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The Online Journal on Electronics and Electrical Engineering (OJEEE) Vol. (1) – No. (2) On-Line Trained Adaptive Neuro-Fuzzy Inference System for Distance Relay of Transmission line Protection Tamer S. Kamel M. A. Moustafa Hassan Electrical Power and Machines Department, Faculty of Engineering, Cairo University. Corresponding E-Mail: mmustafa@eng.cu.edu.eg Abstract-This paper presents a new distance relay commercial developments. However, both these approaches technique for transmission line protection by using well are based on deterministic computations on a well defined known control technique; Adaptive Neuro-Fuzzy model of the system to be protected. This results in difficulty Inference System (ANFIS). The ANFIS can be viewed in taking system variation into account as the rules are fixed. either as a fuzzy system, a neural network or fuzzy neural They do not have the ability to adapt dynamically to the network FNN. The structure is seen as a neural network system operating conditions, and to make correct decisions if for training and a fuzzy viewpoint is utilized to gain the signals are uncertain. Recently, intelligent soft insight into the system and to simplify the model. The computational techniques such as Artificial Neural Network integration with neural network technology enhances (ANN), Fuzzy Inference System (FIS) and (ANFIS) can fuzzy logic systems on learning capabilities. The model superiority of human knowledge features. They also integration with neural network technology enhances re-establish the process without plenty of analysis. Thus these fuzzy logic systems on learning capabilities. It also techniques are attracting great attention in an environment provides a natural framework for combining both that is obvious with the absence of a simple and well-defined numerical information in the form of input/output pairs mathematical model. Besides, these models are characterized and linguistic information in the form of IF–THEN rules by nonrandom uncertainties which associated with in a uniform fashion. The proposed technique is imprecision and elusiveness in real-time systems [3-4]. Many accomplished by one ANFIS that achieves accurate and researchers have studied the application of neural networks to fast estimation of distance to fault from the relay point. overcome most of the problems above outlined. The fuzzy set The normalized positive sequence impedance of the three theory is also used to solve uncertainty problems [5-11]. The phases are considered as inputs to the network The input use of neural nets in applications is very sparse due to its data of the ANFIS were firstly derived from the implicit knowledge representation, the prohibitive fundamental values of the voltage and current computational effort and so on. The key benefit of fuzzy logic measurements after making Fourier transform. Computer is that its knowledge representation is explicit, using simple simulation results are shown in this paper and they IF-THEM relations. However, it is at the same time its major indicate this approach can be used as an effective tool for limitation. The power system operation in transient period location of faults for different fault conditions in fault cannot be easily described by artificial explicit knowledge, inception time, fault impedance, fault distance and fault because it is affected by many unknown parameters. The types integration of neural network into the fuzzy logic system makes it possible to learn from the prior obtained data sets. Keywords: Fuzzy Neural Networks FNN, Adaptive Neuro This research employs adaptive-network-based fuzzy Fuzzy Inference System ANFIS, fault location, inference system for fault location in the transmission lines. transmission line protection. This novel approach overcomes the difficulties associated with conventional voltage and current based measurements I. INRODUCTION for transmission line protection algorithms. These difficulties are due to effect of factors such as fault inception time, fault Protection of transmission lines is very important for impedance and fault distance. This research is integrating the preserving of the power system. With the advent of learning capabilities of neural network to the robustness of microprocessors and digital electronics, digital-based relaying fuzzy logic systems in the sense that fuzzy logic concepts are has been developed since the late 1960s. Research activity has embedded in the network structure. It also provides a natural covered virtually every protection technique. Furthermore, framework for combining both numerical information in the many novel algorithms and associated hardware form of input/output pairs and linguistic information in the implementations have emerged. The fundamentals of form of IF–THEN rules in a uniform fashion. transmission lines protection were developed many years ago [1-2]. Some of them such as representing transmission lines by either first- or second order differential equations and traveling-wave techniques have resulted in several Reference Number: W09-0013 73 The Online Journal on Electronics and Electrical Engineering (OJEEE) Vol. (1) – No. (2) II. ADAPTIVE NEURO FUZZY INFERENCE It has no rule sharing. Different rules do not share SYSTEM (ANFIS) the same output membership function, namely the number of output membership functions must be A neural network which can perform pattern matching task equal to the number of rules. has a large number of highly interconnected processing It has unity weight for each rule. elements (nodes). These elements demonstrate the ability to learn and generalize from training patterns. Distributed Figure (1) shows the architecture of the ANFIS, comprising representation and strong learning capabilities are the major by input, fuzzification, inference and defuzzification layers. features of neural network. On the other hand, decisions using The network can be visualized as consisting of inputs, with N fuzzy logic systems are based on inputs in the form of neurons in the input layer and F input membership functions linguistic variables. These linguistic variables are derived for each input, with F*N neurons in the fuzzification layer. from membership functions which are formulas used to There are F^N rules with F^N neurons in the inference and determine the fuzzy set to which a value belongs and the defuzzification layers and one neuron in the output layer. degree of membership in that set. The variables are then matched with the specific linguistic IF-THEN rules and the Input inputmf rules outputmf response of each rule is obtained through fuzzy implication. Output To perform compositional rule of inference, the response of each rule is weighted according to the values or degree of membership of its inputs and the centroid of response is calculated to generate the appropriate output. Neural network has the shortcoming of implicit knowledge representation. However, fuzzy logic systems are subjective and heuristic. The determination of fuzzy rules, input and output scaling factors and choice of membership functions depend on trial and error that makes the design of fuzzy logic system a time consuming task. These drawbacks of neural network and fuzzy logic systems are overcome by the integration between the neural network technology and the fuzzy logic systems. The ANFIS could be viewed as a fuzzy system, a neural network or fuzzy neural network. The structure is seen as a neural network for training and a fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The Figure (1): The architecture of the ANFIS neuro-adaptive learning method works similarly to that of For simplicity, it is assumed that the fuzzy inference system neural networks. Neuro-adaptive learning techniques provide under consideration has two inputs x and y and one output z a method for the fuzzy modeling procedure to learn as shown in Figure (1). For a zero-order Sugeno fuzzy model, information about a data set. It computes the membership a common rule set with two fuzzy if-then rules is the function parameters that best allow the associated fuzzy following: inference system to track the given input/output data. A Rule 1: If x is A1 and y is B1, Then f1=r1 (1) network-type structure similar to that of a neural network can Rule 2: If x is A2 and y is B2, Then f2 = r2 (2) be used to interpret the input/output map. So it maps inputs Here the output of the ith node in layer n is denoted as On,i: through input membership functions and associated parameters. This will be translated through output Layer 1 membership functions and associated parameters to outputs. Every node i in this layer is an adaptive node with a node The parameters associated with the membership functions function: changes through the learning process. The computation of O1,i=μAi(x) for i=1,2,3 or (3) these parameters (or their adjustment) is facilitated by a O1,i =μBi-3(y) for i=4,5,6 (4) gradient vector. This gradient vector provides a measure of how well the fuzzy inference system is modeling the Where x (or y) is the input to node i and Ai (or Bi) is a input/output data for a given set of parameters. When the linguistic label associated with this node. In other words, O1,i gradient vector is obtained, any of several optimization is the membership grade of a fuzzy set A1, A2 and A3 (or B1, routines can be applied in order to adjust the parameters to B2 and B3) and it specifies the degree to which the given reduce some error measure. This error measure (performance input x (or y) satisfies the quantifier A (or B). Here the index) is usually defined by the sum of the squared difference membership function for A (or B) is triangular membership between actual and desired outputs. ANFIS uses a function and is given as: combination of least squares estimation and back propagation 1 if u c L for membership function parameter estimation. The suggested ANFIS has several attributes: Left : u \ L 0 ,1 c L u (5) The output is zero th order Sugeno-type system. Max otherwise 0.5 w L It has a single output, obtained using weighted average defuzzification. All output membership functions are constant. Reference Number: W09-0013 74 The Online Journal on Electronics and Electrical Engineering (OJEEE) Vol. (1) – No. (2) 0 ,1 c u wi. fi Max otherwise w1 0 .5 w O4 i i Centers : u \ f1 wi C (6) w1 w2 w3 Max 0 , 1 u c if u c i 0 .5 w w2 w3 f 2 f3 (11) w1 w2 w3 w1 w2 w3 1 otherwise Right : u \ Max 0 , 1 u c R R (7) As w1, w2 and w3 are assumed to be constant. Therefore, if u c R 0 .5 w L equation (2) can be rewritten as follows: L Notice that for Equation (5) c specifies the “saturation point” O4,i =c1.r1+c2.r2+c3.r3 (12) and wL specifies the slope of the nonunity and nonzero part of where μL as shown in Figure (2) Similarly, for μR. For μC notice that w1 c is the center of the triangle and w is the base-width. cL, cR, c, c1 (13) w1 w2 w3 wL, wR, and w are the parameters set. As the values of these parameters change, the triangular function varies accordingly, w2 c2 (14) thus exhibiting various forms of membership functions for w1 w2 w3 fuzzy set A. Parameters in this layer are referred to as premise w3 parameters. c3 (15) w1 w2 w3 u This is linear in the consequent parameters r1, r2, and r3. From this observation, It can be concluded that: S = set of total parameters, S1 = set of premise (nonlinear) parameters, S2 = set of consequent (linear) parameters Therefore the overall output will be: Figure (2) : Input triangular membership functions Layer 2 O4,i = F(i, S) (16) Every node in this layer is a fixed node whose output is the Where i is the vector of input variables, F is the overall product of all the incoming signals: function implemented by the adaptive network, and S is the set of all parameters which can be divided into two sets: O2,i= wi = μAi (x) μBi (y) i=1,2,3 (8) Each node output represents the firing strength of a rule. S = S1 S2 (17) Where represents direct sum. Layer 3 Every node i in this layer is an adaptive node with a node Therefore, the hybrid learning algorithm can be applied function: directly. More specifically, the error signals propagate backward and the premise parameters are updated by O3,i = wi fi = wi ri i=1,2,3 (9) Gradient Descent (GD) and node outputs go forward until Where ri is the parameter set of this node. Parameters in this layer 3 and the consequent parameters are identified by the layer are referred to as consequent parameters. Least Squares (LS) method. This hybrid learning is organized as follows: Layer 4 a) Linear and nonlinear parameters are distinguished The single node in this layer is a fixed node which computes b) Each iteration (epoch) of GD update the nonlinear the overall output as the summation of all incoming signals: parameters c) LS method follows to identify the linear parameters. wi f i Overall output = O4i i i=1,2,3 (10) III. SIMULATION ENVIROMENT wi i The simulation environment based on MATLAB software From the ANFIS architecture shown in Figure (1), it is package [12] is selected. It is used as the main engineering observed that when the values of the premise parameters are tool for performing modeling and simulation of power fixed, the overall output can be expressed as a linear systems and relays, as well as for interfacing the user and combination of the consequent parameters. In symbols, the appropriate simulation programs. ATP [13] is used for final output in Layer 4 can be rewritten as: detailed modeling of power network and simulation of interesting events. It possesses excellent power networks modeling capabilities, exceptional libraries of elements and provides fast and accurate simulation results. Scenario setting and neural network relaying algorithm will be implemented in MATLAB and interfaced with the power network model Reference Number: W09-0013 75 The Online Journal on Electronics and Electrical Engineering (OJEEE) Vol. (1) – No. (2) implemented in ATP. MATLAB has been chosen due to ii) All type of faults (i.e. single phase to ground, phase to availability of the powerful set of programming tools, signal phase, double phase to ground or three-phase fault) processing, numerical functions, and convenient user-friendly iii) Inception fault time (Tf) 2 msec interface. In this specially developed simulation environment, iv) Fault resistances (Rf) 0, 25, 50 and 100 ohms. the evaluation procedures can be easily performed. There are 444 training data. The input data to the ANFIS of So the power system model was simulated and the different the locating unit are the impedances of the three phases fault situations were performed by using ATP. Then the (magnitude and phase i.e. 6 inputs) after dividing them by voltage and current measurements have been sent to their non fault values. They are taken from the fundamental MATLAB to demonstrate the ANFIS protective relay. values of the voltage and current measurements after evaluating Fourier transform every 20 msec. The output data IV. THE PROTECTION SCHEME from the ANFIS are the normalized fault distance value. A single line diagram for the protected transmission line c) The ANFIS Locator: (T.L) is illustrated in Figure (3). It consists of two circuits The ANFIS locator consists of six neurons in the input layer of 80 km length, 66 kV voltage level and 2 GVA short (i.e. N=6), four triangular membership functions for each circuit level. input (i.e. F=4), and constant membership function for the output. d) Testing Data: The testing data are chosen at different fault conditions which are carried out at different fault distances, different fault Figure (3): Single line diagram for the Transmission line resistances, different fault inception times and different fault types which are not chosen for the training data. Besides that The overall protection scheme can be demonstrated as in a white noise in introduced in the testing data to model the Figure (4). Where: errors in the voltage and current measurements. Some of the simulation results are shown in Table 1. Vabc (VFabc) and Iabc (IFabc) are the instantaneous Table 1 can be explained as follows; the first three columns values of the three phase's voltage and current are: respectively (at fault condition). Fault inception time (Tf); V*abc (VF*abc) and I*abc (IF*abc) are the Fault resistance (Rf); and fundemantal compontents (peak values and the Fault type respectively. phases) of the three phases voltage and current respectively after Fourier transformation (at fault Then the next six columns are impedances (magnitude and condition). phase) of the three phases and these six values are used as Z*abc (ZF*abc) are the fundemantal compontents input to the ANFIS detector. Then target fault distance (Df (magnitudes and the phases) of the three phases p.u), finally the output of the ANFIS locator is shown in the impedances (at fault condition). next column which is the estimated per unit fault distance and IoF is the zero sequence current at fault condition. the final column is the percentage error between the accurate CU is the control unit that receives the outcomes of value and the estimated one where: the two units and only activates the fault classifier Dactual Destimated % Error * 100% (18) block diagram when a fault is detected. Dtotal The testing data is chosen taking into consideration the faults on transmission lines are quite random in nature with respect to the time of occurrence, location, type and fault resistance. So, the testing data are taken randomly with random fault distances, fault resistances, fault inception times and fault types in each training vector. Figure (4): The proposed protection Layout V. CONCLUISON a) Fault Locating Unit: A new digital distance relaying technique based on ANFIS The fault Location unit is built at different situations of all technique has been developed. ANFIS as control technique fault types (i.e. single line to ground, double lines, double was used to implement this relay. The relay has been tested lines to ground and three lines fault). After that, it is tested for different fault resistances, fault locations, fault types and using different situations of the faulted power system. different system conditions. In all these test cases, the b) Training data maximum error was found to be less than 8%. The proposed The training data used to train the ANFIS of the fault location relaying technique has the ability to provide accurate and unit are taken at: vigorous estimation for the fault distance in transmission i) Fault distance (Df) 5%, 10%, 15%, 20%, 30%, 40%, lines. 50%, 60%, 70% and 80% Reference Number: W09-0013 76 The Online Journal on Electronics and Electrical Engineering (OJEEE) Vol. (1) – No. (2) Table 1: Testing data of the Fault Location Unit and their percentage errors. fault Za Zb Zc Df Tf Rf type p.u Za ph p.u Zb ph p.u Zc ph p.u % Error 0.013 91 DL 0.98 142.3 2.59 147.1 1.38 -250 0.76 1.2 0.007 54 TL 0.47 21 0.67 25.3 0.61 21.3 0.56 5.4 0.002 78 DLG 0.09 75.4 1.02 138.7 0.14 5.9 0.79 3.6 0.012 22 TL 0.17 15.4 0.21 -344 0.19 20 0.4 2.5 0.006 86 DL 1 -220 4.95 94.9 1.03 92.8 0.51 0.5 0.014 18 DLG 0.01 -8.7 0.01 128.4 0.8 -141 0.05 2.8 0.003 61 DL 1.03 134.3 1.15 4.2 0.53 69.8 0.18 2.1 0.002 0 DLG 1.06 144.4 0.09 5.3 0.06 56.2 0.56 1.9 0.012 100 DLG 0.58 103 0.03 -1 0.02 -272. 0.15 1.1 0.002 37 TL 0.3 16.5 0.39 18.4 0.36 19.9 0.5 2 0.003 90 DL 0.55 70.4 1 141.5 2.51 -8.7 0.15 3.6 0.016 27 SLG 0.53 37 0.98 143.7 0.98 -209 0.39 3.7 0.009 60 DL 1 -10.4 0.64 75 0.99 -206 0.35 5.4 0.001 66 SLG 1.01 53.1 0.98 145.2 0.97 -209 0.15 0.6 0.006 58 DL 0.59 66.5 1 141.2 2.67 -26.3 0.53 1.1 0.004 66 DLG 0.03 -6.7 0.02 82.9 0.92 -169 0.17 0.9 [10] Transactions on Power Delivery, Vol. 13, No. 1, pp. VI. REFERENCE 102-108, 1998. [11] Huisheng Wang and W. W. Keerthipala, "Fuzzy-Neuro [1] Sunil S. Rao, "Switchgear and Protection", 10th Approach to Fault Classification for Transmission Edition, KHANNA Publishers, Delhi, 1994. Line Protection" IEEE Transactions on Power [2] W. Mark Carpenter "IEEE Guide for Protective Relay Delivery, Vol. 13, No. 4, pp.1093-1104, 1998. Applications to Transmission Lines", IEEE Std [12] M. Jayabharata Reddy and D.K. Mohanata, C37.113, 1999. "Performance Evaluation of Adaptive Network Based [3] Jacek M. Zurada, "Introduction to Artificial Neural Fuzzy Inference System Approach for Location of Systems",1st Edition, PWS Publishing Company, Faults on Transmission Lines Using Monte Carlo Boston, 1995. Simulation" This paper has been accepted for [4] Kevin M. Passino and Stephen Yurkovich, "Fuzzy publication in a future issue of IEEE journal, but has Control", 1st Edition, Addison Wesley Longman, Inc., not been fully edited, 2007. California, 1998. [13] MATLAB R2008a, 2008 The Math Works, [5] Dalia Farouk Mohamed, "A New Design of an Inc. (MATLAB and Simulink are registered Intelligent Digital Distance Protective Relay" PhD trademarks of The Math Works, Inc.) Dissertation Submitted to the Office of Graduate [14] ATP Draw version 3.5 Studies of Cairo University, 2007. [6] Slavko Vasilic, "Fuzzy Neural Network Pattern Recognition Algorithms For Classification Of The events In Power System Network", Ph. D. Dissertation Submitted to the Office of Graduate Studies of Texas A&M University 2004. [7] Abeer Galal Saad, "Digital Relaying of High Voltage Transmission Lines by Artificial Neural Networks", Master Dissertation Submitted to the Office of Graduate Studies of Cairo University 2004. [8] P. K. Dash, A. K. Pradhan, and G. Panda "A Novel Fuzzy Neural Network Based Distance Relaying Scheme", IEEE Transactions on Power Delivery, Vol. 15, No. 3, pp.902-907, 2000 [9] D. V. Coury and D. C. Jorge, "Artificial Neural Network Approach to Distance Protection" IEEE Reference Number: W09-0013 77

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