A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Fault section identification and determining its location are important aspects to reduce down/repair time, speed up restoration of power supply and to improve the reliability. In this paper combined wavelet and artificial neural network based directional protection scheme is proposed for double circuit transmission lines using single end data to identify the faulty section and its location with reach setting up to 99% of line length. The proposed method requires the three phase currents and voltage to be measured at one end of the double circuit transmission line modelled using distributed parameter line model which also considers the effect of shunt capacitance. Approximate coefficients feature vector of the three phase voltage and current are extracted using discrete wavelet transform to train the ANN with Levenberg Marquardt algorithm. The proposed scheme involves two stages. The first stage identifies the zone/section of the fault and the second stage calculates the fault location from the relaying point. The proposed combined Wavelet and ANN based fault location scheme is also compared with ANFIS based fault location scheme. The test results of the proposed scheme show that the fault section identification and location estimation is very accurate and the average percentage error in fault location estimation is within 0.001%. This method is adaptive to the variation of fault type (both forward and reverse), fault inception angle, fault location and fault resistance. The main advantage of the proposed scheme is that it offers primary protection to 99% of line length using single end data only and also backup protection to the adjacent forward and reverse line sections.     

A new faulty section identification and fault localization technique for three-terminal transmission line

Owing to mal-operation of the conventional scheme during high resistance ground fault near tap point, a new faulty section identification and fault localization technique for three-terminal transmission line is presented in this paper. The proposed technique utilizes time-synchronized voltage and current signals from all the three terminals. Initially, fault detection based on estimation of superimposed voltage of tap point with reference to all three terminals has been carried out. Subsequently, utilizing the above three estimated superimposed voltages; faulty section identification criterion is formed. Finally, fault localization i.e. estimation of the value of fault distance and resistance has been performed. The authenticity of the proposed technique has been verified by simulating an existing 400 kV Indian three-terminal transmission network in PSCAD/EMTDC software. The simulation results point out that the proposed technique is able to identify the faulty section correctly. Moreover, it precisely estimates the value of fault distance and fault resistance as the percentage error for fault location and resistance remains within ±1.5% and ±3.5%, respectively. Likewise, its performance remains unaffected during wide variation in fault and system parameters. At the end, comparative evaluation of the proposed technique with the existing protection scheme clearly shows its superiority.     

Development and implementation of an ANN-based fault diagnosisscheme for generator winding protection

In this paper, the development and implementation of a new fault diagnosis scheme for generator winding protection using artificial neural networks (ANN) is introduced. The proposed scheme performs internal fault detection, fault type classifications and faulted phases identification. This scheme is characterized with higher sensitivity and stability boundaries as compared with the differential relay. Effect of the presence of nonsynchronous frequencies on the scheme performance is examined. Effect of different values of ground resistance on ground fault detection sensitivity is outlined. The scheme hardware is implemented based on a digital signal processing (DSP) board interfaced with a multi input/output (MIO) board. Test results of the proposed scheme corroborate the scheme stability and sensitivity     

Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations

This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations.     

基于PIC单片机的电站故障保护模块设计与应用

提出了一种基于PIC单片机的电站故障保护模块没计方案.介绍了电站故障保护的类型,论述了系统的结构及其工作原理,给出了系统的软硬件没计流程及实现方法.该模块已得到了大量应用,实践证明,该模块使用方便,可靠性高,能够满足现场使用要求.     

New generator split-phase transverse differential protection based on wavelet transform

This paper presents a new split-phase transverse differential protection for a large generator based on wavelet transform. Research results show that there is almost no harmonic component on normal conditions, but it will produce great high-frequency current component when an internal fault occurs, which can be used to detect generator internal fault. With decomposition and reconstruction of the transient currents with wavelet transform, the high-frequency band fault currents are exploited in the new scheme. And the realization of the proposed protection device is also described in this paper, including the relay software and hardware design. The results from the experimental and field tests demonstrate that the new scheme is successful in detecting the generator internal fault. It has higher sensitivity and selectivity than the traditional protection scheme.     

A new wavelet network based method to estimate the lightning-related risk of failure of power system apparatus

This paper proposes a new and fast wavelet network based method for estimating the risk of failure caused by lightning overvoltages in arrester protected networks. First, failure risks obtained by simulations are used as the training data for training the wavelet network. The trained wavelet network is then used for accurate and fast estimating of the lightning-related risk of failure of power system apparatus for all possible conditions. The accuracy of the proposed method has been tested and verified under various conditions in the 230 kV network of Sistan–Baluchestan. Performance of the new method has also been compared with several existing methods under same conditions, and the test results show better accuracy of the proposed method. The proposed method not only does not have the restriction of conventional methods, but also it does not have the limitations associated with traditional neural networks based algorithms such as convergence to local optimum points, over-fit and/or under-fit problems. The main contribution of the paper is an accurate (due to proper selection of the training data set based on the k-fold cross validation technique and using wavelet network for estimation), fast (mean calculation time for the network risk of failure computation is 54 s) and simple wavelet network-based algorithm (as compared to the conventional algorithms) for estimating the lightning-related risk of failure of power system apparatus.     

基于小波变换的发电机定子单相接地保护能量法

根据发电机发生单相接地故障时机端和中性点零序电压存在故障分量，并且两侧故障分量近似相同的特点，提出了利用小波变换检测发电机定子单相接地故障的能量法。该方法在不同尺度下对两侧零序电压的故障分量进行小波变换，将高频部分之和与差分别作为保护的动作信号和制动信号，计算数据窗内相应信号的谱能量作为保护的动作量和制动量，通过比较动作量和制动量的大小检测发电机单相接地故障。通过在制动信号中引入噪声估计考虑噪声的影响，提高了保护判据的可靠性。仿真和试验结果验证了该保护方案具有较高的灵敏度与可靠性。     

基于小波分析的超高压输电线路保护新方法

暂态信号分析是电力系统故障诊断和暂态保护的基础和依据,小波变换为暂态信号分析提供了强有力的数学工具.应用小波变换对超高压输电线路的暂态电压、电流分量进行分析和处理,提出了一种利用经小波滤波后综合电压量确定故障方向和相别.在电压增量中提取行波信号来快速计算故障距离的新方法.ATP-EMTP和Matlab/Wavelet Tool- box仿真结果表明,提出的保护方案具有很好的快速性和可靠性.     

Grid integration of single stage SPV-STATCOM using cascaded 7-level VSC

A multi-functional single-stage grid-tied solar photovoltaic (SPV) system with STATCOM (Static Compensator) capabilities using a cascaded three phase seven level voltage source converter (VSC) is presented in this paper. PS-PWM (Phase Shifted Pulse Width Modulation) technique with a low switching frequency (450 Hz) is used to operate the VSC. The proposed SPV-STATCOM system works in three modes i.e. in Mode-1, only active power is supplied to the grid; in Mode-2, both active and reactive powers are supplied to the grid and in Mode-3, only reactive power is supplied to the grid thereby utilizing the proposed system to its fullest capacity in 24 h of a day. To extract the maximum power from the SPV array, the incremental conductance maximum power point tracking scheme is utilized. To synchronize the SPV-STATCOM power to the grid and to maintain power factor close to unity, a decoupled current controller, feed-forward term and positive sequence detector dq phase locked loop (PSD-dqPLL) control approach are used. Lower switching losses, harmonic distortions, high output voltage and power are some of the advantages of using a single-stage 7-level cascaded H-bridges. The design and the control scheme performances in all modes are simulated in MATLAB and validated through real time hardware in loop (HIL) system.     

An online power system static security assessment module using multi-layer perceptron and radial basis function network

Efficient contingency screening and ranking method has gained importance in modern power systems for its secure operation. This paper proposes two artificial neural networks namely multi-layer feed forward neural network (MFNN) and radial basis function network (RBFN) to realize the online power system static security assessment (PSSSA) module. To assess the severity of the system, two indices have been used, namely active power performance index and voltage performance index, which are computed using Newton–Raphson load flow (NRLF) analysis for variable loading conditions under N − 1 line outage contingencies. The proposed MFNN and RBFN models based PSSSA module, are fed with power system operating states, load conditions and N − 1 line outage contingencies as input features to train the neural network models, to predict the performance indices for unseen network conditions and rank them in descending order based on performance indices for security assessment. The proposed approaches are tested on standard IEEE 30-bus test system, where the simulation results prove its performance and robustness for power system static security assessment. The comparison of severity obtained by the neural network models and the NRLF analysis in terms of time and accuracy, signifies that the proposed model is quick, accurate and robust for power system static security evaluation for unseen network conditions. Thus, the proposed PSSSA module implemented using MFNN and RBFN models are found to be feasible for online implementation.     

SVM based zonal setting of Mho relay for shunt compensated transmission line

Presence of shunt FACTS (Flexible Alternating Current Transmission System) device significantly affect the performance of protection system and may create security and reliability issues. This paper introduces a novel approach for zone identification in transmission line employing shunt FACTS devices such as Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM). The technique depends on the modified apparent impedance seen by the impedance relay for all possible operating conditions. In this technique the Support Vector Machine (SVM) is used for discriminating faulty zone (zone 1 or zone 2). In addition an optimization technique viz. Genetic Algorithm (GA) is used to optimize SVM parameters. A typical 230 kV system was simulated in PSCAD/EMTP software and the results show that the proposed scheme is secure, accurate and reliable under the wide variation in power system parameters like load angle, fault resistance, fault location, inception angle and compensation level.     

Application of wavelet multiresolution analysis for identification and classification of faults on transmission lines

This paper presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line fed from both ends are used. The peak absolute value, the mean of the peak absolute values and summation of the 3rd level output of MRA detail signals of current in each phase extracted from the original signals are used as the criterion for the analysis. The effects of fault distance, fault inception angle and fault impedance are also examined. Extensive simulations are carried out to generate time domain input signal using EMTP (Microtran) on a 230 kV, 200 km long line fed from both ends and simulation results show that the proposed method is a simple, effective and robust method suitable for high impedance faults also.     

Experimental results and technique evaluation based on alienation coefficients for busbar protection scheme

In modern digital power protection systems, statistical coefficients technique is recently used for fault analysis. An alienation technique is developed for busbar protection against all ten types of shunt faults, which may locate in busbar protection zone, under different loading levels, fault resistances and fault inception angle. It does not need any extra equipment as it depends only on the three-line currents measurements, of all feeders connected to the protected busbar, which are mostly available at the relay location. It is able to perform fault detection, fault confirmation, faulty phase selection and determine the fault location in about a half-cycle period. Thus, the alienation technique is well suited for implementation in digital protection schemes. The technique is efficient to detect current transformer saturation conditions without needing any additional algorithm. The effects of DC components and harmonics are eliminated with estimation of alienation coefficients. The proposed scheme is applied for an experimental circuit. LABVIEW program and MATLAB package are used to implement the proposed technique.     

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.     

基于非二进小波变换的电力系统微机保护数字滤波器与算法

提出了非二进小波变换方法。非二进小波变换方法简便 ,不需要迭代 ,可以提出信号的任何整数次和分数次谐波分量 ,具有比二进小波变换更强的信号处理能力和灵活性 ,因而适用于电力系统故障信号分析。此外 ,文中利用非二进小波变换的原理 ,提出了基于非二进小波变换的电力系统微机保护数字滤波器与算法 ,这对于提高电力系统微机保护的可靠性具有重要意义。     

基于非二进小波变换的电力系统微机保护数字滤波器与算法

提出了非二进小波变换方法。非二进小波变换方法简便,不需要迭代,可以提出信号的任何整数次和分数次谐波分量,具有比二进小波变换更强的信号处理能力和灵活性,因而适用于电力系统故障信号分析。此外,文中利用非二进小波变换的原理,提出了基于非二进小波变换的电力系统微机保护数字滤波器与算法,这对于提高电力系统微机保护的可靠性具有重要意义。     

A performance comparison of a nonlinear and a linear control for grid connected PMSG wind energy conversion system

With the increased penetration of wind energy on modern power systems all over the world, the Wind Farm Systems (WFS) are today required to participate actively in electric network operation by an appropriate generation control strategy. This paper presents a comparative study of two control strategies for wind farm based on Permanent Magnet Synchronous Generator (PMSG) and interconnected to the distribution network. The 4 MW wind farm consists of 2 PMSGs based on 2 MW generators connected to a common DC-bus system. Each PMSG of the WFS is connected to the DC-bus through a rectifier, but the DC-bus is connected to the grid through only one inverter system. The proposed control laws are based on a sliding mode algorithm and classical Proportional Integral (PI) controllers to regulate both generator and grid-side converters. The control strategy combines a pitch control scheme and Maximum Power Point Tracking (MPPT) to maximize the total generated power of WFS. Furthermore, the aim of the control strategy is to maximize the extracted power with the lowest possible impact in the power network voltage and frequency for fault conditions as well as for normal working conditions. Finally, simulation results with Matlab/Simulink environment confirm that the proposed strategy has excellent performance.     

Nonlinear intelligent DC grid stabilization for fuel cell vehicle applications with a supercapacitor storage device

This paper presents an intelligent DC link control using a fuzzy logic controller based on the differential flatness control theory for hybrid vehicle applications supplied by a fuel cell (FC) (main source) and a supercapacitor (auxiliary source). The energy in the system is balanced by dc bus energy stabilization (or indirect voltage regulation). A supercapacitor module functions by supplying energy to regulate the dc bus energy. The FC, as a slow dynamic source in this system, supplies energy to the supercapacitor module to maintain its charge. The FC converter combines four-phase parallel boost converters with interleaving, and the supercapacitor converter employs four-phase parallel bidirectional converters with interleaving. These two converters are called a multi-segment converter for high power applications. Because the model of the power switching converters is nonlinear, it is preferable to apply model-based nonlinear control strategies that directly compensate for the nonlinearity of the system without requiring a linear approximation. Using the intelligent fuzzy control law based on the flatness property, we propose straightforward solutions to hybrid energy management and to the dynamic and regulation problems. To validate the proposed method, a hardware system is developed with analogue circuits, and a numerical calculation is generated with a dSPACE controller DS1104. Experimental results for a small-scale power plant (a polymer electrolyte membrane FC (PEMFC) of 1200 W and 46 A with a supercapacitor module of 100 F, 500 A, and 32 V) in the laboratory corroborate the excellent performance of this control scheme during vehicle motor drive cycles.     

A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model

Out-of-step protection of one or a group of synchronous generators is unreliable in a power system which has significant renewable power penetration. In this work, an innovative out-of-step protection algorithm using wavelet transform and deep learning is presented to protect synchronous generators and transmission lines. The specific patterns are generated from both stable and unstable power swing, and three-phase fault using the wavelet transform technique. Data containing 27,008 continuous samples of 48 different features is used to train a two-layer feed-forward network. The proposed algorithm gives an automatic, setting free and highly accurate classification for the three-phase fault, stable power swing, and unstable power swing through pattern recognition within a half cycle. The proposed algorithm uses the Kundur 2-area system and a 29-bus electric network for testing under different swing center locations and levels of renewable power penetration. Hardware-in-the-loop (HIL) tests show the hardware compatibility of the developed out-of-step algorithm. The proposed algorithm is also compared with recently reported algorithms. The comparison and test results on different large-scale systems show that the proposed algorithm is simple, fast, accurate, and HIL tested, and not affected by changes in power system parameters.