Journal for Research| Volume 02| Issue 03 | May 2016 ISSN: 2395-7549
Fuzzy Logic Approach for Fault Diagnosis of Three Phase Transmission Line (March 2016) Farhin R. Sheikh M.E Student Department of Electrical Engineering MGITER Navsari
Dr. Ami T. Patel Head of Department Department of Electrical Engineering MGITER Navsari
Abstract Transmission line among the other electrical power system component suffers from unexpected failure due to various random causes. Because transmission line is quite large as it is open in environment. A fault occurs on transmission line when two or more conductors come in contact with each other or ground. This paper presents a proposed model based on MATLAB software to detect the fault on transmission line. Fault detection has been achieved by using Fuzzy Logic based intelligent control technique. The proposed method aims in presenting a fast and accurate fault diagnosis method to classify and identify the type of fault which occurs on a power transmission system. In this paper, some of the unconventional approaches for condition monitoring of power systems comprising of relay Breaker, along with the application of soft computing technique like fuzzy logic. Results show that the proposed methodology is efficient in identifying fault in transmission system. Keywords: Transmission Line Faults, Fuzzy Logic, Transmission Lines Protection _______________________________________________________________________________________________________ I.
INTRODUCTION
The objective of the faulted section diagnosis method is to identify faulted components in the power station e.g. generation units, power transformers, autotransformers, service transformers, buses and lines that based on the status of protective relays and circuit breakers. To reduce the outage time and ensure stable and reliable supply for electric power for customers, it is essential for control centers to quickly identify the faulted section in power system prior to start restoring actions. Therefore, the operators must have the capability to estimate and restore the faulted section in an optimal procedure. An effective diagnosis system is required to suggest the possible way to remove faults and assist the operator to protect the systems. Recently, the possibility of implementing the heuristic rules using expert systems has motivated extensive works on the application of expert systems in fault diagnosis. Considerable efforts have been made toward developing fault diagnosis system. Most of these efforts are based on Expert Systems (ES) [1 – 3]. Although ES based approach offers powerful solutions to the fault diagnosis, but it has shortcomings, e.g. the procedure of knowledge acquisition and knowledge base revision or maintenance is quite burdensome. In addition, dealing with the large amount of data is difficult due to the conventional knowledge representation and inference mechanisms. During the last two decades, much research work has been done for estimating the fault section diagnosis in a power system by using several artificial intelligence approaches. Such as, artificial neural networks [4, 5], genetic algorithm (GA) [6], fuzzy Petri nets [7,8], family eugenics based evolution theory [9] and immune algorithm [10]. However, the only wor k addressing the power plant control and fault diagnosis [11] that aimed to control and supervision the plant system control of the station but not related to the protection system of all station through generation units, transformers, buses and lines. Since there are some wrong and missed signals in a power system, which may be caused by data transmission error or loss, in addition to mal operation and no operation of circuit breakers or relays, uncertainty reasoning is highly recommended to diagnose the system's faulted section. Among the existing uncertainty reasoning approaches, the fuzzy relations approach is accurate, which applied on the power system that include the transmission lines and bus bars [12]. There are 11 possible fault types resulting from the different combinations of three phases and the ground. The fault resistance range from a few ohms, as occur in cases of arc between phases, to hundreds of ohms, as occur in cases when a fallen conductor touches a dry surface. The fault resistance has considerable effect on the accuracy of fault location algorithms. Over current relays and fuses are responsible for isolating permanent faults, leading to customer outages. Transmission protection systems are designed to identify the location of faults and isolate only the faulted section. The key challenge to the transmission line protection lies in reliably detecting and isolating faults compromising the security of the system. The appropriate percentage of occurrence of various types of faults is given below: 1) Single line to ground fault – 70-80% 2) Line-to-Line to ground fault – 10 -17% 3) Line-to- Line fault – 8-10% 4) Three phase fault - 3 %
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Fuzzy Logic Approach for Fault Diagnosis of Three Phase Transmission Line (March 2016) (J4R/ Volume 02 / Issue 03 / 001)
Thus, a well-coordinated protection system must be provided to detect & isolate various types of faults rapidly so that damage & disruption caused to power system is minimized. And, the time required in determining the fault point along the transmission line affects the quality of power to be delivered. II. PROBLEM FORMULATION The commonly model used for AC overhead transmission lines is called pi model network, where shunt admittance has been even divided into two shunt elements connecting to both ends of a pi equivalent network. A 735 KV, 600km long transmission line system connects with generator having capacity of 1500 MW is used to develop and implement the proposed architectures and algorithms for this problem. Transmission Line is also loaded with resistive as well as inductive loads. The simulation model of three phase transmission line is shown in fig. 1.
Fig. 1: Block Diagram of proposed scheme
For a modern power system, high speed fault clearance is very critical and to achieve these objective various techniques have been developed. III. CONVENTIONAL CONTROL TECHNIQUE (RELAY BREAKER CONTROL) The fault Diagnosis of Electrical power system is a process of discriminating the faulted system element by protective relays and subsequent tripping by Circuit Breaker. When a fault occurs in a power system, operators in a control center need to interpret a large amount of information. In such an emergency condition, it is difficult for the operators to quickly identify the actual fault location. Fault type and malfunctioning type of protective devices such as relays and breakers. Consequently, there is a need for decision making support to help the operator to take proper actions following a fault event in the power system. Thus, Good Fault Diagnosis method can provide accurate and effective diagnostic information to dispatch operator and help them to take necessary measures in fault situation so as to guarantee secure and stable operation of the electrical power system network. The techniques for protection of transmission lines can be broadly classified into the following categories 1) _ Impedance measurement based methods 2) _ Travelling-wave phenomenon based methods 3) _ High-frequency components of currents and voltages generated by faults based methods 4) _ Intelligence based method From quite a few years, intelligent based methods are being used for protection of transmission line. IV. INTELLIGENT CONTROL TECHNIQUE (FUZZY LOGIC CONTROL) Fuzzy logic control (FLC) [14] has gained much interest in the application of system control for the past few decades. It has a real time basis as a human type operator, which makes decision on its own basis. The controller can incorporate easily the dynamic changes of the system due to the operating point shifting; hence it can tackle efficiently nonlinearity of any system. Zadeh has developed the concept of fuzzy logic. A FIS can utilize human expertise by storing its essential components in knowledge base, and perform fuzzy reasoning to infer the overall output value. However, there is no systematic way to transform experiences of knowledge of human experts to the knowledge base of a FIS. For building a FIS, the fuzzy sets, fuzzy operators and the Knowledge base has to be specified. The main feature of FLC is that it is governed by symbolic rules (generally if-then rules) and qualitative fuzzy variables and values. It deals with linguistic variables. Fuzzy logic approximates the relation between variables regardless of their analytical dependence. The fuzzy controller is composed of the following four elements: 1) A rule-base (a set of if-then rules), which contains a fuzzy logic quantification of the expert’s linguistic description of how to achieve good control. 2) An inference mechanism (also called a fuzzy inference engine), which emulates the expert’s decision making in interpreting and applying knowledge about how best to Control the plant.
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Fuzzy Logic Approach for Fault Diagnosis of Three Phase Transmission Line (March 2016) (J4R/ Volume 02 / Issue 03 / 001)
3) A fuzzification interface, which converts controller inputs into information that the inference mechanism can easily use to activate and apply rules. 4) A defuzzification interface, which converts the conclusions of the inference mechanism in to actual inputs for the process. For Fuzzy Logic controller design, three phase Transmission Line Currents and Voltages (in all three phases Ia, Ib, Ic and Va, Vb, Vc) can be observed and have been chosen as the input signal of the fuzzy. The response of different types of fault in the system could be evaluated by examining the response curve of these six variables in output curves. The output of the fuzzy logic controller is different 11 types of fault both symmetrical an unsymmetrical. The eleven membership functions for fuzzy variable are listed below: Table – 1 Membership functions for fuzzy variables NEL Negative Extra Large NVL Negative Very Large NL Negative Large NM Negative Medium NS Negative Small ZE Zero PEL Positive Extra Large PVL Positive Very Large PL Positive Large PM Positive Medium PS Positive Small
A set of rules which define the relation between the inputs and output of fuzzy controller can be found using the available knowledge in the area of designing Fault Diagnosis. These rules are defined using the linguistic variables. The six inputs, voltages (Va, Vb, Vc) and Currents (Ia, Ib, Ic), results in 117for each transmission system. The typical rules are having the following structure. Total 117 rules can be developed according to this rule base. But it is not convenient to take all rules because it takes more simulation time. Thus from that 117 rules, I have selected 11 rules which gives better performance under wide range of operating condition.
Va PEL PVL PL PM PS ZE NS NM NL NVL NEL
Table – 2 Rule base table for fuzzy logic controller INPUT VECTOR FAULT TYPE Vb Vc Ia Ib Ic PEL PEL PEL PEL PE PEL (A-G ) PVL PVL PVL PVL PV PVL (B-G ) PL PL PL PL PL PL (C-G ) PM PM PM PM PM PM (AB) PS PS PS PS PS PS (BC) ZE ZE ZE ZE ZE ZE (AC) NS NS NS NS NS NS (AB-G) NM NM NM NM NM NM (BC-G) NL NL NL NL NL NL (AC-G ) NVL NVL NVL NVL NVL NVL (ABC) NEL NEL NEL NEL NEL NEL (ABC-G)
V. RESULT AND DISCUSSION Phase A Phase B Phase C
Phase A
30
Phase B
20
Phase C
10 0 -10 -20 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Time in Second Fig. 1: Current Response of System for SLG Fault (Phase A)
0.2
40
Current in PU
Current in PU
40
Phase A
20
Phase A Phase B Phase C
Phase B Phase C
0 -20 -40 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time in Second Fig. 2: Current Response of System for SLG Fault (Phase B)
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Fuzzy Logic Approach for Fault Diagnosis of Three Phase Transmission Line (March 2016) (J4R/ Volume 02 / Issue 03 / 001) 60
Phase A
20
Phase B
Phase A Phase B Phase C
Phase C
0 -20 -40 0
0.02
0.04
0.06
0.08
0.1
0.12
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0.18
Phase A Current in PU
Current in PU
40
40
-20 0.02
0.04
0.06
Current in PU
Phase A Phase B
Phase A Phase B Phase C
Phase C
-20
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Current in PU
40
0.02
0 -20 0.06
0.08
0.1
0.12
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Current in PU
Current in PU
Phase A Phase B Phase C
Phase C
0.04
Phase A Phase B Phase C
Phase C
-40 0.02
0.04
0.06
0.08
0.1
0.12
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Phase A Phase B Phase C
20
0.2
Phase A Phase B Phase C
0 -20 -40 0
0.2
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time in Second
Fig. 7: Current Response of System for LLG fault (phase AB – G)
Fig. 1: Current Response of System for LLG fault (phase BC – G)
Phase A
Phase A Phase B Phase C
Phase B 0
Phase C 0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time in Second Fig. 9: Current Response of System for LLG fault (phase AC – G)
Current in PU
60
50
Current in PU
0.2
-20
Time in Second
-50 0
0.18
0
40
Phase A Phase B
0.02
0.16
Fig. 6: Current Response of System for LL fault (phase AC)
60
-40 0
0.14
Time in Second
Fig. 5: Current Response of System for LL fault (phase BC)
20
0.12
Phase A Phase B
20
-60 0
0.2
Time in Second
40
0.1
Fig. 4: Current Response of System for LL fault (phase AB)
0
-40 0
0.08
Time in Second
Fig. 3: Current Response of System for SLG Fault (Phase C)
20
Phase C
0
Time in Second
40
Phase A Phase B Phase C
20
-40 0
0.2
Phase B
Phase A Phase B Phase C
Phase A Phase B
40 20 0 -20
Phase C
-40 -60 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time in Second Fig. 10: Current Response of System for LLL fault (phase ABC)
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Fuzzy Logic Approach for Fault Diagnosis of Three Phase Transmission Line (March 2016) (J4R/ Volume 02 / Issue 03 / 001)
Current in PU
60 Phase A Phase B Phase C
Phase A Phase B
40 20
Phase C
0 -20 -40 -60 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Time in Second Fig. 2: 3 Current Response of System for LLL G fault (phase ABCG)
VI. CONCLUSION The proposed method uses a FUZZY based scheme for fast and reliable fault detection. Various Asymmetric fault (Single line to ground and double line to ground fault) are simulated and an Breaker based algorithm is used for detection of these faults. Performance of the proposed scheme is evaluated using various fault types and encouraging results are obtained. The simplicity of this design based on fuzzy logic, causes a drastic reduction in loss on distribution systems due to prolonged outages of feeder downtime during faulted conditions. As shown in above simulation fuzzy logic can reduce isolation time of fault. Further work can be carried out by developing detection system to detect other asymmetric and symmetric fault. REFERENCES Basic format for books: [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]
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