A neural network-based scheme for fault diagnosis of power transformers

This research presents an artificial neural network (ANN)-based scheme for fault diagnosis of power transformers. The scheme is designed to detect the fault, estimate the faulted side, classify the fault type and identify the faulted phase.The proposed fault diagnosis scheme (FDS) consists of three hierarchical levels. In the first level, a pre-processing of input data is performed. In the second level, there is an ANN which is designed to detect the fault and determine the faulted side if any. In the third level, there are two sides diagnosis systems. Each system is dedicated to one side and consists of one ANN in series with four paralleled ANNs (for fault type classification).The proposed FDS is trained and tested using local measurements of three-phase primary voltage and primary and secondary currents. These samples are generated using EMTP simulation of the High Dam 15.75/500 kV transformer substation in Upper Egypt. All the possible fault types were simulated. The fault locations and fault incipience time were varied within each fault type. Testing results proved that the performance of the proposed ANN-based FDS is satisfactory.     

A Voltage Phasor Based Fault-classification Method for Phasor Measurement Unit Only State Estimator Output

Abstract—This article presents a fault-classification method for transmission lines based on voltage phasors using classification and regression trees. The proposed method is intended to aid system operators in understanding the outputs of a phasor measurement unit only state estimator. Faults are classified into four categories when the estimator is positive sequence and into ten categories when the estimator is three phase. The fault data are generated in PowerWorld® (PowerWorld Corporation, Champaign, IL, USA) and DSA Tools® (Powertech Labs Inc., Surrey, British Columbia, Canada). The pre-fault state consists of a variety of operating conditions and loading angles of faulted lines. The fault condition comprises different fault types, fault locations, fault impedances, and fault incidence angles. Fault classification is done using MATLAB® (The MathWorks, Natick, Massachusetts, USA).The approach is successfully tested on the IEEE-118 bus system. The results demonstrate that the technique developed here is effective and robust, irrespective of the pre-fault and fault conditions.     

Modern approaches for protection of series compensated transmission lines

Series compensation has been employed to improve power transfer in long-distance transmission systems worldwide. However, this in turn introduces problems in conventional distance protection. The complex variation of line impedance is accentuated, as the capacitor's own protection equipment operates randomly under fault conditions. This paper proposes two approaches based on travelling waves and artificial neural networks (ANN) for fault type classification and faulted phase selection of series compensated transmission lines.A modal transformation technique, which decomposes the three-phase line into three single-phase lines, is used for this purpose. Algorithms based on two different modal transformations are developed for phase selection and fault classification. Each algorithm is derived from a corresponding truth table. The truth tables are constructed for different types of faults with different faulted phases and different transformation bases.The proposed ANN topology is composed of two levels of neural networks:

• In level-1, a neural network (ANN_F) is used to detect the fault. In level-2, four neural networks (ANN_A, ANN_B, ANN_C and ANN_G) are used to identify faulted phase(s), and activated by the output of ANN_F if there is a fault.
• System simulation and test results, which are presented and analyzed in this paper indicate the feasibility of using travelling waves and ANN in the protection of series compensated transmission lines.

     

基于有限PMU的电网故障在线识别算法研究

为了实现有限PMU布局方式下的快速有效电网故障检测,研究了电网故障线路在线识别算法。根据PMU布局将电网划分为多个区域。分析了故障发生时流入故障区域的正序、负序和零序电流明显增大的特性,研究了基于边界PMU的故障区域搜索方法。基于虚拟节点构建了纯故障等值模型及其故障分量电压方程。利用一个区域的各边界节点电气量,构造故障分量电压的超定方程组,采用最小二乘法求解故障线路的故障点位置,准确检测故障区域内的故障线路。IEEE14节点系统上大量仿真实验验证了该算法的有效性,它不受故障位置、故障类型、过渡电阻的影响。仿真实验也表明了采用最小二乘法求解的故障点精度能够满足要求。     

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.     

Design of robust intelligent protectiontechnique for large-scale grid-connectedwind farm

This paper presents a design of robust intelligent protection technique using adaptive neuro-fuzzy inference system (ANFIS) approach to detect and classify the fault types during various faults occurrence in large-scale grid-connected wind farm. Also, it is designed to determine the fault location and isolate the wind turbine generators located in the faulted zone during fault occurrence and reconnect them after fault clearance. The studied wind farm has a total rating capacity of 120 MW, where it consists of 60 doubly fed induction generator (DFIG) wind turbines each has a capacity of 2 MW. Moreover, the wind farm generators are positioned in 6 rows, where each row consists of 10 generators. The impacts of fault type, fault location, fault duration, cascaded faults, permanent fault and external grid fault on the behaviours of the generated active and reactive power are investigated. Also, the impacts of internal and external faults in cases of different transition resistances are investigated. The simulation results indicate that, the proposed ANFIS protection technique has the ability to detect, classify and determine the fault location, then isolate the faulted zones during fault occurrence and reconnect them after fault clearance. Furthermore, the wind turbines generators which are located in un-faulted zones can stay to deliver their generated active power to the grid during fault period.     

A WAMS/PMU-based fault location technique

A WAMS (wide-area measurement/monitoring system)/PMU (phasor measurement unit)-based fault location technique is proposed in this paper. The technique uses synchronized fault voltages of two nodes of the faulted line and their neighboring nodes for fault location. Based on these fault node voltages measured by PMUs, line currents between these nodes can be calculated. Then, node injection currents at two terminals of the faulted line are formed from the line currents. Based on the calculated fault node injection currents, fault node can be deduced or fault location in transmission lines can be calculated accurately. Fault location formulas are derived in full details. Case studies on IEEE-14-bus system and a testing network with 500 kV transmission lines including ATP/EMTP simulations are given to validate the proposed technique. Various fault types and fault resistances are also considered.     

Rule based decision support system for single-line fault detectionin a delta-delta connected distribution system

Single-line fault detection, faulted feeder identification, fault type classification, fault location and fault impedance estimation, continue to pose a problem to delta-delta connected distribution systems such as the Los Angeles Department of Water and Power (LADWP) which has over 1500 feeder circuits at the 4.8 kV voltage level. This paper describes a rule based decision support (RBDS) system application to single-line fault detection in a delta-delta connected distribution system. The RBDS system is built from knowledge acquired through exhaustive simulation based on non-arcing type fault situations. It is primarily designed to detect the presence of a fault, identify the faulted feeder, the faulted phase and classify the fault type. It is also designed to gauge the proximity of the fault to the substation and to assess the fault impedance. A fault in the distribution system, upon identification, triggers an alarm with explanatory facility leading to the fault. The RBDS system was tested with different sets of simulated data and proved successful in most cases. Additional tests will be done using field data made available by LADWP. The RBDS system module is a prototype integrated fault detection scheme to be installed in a LADWP distribution substation     

故障限流器控制系统的设计与实现

针对故障限流器高速性、高实时性的应用需求,设计并研制了一套基于嵌入式平台的故障限流器控制系统。系统采用模块化设计,硬件部分包括数据采集单元、主控单元和子模块控制单元三部分,其中主控单元中的核心控制模块采用高性能DSP和FPGA的硬件模式。软件部分包括故障识别模块、状态监测模块和逻辑控制模块,其中故障识别模块采用基于短数据窗的幅值计算方法,对控制逻辑及相关定值也进行了详细设计。通过型式试验和数模仿真试验验证,控制系统能够快速识别短路故障并控制故障限流器动作,为故障限流器的工程应用奠定基础。     

An accurate fault classification technique for power system monitoring devices

This paper presents a novel method of classifying transmission line shunt faults. Most algorithms employed for analyzing fault data require that the fault type be classified. The older fault-type classification algorithms are inefficient because they are not effective under certain operating conditions of the power system and may not be able to accurately select the faulted transmission line if the same fault recorder monitors multiple lines. The technique described in this paper has been proven to accurately identify all ten types of shunt faults that may occur in an electric power transmission system. The other advantage of this technique is that it can be used where multiple transmission lines are present. It is able to identify the faulted line even if secondary effects are recorded in the unfaulted lines.     

A Fault-Location Method for Application With Current Differential Relays of Three-Terminal Lines

This paper presents a new method for locating faults on three-terminal power lines. Estimation of a distance to fault and indication of a faulted section is performed using three-phase current from all three terminals and additionally three-phase voltage from the terminal at which a fault locator is installed. Such a set of synchronized measurements has been taken into consideration with the aim of developing a fault-location algorithm for applications with current differential relays of three-terminal lines. The delivered fault-location algorithm consists of three subroutines designated for locating faults within particular line sections and a procedure for indicating the faulted line section. Testing and evaluation of the algorithm has been performed with fault data obtained from versatile Alternate Transients Program-Electromagnetic Transients Program simulations. The sample results of the evaluation are reported and discussed.     

带同杆双回线的T型支接线的故障测距的研究

针对T型线路中出现同杆双回线的线路结构,提出了首先做故障分支判定,然后进行精确测距的新方法。当某支路发生故障时,求取各支路保护安装处的突变量,通过正序网络图,计算两个必要参数用于判别故障支路,区分出非同杆双回线故障、同杆双回线的跨线故障,以及同杆双回线的单回线故障。在此基础上,将非故障支路化简合并,并对同杆双回线故障以及非同杆双回线故障进行分别测距。该方法不仅能够对单回线准确测距,而且针对双回线,也保证了故障测距的精确性,一定程度上克服了T型线路中出现双回线给测距带来的困难。大量的ATP仿真结果表明故障测距的精度基本上不受故障支路,故障类型、系统运行方式、故障点过渡电阻等因素的影响。     

Wavelet Packet Entropy and RBFNN Based Fault Detection,Classification and Localization on HVAC Transmission Line

The article presents a technique for fast and accurate detection, classification and localization of faults on the high voltage transmission systems considering the alternator's dynamics and the effect of transformers. The systems have been simulated by ATP/EMTP software and three phase fault currents at one end of the transmission line are recorded with a sampling frequency of 50 kHz. The fault signals are decomposed by wavelet packet decomposition (WPD) up to 3^rd level with mother wavelet db6 to calculate wavelet packet entropy (WPE) which has the ability to measure the uncertainty of fault signals during feature extraction. A properly designed radial basis function neural network (RBFNN) trained with these features can recognize, classify and locate faults faster as it utilizes only half cycle data after fault initiation. This technique has been verified for different fault categories, fault impedances and fault inception angles (FIA) at different locations for two different transmission systems. The investigated results demonstrate that the wavelet packet entropy is very powerful for extracting the features from the fault signals and RBFNN is very accurate for classification and localization of faults on the transmission line including locations close to the generator's end.     

运用MAS的配电网保护和控制

随着信息设施的不断改善,大量的分布式发电机(DGs)将会接入配电网.为此,运用多代理技术提出了一个新的配电网保护方案.该方案中,各种不同的继电器被设计成继电器代理,区域内的继电器相互合作以定位和隔离故障.通过感知在继电器处的电流突然变化,来选择二元状态信号(电流的方向,幅值大小等)作为保护用信号.每个继电器同时利用同一...     

一种适合于同杆4回线的故障测距方法

在对同杆4回线解耦的基础上,提出了一种适合于同杆4回线的故障测距方法.同杆4回线内部发生故障时,可以将故障分量分解为12序分量,且利用该12序分量进行故障测距.考虑到在任何故障类型下,同杆4回线内部都会存在反序分量,所以提出了基于反序环流分量的故障测距方法.当同杆4回线内部发生故障时,从线路两端计算到故障点的反序环流分量电压相等,利用该关系可以求出故障点的精确位置.由于相互独立的12序分量完全能够解决线间耦合问题,文中提出的测距算法能够精确测距,测距精度不受故障类型影响.大量ATP仿真结果表明所提出的测距算法完全适用于同杆4回线的故障测距,测距精度不受过渡电阻、系统运行方式、线路两端功角的影响.     

Accurate fault locator for EHV transmission lines based on radial basis function neural networks

This paper describes the design and implementation of an artificial neural networks-based fault locator for extra high voltage (EHV) transmission lines. This locator utilizes faulted voltage and current waveforms at one end of the line only. The radial basis function (RBF) networks are trained with data under a variety of fault conditions and used for fault type classification and fault location on the transmission line. The results obtained from testing of RBF networks with simulated fault data and recorded data from a 400 kV system clearly show that this technique is highly robust and very accurate. The technique takes into account all the practical limitations associated with a real system. Thereby making it possible to effectively implement an artificial intelligence (AI) based fault locator on a real system.     

基于遥信数据故障编码技术及DHNN校正的电网故障诊断方法

提出采用故障编码技术形成故障空间最优编码集,然后通过模板匹配的方式进行电网故障诊断的方法。针对由于缺少前端故障遥信数据处理的清洗算法,造成故障诊断算法诊断正确率不高的问题,提出了建立离散Hopfield神经网络模型用于故障遥信数据的前端数据清洗的算法。利用故障遥信数据之间的相关性对遥信变位数据进行分组,并对各组数据分别采用所提出的算法进行数据清洗,利用穷举输入状态数据的方法求取了算法的修正域,从而建立了DHNN清洗模型。最终形成具有纠错能力的电网故障智能诊断方法,实现在故障诊断空间内对故障元件的诊断。通过实际电网的故障遥信数据的测试,验证了DHNN神经网络信息纠正模型和故障诊断模型对电网故障元件诊断的有效性。     

输电线路故障层次化变步长Tsallis小波奇异熵诊断方法

为提高熵方法输电线路故障信号时-频域的特征提取能力,提出层次化变步长Tsallis小波奇异熵(Tsallis Wavelet Singular Entropy, TWSE)方法用于电力系统故障诊断。首先,对采集到的电压信号进行小波分解与单支重构,构建时-频矩阵;之后,将奇异值分解与Tsallis熵理论相结合,对该时-频矩阵求滑动步长为1的Tsallis奇异熵,确定故障发生时刻;然后,对故障发生后1周期内的三相电压重构系数求滑动步长为1/4周期的TWSE,构建用于故障诊断的特征向量;最后,将TWSE特征向量输入到极限学习机(Extremly Learning Machine, ELM)分类器中,实现输电线路故障诊断。仿真结果表明,新方法具有更好的故障暂态信号特征表现能力,且分类结果不受故障时间、过渡电阻和故障位置等因素影响,相较基于小波奇异熵的线路故障诊断方法具有更好的诊断效果。     

A combined impedance and traveling wave based fault location method for multi-terminal transmission lines

A new fault location method suitable for multi-terminal transmission lines that combines the advantages of both impedance and traveling wave based methods has been developed and presented in this paper. The proposed method first determines whether the fault is grounded or ungrounded by comparing the magnitude of the ground mode wavelet coefficients at the measurement end. Next, the impedance based method is used to identify the faulted half of the line in the case of two-terminal line and the faulted line section as well as the faulted half of the line section in the case of multi-terminal lines. Finally the fault location is determined by taking the time difference between the first two consecutive aerial modes of the current traveling waves observed at one end of the multi-terminal line. The proposed method has been tested on four- and five-terminal transmission lines with different types of faults, fault resistances and fault inception angles using ATP simulation.     

Hybrid Fault Diagnosis Scheme Implementation for Power Distribution Systems Automation

Power distribution automation and control are important tools in the current restructured electricity markets. Unfortunately, due to its stochastic nature, distribution systems faults are hardly avoidable. This paper proposes a novel fault diagnosis scheme for power distribution systems, composed by three different processes: fault detection and classification, fault location, and fault section determination. The fault detection and classification technique is wavelet based. The fault-location technique is impedance based and uses local voltage and current fundamental phasors. The fault section determination method is artificial neural network based and uses the local current and voltage signals to estimate the faulted section. The proposed hybrid scheme was validated through Alternate Transient Program/Electromagnetic Transients Program simulations and was implemented as embedded software. It is currently used as a fault diagnosis tool in a Southern Brazilian power distribution company.