNN is tested with unseen load patterns. Sequential Forward Selection (SFS) belonging to greedy wrapper method is used for the selection of best optimal input features. Simulations are performed on 39 bus New England test system and IEEE 118 bus system. Solution accuracy and computation time are analyzed. The results obtained illustrate that, for on-line evaluation of loadability limit of pool model with TCSC, BPNN is accurate with minimal Mean Squared Error (MSE) and less computation time.     

Electricity market price spike analysis by a hybrid data model and feature selection technique

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets.     

Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines

In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy.     

开关电源监控信号接入微波系统网管的实例

通过实例介绍了利用微波设备外围告警输入功能实现监控远端电源告警信息的方法。     

地理信息环境下配电网潮流实时计算与显示

分析了组件式地理信息系统(GIS)的特点和前推回带法的基本原理,在此基础上研究并开发了一个基于地理信息系统的配电网潮流管理软件.介绍了GIS环境下潮流计算数据库的总体结构,利用GIS的图层绑定功能实现了电压、功率在GIS上的实时显示.     

改进F-score特征选择的MPSO-BP神经网络短期负荷预测

针对海量数据提出一种基于改进Fisher分数(F-score)特征选择的改进粒子群优化的BP(Modified Particle Swarm Optimization and Back Propagation,MPSO-BP)神经网络短期负荷预测方法。首先采用改进F-score特征评价准则计算影响负荷预测精度各个特征的F-score值,再通过F-score Area法设定阈值筛选出最优特征子集,然后将最优特征子集作为MPSO-BP神经网络模型的输入变量完成对预测日一天24点负荷的预测,并与MPSO-BP神经网络短期负荷预测和传统BP神经网络短期负荷预测进行对比。算例表明,文中提出的短期负荷预测方法可以较好地对海量数据进行挖掘,具有较高的预测精度。     

利用粒子群算法解决电网优化购入电量

利用粒子群算法对购电优化问题进行了研究,建立了考虑供求约束、发电能力约束和输电容量约束的电网优化购入电量模型.按价格最低原则预先确定供电商,运用粒子群算法解决了当约束中出现非连续变量的问题.提供了一种解决电网优化购入电量问题的方法,节约了购电成本.     

含风电场配电网随机潮流计算及其电压安全分析

风电场的输出功率具有较为明显的随机性和波动性,传统确定性潮流计算方法不能全面分析其在并入配电网这类弱电网后对系统安全运行可能造成的影响。基于Cornish-Fisher正态分布展开及协方差矩阵转换法,提出一种考虑风电场输出功率模型特性以及系统负荷相关性的随机潮流计算方法。以浙江某地配电网为例,对风电场并网后系统电压分布进行计算,并对风电场并网位置、风电电源渗透率和风电场机组构成比例等因素对配电网系统电压安全影响作了对应分析,结果证明了该算法的有效性。     

A hybrid model for transient stability evaluation of interconnectedlongitudinal power systems using neural network/pattern recognitionapproach

A methodology for evaluation of transient stability of medium size interconnected longitudinal power systems has been developed using a hybrid neural network pattern recognition approach. Assessment of transient stability is done using a fast pattern recognition algorithm at each load level, accurately predicted by a neural network on a half-hourly basis. As opposed to the conventional approaches, this hybrid strategy can make fast decisions with less computations     

应用复幅度实时检测电网频率的新算法

首先简要分析了频谱泄漏的成因和其对频率分析中DFT算法的影响.在理想同步采样的条件下,提出了一种基于简谐信号复幅度的频率偏差求取算法.再依据实际采样和电网信号的特点,修正了该算法,并得出了电网频率实时检测的迭代公式.仿真结果表明,该算法在非同步采样条件下能精确、快速地跟踪频率变化,且运算量小、收敛性好、抗谐波干扰能力强,在以交流电信号实时检测为基础的系统中有较强的应用价值.