k-NN based fault detection andclassification methods for powertransmission systems

This paper deals with two new methods, based on k-NN algorithm, for fault detection and classification in distance protection. In these methods, by finding the distance between each sample and its fifth nearest neighbor in a predefault window, the fault occurrence time and the faulty phases are determined. The maximum value of the distances in case of detection and classification procedures is compared with pre-defined threshold values. The main advantages of these methods are: simplicity, low calculation burden, acceptable accuracy, and speed. The performance of the proposed scheme is tested on a typical system in MATLAB Simulink. Various possible fault types in different fault resistances, fault inception angles, fault locations, short circuit levels, X/R ratios, source load angles are simulated. In addition, the performance of similar six well-known classification techniques is compared with the proposed classification method using plenty of simulation data     

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 B-Spline Wavelet Based Fault Classification Scheme for High Speed Protection Relaying

This paper presents a new fault detection and classification algorithm based on the wavelet transform. The B-spline wavelet transforms of three phase currents on transmission lines are employed. By comparing the moving average of these transforms, fault types are classified easily. Effects of the fault inception angle, fault distance, and fault impedance are examined. Simulation studies using EMTP show that the wavelet-based algorithm is simple, effective, and robust. Without any modification, the proposed algorithm can be applied to power systems of any voltage level. It is suitable for the high-speed protection relaying.     

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.     

A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines.     

Research on the protection coordination ofpermanent magnet synchronous generatorbased wind farms with low voltage ridethrough capability

This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation (SST or simply the sparse S-Transform). The average and differential current components are passed through a change detection filter, which senses the instant of fault inception and registers a change detection point (CDP). Subsequently, if CDP is registered for one or more phases, then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique, which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault. The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location. The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification, which is least effected from bias setting, has a faster relay trip response (less than one cycle from fault incipient) and a better accuracy in fault location. The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system, subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications     

Modeling methodology and faultsimulation of distribution networksintegrated with inverter-based D

This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation (SST or simply the sparse S-Transform). The average and differential current components are passed through a change detection filter, which senses the instant of fault inception and registers a change detection point (CDP). Subsequently, if CDP is registered for one or more phases, then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique, which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault. The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location. The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification, which is least effected from bias setting, has a faster relay trip response (less than one cycle from fault incipient) and a better accuracy in fault location. The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system, subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications     

A new approach of fast fault detection in HV-B transmission lines based on Detail Spectrum Energy analysis using oscillographic data

The main focus of this research is to develop an accurate real-time method for fault detection and analysis of HVB (High Voltage Class-B) transmission lines. The current and voltage signals of oscillographic records are acquired by the distance protections relays with minimum impedance GE D60-1 installed in the electrical network of SONELGAZ (Algerian Company of Electricity and GAS). This method deals on the evaluation of the Detail Spectrum Energy (DSE) calculated from the Discrete Wavelet Transform (DWT) applied on the current phases by moving data windows with length equivalent to one cycle of the fundamental power frequency. The fault detection algorithm is processed at first scale superimposed in the fault current signals (phases and ground) by the sharp variation of (DSE). Most of the existing methods treat the disturbances and faults simultaneously exist in transmission line as a single type. The proposed method has the ability to discriminate between the disturbances and the faults. This study is compared with the “Powerful Analysis of all Protection Fault Records” SIGRA software for determining the start fault inception and it’s clearing time. The performance of this method was tested and evaluated on a real data records and can accurately detect the fault within only half a cycle from the instant of fault occurrence.     

An analytical approach for assessment of voltage sags

In this paper, analytical expressions for the calculation of remaining voltages due to fault at bus and along the line are derived. Balanced and unbalanced faults are considered and the effects of different fault distributions are taken into account. The proposed analytical methods are compared with the method of critical distance in order to achieve the acceptability of the proposed method. The developed method is applied to the IEEE 30-bus test system and a real Indian distribution system.     

Support vector classification and regression for fault location in distribution system using voltage sag profile

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location.     

A real time fault analysis tool for monitoring operation of transmission line protective relay

This paper proposes an integrated real time fault analysis tool for transmission line. The two primary techniques used in the fault analysis tool, fuzzy adaptive resonance theory (ART) neural network and synchronized sampling, can offer accurate fault detection, classification, internal/external fault differentiation, and fault location. The paper makes several extensions of the two techniques so that they can fit well in the realistic situations. The hardware configuration and software implementation are proposed in the paper. A comprehensive evaluation study is implemented to compare the proposed fault analysis tool with the traditional distance relay. Simulation results indicate that the integration exemplifies the advantages of both techniques and that the integrated solution has much better performance in different system conditions compared to distance relay. Both dependability and security of transmission line protection system are improved by using the proposed tool.     

Directional relaying using support vector machine for double circuit transmission lines including cross-country and inter-circuit faults

The conventional distance relaying algorithms are unable to detect the inter-circuit faults, cross-country faults, high resistance faults which may occur in a double circuit line. This paper presents combined Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) based directional relaying and fault classification scheme including inter-circuit faults, cross-country faults and high resistance faults. SVM modules are designed for forward or reverse fault identification and fault classification using single terminal data. The 3rd level approximate discrete wavelet transform coefficients of three phase current signals only have been used. Proposed method is tested with variations in fault type, fault location, fault inception angle, fault resistance, inter-circuit faults, and cross-country faults. The proposed method based on SVM does not need any threshold to operate which is an exceptional attribute for a protective function. As SVMs are not based on comparing with some threshold, rather initially the SVMs are trained with the wide variety of fault patterns which is an offline process and then the trained SVMs are tested online to detect and classify the fault within short time. The test results show that all types of shunt faults can be identified within half cycle time. The proposed scheme offers both primary protection to 95% of the line section and also backup protection to 95% of the adjacent reverse and forward line section also.     

小波熵理论及其在电力系统中应用的可行性探讨

电力系统采集的丰富实时数据包含系统模型的复杂性和不确定性,从这些数据中挖掘和融合出一个或系列普适量来检测系统的故障或稳定性至关重要。章分析了小波熵(Wavelet Entropy)在电力系统故障检测与判别中应用的可行性,探讨了基于小波分析理论的小波熵概念,提出了两种小波熵的定义和计算方法,仿真验证了小波熵可以用在输电线路的故障检测中,探讨了小波熵理论在电力系统故障检测与判别、系统故障分类等应用中的前景。     

Further enhancements in the symmetrical components based improved fault impedance estimation method Part II. Performance evaluation

An approach that can be used for further enhancing the symmetrical components based improved fault impedance estimation method has been proposed by the authors in Part-I of this companion paper. The PC based alternative transients program (ATP) was used to model a six bus system and generate fault data. The performance of the proposed method was assessed using the generated fault data. The results of performance assessment studies are presented and discussed in this paper. The proposed method was also applied for calculating an exact fault location. It is also shown in the paper that the proposed method provides highly accurate fault location estimates based on the distance relaying information.     

基于形态学梯度和参数识别的含串补供电牵引网故障测距

电气化铁道供电牵引网区间增加串联电容补偿装置,当线路发生故障时,短路电抗与故障距离对应关系遭到了破坏。通过对该种类型牵引网馈线故障模型和短路特性的研究,对比了短路故障引起电压变化的基于几种形态学梯度的检测方法,提出了一种基于二阶形态学梯度检测电容放电间隙短路的方法。根据某区间含串联电容补偿装置的电气化铁路牵引网的情况,对不同的电容电压相角和不同过渡电阻条件下的ATP仿真以及Matlab程序分析结果验证,基于二阶形态学梯度的方法能正确识别电容放电间隙短路。结合该识别方法和已有文献所述的电容参数识别的判别故障点在区间电容前或电容后的方法,最后根据傅里叶算法计算的短路电抗值,实现基于电抗法的查表测距。     

A new wavelet based fault detection,classification and location in transmission lines

This paper deals with the application of wavelet transforms for the detection, classification and location of faults on transmission lines. A Global Positioning System clock is used to synchronize sampling of voltage and current signals at both the ends of the transmission line. The detail coefficients of current signals of both the ends are utilized to calculate fault indices. These fault indices are compared with threshold values to detect and classify the faults. Artificial Neural Networks are employed to locate the fault, which make use of approximate decompositions of the voltages and currents of local end. The proposed algorithm is tested successfully for different locations and types of faults.     

Gear Transmission Fault Classification using Deep Neural Networks and Classifier Level Sensor Fusion

Gear transmissions are widely used in industrial drive systems. Fault diagnosis of gear transmissions is important for maintaining the system performance, reducing the maintenance cost, and providing a safe working environment. This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks (CNNs) and decision-level sensor fusion. In the proposed approach, a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors. Raw sensory data is sent directly into the CNN models without manual feature extraction. Then, classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models. Experimental study is conducted, which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine. The presented approach also achieves end-to-end learning that can be applied to the fault classification of a gear transmission under various operating conditions and with signals from different types of sensors.     

Fast fault detection scheme for series-compensated lines during power swing

This paper reports the use of a novel ultra-high speed scheme to release the distance relay to operate for a fault during a power swing in the series compensated line. In the scheme, in order to extract the fault induced voltage and current components, voltage and current samples are analyzed by the multi-resolution morphological gradient (MMG), first. Then, the fault initiated forward travelling wave is computed at the distance relay point. Next, Likelihood ratio [LR] test is utilized to detect a jump in the statistical mean of the calculated forward travelling wave. Finally, a support vector machine (SVM) classifier is employed to distinguish faults from other normal capacitor and switching transients. It is shown that in all of the simulated cases, our ultra-high speed algorithm was successful in fault detection across a wide range conditions including, fault type, fault resistance, fault location, pre-fault loading and fault inception time. Moreover, we found that using the proposed scheme significantly speeded the fault detection, in comparison with the existing phasor based methods. In addition, the improvements noted in our algorithm are achieved with a low computational burden.     

基于三相电流平方和比值的短路故障快速检测方法

为了快速准确地检测出短路故障,并使故障限流器快速投入运行,提出了一种基于三相电流平方和比值的短路故障快速检测方法。该方法利用前后时间窗内三相电流平方和均值之比作为故障检测量,在三相短路故障时不受故障初相角影响,不受谐波影响。基于PSCAD/EMTDC仿真平台,建立了500 kV输电线路模型,合理选取了该方法的整定值与闭锁值。考虑不同故障类型、不同故障初相角、不同过渡电阻、空载合闸、谐波及噪声等因素,验证了该方法的快速性及有效性。在各类故障工况及干扰工况下,比较了该方法与常用三种检测方法的性能差异。仿真结果表明,该方法性能良好,能够快速可靠地检测出短路故障。     

A novel fault location method for distribution networks with distributed generations based on the time matrix of traveling-waves

To improve location speed, accuracy and reliability, this paper proposes a fault location method for distribution networks based on the time matrix of fault traveling waves. First, an inherent time matrix is established according to the normalized topology of the target distribution network, and a post-fault time matrix is obtained by extracting the head data of initial waves from traveling wave detection devices. A time determination matrix is then obtained using the difference operation between the two matrices. The features of the time determination matrix are used for fault section identification and fault distance calculation, to accurately locate faults. The method is modified by considering economic benefits, through the optimal configuration of detection devices of traveling waves when calculating fault distances. Simulation results show that the proposed method has good adaptation with higher fault location accuracy than two other typical ones. It can deal with faults on invalid branches, and the error rate is under 0.5% even with connected DGs.