1. A Novel Integrated Protective Scheme for Transmission Line Using ANFIS Faculty of Engineering, Minia University, Minia, Egypt Slide 1 of 51

2. To implement a novel application of ANFIS approach to fault detection, classification and location in transmission lines a proposed computer program based on Matlab software to calculate all ten types of shunt faults that may occur in a transmission line should be presented first. This work divided into two papers The first paper concerns with Modeling and Computer Simulation of Fault Calculations for Transmission Lines. The second paper concerns with A Novel Integrated Protective Scheme for Transmission Line Using ANFIS. Slide 2 of 51

3. In the first paper: Part I This paper presents a proposed computer program based on Matlab software to calculate all ten types of shunt faults that may occur in a transmission line. Various fault scenarios (fault types, fault locations and fault impedance) are considered in this paper. The inputs of the proposed program are line length, source voltage, positive, negative and zero sequence for source impedance, line charging, and transmission line impedance. The output of the algorithm is used to train an artificial intelligence networks to detect, classify and locate transmission lines faults. Simulation results have shown the effectiveness of the algorithm under the condition of all types of shunt faults. Slide 3 of 51

4. Presents a novel application of ANFIS approach to fault detection, classification and location in transmission. Three ANFIS’s have been proposed to provide an integrated protective scheme for the transmission line. The first ANFIS has been proposed to detect all shunt faults. In the Second paper: Part II Slide 4 of 51 The last one has been proposed to determine the distance of the fault from sending end. The second ANFIS has been proposed to identify the type of faults.

5. Methodology Figure 1. Faulted Transmission Line The fault formula for balanced and various unbalanced faults are summarized below:- II-I Modeling of SLG Faults Using Z bus Figure 2. Single-line-to-ground fault at point F Slide 5 of 51

6. The conditions at the fault bus 3 (point F) are expressed by the following equations:- I fb =0, I fc =0 (1) The following equations give relationships between sequence components of fault currents at the fault point. The fault line current at bus 3 can be calculated as follows [2,3,4,5]: V Pre f (0):The prefault voltage at point F (bus 3) and can be calculated by using two port network as shown in Fig. 3 Slide 6 of 51

7. Figure 3. Power system in SLG fault Slide 7 of 51

8. II-III Modeling of DL Faults Using Z bus [2,5] Figure 4. Double line fault at point F The following relations must be satisfied at the fault point I fa =0, I fb = -I fc The symmetrical components of fault current is (15) The symmetrical components of fault current is Slide 8 of 51

9. II-III Modeling of DLG Faults Using Z bus [2,5] Figure 5. Double line to ground fault at point F The symmetrical components of fault current is Slide 9 of 51

10. The fault line currents can be obtained from Equation Where T is known as symmetrical components transformation matrix II-IV Modeling of three-phase Faults Using Z bus Figure 6. A balanced three-phase fault at point F Slide 10 of 51

11. The symmetrical components of fault current are given by the following Equation:- The fault line currents can be obtained from Equation (6) II-V Modeling of Sending Voltage and Line current during Fault [5] Using sequence components of the fault currents the symmetrical components of the sending end bus voltage during fault can be obtained by the following Equations Slide 11 of 51

12. Where Z 1, Z 2 and Z 0 : The matrix impedance of transmission line per unit length. Vsa (0): The prefault phase voltage at sending end. The phase voltages at sending end during fault are Slide 12 of 51

13. The symmetrical components of fault current in line 1 to 3 is given by The line fault currents from Bus 1 to Bus 3 (point F) can be obtained as follows:- Slide 13 of 51

14. Computer Simulation of Fault Calculation The flow chart of the proposed computer program is shown in Fig. 7. The proposed computer program simulates various faults for different fault conditions. i.e. a-g, b-g, c-g, ab, bc, ca, ab-g, bc-g, ca-g, and abc fault) based on Zbus. The condition parameters that have been taken into account for each fault type are: 1) Variation of fault impedance, [0: 200] (Ω). 2) Variation of fault angle, [0: 90] (degree). 3) Variation fault locator [1:200] km. Slide 14 of 51

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16. Application and Results The faulted transmission line is represented by distributed parameters. As an application, a 200 km overhead transmission line with the parameters of the transmission line model of Fig. 1 is as follows Source voltages: Source S: V 1 = 400 kV; source R: V 2 = 400 kV. Source impedance (both sources): Positive sequence impedance = 1.31 + j15.0; Zero sequence impedance = 2.33 + j26.6; Frequency = 50 Hz; Transmission line impedance: Positive sequence impedance = 8.25 + j94.5; Zero sequence impedance = 82.5 + j308; Positive sequence capacitance = 13 nF/km; Zero sequence capacitance = 8.5 nF/km. The significant parameters above are selected based on in real operations [8]. Slide 16 of 51

17. Influence of the Fault Distance and Fault Impedance Figure 8. Influence of Fault Impedance, Fault distance at Fault current For Single Line-to-ground Fault at  =10 o. Slide 17 of 51

18. Figure 9. Influence of Fault Impedance, Fault distance at Fault voltage For DLG Fault at  =10 o Slide 18 of 51

19. Figure 10. Influence of Fault Impedance and Fault distance at Fault current For DL Fault at  =10 o. Slide 19 of 51

20. Figure 11. Influence of Fault Impedance, Fault distance at Fault current For Three phase Fault at  =10 o. Slide 20 of 51

21. Figure 15. Influence of Fault Impedance, Fault distance at Fault voltage For Three phase Fault at  =10 o Slide 21 of 51

22. Figure 25. Influence of Fault distance and Fault inception angle at Fault current For DLG Fault at Zf =30 ohm. Slide 22 of 51

23. Figure 20. Influence of Fault Impedance and Fault distance at Fault voltage For SLG Fault at Lf =5km. Slide 23 of 51

24. Figure 21. Influence of Fault Impedances and Fault distance at Fault voltage For DLG Fault at Lf =5km. Slide 24 of 51

25. Figure 22. Influence of Fault Impedance and Fault distance at Fault voltage For DL Fault at Lf=5km. Slide 25 of 51

26. Figure 27. Influence of Fault distance, Fault inception angle at Fault current For Three phase Fault at Zf =30 ohm. Slide 26 of 51

27. Figure 29. Influence of Fault distance and Fault inception angle at Fault voltage For DLG Fault at Zf=30 ohm. Slide 27 of 51

28. C ONCLUSIONS This paper presents a computer package to perform transmission line fault analysis based on Z bus methods along with the symmetrical components method. From the results obtained, the salient conclusions of this paper are:- 1. Calculates the fault conditions and to provide protective equipment designed to isolate the faulted zone from the reminder of the system in the appropriate time. 2. Presents a highly accurate transmission line simulation technique which utilized to calculate voltages and currents at the relay location (Sending end S) for different fault types, fault conditions and different power system data. 3. Calculate faults along different line lengths. The results show that the method is suitable for design a protective scheme for transmission line based on artificial intelligence. As the method is easy applicable and it is flexible, it can be used for modeling other any transmission lines Slide 28 of 51

29. A Novel Integrated Protective Scheme for Transmission Line Using ANFIS In the Second paper: Part II This paper presents a novel application of ANFIS approach to fault detection, classification and location in transmission lines using measured data from one terminal of the transmission line. Three ANFIS’s have been proposed to provide an integrated protective scheme for the transmission line. The first ANFIS has been proposed to detect all shunt faults. The second ANFIS has been proposed to identify the type of faults. The last one has been proposed to determine the distance of the fault from sending end. Slide 29 of 51

30. ANFIS Algorithm The ANFIS is a fuzzy Sugeno model of integration where the final fuzzy inference system is optimized via the ANNs training. The ANFIS makes use of a hybrid learning rule to optimize the fuzzy system parameters of first- order Sugeno system, which can be graphically represented by Fig. 1. It maps inputs through input membership functions and associated parameters, and then through output membership functions to outputs. Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Fig. 1. ANFIS architecture for a two-input, two-rule first-order Sugeno model. For a first-order Sugeno fuzzy model, a typical rule set with two fuzzy if-then rules can be expressed as [18]: Rule 1 If x 1 is A 1 and x 2 is B 1, then y 1 = p 1 x 1 + q 1 x 2 +r 1, Rule 2 If x1 is A 2 and x 2 is B 2, then y 2 = p 2 x 1 + q 2 x 2 +r 2, Where [A 1,A 2, B 1, B 2 ] are called the premise parameters. [p i, q i, r i ] are called the consequent parameters, i =1,2. The consequent parameters (p, q, and r) of the n th rule contribute through a first order polynomial of the form: Slide 30 of 51

31. ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using either a back propagation algorithm alone, or in combination with a least squares type of method. ANFIS is much more complex than the fuzzy inference systems, and is not available for all of the fuzzy inference system options. The following is a layer by layer description of a two input two rule first-order Sugeno system. Layer 1. Generate the membership grades: Layer 2. Generate the firing strengths. Each node in this layer calculates the firing strengths of each rule ز The outputs of this layer can be represented as Layer 3. Normalize the firing strengths. The i th node of this layer calculates the ratio of the i th rule’s firing strength to the sum of all rules’ firing strengths: Slide 31 of 51

32. Layer 4. Calculate rule outputs based on the consequent parameters. The output of each node in this layer is simply the product of the normalized firing strength and a first-order polynomial. Thus, the outputs of this layer are given by Layer 5. Sum all the inputs from layer 4. There is only single fixed node labeled with ∑. This node performs the summation of all incoming signals. Hence, the overall output of the model is given by Slide 32 of 51

33. F AULT D ETECTION, CLASSIFICATION AND L OCATION ALGORITHM Power System under Study To evaluate the performance of the proposed ANFIS integrated protective scheme, Let us consider a faulted transmission line extending between two sources as shown in Fig. 2 is considered in this study Fig. 2. Faulted transmission line. PT: Potential Transformer, CT: Current Transformer, CB: Circuit-Breaker, FD: Fault Detector, FC: Fault Classification, FL: Fault Locator. Slide 33 of 51

34. Fault Detection, Classification and Location Methodology Using ANFIS In Fig. 2, the input data are measured based-on RMS values for current in phase a, phase b and measured RMS values for voltage for phase a and phase b. ANFIS employ the theory of fuzzy sets and fuzzy if-then rules to derive outputs. The outputs extracted form ANFIS used not only in discriminating between transmission line healthy and/or faulty states but also used in classifying fault type and determine its location. Three ANFIS’s were trained and tested in this paper to provide fault detection, classification and location for the transmission line. The process of generating outputs of the ANFIS’s are depicted in Fig. 3. Fig. 3. Process for generating input patterns to the ANFIS. Slide 34 of 51

35. The first ANFIS used for fault detector (FD). The ANFIS for fault detector output is indexed with either a value of 1 (the presence of a fault) or 0 (the non-faulty situation). The second ANFIS is used to identify the type of fault located in the first protection zone of the transmission line covering 100% of the line length from the sending end data only and classify the fault (i.e., a-g, b-g, c-g, a-b, b-c, c-a, a-b-g, b-c-g, c-a-g). To classify the fault, the following methodology has been adopted. Initially, in order to represent the fault type correctly, a binary coding system has been developed. In this coding system, a 4-b binary number is used to represent the type of fault. Thus, for a line-to-ground (a-g) fault, the 4-b number would be 0- 0-0-1. Similarly, for a line-to-line (b-c) fault, the corresponding 4-b number would be 0-1-1-0. Similarly, this 4-b binary number also represents the other types of fault. The complete binary coding system and equivalent decimal numbers for representing all possible types of faults is given in Table 1 Slide 35 of 51

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37. Typically an ANFIS scheme performs its action in several steps including. - Fuzzification (comparing the input values with membership functions to obtain membership values of each linguistic term for computing the truth values of the premise of each rule in the rule base. - Fuzzy reasoning (firing the rules and generating their fuzzy or crisp consequents), - Defuzzification (aggregating rule consequents to produce a crisp output). Fig. 4. Fuzzy inference systems for Fault detection, Classification and location Slide 37 of 51

38. Application of ANFIS and Results The design process of the ANFIS fault detector, classifier and locator go through the following steps: 1- Generation a suitable training data. In order to use the ANFIS technique for fault detection, classification and location the input parameters limit should be determined precisely. The input parameters are obtained from recording devices sparsely located at sending end in a power system network. Examples of recording devices may include digital fault recorders (DFR), digital relays, or other intelligent electronic devices (IED).The output indicate where the fault occurred and classified. Due to limited available amount of practical fault data, it is necessary to generate training/testing data using simulation. To generate data for the typical transmission system, a computer program have been designed to generate training data for different faults. 2- Selection of a suitable ANFIS structure for a given application. Various ANFIS are designed to accurately detect, classify and locate all types of faults on transmission lines. 3- Training the ANFIS. Various network configurations were trained in order to establish an appropriate network with satisfactory performances. The ANFIS’s are trained to detect presence of fault, classify fault and finally where the fault position is. 4- Evaluation of the trained ANFIS using test patterns until its performance is satisfactory. When Network is trained, ANFIS’s should be given an acceptable output for unseen data. When output of test pattern and network’s error reached an acceptable range then, fuzzy system is adjusted in the best situation which means the membership functions and fuzzy rules are well adjusted. All of these steps above are done off-line and when the structure and parameters of ANFIS are adjusted, it can be used as an on-line fault detector, classifier and locator. Slide 38 of 51

39. 4.1. ANFIS FOR F AULT DETECTOR AND C LASSIFIACTION Fig. 9. Results of Testing ANFIS for fault Detector Fig. 10. Relation between RMS Error and Number of Test cases Slide 39 of 51

40. Fig. 11. Membership function of Inputs Variable for Fault detection Fig. 12. Structure of ANFIS for Fault Detection Slide 40 of 51

41. The structure of an ANFIS with four inputs and one output is shown in Fig. 12. The ANFIS has the following design parameters: -Type - Sugeno, -Gaussian and Generalized bell-shaped membership functions, -Two linguistic terms for each input membership function, -16 linear terms for output membership functions, -16 rules (resulting from number of inputs and membership function terms), -Fuzzy operators: product (and), maximum (or), product (implication), maximum (aggregation), average weight (defuzzification). There are 16 rules which are sufficient to assign a detector using ANFIS. Some of these rules are as follows: 1-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf1) (1) 2-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf2) then (Output is out1mf2) (1) 3-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf1) then (Output is out1mf3) (1) 4-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf4) (1) 5- If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf5) (1) 15-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf15) (1) 16- If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf16) (1) Slide 41 of 51

42. Fig. 13. Results of Testing ANFIS for Fault classification Fig. 14. Relation between RMS Error and Number of Test cases for Fault classification Slide 42 of 51

43. Fig. 15. Membership function of Input Variables for Fault Classification Slide 43 of 51

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45. Slide 45 of 51 The ANFIS has the following design parameters: Type - Sugeno, Gaussian and Generalized bell-shaped membership functions, Two and three linguistic terms for each input membership function, 36 linear terms for output membership functions, 36 rules (resulting from number of inputs and membership function terms), Fuzzy operators: product (and), maximum (or), product (implication), maximum (aggregation), average weight (defuzzification). There are 36 rules which are sufficient to assign a detector using ANFIS. S OME OF THESE RULES ARE AS FOLLOWS : If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf1) (1) If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf2) then (Output is out1mf2) (1) If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf1) then (Output is out1mf3) (1) If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf4) (1) If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf5) (1) If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf35) (1) If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf36) (1)

46. Fig. 17. Results of Testing ANFIS for Single Line-To-Ground fault Fig. 18. Relation between RMS Error and Number of Test cases for Fault Locator Slide 46 of 51

47. Fig. 19. Membership function of Input Variables for Fault Locator Fig. 20. Structure of ANFIS For fault locator Slide 47 of 51

48. C ONCLUSION This paper presented a novel application based on ANFIS for fault detection, classification and location in transmission line for integrated protective scheme. The proposed system consists of four procedures as in Fig. 1, Data acquisition and three ANFIS’s. In this work, the measured of RMS currents and voltages are utilized for detecting, classifying and locating the faults occurs on transmission line. From results obtained above, the following are the salient conclusions that can be drown from this paper: 1- A new computer program to simulate transmission line and calculated voltages and currents for each type of fault which used in training/testing ANFIS has been proposed. 2- Various tests in different fault condition of transmission line illustrate that this method is an accurate and has error less than 0.20%. The obtained results show that the proposed method gives good estimations. Slide 48 of 51

49. 3- The proposed ANFIS’s can be implemented for all types of shunt fault including high impedance and low impedance fault. 4- The ANFIS has the following design parameters for the configuration for detecting fault are: Type - Sugeno, Gaussian and Generalized bell-shaped membership functions, Two linguistic terms for each input membership function, 16 linear terms for output membership functions, 16 rules (resulting from number of inputs and membership function terms), 4- The ANFIS, which used for fault classification has the following design parameters: Type - Sugeno, Gaussian and Generalized bell-shaped membership functions, Two and three linguistic terms for each input membership function, 36 linear terms for output membership functions, 36 rules (resulting from number of inputs and membership function terms), Slide 49 of 51

50. 5- The ANFIS, which used for fault location has the following design parameters: Type - Sugeno, Gaussian membership functions, Three and Four linguistic terms for each input membership function, 144 linear terms for output membership functions, 144 rules (resulting from number of inputs and membership function terms), 6- The proposed methodology for output based on ANFIS can be used for integrated protective scheme of transmission line. Slide 50 of 51

51. Thanks for your attention and listening Slide 51 of 51