An Overview- Protection of Transmission Line Using Artificial Intelligence Techniques

An Overview- Protection of Transmission Line Using Artificial Intelligence Techniques

Abstract

This article presents a survey of the developments in digital relays for protection of transmission lines. For a modern power system, selective high speed clearance of faults on high voltage transmission lines is critical and this survey indicates the efficient and promising implementations for fault detection, classification and fault location in power transmission line protection. The work done in this area favor computerized relays, digital communication technologies and other technical developments, to avoid cascading failures and facilitate safer, secure and reliable power systems. Efforts have been made to include almost all the techniques and philosophies of transmission line protection reported in the literature up to 2012. The focus of this article is on the most recent techniques, like artificial neural network, fuzzy logic, fuzzy- neuro, fuzzy logic-wavelet based and phasor measurement unit-based concepts as well as other conventional methods used in transmission line protection.

Keywords: Artificial intelligence, Digital relay, Distance protection, Fuzzy logic, Neural network, Wavelet transform.

1. Introduction

   Transmission lines are among the power system components with the highest fault incidence rate, since they are exposed to the environment. Line faults due to lightning, storms, vegetation fall, fog and salt spray on dirty insulators are beyond the control of man. The balanced faults in a transmission line are three phase shunt and three phases to ground circuits. Single line-to-ground, line-to-line and double line-to-ground faults are unbalanced in nature. On a transmission system the protective relaying system is incorporated to detect the abnormal signals indicating faults and isolate the faulted part from the rest of the system with minimal disturbance and equipment damage. This survey attempts to cover the various developments in digital relays for transmission line protection up to 2012 and point to some of the references showing promising directions.

   Rockefeller first presented the implementation of digital relaying in 1969 [2]. The advances in the very large scale integrated (VLSI) technology and software techniques led to the development of microprocessor based relays that were first offered as commercial devices in 1979 [3]. Selective, high speed clearance of faults on high voltage transmission lines is critical to the stability of the highly complex, modern power system. In this respect, lot of work have been developed to improve the performance of digital protective relays and use of intelligent techniques for analysis of faults and protective relay operations.

   Distance relaying principle, due to their high speed fault clearance compared with the over current relays is a widely used protective scheme for the protection of high and extra high voltage (EHV) transmission and sub-transmission lines. A distance relay estimates the electrical distance to the fault and compares the result with a given threshold, which determines the protection zone. In terms of hardware, distance relays have evolved from electromechanical relays to static relays and to microprocessor based (digital) relays. When a fault occurs in an electrical transmission line, the distance relays detect the faulty line and type of fault but they may under reach/over reach depending upon pre-fault loading, fault resistance and remote end in-feeds. The impedance estimated by a digital distance relay reduces with the increase in the speed at which the estimate is obtained. Hence an impedance relay with a specified reach setting cannot operate at arbitrarily high speeds [3]. The first installation of digital computer for relaying began in 1960s which made it possible to store information so that the relay engineer can control the reach characteristics of a distance relay to suit the application and develop fault location algorithms [5,6]. Such digital fault locators calculate the reactance of a faulty line estimated from the computation of voltage and current phasors at the line terminals [9, 59]. But these fault location methods need some simplifying hypothesis to allow the fault distance calculation, affecting the accuracy of the results. The one terminal approach is simple and easy to implement [7,60,96,97] although the two-end algorithms which process signals from both terminals

   of the line are superior in comparison to the one-end approaches [16].

   In the 70s research was concentrated on ultra high speed protection based on the travelling wave. The post fault wave forms in the first one or two cycles after the occurrence of a fault contain high frequency transient wave fronts. Based on the analysis of these transient state signals the fault location can be calculated within a few milliseconds of the fault initiation. Different algorithms proposed for implementation of travelling wave distance protection are reported by [8,12,17,98]. The positional protection utilizes the transit times the high frequency fault generated transients to identify the faulted line section [47]. It has been noted by most of the researchers that the travelling wave based method does not perform well for faults close to the relaying point and for faults with small fault inception angle;They require a very high sampling rate and their implementation are more costly than implementation of impedance techniques. As the complexity of the power network increases, the transmission line protection and control must be based on real time power system changes and it must be at high speeds to ensure that the power system will not run into transient stability problems. Several papers have considered the real time power system changes and have reported about accurate, fast faulty phase selection and fault location [9,10,13,14,24,30,31,41,48,57,61,72,73,74,78,79,83,8

   4,89,90,91,92,99,100,101,102].

   In the late 80s synchronized measurement technology emerged as a promising prospect in achieving real time protection. With global positioning system (GPS), digital measurement at different line terminals can be performed synchronously [32,42]. They are more accurate than distance relaying algorithms which are affected with inadequate modeling of transmission lines and parameter uncertainty due to line aging, line asymmetry and environmental factors. The Phasor Measurements Units (PMU) are the most widely used synchronized measurement devices for power system applications, whose measurements are synchronized with respect to a GPS clock and PMU-based fault locators are more accurate than the method based on unsynchronized phasors [43,55,62,63,93]. The unsynchronized approaches are cheaper, since there is no need to use GPS and are not affected by errors due to different sampling rates or phase shifts introduced by the different recording devices and transducers. Such impedance based fault location methods have been presented by [25,80,94], having negligible fault location error if phasor and transmission line parameters are accurate. Although the use of GPS, phasor measurement units (PMUs),

   digital communication technologies, high precision signal transducers have facilitated accurate protection of power system over a wide area, they are subjected tosoftware insecurity and communications latency.

   There is a need for the measuring algorithms to have the ability to adapt dynamically to the system operating conditions such as changes in the system configuration, source impedances and fault resistance. Keeping this in view, intelligent techniques are under investigation to increase reliability, speed and accuracy of existing digital relays based on Artificial Neural Network (ANN), Fuzzy Logic (FL), Fuzzy-Neuro and Fuzzy Logic- Wavelet based systems. These developments are discussed in section 2, 3 and 4 respectively.

2. Artificial Neural Network Approach

   To reach accuracy, an electromechanical, static or a microprocessor based distance relay is affected by different fault conditions and changed network configuration. So ANN techniques are under investigation over the past 15-20 years, which can adapt dynamically to the system operating conditions at a high speed. The ability of ANN to learn by training any complex input/output mapping and recognize.The noisy patterns (those with desired segments missing and/or undesired segments added), gives them the powerful property of pattern recognition and classification [18]. ANNs can solve the overreach and the under reach problems which are very common in the conventional distance relay design. ANN utilizes samples of currents and voltages directly as inputs without computation of phasors and related symmetrical components. Various kinds of neural network such as multi-layer perceptron (MLP), recurrent, radial basis function (RBF), probabilistic neural network etc. are being applied for fault classification and fault location. These are designed by different training algorithms like back propagation, orthogonal least square, extended kalman filter etc. The use of ANNs can extend the first zone of distance relays and enhance system security [33]. For selecting the appropriate network configurations, the performance criteria are fault tolerance, minimal response time and generalization capabilities. ANN approach has been used to improve some of the standard functions used in protection of transmission lines. They have been related to fault direction discrimination (Sidhu et al., 1995; Sidhu et al., 2004), fault detection and classification [21,26,56,81,82], distance protection [15,34,38], improvements in fault distance computation [44,45,49,54,71,103], protection of

   series compensated lines [27], adaptive distance protection[28,29,39,85] and adaptive reclosing [19].

   To make the ANN responsive to time varying voltage and current waveforms different types of recurrent networks were considered that allow the hidden units of the network to see their own previous output, so that the subsequent behavior can be shaped by previous response. Such an Elman recurrent network is proposed by [35,40]. Inside these ANNs, the operations that take place are not clearly defined and hence they are not considered highly reliable. Further development is the concept of supervised clustering to reduce the number of iterations in the learning process of multi layer feed forward networks [22]. A neural network simulator is developed by [50], to identify the optimum ANN structure required for training the data and to implement the ANN in hardware. Still the problem with ANNs is that no exact rule exists for the choice to the number of hidden layers and neurons per hidden layer. So it is uncertain whether the ANN based relay gives the optimum output, to maintain the integrity of the boundaries of the relay characteristics or not a high speed distance relaying scheme based on radial basis function neural network (RBFNN) is proposed by [51], due to its ability to distinguish faults with data falling outside the training pattern. A sequential procedure is presented by [52], for distance protection using a minimal radial basis function neural network (MRBFNN), to determine the optimum number of neurons in the hidden layer without resorting to trial and error. The use of separate ANNs, for faults involving earth and not involving earth has proved to be convenient way of classification of transmission faults based on RBF neural networks by [65]. For simple and reduced architecture and better learning capability a modular neural network, is proposed by [53,75], to discriminate the direction of faults for transmission line protection. Such a network considers corresponding phase/ground voltage and current information as input and thereby the redundant inputs in conventional approaches are eliminated. The existing ANN based solutions easily converge on local minima whenever input patterns with large dimensionality are present and when designed for specific applications, are prohibitively expensive or infeasible for real time implementations. It is observed that the ANN based distance relays need much larger training sets and hence the training of these networks is time consuming and further research work can produce a hardware realization with proper modification in the learning methodology and preprocessing of input data that would improve the learning rate performance, efficiency and the reliability many folds. Presently research efforts are

   in the direction of evolutionary computational techniques such as genetic algorithms (GA) for determining the neural network weights and thereby avoid training of ANN.

3. Fuzzy Logic and Combined Neural Network/Fuzzy Logic Approach

   Zadeh introduced the concept of fuzzy set theory in 1965 for dealing with uncertain and ambiguous properties of events (Zadeh, 1965). It was introduced in power system networks to solve uncertainty problems that arise due to the continuously varying power system parameters. The key benefit of fuzzy logic is that its knowledge representation is explicit, using simple IFTHEN relations. The fuzzy set theory is used for fault type identification on a transmission line by [23,76], without any computationally expensive training of ANN or expert domain knowledge. These algorithms are fairly accurate only under certain assumptions of fault distance, prefault power flow, fault resistance and line length. Fuzzy sets are good at various aspects of uncertain knowledge representation, while neural networks are efficient structures capable of learning from examples. Neural network has the shortcoming of implicit knowledge representation, whereas fuzzy logic systems (FLS) are subjective and heuristic. In a fuzzy neural network (FNN), a neural network is used to implement a fuzzy rule-based system from input/output data to enhance the learning capabilities, plus knowledge illustration of fuzzy logic system. [36], proposed three different neuro-fuzzy networks in series to classify the fault in transmission line protection using both designers experiences and sample data sets. A distance relaying scheme based on FNN is proposed by [46], in which the fuzzy view point is utilized to simply the model, but the FNNs calculate the fault distance within 80% of the line. A decision rule is proposed by [77], to improve algorithm selectivity for a variety of real events not necessarily anticipated during training have been a new concept of transmission line fault classification algorithm using a self-organized neural network based on adaptive resonance theory (ART) with fuzzy K-nearest neighbor (K-NN). An algorithm is developed by [58], using the adaptive network-based fuzzy inference system (ANFIS) for fault detection and classification in transmission lines based on root mean square value of phase current and zero sequence current, under a wide variety of system and fault conditions including contingencies such as high impedance faults.

   In fuzzy logic based protection system, accuracy cannot be guaranteed for wide variations in system

   conditions. So consequently a more dependable and secure relaying algorithm during real time implementation is needed for classifying the faults under a variety of time-varying network configurations. The fuzzy-neuro approaches are sensitive to system frequency changes and require largetraining sets and a large number of neurons affecting their accuracy and speed in protecting large power networks.

4. Wavelet Approach

   The fundamental frequency components of the post fault voltages and currents need to be extracted as quickly and accurately as possible for the quick response of a digital distance relay. Wavelet approach is one of the new tools in this direction which is useful for power system transient analysis, since the conventional signal processing techniques have the inherent disadvantages of long discrimination time, errors in impedance calculations and misclassifications (during CT saturation and in presence of fault resistance) [37,66,104]. Wavelet transform (WT) has the ability to perform local analysis of relaying signals without losing the time- frequency information. WT in conjunction with AI/Fuzzy/Expert system/SVM based techniques have the advantages of fast response and increased accuracy in fault type and location identification. A preprocessing module based on discrete wavelet transform (DWTs) considerably simplifies the input signal, reducing the volume of input data fed into an ANN that classifies the fault events. A solution for protection of parallel transmission lines by decomposing fault current signals using WT and by comparing the magnitudes of line currents in the corresponding phases is presented by [67].

   The ability of wavelets to decompose the signal into different frequency bands using multi resolution analysis (MRA) allows detecting and classifying faults as well as extracting the voltage and current fundamental phasors needed to calculate the impedance to the fault point in distance protection

   [68] and with filtering algorithms proposed by [106], fast relay operating times are obtained. Discrete wavelet transform based MRA is used for feature extraction by [86] and the features extracted from fault current signals are used to train and test the support vector machine (SVM) for fault classification. Fault location from the relaying point is computed by RBFNN, A solution to the complexities of the protection of series compensated transmission lines is proposed by [95] which is a combination of wavelet-SVM technique for fault zone identification. In a Fuzzy-logic-wavelet based

   technique the wavelet transform of current signal provides hidden information of a fault situation to FLS, to classify the fault and these are reported in [69,70,87]. These fuzzy procedures solve the problem with simple computational procedures rather than using more complex algorithms in the deterministic way. Some more improved solutions to detect the faults precisely with wavelet transform based digital protection for transmission lines are proposed by [88,105,107]. The combined techniques of WT with ANN and WT with Fuzzy Logic depend on huge samples and trainings for knowledge representation, leading to an excessively complicated job. Wavelet singular entropy (WSE) technique which indicates the uncertainty of the energy distribution in the time- frequency domain is used to extract features from fault transients for the fault diagnosis in EHV transmission lines [108].The capabilities of wavelets are affected owing to the existence of noises riding high on the signal and the problem lies in identification of the most suitable wavelet family that is more approximate for use in estimating fault location. Most of the wavelet based techniques employ multi-level wavelet decomposition, which requires multi-level filtering followed by complex computations. Wavelet transform will emerge as a powerful tool in transmission line protection provided further work is done in reducing the algorithm complexity, computational burden and response time.

   The adaptive wavelet [109-110] presents advantages for transmission line protection rather than predefined mother wavelets.

5. Conclusions

A survey of transmission line protection is done through this article. For implementation of digital relaying, a lot of work has been done to improve the performance of digital protective relays. In the context of reformation in the power industry and operation of transmission lines close to the stability limits, new tools and algorithms are needed to maintain system reliability and security within an acceptable level. The ANN, fuzzy logic, genetic algorithm, SVM and wavelet based techniques are quite successful but are not adequate for the present time varying network configurations, power system operating conditions and events. Therefore, it seems that there is a significant scope of research in AI techniques which can simplify the complex nonlinear systems, realize the cost effective hardware with proper modification in the learning methodology and preprocessing of input data which are computationally much simpler. Also development of reliable software and communication system will

pave the way for better relaying and fault location performance using multi terminal synchronized phasor measurement based on GPS. This article is an effort to present the most comprehensive set of references on the subject of recent techniques in transmission line protection.

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