A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Fault section identification and determining its location are important aspects to reduce down/repair time, speed up restoration of power supply and to improve the reliability. In this paper combined wavelet and artificial neural network based directional protection scheme is proposed for double circuit transmission lines using single end data to identify the faulty section and its location with reach setting up to 99% of line length. The proposed method requires the three phase currents and voltage to be measured at one end of the double circuit transmission line modelled using distributed parameter line model which also considers the effect of shunt capacitance. Approximate coefficients feature vector of the three phase voltage and current are extracted using discrete wavelet transform to train the ANN with Levenberg Marquardt algorithm. The proposed scheme involves two stages. The first stage identifies the zone/section of the fault and the second stage calculates the fault location from the relaying point. The proposed combined Wavelet and ANN based fault location scheme is also compared with ANFIS based fault location scheme. The test results of the proposed scheme show that the fault section identification and location estimation is very accurate and the average percentage error in fault location estimation is within 0.001%. This method is adaptive to the variation of fault type (both forward and reverse), fault inception angle, fault location and fault resistance. The main advantage of the proposed scheme is that it offers primary protection to 99% of line length using single end data only and also backup protection to the adjacent forward and reverse line sections.     

基于小波奇异值和支持向量机的高压线路故障诊断

提出了基于小波奇异值（WSV）和支持向量机（SVM）的电力系统故障类型识别的新方法。利用WSV来量化故障特征,再与SVM结合进行故障类型识别。对故障线路三相电流信号进行小波包变换分解,获取故障信号的小波细节系数;利用相重构技术将小波细节系数向量形成系数矩阵,并对该矩阵作奇异值分解,获取小波奇异值;将小波奇异值向量输入到SVM分类器进行故障类型识别。仿真表明,对于不同的故障类型,其小波奇异值分布明显不同,而对于同一类型故障,其小波奇异值分布在不同的故障位置、过渡电阻的情况下仍保持很大的相似性。SVM具有训练样本少、训练时间短、识别率高等优点。     

Discrete wavelet transform and support vector machine‐based parallel transmission line faults classification

This paper presents a scheme for classification of faults on double circuit parallel transmission lines using combination of discrete wavelet transform and support vector machine (SVM). Only one cycle post fault of the phase currents was employed to predict the fault type. Two features for each phase current were extracted using discrete wavelet transform. Thus, a total of 12 features were extracted for the six phase currents. The training data were collected, and SVM was employed to establish the fault classification unit. After that, the fault classification unit was tested for different fault states. The power system simulation was conducted using the MATLAB/Simulink program. The proposed technique took into account the mutual coupling between the parallel transmission lines and the randomness of the faults on transmission line considering time of occurrence, fault location, fault type, fault resistance, and loading conditions. The results show that the proposed technique can classify all the faults on the parallel transmission lines correctly. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Wavelet transform based digital protection for transmission lines

This paper presents a high speed, computationally efficient scheme for protection of transmission lines. The relay logic consists of three parts: directional protection, fault classification and fault location. Wavelet transform is used for extracting information from the fault transients and only the first level high frequency details of the voltages and currents are used. Proposed protection logic compares the directional signals from both terminals to discriminate between faults inside and outside the zone of interest. Fault classification is achieved using local terminal current information. An estimate of the location of the faults is obtained utilizing single faulted phase current information from both terminals. The logic is deterministic and can work reliably in the presence of fault resistance, load variation and CT saturation. The validity of the proposed logic was exhaustively tested by simulating various types of faults on a four bus meshed system modeled in EMTP/ATP.     

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.     

Intelligent relaying scheme for series-compensated double circuit lines using phase angle of differential impedance

The paper presents a comprehensive intelligent relaying scheme using phase angle of differential impedance (PAODI) for series compensated double circuit transmission lines. The differential impedance (DI) is the ratio of differential voltage phasors of any phase across two ends of the transmission line to the differential current phasors of the same phase of the same transmission line. The PAODI of each phase are used as inputs to the data mining model known as decision tree (DT) to generate final relaying decision to identify the faulty phase(s) involved. The proposed scheme is extensively validated for different fault scenarios including inter-circuit and cross-country faults on the series-compensated parallel transmission developed on Real Time Digital Simulator (RTDS) platform. The test results obtained indicate that the proposed PAODI based intelligent relaying scheme is both dependable and secure in protecting series compensated double circuit transmission lines with a response time of less than 1 and 1/2 cycles.     

Fault classification in SEIG system using Hilbert-Huang transform and least square support vector machine

This paper presents the transient performance analysis of self excited induction generator (SEIG) during both balanced and unbalanced faults using stationary frame d–q axis. Significance of fault detection and fault classification is also investigated in this study. Current signal of SEIG is extracted. Non stationary distorted current waveforms of SEIG during fault condition are considered as superimposition of various oscillating modes. To separate out these oscillating components known as intrinsic mode functions (IMFs), empirical-mode decomposition (EMD) is used. Hilbert transform (HT) is applied on the first four IMFs to extract instantaneous amplitude and frequency. Combination of EMD and HT is known as Hilbert-Huang transform. To classify different faults of SEIG system, least square support vector machine (LSSVM) is used. Finally the superiority of the proposed SVM is established through comparison with support vector machine and probabilistic neural network.     

Fault location in a series compensated transmission line based on wavelet packet decomposition and support vector regression

This paper proposes a novel transmission line fault location scheme, combining wavelet packet decomposition (WPD) and support vector regression (SVR). Various types of faults at different locations, fault resistance and fault inception angles on a series compensated 400 kV-285.65 km power system transmission line are investigated. The system only utilizes a single-end measurements. WPD is used to extract distinctive fault features from 1/2 cycle of post fault signals after noises have been eliminated by a low pass filter, and SVR is trained with features obtained from WPD. After training, SVR was then used in precise location of fault on the transmission line. The result shows that fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance.     

Detection,classification, and location of faults in power transmission lines

This paper presents a pattern recognition approach for current differential relaying of power transmission lines. The current differential method uses spectral energy information provided through a new Fast Discrete S-Transform (FDST). Unlike the conventional S-Transform (ST) technique the new one uses different types of frequency scaling, band pass filtering, and interpolation techniques to reduce the computational cost and remove redundant information. Further due to its low computational complexity, the new algorithm is suitable for real-time implementation. The proposed scheme is evaluated for current differential protection of a transmission line fed from both ends for a variety of faults, fault resistance, inception angles, and significant noise in the signal using computer simulation studies. Also the fundamental amplitude and phase angle of the two end currents and one end voltage are computed with the help of the new formulation to provide fault location with significant accuracy. The results obtained from the exhaustive computation show the feasibility of the new approach.     

基于Hilbert-Huang变换和最小二乘支持向量机的电力市场出清电价预测

随着世界各国电力工业改革的发展趋势,我国于20世纪90年代也开始了以打破垄断、引入竞争、放松管制为目标的电力市场化改革。如何合理制定相应的运营模式以及怎样根据电力市场的相关历史数据准确的预测出未来的市场出清电价,对于市场中的各个参与者都具有十分重要的意义。而实际电力市场的出清电价数据具有很强的非平稳性,Hilbert-Huang变换是分析处理非平稳性信号数据非常有效的方法,本文应用Hilbert-Huang变换首先对电力市场出清电价数据进行平稳化处理,然后运用最小二乘支持向量机（LS-SVM）对处理后的数据进行预测。预测结果表明,此模型显著的提高了出清电价预测的精度。     

基于多重分形谱和支持向量机的风电机组行星齿轮箱故障诊断与研究

风电机组行星齿轮箱振动信号是一种典型的非平稳、非线性信号,传统故障检测方法对于此类信号处理能力有限。为了克服传统方法的不足,提高故障诊断能力,提出了一种基于多重分形谱和支持向量机相结合的故障检测方法。首先通过多重分形定义求取信号的多重分形谱。然后在多重分形谱中提取八个特征量。最后将特征量作为支持向量机的输入向量,实现了在不同转速情况下对正常信号和四种太阳轮故障信号的分类与识别。实验结果证实了所提方法对行星齿轮箱信号特征进行提取是有效的,在不同转速情况下均提高了故障识别率。     

基于小波分解和微分进化支持向量机的风电场风速预测

针对因风速具有很强的波动性和间歇性而导致其难以预测的问题,提出了一种新的基于小波分解和微分进化支持向量机的预测方法,通过小波变换对风速数据进行多分辨率分解,并以微分进化优化的支持向量机对各分解层的风速分别建立预测模型,然后将各模型的预测结果叠加后作为最终的预测值。用某风电场实测风速数据进行仿真预测,结果表明,所提方法与交叉验证支持向量机和BP神经网络等常用的预测方法相比,具有更高的预测精度。     

Remote monitoring system for real timedetection and classification of transmissionline faults in a power grid using PMUmeasurements

Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns. This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid. In contradiction to conventional methods, transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit (PMU) at one of the generator buses. This minimal requirement makes the proposed methodology ideal for providing backup protection. Spectral analysis of equivalent power factor angle (EPFA) variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid. Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence. The proposed system can considerably assist system protection center (SPC) in fault localization and to restore the line at the earliest. Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.     

A geometry‐based two‐step method for nonlinear classification using quasi‐linear support vector machine

This paper proposes a two‐step method to construct a nonlinear classifier consisting of multiple local linear classifiers interpolated with a basis function. In the first step, a geometry‐based approach is first introduced to detect local linear partitions and build local linear classifiers. A coarse nonlinear classifier can then be constructed by interpolating the local linear classifiers. In the second step, a support vector machine (SVM) formulation is used to further implicitly optimize the linear parameters of the nonlinear classifier. In this way, the nonlinear classifier is constructed in exactly the same way as a standard SVM, using a special data‐dependent quasi‐linear kernel composed of the information of the local linear partitions. Numerical experiments on several real‐world datasets demonstrate the effectiveness of the proposed classifier and show that, in cases where traditional nonlinear SVMs run into overfitting problems, the proposed classifier is effective in improving the classification performance. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

SVD与SVM叠加算法在玻璃温度表图像识别中的应用

基于温度表图像受光线及角度影响模糊不清,提取液柱不便于精准识别的特征,提出一种把奇异值分解和支持向量机结合起来进行玻璃温度表图像识别的方法.随机选取100支不同型号,不同刻度的玻璃温度表图像作为训练样本,采用MATLAB 7.0对本文方法进行了仿真实验.从仿真结果可以看出,随着样本数目的减小,文中提出的SVD与SVM组合的方法识别率降低较轻微,而小波+SVD和PCA的识别率则产生了明显的下降.因此,基于SVD与SVM叠加算法的温度表图像识别方法在处理液柱提取准确度问题时优势明显,解决传统识别方法普遍不能克服的难题.     

Low cost microcontroller based fault detector,classifier, zone identifier and locator for transmission lines using wavelet transform and artificial neural network: A hardware co-simulation approach

A low cost, fast and reliable microcontroller based protection scheme using wavelet transform and artificial neural network has been proposed and its effectiveness evaluated in real time. The proposed scheme, based on the hardware co-simulation approach performs all the functions of transmission line protection i.e. fault detection/classification, fault zone/section identification and location estimation. The fault detection/classification and zone identification algorithms use fundamental frequency current component to estimate a fault index. The fault location estimation module uses wavelet transform coefficients in hybridization with a parallel artificial neural network structure. For hardware implementation, a 8-bit ATmega microcontroller is used and interfaced with the simulated power system model using Integrated Development Environment (IDE). The scheme is tested on a power system model of 400 kV, 50 Hz three phase double circuit line with source at both the ends. Laboratory tests have been performed in real time for 20,000 fault cases including evolving faults with varying fault resistance, fault inception angle, fault distance, direction of power flow angle and its magnitude. The tests confirm the suitability and reliability of proposed scheme even with Current Transformer (CT) saturation. The implementation of the proposed approach on a low cost microcontroller with the lesser execution time, makes the prototype ideal for implementation on a digital platform (digital relay), thus leading to financial viability and sustainability of the protection scheme.