自适应和模糊推理结合的故障分类新方法

输电线上许多类型的故障伴随低阻抗故障和高阻抗故障发生,传统的保护方法不能正确地检测和分类.提出了一种基于自适应网络的模糊推理机制(ANFIS)对低阻抗故障和高阻抗故障进行故障检测和分类的新算法.算法的性能已得到韩国典型的154 kV输电线系统在各种故障条件下的测试.测试结果表明ANFIS能在半个周期内准确地检测故障和区分故障类型(包括低阻抗故障和高阻抗故障).     

High impedance fault detection methodology using wavelet transform and artificial neural networks

This paper presents a new technique based on the combination of wavelet transform (WT) and artificial neural networks (ANNs) for addressing the problem of high impedance faults (HIFs) detection in electrical distribution feeders. The change in phase current waveforms caused by faults and normal switching events has been used in this methodology. The discrete wavelet transform (DWT) used decomposes the time domain current signals into different harmonics in time-frequency domain and extracts special features to train ANNs. This preprocessing reduces the number of inputs to ANN and improves the training convergence. The ANN structure and learning algorithm used in this method is the multilayer perceptron network and Levenberg-Marquardt back-propagation algorithm, respectively.The signal data of several HIFs, low impedance faults (LIFs) and normal switching events have been obtained by the simulation of a real distribution network, with five feeders, under these different operations conditions, using SimPowerSystem Blockset of MATLAB. The results obtained have validated the effectiveness of the proposed methodology to detect HIFs and discriminate them from normal transient operations.     

基于介质击穿原理的配电线路高阻接地故障精确建模

配电线路负荷率快速增加和电缆线路的广泛应用使得配电网中性点有效接地方式得到越来越多的应用。中性点有效接地配电线路受送电走廊、自然环境等因素影响,容易发生弧光高阻接地故障;此类故障电流幅值小,保护难以检测跳闸,很少能获取到现场录波数据;而人工接地试验成本高,难以频繁使用,因此挖掘有限的试验数据,实现弧光高阻接地模型的精确建模尤为重要。文中在详细分析基于热平衡原理的电弧模型机理的基础上,指出了该类模型不完全适用于开放空间,以及弱故障电流的高阻弧光接地故障。针对以上问题,文中基于固体介质电击穿原理,提出并建立了配电线路高阻接地故障点非线性电阻模型,理论分析及现场试验数据验证了模型的准确性;为后续的高阻接地故障检测算法研究奠定了理论基础。     

Fault diagnosis system for tapped power transmission lines

This paper presents a design for a fault diagnosis system (FDS) for tapped HV/EHV power transmission lines. These lines have two different protection zones. The proposed approach reduces the cost and the complexity of the FDS for these types of lines. The FDS consists basically of fifteen artificial neural networks (ANNs). The FDS basic objectives are mainly: (1) the detection of the system fault; (2) the localization of the faulted zone; (3) the classification of the fault type; and finally (4) the identification of the faulted phase. This FDS is structured in a three hierarchical levels. In the first level, a preprocessing unit to the input data is performed. An ANN, in the second level, is designed in order to detect and zone localize the line faults. In the third level, two zone diagnosis systems (ZDS) are designed. Each ZDS is dedicated to one zone and consists of seven parallel-cascaded ANN's. Four-parallel ANN's are designed in order to achieve the fault type classification. While, the other three cascaded ANN's are designed mainly for the selection of the faulted phase. A smoothing unit is also configured to smooth out the output response of the proposed FDS.     

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     

基于故障电阻测量的小电流接地系统保护方法

为解决小电流接地系统高阻接地故障检测的难题,分析了单相接地故障前后相电流变化特征:故障线路的故障相与非故障相电流变化量之差等于接地故障电流,非故障线路的故障相与非故障相电流变化量相等;并由故障相电压与故障电流之比计算出接地故障电阻,发明了基于接地故障电阻测量的高阻接地保护方法。EMTP仿真分析和动模实验测试结果表明,该保护方法能够保护各种高阻接地故障,具有较高的保护精度和可靠性,适合在配电自动化终端单元(FTU)上就地实现。     

Broken conductors protection system using carrier communication

This paper presents a detection and signaling system designed to identify and locate high impedance faults caused by broken conductors on distribution primary feeders. Unlike conventional protection systems, which perform current sensing, the working principle of the proposed system consists on monitoring the voltage unbalance along a feeder. This allows the system to detect a fault occurrence even in cases when the conductor touches a high impedance earth surface (for instance asphalt). This system has an additional advantage of giving an indication of the location of a fault since it involves measurements at multiple points on a feeder. In order to detect the voltage unbalance produced by a broken conductor, a new sensor was developed which is sensitive to the electric field generated by primary feeders. A carrier communication channel is associated to each sensor allowing the high impedance fault occurrence information to reach the protection equipment located closer to or at the substation     

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.     

An alternative approach to fault location on power distribution feeders with embedded remote-end power generation using artificial neural networks

In this paper, the design and implementation of a feed-forward artificial neural network (ANN)-based fault locator to classify and locate shunt faults on primary overhead power distribution lines with load taps and embedded remote-end power generation is presented. In the ANN algorithm, the standard back-propagation technique with a sigmoid activation function is used. The fault locator utilizes fault voltage and current samples obtained at a single location of a typical radial distribution system. The ANNs are trained with data under a wide variety of fault conditions and used for the fault type classification and fault location on the distribution line. A 34.5?kV distribution system is simulated using electro-magnetic transients program and their results are used to train and test the ANNs. The ANN-based fault locator gives high accuracy for the vast majority of the practically encountered systems and fault conditions, including the presence of load taps and the remote-end in-feed source.     

New ANN-Based Algorithms for Detecting HIFs in Multigrounded MV Networks

Application of two new ANN-based algorithms for arcing high impedance fault (HIF) detection in multigrounded medium-voltage (MV) distribution networks is presented in this paper. The paper provides an evaluation of two new structures of artificial neural networks (ANNs) that may be used for reliable HIF detection in multigrounded as well as isolated, compensated, and grounded via small resistance distribution grids. The results obtained by use of both neural nets are presented. The performance was tested using data obtained from staged HIFs in real MV network as well as from electromagnetic transients program-alternative transients program simulations. A small number of necessary neurons in developed ANNs, short measuring sliding data window, and easy interpretation of obtained output signals are the main advantages of the proposed approach. Satisfactory results of ANN performance were observed for all examined HIF cases in which the ground fault current was greater than 16 A. The selected ANNs of best performance show high reliability and immunity to transients resulting from switching operations in protected feeders and from capacitor bank switching.     

Detection of Distribution High Impedance Faults Using Burst Noise Signals near 60 HZ

Previous papers have described a method for the detection of arcing fallen distribution primiary conductor faults using the electrical noise in feeder current above 2kHz. While this method provided improved detection of such faults, this high frequency signal often would not propagate past capacitor banks. In the present paper, we describe a technique for the identification of arcing high impedance faults using burst noise signals at frequencies near the power system fundamental and low order harmonics. Arcing generates non-synchronous burst noise signals which approximate white noise, providing a signal which can be differentiated from synchronous power system signals in the frequency bands of interest. The primary advantage of monitoring frequencies near the fundamental is that this arcing fault signal at low frequencies will exhibit little attenuation from capacitor banks or other sources. This paper provides preliminary results that arcing faults can be detected effectively using frequency components below 60 Hz or between low order harmonics of 60 Hz. The technique is demonstrated through analysis of analog signals recorded during numerouis staged utility downed conductor tests.     

信息融合技术在电力系统故障检测中的应用探讨

电力系统故障产生各种故障信息，对故障信息全面分析、综合处理，能提高故障检测的精度和鲁棒性。为实现对各种传感器检测到的多源故障信息进行有机综合处理，需研究信息综合处理技术。信息融合技术是研究多源信息综合处理的新兴边缘学科，已在军事、信息处理等领域中有着成熟的应用。该文把信息融合技术应用于电力系统故障检测，介绍信息融合故障检测的模型与方法；分析信息融合技术在状态监测、继电保护中的应用技术；并以小电流接地系统故障选线为例，提出研究了模糊信息融合故障选线方法技术，提高了故障选线的灵敏度和可靠性。     

A novel protection scheme for synchronous generator stator windings based on SVM

This paper proposes a novel scheme for detecting and classifying faults in stator windings of a synchronous generator (SG). The proposed scheme employs a new method for fault detection and classification based on Support Vector Machine (SVM). Two SVM classifiers are proposed. SVM1 is used to identify the fault occurrence in the system and SVM2 is used to determine whether the fault, if any, is internal or external. In this method, the detection and classification of faults are not affected by the fault type and location, pre-fault power, fault resistance or fault inception time. The proposed method increases the ability of detecting the ground faults near the neutral terminal of the stator windings for generators with high impedance grounding neutral point. The proposed scheme is compared with ANN-based method and gives faster response and better reliability for fault classification.     

A novel protection scheme for synchronousgenerator stator windings based on SVM

Integration of electric vehicles (EVs), demand response and renewable energy will bring multiple opportunities for low carbon power system. A promising integration will be EV battery swapping station (BSS) bundled with PV (photovoltaic) power. Optimizing the configuration and operation of BSS is the key problem to maximize benefit of this integration. The main objective of this paper is to solve infrastructure configuration of BSS. The principle challenge of such an objective is to enhance the swapping ability and save corresponding investment and operation cost under uncertainties of PV generation and swapping demand. Consequently this paper mainly concentrates on combining operation optimization with optimal investment strategies for BSS considering multiscenarios PV power generation and swapping demand. A stochastic programming model is developed by using state flow method to express different states of batteries and its objective is to maximize the station’s net profit. The model is formulated as a mixed-integer linear program to guarantee the efficiency and stability of the optimization. Case studies validate the effectiveness of the proposed approach and demonstrate that ignoring the uncertainties of PV generation and swapping demand may lead to an inappropriate batteries, chargers and swapping robots configuration for BSS.     

Fault location for underground distribution feeders: An extended impedance-based formulation with capacitive current compensation

Underground distribution systems are normally exposed to permanent faults, due to specific construction characteristics. In these systems, visual inspection cannot be performed. In order to enhance service restoration, accurate fault location techniques must be applied. This paper describes an extended impedance-based fault location algorithm for underground distribution systems. The formulation is developed on phase frame and calculates the apparent impedance using only local voltage and current data. The technique also provides an iterative algorithm to compensate the typical capacitive component current of underground cables. Test results are obtained from numerical simulations using a real underground distribution feeder data from the Electrical Energy Distribution State Company of Rio Grande do Sul (CEEE-D), southern Brazil. Comparative results show the techniques accuracy and robustness in respect to fault type, distance and resistance.     

A novel technique for high impedance fault identification

A novel technique using wavelet analysis filter banks (WAFB) to identify distribution high impedance faults (HIFs) is presented. A new model of HIF is used. HIFs and capacitor bank switching operations are simulated by the Electromagnetic Transients Program (EMTP) and their current signals are studied. High frequency components with the time localization information of both HIFs and capacitor bank switching operations are obtained using WAFB and their behavior is differentiated clearly. Results demonstrate that WAFB can be used as an element in a HIF detector for fast and accurate identification of distribution HIFs     

High impedance fault detection based on wavelet transform and statistical pattern recognition

A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.     

Practical high-impedance fault detection on distribution feeders

Distribution protection systems must balance dependability with security considerations to be practical. This is quite difficult for high-impedance faults. Only highly sensitive algorithms can achieve absolute dependability in detecting very low current faults. This high sensitivity results in a propensity for false tripping, creating a less secure, system and resulting in the potential for decreased service continuity and lower reliability. Researchers at Texas A&M University have balanced fault detection with fault discrimination, resulting in a practical combination of detection algorithms in a commercially viable system. This device has many “intelligent” features, including the ability to analyze and correlate numerous fault characteristics in real time, so that a correct determination of the status of the feeder can be made with a high probability of accuracy. This paper describes the use of multiple algorithms to detect various types of faults and the use of an expert decision maker to decipher incoming data, to determine the status and health of a distribution feeder. Requirements for a practical, secure high-impedance fault relay are also discussed. Finally, Texas A&M has licensed this technology to a commercial partner, which manufactures a device that detects high-impedance faults, in addition to performing numerous other monitoring and protection functions     

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.     

基于配电自动化终端配置方案的供电可靠性评估

建立了架空系统供电可靠性评估模型,分别以架空主干馈线故障和柱上开关故障为研究场景,在馈线满足N-1和不满足N-12种情形下,运用集中式馈线自动化故障处置原理,分析并推导了架空主干馈线故障和柱上开关故障时的户均停电时间计算公式。特别是针对“二遥”和“三遥”终端混合配置情形,枚举“二遥”和“三遥”终端不同数量、不同位置的配置方案,详细分析故障处理过程,推导得出基于“二遥”和“三遥”终端配置数量的架空系统故障平均停电时间计算公式。最后,以某架空区域配电自动化终端建设为例,对研究成果进行了应用。