FUZZY LOGIC FOR SWITCHING FAULT DETECTION OF INDUCTION MOTOR DRIVE SYSTEM Sumy Elizabeth Varghese 1 and Reema N 2 1 PG Scholar, Sree Buddha College of Engineering,Pattoor,kerala 2 Assistance.Professor, Sree Buddha College of Engineering,Pattoor,kerala Abstract Induction motors are mainly preferred in many applications. But these machines are subjected to different kinds of faults. But the manufacturers as well as the users of the induction motor drives are concerned about the reliability of the drives when used in an application. Thus it has become extremely important that any faults occurring in these motor should be detected at the earliest. In this paper a 5HP squirrel cage induction motor was simulated in MATLAB and the different kinds of switching faults were analyzed using fuzzy logic. Keywords Induction Motor, Switching fault, IGBT fault, DC link capacitor, fuzzy logic I. INTRODUCTION Three phase induction motor drive system has become the workhorse in many industries and about 8% of the mechanical output in these industries is provided by induction motors. The reliable operation of these drives ensures energy efficiency and maximum financial benefit in the industry. But these motors are used in many harsh processes and hence they are subjected to many kinds of faults. The faults in an induction motor can be classified into switching fault and machine fault. Machine faults can again be classified into electrical faults, mechanical faults and environment related faults. Switching faults occurs in rectifier diodes, DC link capacitor and IGBT switches of the inverter [1]. Electrical faults consist of unbalanced faults, under voltage and overvoltage. The rotor winding failure, stator winding failure and bearing faults are included in mechanically related faults whereas external moisture, contamination in the ambient temperature which affects the motor performance is termed as environmental faults [3]. The machine when subjected to these kinds of faults is thus having an adverse impact on the motor performance. So the fault has to be detected by some means. There are many techniques to detect these faults.out of the many available techniques fuzzy logic is used in this work. Fuzzy logic is mainly preferred since multiple faults that come under switching fault has to be detected. The work is explained in three phases. The first phase explains the causes and effects of various switching faults in an induction motor drive system. The simulation of three phases squirrel cage induction motor drive system is included in the second phase.the fuzzy logic fault detection are explained in the third phase. II. INFLUENCE OF SWITCHING AND ELECTRCAL FAULTS ON INDUCTION MOTOR Power electronics part is considered as the weakest component in drive system and about 38% of the induction motor faults are due to switching faults. In this section the causes and effects of various switching faults on induction motor performance is described. Switching faults may occur due to many reasons which include electrical stress due to stored charge carriers, maximum reverse current, faulty base drive system, manufacturing defects, ageing on capacitor, loose connections, abnormal transients etc. It can result in reduced performance of motor, increase in temperature thereby resulting in an increase in stator current or may even result in shut down of the motor. @IJMTER-216, All rights Reserved 289
Current(A) Speed(RPM) Volume 3, Issue 6, [June 216] ISSN (Online):2349 9745; ISSN (Print):2393-8161 Figure 1. Possible Switching faults The various switching faults discussed in this work are rectifier diode short circuit fault(f1),rectifier diode open circuit (F2),DC link capacitor short circuit (F3),DC link capacitor earth fault (F4),IGBT short circuit fault(f5),igbt open circuit fault (F6).All these faults have negative impact on the motor performance. III. SIMULATION OF INDCUTION MOTOR DRIVE SYETEM A 5hp three phase voltage source inverter driven induction motor was simulated in MATLAB SIMULINK[5] and it was made to work at a load of 75% of the rated load. Under normal condition the speed was observed to be 1418 RPM and the current as 1.5 A. For the purpose of simulation a breaker switch was used which was made either to open or close at an instant of.5 sec. So the changes in the value of waveform after.5 sec were used for the fault detection. 16 14 12 1 Speed -Time Characterstics 8 6 4 2-2.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Fig 2. Speed response of induction motor drive under normal condition 7 6 Current -Time Characterstics 5 4 3 2 1.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Fig 3. Current response of induction motor drive under normal condition The induction motor when simulated with diode short circuit fault (F1) and diode open circuit fault (F2) distortions in current was observed. Similarly there was decrease in the speed when the fault was applied at.5 sec [8]. @IJMTER-216, All rights Reserved 29
Speed (RPM) Current (A) Current (A) Current(A) Volume 3, Issue 6, [June 216] ISSN (Online):2349 9745; ISSN (Print):2393-8161 7 6 5 Current-Time Characterstics 4 3 2 1 7 6.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 4. Current response during fault F1 Current Characteristics (A) (A) 5 4 3 2 1.2.4.6.8 1 1.2 1.4 1.6 1.8 2 When the capacitor is shorted at an instant of.5 sec the voltage across the dc link become zero which causes the speed of the motor to go to zero Due to short circuit a high magnitude of current flows at the instant of fault. Fig 5.7 shows that when a capacitor short circuit fault (F3) is applied at.5 sec and there was an increase in the current at that instant. 7 6 Figure 5. Current response during fault F2 Current Time Characterstics 5 4 3 2 1.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 6. Current response during fault F3 15 1 5-5 -1-15 -2-25 -3.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 7. Speed response during fault F3 DC capacitor earth fault (F4) result in distortion of current and decrease in speed as seen in the fig 8 and 9. @IJMTER-216, All rights Reserved 291
Current (A) Speed (RPM) Current (A) Speed (RPM) Volume 3, Issue 6, [June 216] ISSN (Online):2349 9745; ISSN (Print):2393-8161 2 15 1 5-5.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 8. Speed response during fault F4 7 Current Characterstics 6 5 4 3 2 1.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 9. Speed response during fault F4 IGBT short circuit (F5) is considered as the severe fault as it results in a decrease of speed to zero value as well as an increase in current to a very high value. 16 14 12 1 8 6 4 2-2 -4.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 1. Speed response during fault F5 7 6 5 4 3 2 1.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 11. Speed response during fault F5 @IJMTER-216, All rights Reserved 292
Current (A) Speed (RPM) Volume 3, Issue 6, [June 216] ISSN (Online):2349 9745; ISSN (Print):2393-8161 Compared to IGBT short circuit fault (F6), the effect of IGBT open circuit fault is not so severe that it will not result in stop of operation of the motor. It causes a decrease in motor speed as shown in fig 12 and an increase in current as in fig 13 16 14 12 1 Speed Characteristics 8 6 4 2-2.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 12. Speed response during fault F6 7 6 Current Characterstics 5 4 3 2 1.2.4.6.8 1 1.2 1.4 1.6 1.8 2 Figure 13. Speed response during fault F6 IV.FUZZY LOGIC FAULT DETECTION Fuzzy logic is an approach to computing based on degrees of truth rather than the usual true or false. Thus fuzzy logic is a form of many valued logic in which the truth values may be a real number from to 1. ie, it s a concept of partial truth where the truth value range from a completely true to completely false case. Three major steps are involved in the fault detection process[9]. Fuzzification: The process of converting crisp inputs into fuzzy variables is called fuzzification process. In this work the input variables are stator currents,, and Speed N. The output variable is the motor condition. Figure 14. Output Membership Function @IJMTER-216, All rights Reserved 293
Volume 3, Issue 6, [June 216] ISSN (Online):2349 9745; ISSN (Print):2393-8161 Fuzzy Inference Engine: The fuzzy inference engine consists of fuzzy rule base and fuzzy implications. Rules are established and relation between different input and outputs are established using the rules created. Various linguistic variables are created to provide a means of systematic manipulation of vague and imprecise concepts.. The term set T(MC), interpreting motor condition, T(MC)={N,DS,DO,CS,CE,IOF,ISF}.Here the input variable stator currents are interpreted as linguistic variables with T(I)={VL,L,N,BM,M,H,VH,VVH} and speed as T(N)={VVS,VS,S,VVL,VL,L,M,N,H}.Fuzzy membership function are also built so that the value of current ranges from to 6A and that of speed ranges from to 15 RPM. Figure 15:Fuzzy Rule viewer for capacitr short circuit. Defuzzification: In this process the fuzzy variables are converted to crisp inputs. There are various methods available for defuzzification. In this work centre of area method is used. V. CONCLUSION This paper presents fault detection of induction motor drive system using fuzzy logic. Modeling and simulation was done in MATLAB. Among the different kinds of faults IGBT fault was found to be the most severe fault which has severe impact on the motor performance. REFERENCES [1] Reema. N., Mini V. P., and S.Ushakumari. Switching fault detection and analysis of induction motor drive system using fuzzy logic, Conference Proceedings ICAGE 214. [2] Mini V. P., and S. Ushakumari. "Incipient fault detection and diagnosis of induction motor using fuzzy logic." Recent Advances in Intelligent Computational Systems (RAICS), 211 IEEE. IEEE, 211. [3] Sreeja V, Mini.V.P, Sreedevi.G, Fault Analysis of Induction Motor Drive System using Maxwell-Simplorer, Proceedings of 32nd IRF International Conference, 12th July 215 [4] D. Kastha et al., Investigation of fault modes of voltage-fed inverter system for induction motor drive, IEEE Trans. Industry Applications, vol. 3, n 4, July-August 1994, pp. 426-43 [5] Ozpineci, Burak, and Leon M. Tolbert. "Simulink implementation of induction machine model-a modular approach." Electric Machines and Drives Conference, 23. IEMDC'3. IEEE International. Vol. 2. IEEE, 23. [6] Aleck W. Leedy. "Simulink / MATLAB Dynamic Induction Motor Model for Use as A Teaching and Research Tool ",International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-237, Volume-3, Issue-4, September, 213 [7] Mat Siddique, Effects of Voltage Unbalance on Induction Motors, Conference Record of the 24 IEEE International Symposium on Elecnical Insulation, Indianapolis, IN USA, 19-22 September 24. [8] Dr. P.S. Bimbra, Power Electronics, Khanna publications, Fourth edition 212 [9] Dr. K. Sundareswaran. A learner s guide to fuzzy logic systems. Jaico publishing house @IJMTER-216, All rights Reserved 294