Differential equation-based fault locator for unified power flow controller-based transmission line using synchronised phasor measurements

The fault location algorithm based on a differential equation-based approach for a transmission line employing a unified power flow controller (UPFC) using synchronised phasor measurements is presented. First, a detailed model of the UPFC and its control is proposed and then, it is integrated into the transmission system for accurately simulating fault transients. The method includes the identification of fault section for a transmission line with a UPFC using a wavelet-fuzzy discriminator. Features are extracted using a wavelet transform and the normalised features are fed to the fuzzy logic systems for the identification of fault section. After the identification of the fault section, the control shifts to the differential equation-based fault locator that estimates the fault location in terms of the line inductance up to the fault point from the relaying end. Shunt faults are simulated with wide variations in operating conditions and a pre-fault parameter setting. The instantaneous fault current and voltage samples at the sending and receiving ends are fed to the designed algorithm sample by sample, which results in the fault location in terms of the line inductance. The proposed method is tested for different fault situations with wide variations in operating conditions in the presence of a UPFC.     

Wavelet packet-based digital relaying for advanced series compensated line

A new approach for the protection of thyristor-controlled series-compensated (TCSC) line using wavelet packets transform (WPT) is presented. The proposed method uses one cycle post-fault-current samples just after fault inception, which is processed through WPT and decomposed into various decomposition levels. The decomposed components are grouped together to provide different frequency sub-bands. Then the phase selection signal (PSS) and section identification signal (SIS) are computed to identify the faulty phase and faulty section, respectively, involved in the fault process in transmission line including TCSC. A threshold value (THD) is selected for PSS, and PSS above THD describes the faulty phase involved, otherwise not. Similarly, another THD is selected for SIS, and SIS below THD describes fault that includes TCSC, otherwise fault that does not include TCSC. As PSS takes half cycle after fault inception to identify the faulty phase and then triggers SIS, the faulty phases and faulty sections are identified within one cycle of fault inception. The proposed WPT algorithm is also tested on physical transmission line model with TCSC, under wide variations in operating conditions and provides accurate results. Thus, the proposed method provides accurate and fast protection measures for TCSC-based line.     

Time?frequency transform approach for protection of parallel transmission lines

A new approach for protection of parallel transmission lines is presented using a time-frequency transform known as the S-transform that generates the S-matrix during fault conditions. The S-transform is an extension of the wavelet transform and provides excellent time localisation of voltage and current signals during fault conditions. The change in energy is calculated from the S-matrix of the current signal using signal samples for a period of one cycle. The change in energy in any of the phases of the two lines can be used to identify the faulty phase based on some threshold value. Once the faulty phase is identified the differences in magnitude and phase are utilised to identify the faulty line. For similar types of simultaneous faults on both the lines and external faults beyond the protected zone, where phasor comparison does not work, the impedance to the fault point is calculated from the estimated phasors. The computed phasors are then used to trip the circuit breakers in both lines. The proposed method for transmission-line protection includes all 11 types of shunt faults on one line and also simultaneous faults on both lines. The robustness of the proposed algorithm is tested by adding significant noise to the simulated voltage and current waveforms of a parallel transmission line. A laboratory power network simulator is used for testing the efficacy of the algorithm in a more realistic manner.     

Transmission line distance relaying using machine intelligence technique

A new approach for distance relaying of transmission line using machine intelligence technique such as support vector machine (SVM) is presented. The proposed SVM technique is used for faulty phase selection and ground detection in different fault situations that occur on large power transmission line. Post-fault current and voltage samples for one-fourth cycle (five samples) are used as inputs to SVM 1, which provide output for faulty phase selection. SVM 2 is trained and tested with zero-sequence components of fundamental, third and fifth harmonic components of the post-fault current signal to provides the involvement of ground in the fault process. The polynomial and Gaussian kernel SVMs are designed to provide the most optimised boundary for classification. The total time taken for faulty phase selection and ground detection is 10 ms (half cycle) from the inception of fault. Also the proposed technique is tested on experimental set-up with different fault situations. The test results are compared with those of the radial basis function neural network and were found to be superior with respect to efficiency and speed. The classification test results from SVMs are accurate for simulation model and experimental set-up, and thus provide fast and robust protection scheme for distance relaying in transmission line.     

Probabilistic technique for sizing FACTS devices for steady-state voltage profile enhancement

A novel and simple stochastic-based approach to determine the optimal sizing of multiple flexible AC transmission systems (FACTS) devices in a power system for steady-state voltage profile enhancement is presented. In this context, investigations have been conducted on a published test system taking into consideration the uncertainty of the system load and generator scheduling. Two FACTS schemes are considered, namely a thyristor controlled series capacitor (TCSC) and two static synchronous compensators (STATCOMs) and a unified power flow controller (UPFC) and a STATCOM. The TCSC and UPFC are employed in the system to adjust the natural power sharing of two different parallel transmission lines and therefore enable the maximum transmission capacity to be utilised. Risk indices to estimate the likelihood that the voltage magnitude at a certain bus falls below a desired value is also presented.     

Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings

In the present milieu, changes in regulations and the opening of power markets have manifested in the form of large amount of power transfer across transmission lines with frequent changes in loading conditions based on market price. Since conventional distance relays may consider power swing as a fault, tripping because of such malfunctioning would lead to serious consequences for power system stability. A frequency domain approach for digital relaying of transmission line faults mitigating the adverse effects of power swing on conventional distance relaying is presented. A wavelet-neuro-fuzzy combined approach for fault location is also presented. It is different from conventional algorithms that are based on deterministic computations on a well-defined model for transmission line protection. The wavelet transform captures the dynamic characteristics of fault signals using wavelet multi-resolution analysis (MRA) coefficients. The fuzzy inference system (FIS) and the adaptive-neuro-fuzzy inference system (ANFIS) are both used to extract important features from wavelet MRA coefficients and thereby to reach conclusions regarding fault location. Computer simulations using MATLAB have been conducted for a 300 km, 400 kV line and results indicate that the proposed localisation algorithm is immune to effects of fault inception, angle and distance. The results contained here validate the superiority of the ANFIS approach over the FIS for fault location.     

An online fault-locating scheme for EHV/UHV transmission lines

The authors describe the principle of an online fault-locating scheme using the voltage and current synchronously sampled from both ends of a long EHV/UHV transmission line. This is based on a distributed parameter model of the line. Steady-state transmission line equations are used to construct a fault-locating function, with the effect of distributed capacitive current taken into account. Application is extended to practical situations where in-line shunt reactors are present for compensation. The fault-locating scheme is insensitive to fault path resistance, fault inception angle, load current and source impedance. Test results verify the high accuracy and speed of the scheme for different types of faults.     

基于小波包变换和随机森林算法的光伏系统故障分类

针对光伏系统故障分类问题,提出一种小波包变换和随机森林算法相结合的故障分类方法。采集光伏系统的故障电压数据,利用小波包变换对电压信号进行分解,提取各频带能量作为故障特征,将特征样本送入随机森林算法中进行分类。随机森林算法是结合集成学习理论和随机子空间方法的一种算法,可以对多种故障做出准确分类。使用PSCAD/EMTDC搭建独立光伏发电系统,选取12种故障进行模拟,得到600个故障样本,选取其中360个样本用于训练分类器,240个样本用于测试分类器的分类性能。仿真结果表明:该方法可有效辨别光伏系统的12种故障,分类准确率达到97.92%。与RBF神经网络分类器相比,故障分类准确率提高了4.17%,对进一步实现光伏系统故障诊断研究具有重要意义。     

Resistance varying characteristics of DC superconducting fault current limiter in MTDC system

Recent years, voltage source converter-based multi-terminal high voltage DC power transmission (MTDC) is widely developed in the world. However, it is difficult for the existing DC breaker to cut off the fault transmission line with large short-circuit fault current. Then, it would be helpful to develop DC fault current limiter for the MTDC system. In this paper, DC superconducting fault current limiter (DCSFCL) is proposed to limit fault current. In order to study the resistance-time performance of the DCSFCL under the rapid change of fault current, a simulation model of Zhoushan MTDC system with DCSFCL is established, and the current-limiting performance of the DCSFCL at different location of the grid is studied. The simulation results show that DCSFCL can effectively limit short-circuit current and improve the operation reliability of MTDC system.     

Software reliability allocation of digital relay for transmission line protection using a combined system hierarchy and fault tree approach

Digital relay is a special purpose signal processing unit in which the samples of physical parameters such as current, voltage and other quantities are taken. With the proliferation of computer technology in terms of computational ability as well as reliability, computers are being used for such digital signal processing purposes. As far as computer hardware is concerned, it has been growing steadily in terms of power and reliability. Since power plant technology is now globally switching over to such computer-based relaying, software reliability naturally emerges as an area of prime importance. Recently, some computer-based digital relay algorithms have been proposed based on frequency-domain analysis using wavelet-neuro-fuzzy techniques for transmission line faults. A software reliability allocation scheme is devised for the performance evaluation of a multi-functional, multi-user digital relay that does detection, classification and location of transmission line faults.     

High impedance fault detection in power distribution networks using time?frequency transform and probabilistic neural network

An intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time-time and time-frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and classify HIF from no fault (NF). In the proposed approach, S- and TT-transforms are used to extract time-frequency and time-time distributions of the HIF and NF signals, respectively. The features extracted using S- and TT-transforms are used to train and test the probabilistic neural network (PNN) for an accurate classification of HIF from NF. A qualitative comparison is made between the HIF classification results obtained from feed forward neural network and PNN with same features as inputs. As the combined signal-processing techniques and PNN take one cycle for HIF identification from the fault inception, the proposed approach was found to be the most suitable for HIF classification in power distribution networks with wide variations in operating conditions.     

New approach to adaptive single pole auto-reclosing of power transmission lines

In this study, a novel digital algorithm is introduced for recognition of arcing (transient) faults and determination of dead time for adaptive auto-reclosing. The algorithm distinguishes between arcing and permanent faults by using the zero sequence voltage measured at the relaying point. If the fault is recognised as an arcing fault, then the third harmonic of the zero sequence voltage is used to evaluate the extinction time of secondary arc and to initiate reclosing signal. The proposed algorithm uses an adaptive threshold level and therefore no significant adjustment is needed for different transmission systems. Moreover, its performance is independent to fault location, line parameters and the system pre-fault operating conditions. The algorithm has been successfully tested for various faults and operating conditions on a 400 kV overhead line using the electro-magnetic transient program (EMTP). The test results have demonstrated validity of the algorithm in determining the secondary arc extinction time and blocking unsuccessful automatic reclosing during permanent faults.     

Radial basis probabilistic neural network for differential protection of power transformer

Protection of medium- and large-power transformers has always remained an area of interest of relaying engineers. Conventionally, the protection is done making use of magnitude of various frequency components in differential current. A novel technique to distinguish between magnetising inrush and internal fault condition of a power transformer based on the difference in the current wave shape is developed. The proposed differential algorithm makes use of radial basis probabilistic neural network (RBPNN) instead of the conventional harmonic restraint- based differential relaying technique. A comparison of performance between RBPNN and heteroscedastic-type probabilistic neural network (PNN) is made. The optimal smoothing factor of heteroscedastic-type PNN is obtained by particle swarm optimisation technique. The results demonstrate the capability of RBPNN in terms of accuracy with respect to classification of differential current of the power transformer. For the verification of the developed algorithm, relaying signals for various operating conditions of the transformer, including internal faults and external faults, were obtained through PSCAD/EMTDC. The proposed algorithm has been implemented in MATLAB.     

Ground distance relaying algorithm for high resistance fault

This study proposes a new fault impedance estimation algorithm of phase-ground fault for ground distance relaying based on the negative-, zero- and comprehensive negative-zero-sequence current component. The principle is based on the assumption that fault path is purely resistive, and the phase angle of fault point voltage and fault path current is equal to construct the fault impedance estimating equations for ground distance relay, which can eliminate the effect of fault path resistance, load current and power swing. PSCAD software simulations show the accuracy of proposed algorithm.     

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We built a circuit simulation model in MATLAB to obtain its DC output voltage. Using Fourier analysis, we extracted fault features. These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization (PSO) and the ASAPSO algorithm. The accuracy of fault diagnosis was compared for the three networks. The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy, better reliability, and adaptability and can more effectively diagnose and locate faults in power electronic circuits.     

High-impedance fault detection using multi-resolution signal decomposition and adaptive neural fuzzy inference system

High-impedance faults (HIFs) on distribution systems create unique challenges to protection engineers. HIFs do not produce enough fault current to be detected by conventional overcurrent relays or fuses. A method for HIF detection based on the nonlinear behaviour of current waveforms is presented. Using this method, HIFs can be distinguished successfully from other similar waveforms such as nonlinear load currents, secondary current of saturated current transformers and inrush currents. A wavelet multi-resolution signal decomposition method is used for feature extraction. Extracted features are fed to an adaptive neural fuzzy inference system (ANFIS) for identification and classification. The effect of choice of mother wavelet is also analysed by investigating a large number of wavelet families. Various simulation results, which are obtained using an appropriate model, are summarised and efficiency of the proposed algorithm for dependable and secure HIF detection is determined.     

Structure-preserved power-frequency slow dynamics simulation of interconnected ac/dc power systems with AGC consideration

A structure-preserved power-frequency slow dynamics simulation model is suggested for interconnected ac/dc power systems with automatic generation control (AGC) consideration, which will be applied to study relevant emergency control in future so that the bulk system viability crisis caused by load-frequency slow dynamics can be released. In the model, the network structure of interconnected power systems is entirely preserved, and the multi-area dynamic load flow (DLF) is developed for simulation. The generator speed governor and rotor dynamics, load-frequency characteristics, simplified models for high voltage direct current (HVDC) transmission and flexible ac transmission systems (FACTS) device thyristor controlled series capacitor (TCSC) suitable for long-term dynamics are considered with their AGC interfaces kept for future emergency-AGC study. However, at this stage, the sub-problem of reactive power and voltage is neglected for modelling simplicity and dc load flow is thus used for network solution. The concept of area centre of inertia (ACOI) is used based on the assumption of uniform frequency in each control area similar to that of the conventional single-area DLF calculation. The application of ACOI concept is attractive because the signal can be obtained from wide-area measurement systems (WAMSs) in real time and used to enhance long-term frequency stability through advanced control in future. The computer test results from 2-area 4-machine and IEEE 30-bus power systems demonstrated the validity and effectiveness of the suggested model and corresponding algorithm.     

基于深度度量学习的轴承故障诊断方法

针对机械大数据因故障类内离散度和类间相似度较大而导致诊断精度低的问题,提出一种深度度量学习故障诊断方法,采用深度神经网络(Deep Neural Network, DNN)对故障特征进行自适应提取,并利用基于欧氏距离的边际Fisher分析(Marginal Fisher Analysis, MFA)方法进行了优选,在构建的深度度量网络(Deep Metric Network, DMN)顶层特征输出层添加BPNN(Back Propagation Neural Network, BPNN)分类器对网络参数进行微调,并实现故障的分类识别。通过对不同类型和严重程度的轴承故障进行了诊断分析,验证了该方法可以有效地对轴承故障进行高精度诊断,效果优于传统深度信念网络(Deep Belief Network, DBN)故障诊断方法以及常用时域统计特征结合支持向量机(Support Vector Machine, SVM)分类的故障诊断方法。     

Maximal network reliability for a stochastic power transmission network

Many studies regarded a power transmission network as a binary-state network and constructed it with several arcs and vertices to evaluate network reliability. In practice, the power transmission network should be stochastic because each arc (transmission line) combined with several physical lines is multistate. Network reliability is the probability that the network can transmit d units of electric power from a power plant (source) to a high voltage substation at a specific area (sink). This study focuses on searching for the optimal transmission line assignment to the power transmission network such that network reliability is maximized. A genetic algorithm based method integrating the minimal paths and the Recursive Sum of Disjoint Products is developed to solve this assignment problem. A real power transmission network is adopted to demonstrate the computational efficiency of the proposed method while comparing with the random solution generation approach.     

Estimation methods using dynamic phasors for numerical distance protection

Several new methods for faulted transmission line parameters estimation in phasor and time domain are proposed, that eventually improve the overall performance of numerical distance relays. The concept of dynamic phasors is introduced to accommodate the time-variant nature of the current and voltage signals during transients and faults. Based on dynamic phasor transmission line models, direct and indirect estimation methods are derived. For the proposed indirect estimation method, stability of prediction error dynamics is assured by using the Lyapunov direct method. Presented estimation techniques are compared with a conventional stationary phasor solution as well as with a recursive least-square estimator derived in the discrete time domain. In the evaluation, more realistic assumptions are considered with regards to distortion of the input voltage and current signals along with the variable fault resistance because of arcing faults. Simulation results and actual field measurements are included for performance evaluation of the proposed estimators.