Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.     

基于离散小波变换的输电线路故障精准定位

为了提高架空线路和地下电缆组合输电线路发生故障时的定位精度,提出了一种基于离散小波变换(DWT)的输电线路故障定位新方法。通过DWT对输电线路单端测得的瞬态信号进行多分辨率分析(MRA),在低故障起始角情况下,利用线模电流和零模电流检测故障,结合小波模量极大值(WMM)求解从故障点到变电站的行波到达时间,从而对输电线路故障进行精准定位。采用半正弦电压响应的方法克服了采样频率有限和故障起始角低的缺点,运用100kHz半正弦信号的发送时间与接收导数信号的时间之差计算故障距离。在考虑谐波畸变和故障电阻、接地电阻、故障位置和起始角变化的情况下对所提方法进行测试,结果表明：对于100km的输电线路,即使在故障靠近总线（<2%）且故障起始角较低的情况下,所提出的方法得到的故障定位误差仅为0.14km。     

Support Vector Machines for classification and locating faults on transmission lines

This paper presents a new approach to classify fault types and predict the fault location in the high-voltage power transmission lines, by using Support Vector Machines (SVM) and Wavelet Transform (WT) of the measured one-terminal voltage and current transient signals. Wavelet entropy criterion is applied to wavelet detail coefficients to reduce the size of feature vector before classification and prediction stages. The experiments performed for different kinds of faults occurred on the transmission line have proved very good accuracy of the proposed fault location algorithm. The fault classification error is below 1% for all tested fault conditions. The average error of fault location in a 380 kV–360-km transmission line is below 0.26% and the maximum error did not exceed 0.95 km.     

基于小波包和神经网络的电力输电线故障诊断研究

提出了一种将小波包分析方法和改进的概率径向基神经网络以及归一化径向基神经网络相结合．进行输电线短路故障分类和定位的新方法。对新方法进行了较为深入的分析和讨论，建立了一个电力输电线故障诊断系统，该系统能够自动监测故障分类的正确性，并且具有较高的故障定位精确度。通过对电力输电系统短路故障类型及其故障地点的仿真测试表明，所提出的方法切实可行、有效。     

基于改进型蚁群算法的输电线路故障测距模型研究

为了解决输电线路运行过程中容易出现的单相短路、两相接地、相间短路、三相短路等故障,构建一种基于改进型蚁群算法的输电线路故障测距模型,以实现输电线路故障测距优化。通过在常规蚁群算法的基础上优化寻优路径,以解耦方式消除三相线路中的互感作用。利用阻抗矩阵与导纳矩阵获取电力系统的相模矩阵。根据相模变化结合傅氏补偿算法,解决影响输电线路故障测距精度的多项因素,并以此为基础构建输电线路故障测距模型最优解。采用MATLAB仿真软件对所构建的模型进行了仿真试验。试验结果表明,所设计的模型受故障测距影响因素的影响较小,测距精度更高。该研究为后续的输电线路故障测距算法改进奠定了理论基础。     

A modular neuro-wavelet based non-unit protection scheme for zone identification and fault location in six-phase transmission line

To overcome the constraints on land availability, infrastructure and environmental problems, six-phase transmission lines have been proposed as a potential alternative to increase the power transfer capability of existing transmission lines without major modification in the existing structure of three-phase double-circuit system. The non-availability of a proper protection scheme due to large number of possible faults has been the prime reason behind the low popularity and acceptance of six-phase system. In this regard, the present work proposes a protection scheme for six-phase transmission line based on the hybridization of discrete wavelet transform and modular artificial neural network. The fault information (approximate coefficients) in the voltage and current signals is captured using discrete wavelet transform. The standard deviation of the coefficients of voltage and current signals in each phase is then computed and given as input to modular artificial neural network, which aims at identifying the faulty section/zone and estimate its location. Test results exhibit that the proposed scheme effectively discriminates the faulted section and estimates the fault location with maximum error of 0.675 %. It offers primary protection to the total line length and also provides remote backup protection for the adjacent reverse section of the line using data at relaying point only and thus avoids the need of a communication link.

     

An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line

This article proposes an application of the discrete wavelet transform (DWT) and back-propagation neural networks (BPNN) for fault diagnosis on single-circuit transmission line. ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is used to decompose the high-frequency component of these signals. In addition, characteristics of the fault current at various fault inception angles, fault locations and faulty phases are detailed. The DWT is employed in extracting the high frequency component contained in the fault currents, and the coefficients of the first scale from the DWT that can detect fault are investigated, and the decision algorithm is constructed based on the BPNN. The results show that the proposed technique provides satisfactory results.     

深空探测航天器姿态控制系统故障定位

执行机构与敏感器故障检测与定位是深空探测任务卫星平台可靠运行的前提和保障.本文从数据的角度出发,结合姿控系统工作机理,提出一种基于神经网络和支持向量机结合的故障诊断方法用于检测并定位故障.故障诊断方法分为3步,首先采集姿控系统的状态信息,采用神经网络对闭环姿控系统中未知动态特性建模并进行预测;然后将姿控系统敏感器信号与神经网络预测输出比较生成残差并提取故障特征;最后采用支持向量机辨识残差特征检测故障,并结合运动学特性分析定位故障.仿真结果表明本文所提方法可以有效提取、辨识故障特征,实现执行器与敏感器的故障检测定位.     

A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line

This paper presents a new method for fault classification in series-compensated transmission line using multiclass support vector machine (MCSVM) and multi class extreme learning machine (MCELM). These methods use the information obtained from the wavelet decomposition of fault current signals for fault classification. Using MATLAB simulink, data set has been generated with different types of fault and system variables, which include the fault resistance, fault distance, load angle and fault inception angle. The proposed method has been tested on a 400-kV, 300-km transmission line under variety of fault conditions. The performance of MCSVM and MCELM is compared in terms of training time and classification accuracy. The comparisons have been made for both One-Versus-One and One-Versus-Rest methods of SVMs and ELMs. Results show that MCELMs need less training time compared to MCSVMs, and the classification accuracy of MCELMs is more or less similar to MCSVMs. The feasibility of the proposed methods is also tested on a practical 220-kV series-compensated transmission line, and the results obtained are quite promising.     

A current independent method based on synchronized voltage measurement for fault location on transmission lines

This paper presents a new method based on synchronized voltage measurement technique in order to identify the fault locations in two and three-terminal transmission lines. Due to common problems of current transformers in distance protection of power system and as result increasing cost and reduction of protection accuracy, proposed method is independent of current measurement and based on transmission line terminals voltages measurement. Pre-fault and post-fault voltages at both ends of the line are measured synchronously and used to calculate the fault location. Proposed method calculates the fault location using Thevenin model of faulted system and transforms the whole parameters to symmetric components. Using proposed technique, fault location can be calculated with a lower than 0.6% error without using current transformers. EMTP/ATP simulation results and mathematical analysis show that proposed fault location technique is independent of fault type, fault resistance, fault inception angle and loading angle of the transmission line.     

Application of extreme learning machine for series compensated transmission line protection

This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions.     

基于提升小波的输电线路短路故障仿真

输电线路发生故障后的信号是一个突变的、具有奇异性的暂态信号,包含着丰富的故障信息。输电线路的破坏多半是由短路故障引起的,通过分析短路故障,采用提升小波对故障后的电流进行检测。该文依据实际电力系统输电线路进行短路故障实例仿真,仿真结果表明,提升小波不但能有效地检测输电线路短路故障信号的奇异点,而且比db5小波快3 ms,从而为下一步故障测距提供有利依据。     

城轨美式应答查询器状态分析与故障检测

城市轨道交通中应答器传输系统是对行进列车进行精确定位的关键系统,列车定位是保证基于通信的列车控制系统（CBTC）安全高效工作的前提。在运营过程中,应答器传输系统在因线路老化、列车振动、设备故障等原因出现信标丢失的故障,导致无法对列车进行定位而引起列车制动,这对列车运行造成极大的影响,更是线路运营的重大安全隐患。目前城市轨道交通中针对车载设备的故障,采取的检修方法是整体更换故障设备,维修成本高且效率低下。为减少车载设备故障情况,本文通过对应答器传输系统中的美式应答查询器的工作状态进行研究,旨在根据其工作状态判断应答查询器故障的具体位置,提出了一种针对应答查询器的故障的检测流程,为城轨车载设备的故障检测提供了指导,有良好的应用价值与实际意义。     

基于小波变换的配电网故障暂态量选线方法

小电流接地是中压电网中性点的典型接地方式。小电流接地电网的单相接地故障特征表明,故障暂态电流幅值比稳态对地电容电流大得多,而且几乎不受消弧线岗影响。冈此,利用暂态电流进行选线具有一定的优越性。为了精确提取故障时的暂态信号用于选线,利用小波变换工具对发生接地故障时各线路的暂念零序电流进行小波变换,提取线路故障信息。本文建立仿真模型对小电流接地电网的多种单相接地故障型式进行了仿真验证,证实了该方法的有效性。     

Fault location estimation for series-compensated double-circuit transmission line using parameter optimized variational mode decomposition and weighted P-norm random vector functional link network

In this paper, both the proposed parameter optimized variational mode decomposition (POVMD) and weighted P-norm random vector functional link network (WPRVFLN) are integrated for fault detection, location estimation, and classification in a series capacitor compensated double circuit transmission line (SCCDCTL). The required number of decomposition is determined from the magnitude spectrum of full-cycle current signals from the point of fault inception. An entropy index is used to obtain the optimum value of data-fidelity factor for extracting the most suitable band-limited intrinsic mode functions (BLIMFs). The four efficacious features namely standard deviation of the magnitude, energy, Renyi entropy and crest factor are computed from the Hilbert transformed array of the BLIMFs to construct the feature vector. A diagonal matrix W is computed based on zero sequence current of the original current signals as a weighting factor to categorize the ground fault accurately. Numerous faults are generated with a wide variation of the system conditions in MATLAB/Simulink simulation environment. An efficient WPRVFLN computational intelligence technique is proposed to recognize the fault by taking the extracted feature vector with weight factor, and its performances are compared with the recently developed classifiers in the MATLAB interface. The lesser computational complexity, faster learning speed, superior fault location estimation accuracy, and short event detection time prove that the proposed POVMD–WPRVFLN method can be implemented in the real power system for online fault recognition. Finally, the feasibility of the proposed method is tested and validated by using the fast FPGA digital circuitry in a loop.     

A DSP based frequency domain approach for classification of transmission line faults

This paper employs a digital signal processing (DSP) based frequency domain approach using wavelet multi-resolution analysis (MRA) to overcome difficulties such as fault inception angle, fault impedance and fault distance associated with conventional time domain approach employing voltage and current based measurements for fault classification in case of digital relaying of transmission line. The frequency domain approach for fault classification algorithm uses wavelet MRA technique to extract the features of the current signals based on harmonics generated at the instant of occurrence of fault due to abrupt change of currents in a three phase transmission line. Since choice of particular wavelet plays a vital role for extracting features of generated harmonics, therefore an attempt has been made in this proposed research to extensively investigate using 16 wavelets to establish the superiority of Db4 wavelet over other standard wavelets for accurate fault classification.     

Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.     

Spectral observation of power network signals for digital signal processing

A fast digital signal processing algorithm with a view to digital protection of power networks using a dead-beat spectral observer is presented. The spectral observer computes the discrete Fourier transform of a sampled-data band-limited periodic signal (like the differential currents in power transformers and voltages, and currents in power transmission lines) recursively to produce high-speed fault detection. Further progressive incorporation of new samples, discarding the old ones as in running Fourier transforms, requires a single iteration. The scheme is suitable for microprocessor-based realtime application and has been implemented on line with a 16-bit LS1-11/23 microcomputer with signals from a power transformer.     

变速箱振动信号的测点定位与评价方法研究

在变速箱故障诊断实验中,通过振动传感器获取各工况下不同测点位置的箱体振动信号,从振动信号中提取时频域特征值进行分析评价。对于某一固定位置多组采集样本进行了研究,以降低测量误差,在此基础上对不同位置处获取的多组采集样本进行了详细研究。结果表明,不同测点位置获取的振动信号包含的故障特征是不同的,从而选取更能表征各种故障特征的最佳位置,为基于振动信号的变速箱故障诊断奠定了基础,提高了诊断的准确性。     

An IP-ERN architecture to enable hybrid E2E/ERN protocol and application to satellite networking

We propose an architecture based on a hybrid E2E-ERN approach allowing ERN protocols to be inter-operable with current IP-based networks. Without introducing complex operations, the resulting E2E-ERN protocol provides inter and intra protocol fairness and benefits from all ERN advantages when possible. We detail the principle of this novel architecture, called IP-ERN, and show that this architecture is highly adaptive to the network dynamics and is compliant with every TCP feature, IPv4, IPv6 as well as IP-in-IP tunneling solutions. As a possible use case, we test this architecture as a potential candidate to replace Performance Enhancing Proxies (PEPs) commonly-used over satellite IP-based networks. Compared to splitting PEP, the IP-ERN architecture does not break the E2E connectivity, still achieves high satellite link utilization and fairness without needs of extra fault tolerant mechanisms.