Application of Neural Networks in Power Quality

2015 International Conference on Soft Computing Techniques and Implementations- (ICSCTI)

 ABSTRACT: Use of power electronic converters with nonlinear loads produces harmonic currents and reactive power. A shunt active power filter provides an elegant solution to reactive power compensation as well as harmonic mitigation leading to improvement in power quality. However, the shunt active power filter with PI type of controller is suitable only for a given load. If the load is varying, the proportional and integral gains are required to be fine tuned for each load setting. The present study deals with neural network based controller for shunt active power filter. The performance of neural network controller evaluated and compared with PI controller.

 KEYWORDS:

  1. Active Power Filter
  2. Neural Networks
  3. Back Propagation Algorithm
  4. Soft Computing.

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Schematic Diagram of Shunt Active Power Filter

Fig1. Schematic Diagram of Shunt Active Power Filter

  

EXPECTED SIMULATION RESULTS:

 

Fig 2. (a) Waveform of Load Current, Compensating Current, Source Current and Source Voltage for 1kVA with 􀄮=60º and (b) Waveform of Source Voltage and in the phase Source Current of Fig. (a)

 

CONCLUSION:

The active power filter controller with neural network based controller has been seen to eminently minimize harmonics in the source current when the load demands non sinusoidal current, irrespective of whether the load is fixed or varying. Simultaneously, the power factor at source also becomes the unity, if the load demands reactive power. Thus, neural network based controller is far superior to PI type of controller which requires fine tuning of Kp and Ki every time the load changes. In the present work, the performance of a range of values of the load is considered to robustly test the controller. It has been demonstrated that neural network based controller, therefore, significantly improves the performance of a shunt active power filter.

 

REFERENCES:

  • Laszlo Gyugyi, “Reactive Power Generation and Control by Thyristor Circuits”, IEEE Transactions on Industry Applications, vol. IA-15, no. 5, September/October 1979.
  • Akagi, Y. Kanazawa, and A. Nabae, “Instantaneous reactive power compensators comprising switching devices without energy storage components,” IEEE Transaction Industrial Applications, vol. IA-20, pp. 625-630, May/June 1984.
  • Z. Peng, H. Akagi, and A. Nabae, “A study of active power filters using quad series voltage source pwm converters for harmonic compensation,” IEEE Transactions on Power Electronics, vol. 5, no. 1, pp. 9–15, January 1990.
  • Conor A. Quinn, Ned Mohan, “Active Filtering of Harmonic Currents in Three-phase, Four-Wire Systems with Three-phase and Single-phase Non-Linear Loads”, IEEE-1992.
  • A. Morgan, J. W. Dixon, and R. R. Wallace, “A three-phase active power filter operating with fixed switching frequency for reactive power and current harmonic compensation,” IEEE Transactions on Industrial Electronics, vol. 42, no. 4, pp. 402–408, August 1995.

Improved Dynamic Performance of Shunt Active Power Filter Using Particle Swarm Optimization

2017 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNIQUES IN CONTROL, OPTIMIZATION AND SIGNAL PROCESSING

 ABSTRACT: In this paper, a novel particle swarm optimization (PSO) technique is proposed to tune the proportional-integral (PI) controller gain parameters for enhancing the dynamic performance of the shunt active power filter (APF). The shunt APFs are well established filter to compensate current harmonics, reactive power to maintain the power factor unity. The compensation is highly influenced by the DC-link voltage regulation. The calculated PI controller gain parameters conventionally, are giving satisfactory results under steady state condition of the load. However, tuning of the PI controller parameters under fast changing loads are very difficult. To improve the dynamic performance of the system and optimize the gain parameters of the PI controller, a PSO technique is proposed. The modified p-q theory uses a composite observer filter to extract fundamental component of voltage from the distorted supply voltage for the further process of calculating reference current. A complete comparison of conventional and PSO based PI controller gain tuning have been simulated using MATLAB® Simulink software under different supply voltage and load condition of the system. The results show that the dynamic response is improved with PSO based PI tuning compared to conventional PI tuning.

 KEYWORDS

  1. Shunt Active power filters (SAPF)
  2. PI controller
  3. Particle swarm optimization (PSO)

SOFTWARE: MATLAB/SIMULINK

BLOCK  DIAGRAM:

Fig. 1 Optimal design of PI controller gain values using PSO

EXPECTED SIMULATION RESULTS

Fig. 2. Performance of modified p-q control technique under available supply voltage

Fig. 3 FFT analysis of phase a source current under distorted supply voltage

 

Fig. 4 Simulation results under distorted supply voltage with RC-load

Fig. 5 Harmonic spectrum of phase-a source current after Compensation

Fig. 6 Simulation dynamic performance of the shunt APF

Fig.7 Tuning of PI controller: (a) conventional PI method (b) using PSO technique

 CONCLUSION

The performance of the proposed PSO based modified p-q theory has been designed for different types of loads and supply voltage conditions. The modified composite observer filter is an extracted fundamental frequency component of voltage from distorted supply without phase delay which further processed in the calculation of the reference current. The comparison of conventional PI tuning and PSO based tuning is tested for dynamic condition of the load. The proposed control scheme is modelled in MATLAB simulink environment. The simulation results show that the PSO based tuning provide less overshoot, ripples in the DC-link voltage and lesser settling time as compared to convention PI tuning.

 REFERENCES:

  • S. Adamu, H. S. Muhammad and D.S. Shuaibu, “Harmonics Assessment and Mitigation in Medical Diagnosis Equipment”, IEEE international conference on Awerness Science and Technology (iCAST), pp. 70-75, 2014.
  • Akagi, “Active harmonic filters,” Proc. IEEE, Vol. 93, no.12, pp.2128-2141, pp.2128-2141, 2005.
  • H. Bollen, Understanding Power Quality Problems: Voltage Sags and Interruptions, John Wiley & Sons, 1999.
  • Akagi, E. H. Watanabe, and M. Aredes, Instantaneous Power Theory and Applications to Power Conditioning, Piscataway, NJ: IEEE Press, 2007.
  • Gupta, S. P. Singh and S. P. Dubey “Neural network based shunt active filter for harmonic and reactive power compensation under non-ideal mains voltage,” In proc. of IEEE Industrial Electronics and Applications (ICIEA), Taiwan, pp. 370-375, 2010.

Particle Swarm Optimization Based Shunt Active Harmonic Filter for Harmonic Compensation

2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON)

ABSTRACT: This paper presents a performance evaluation of Shunt Active Harmonic Filter (SAHF) for harmonic compensation, using Particle Swarm Optimization algorithm for DC link voltage regulation. Particle Swarm Optimization algorithm is used to search for the optimal PI control parameters. The simulation results show that the performance of Shunt Active Harmonic Filter (SAHF), where current is generated using instantaneous real and reactive power(p-q) theory, using PSO technique for six pulse controlled rectifier under different firing angles is simple in structure and very effective for harmonic compensation. The simulation is done with the help of MATLAB-SIMULINK tool box.

KEYWORDS

  1. Shunt Active Harmonic Filter
  2. PI controller
  3. Hysteresis Current Controller
  4. P-q theory
  5. PSO
  6. Controlled rectifier

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig. 1. Proposed implementation of PI controller

EXPECTED SIMULATION RESULTS

Fig. 2. Convergence graph of PSO for 􀍲􀍲􀀃firing angle

Fig. 3. FFT analysis of source current (phase a) without SAHF.

Fig. 4. FFT analysis of source current (phase a) of SAHF for 􀍲􀍲firing angle.

Fig. 5. FFT analysis of source current (phase a) of optimized SAHF for

􀍲􀍲firing angle.

 CONCLUSION

It can be concluded from the simulation results that with the application of SAHF in parallel to controlled rectifier, harmonics present in the source current are mostly compensated. The DC link voltage is controlled by PI controller, which when optimized using Particle Swarm Optimization Technique further reduces the THD value of source current. The values of THD in phase a, b and c of source current are 30.18%, 31.54%, 31.74% respectively. Further it is analyzed that by optimizing the gains of PI controller the THD values are further reduced from 2.66% to 1.85% for 􀍲􀍲firing angle. Thus we can clearly state that optimization of PI controller using PSO further reduces the harmonics on the source side.

 

REFERENCES:

[1] M.H.J. Bollen, “What is Power Quality?”, Electric Power Systems Research, Vol.66, Iss. 1, pp. 5-14, July 2003.

[2] H. Akagi, Y. Kanazawa and A. Nabae, ”Theory of Instantaneous Reactive Power and Its Applications”, Transactions of the lEE-Japan, Part B, vol. 103, no.7, 1983, pp. 483-490.

[3] Ned Mohan 2002, ‘Power Electronics: Converters, Applications, and Design’ 3rd Edition’, Wiley publications.

[4] F. Z. Peng, H. Akagi and A. Nabae, “A New Approach to Harmonic Compensation in Power System a Combined System of Shunt Passive and Series Active Filter”, IEEE Trans. On Industry App., vol. 27, no. 6, (1990), pp. 983-990.

[5] Hamadi,A , Rahmani,S & Al-Haddad, K 2010, ‘A hybrid passive filter configuration for VAR control and harmonic compensation’, IEEE Trans. Ind. Electron., 57(7): 2419–2434.

An Intelligent Speed Controller for Indirect Vector Controlled Induction Motor Drive

 

ABSTRACT:

This paper presents the speed control scheme of indirect vector controlled induction motor (IM) drive. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PI controller or Fuzzy-PI controller is employed in closed loop speed control. Decoupling of the stator current into torque and flux producing (d-q) current components model of an induction motor is involved in the indirect vector control. The torque component Iq current of an IM is developed by an intelligent based Fuzzy PI controller. Based on settling time and dynamic response the performance of Fuzzy Logic Controller is compared with that of the PI Controller to sudden load changes. It’s provides better control of motor torque with high dynamic performance. The simulated design is tested using various tool boxes in MATLAB. Simulation results of both the controllers are presented for comparison.

KEYWORDS:

  1. Indirect Vector Control (IVC)
  2. Space Vector Pulse Width Modulation (SVPWM)
  3. PI Controller
  4. Fuzzy Logic Controller (FLC)

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig.1 Block diagram of a proposed scheme

EXPECTED SIMULATION RESULTS:

Fig.2 Starting response

Fig.3 Step response

Fig.4 Speed response for with and without load impact

Fig.5 Torque response for with and without load impact

CONCLUSION:

In this paper the concept of fuzzy logic has been presented and the SVM based indirect vector controlled induction motor drive is simulated using both PI and Fuzzy PI controller. The results of both controllers under the dynamics conditions are compared and analyzed. The simulation result support that the FLC settles quickly and has better performance than when PI controller.

REFERENCES:

[1] Bimal K.Bose, “Modern Power Electronics and AC Drives”, Pearson education.

[2] Leonhand.W, ‘Control of Electrical Drives’, Springer Verlag 1990.

[3] Yang Li Yinghong, Chen Yaai and Li Zhengxi “A Novel Fuzzy Logic Controller for Indirect Vector Control Induction Motor

[4] Drive” Proceeding of the 7th World Congress on Intelligent and Automation Jun 25 – 27, 2008, Chongqing,China, pp. 24-28

[5] R.A. Gupta, Rajesh Kumar, S.V.Bhangale “Indirect Vector Controlled Induction Motor Drive with Fuzzy Logic based Intelligent Controller”, ICTES,UK,December 2007,pp.368-373.

 

 

A Comparative Study on the Speed Response of BLDC Motor Using Conventional PI Controller, Anti-windup PI Controller and Fuzzy Controller

 

ABSTRACT:

Brushless dc motors (BLDC) are widely used for various applications because of high torque, high speed and smaller size. This type of motors are non linear in nature and are affected highly by the non-linearities like load disturbance. Speed control of this motor is traditionally handled by conventional PI and PID controllers. This paper presents the speed control of BLDC motor using anti wind up PI controller. Problems like rollover can arise in conventional PI controller due to saturation effect. In order to avoid such problems anti wind up schemes are introduced. As the fuzzy controller has the ability to control and as it is simple to calculate, a fuzzy controller is also designed for speed control of BLDC motor. The control and simulation of BLDC motor have been done using software MATLAB/SIMULINK. The simulation results using anti wind up PI controller and fuzzy controller are compared with PI controller.

KEYWORDS:

  1. BLDC
  2. Speed response
  3. PI controller
  4. Fuzzy
  5. Anti windup

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig.1. Simulation block diagram

 EXPECTED SIMULATION RESULTS:

 

 Fig.2. Speed response under no load

Fig.3. Speed response for step increase in speed

Fig.4. Speed response for step increase in speed

Fig.5. Speed response under loaded condition

Fig.6.Speed response under load condition

CONCLUSION:

 This paper presents the speed control of BLDC motor using anti wind up PI controller and fuzzy controller for three phase BLDC motor. The simulation results are compared with PI controller results. The conventional PI controller results are slower compared to fuzzy and anti wind up controllers. From the simulation results, it is clear that for the load variation anti wind up PI controller gave better response than conventional PI and fuzzy controller. Hence anti wind up PI controller is found to be more suitable for BLDC motor drive during load variation. It can also be observed from the simulation results that performance of fuzzy controller is better during the case of speed variation.

REFERENCES:

[1] R. Arulmozhiyal, R. Kandibanv, “Design of Fuzzy PID Controller for Brushless DC Motor”, in Proc. IEEE International Conference on Computer Communication and Informatics, Coimbatore, 2012.

[2] Anirban Ghoshal and Vinod John, “Anti-windup Schemes for Proportional Integral and Proportional Resonant Controller”, in Proc. National Power electronic conference, Roorkee, 2010.

[3] M. F. Z. Abidin, D. Ishak and A. Hasni Abu Hassan, “A Comparative Study of PI, Fuzzy and Hybrid PI Fuzzy Controller for Speed Control of Brushless DC Motor Drive”, in Proc. IEEE International conference on Computer applications and and Industrial electronics, Malysia, 2011.

[4] J. Choi, C. W Park, S. Rhyu and H. Sung, “Development and Control of BLDC Motor using Fuzzy Models”,in Proc. IEEE international Conference on Robotics, Automation and Mechatronics, Chengdu, 2004.

[5] C. Bohn and D.P. Atherton, “An analysis package comparing PID anti-windup strategies,” IEEE Trans. controls system, Vol.15, No. 2, pp.34-40, 1995.

 

An Intelligent Speed Controller for Indirect Vector Controlled Induction Motor Drive

ABSTRACT:

This paper presents the speed control scheme of indirect vector controlled induction motor (IM) drive. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PI controller or Fuzzy-PI controller is employed in closed loop speed control. Decoupling of the stator current into torque and flux producing (d-q) current components model of an induction motor is involved in the indirect vector control. The torque component Iq current of an IM is developed by an intelligent based Fuzzy PI controller. Based on settling time and dynamic response the performance of Fuzzy Logic Controller is compared with that of the PI Controller to sudden load changes. It’s provides better control of motor torque with high dynamic performance. The simulated design is tested using various tool boxes in MATLAB. Simulation results of both the controllers are presented for comparison.

KEYWORDS:

  1. Indirect Vector Control (IVC)
  2. Space Vector Pulse Width Modulation (SVPWM)
  3. PI Controller
  4. Fuzzy Logic Controller (FLC)

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig.1 Block diagram of a proposed scheme

EXPECTED SIMULATION RESULTS:

 

Fig.2 Starting response

 Fig.3 Step response

 

Fig.4 Speed response for with and without load impact

Fig.5 Torque response for with and without load impact

CONCLUSION:

In this paper the concept of fuzzy logic has been presented and the SVM based indirect vector controlled induction motor drive is simulated using both PI and Fuzzy PI controller. The results of both controllers under the dynamics conditions are compared and analyzed. The simulation result support that the FLC settles quickly and has better performance than when PI controller.

REFERENCES:

[1] Bimal K.Bose, “Modern Power Electronics and AC Drives”, Pearson education.

[2] Leonhand.W, ‘Control of Electrical Drives’, Springer Verlag 1990.

[3] Yang Li Yinghong, Chen Yaai and Li Zhengxi “A Novel Fuzzy Logic Controller for Indirect Vector Control Induction Motor

[4] Drive” Proceeding of the 7th World Congress on Intelligent and Automation Jun 25 – 27, 2008, Chongqing,China, pp. 24-28

[5] R.A. Gupta, Rajesh Kumar, S.V.Bhangale “Indirect Vector Controlled Induction Motor Drive with Fuzzy Logic based Intelligent Controller”, ICTES,UK,December 2007,pp.368-373.

Mitigation of Voltage Sag and Swell in Transmission Line using DPFC with PI and Fuzzy Logic Control

 

ABSTRACT:

The Power Quality problems during the last two decades has been the major concern of the power companies. The operation of power systems has become complex due to growing consumption and increased number of non-linear loads because of which compensation of multiple power quality issues has become an compulsion. A new component within the flexible AC-transmission system (FACTS) family, called Distributed Power-flow controller (DPFC) is presented in this paper. DPFC is derived from the unified power-flow controller (UPFC). DPFC can be considered as a UPFC with an eliminated common dc link. The active power exchange between the shunt and series converters, which is through the common dc link in the UPFC, is now through the transmission lines at the third-harmonic frequency. The DPFC employs the distributed FACTS (D-FACTS) concept, which is to use multiple small-size single-phase converters instead of the one large-size three-phase series converter in the UPFC. Power quality issues are studied and DPFC is used to mitigate the voltage deviation and improve power quality. In this paper, the capability of DPFC is observed for the transmission line based on PI and fuzzy logic controllers (FLC). On comparing the two controllers performance, we can say that Fuzzy Logic Controller based DPFC gives better compensation than PI Controller based DPFC. Simulink models are developed with and without the controllers. The three phase fault is created near the load. Simulation results show the effectiveness between the two controllers.

KEYWORDS:

  1. Power Quality
  2. D-FACTS
  3. DPFC
  4. Voltage Sag
  5. Swell
  6. PI Controller
  7. Fuzzy Logic Controller

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig 1: The DPFC Structure

EXPECTED SIMULATION RESULTS:

 

Fig 2: Voltage Sag without DPFC

Fig 3: Current Swell without DPFC

Fig 4: THD without DPFC

Fig 5: Voltage sag Compensation with DPFC using PI Controller

Fig 6: Current Swell Compensation with DPFC using PI Controller

Fig 7: THD with DPFC using PI Controller

Fig 8: Voltage Sag Compensation with DPFC using Fuzzy Logic Controller

Fig 9: Current Swell Compensation with DPFC using Fuzzy Logic Controller

Fig 10: THD with DPFC using Fuzzy Logic Controller

CONCLUSION:

In this study mitigation of power quality issues like voltage sag and swell are simulated in Matlab/Simulink environment employing a new FACTS device called Distributed Power Flow Controller(DPFC). The DPFC is emerged from the UPFC and inherits the control capability of the UPFC, which is the simultaneous adjustment of the line impedance, the transmission angle, and the bus voltage magnitude. The common dc link between the shunt and series converters, which is used for exchanging active power in the UPFC, is eliminated. This power is now transmitted through the transmission line at the third harmonic frequency. The series converter of the DPFC employs the D FACTS concept, which uses multiple small single phase converters instead of one large size converter. The reliability of the DPFC is greatly increased because of the redundancy of the series converters. The total cost of the DPFC is also much lower than the UPFC, because no high voltage isolation is required at the series converter part and the rating of the components of is low. It is proved that the shunt and series converters in the DPFC can exchange active power at the third harmonic frequency, and the series converters are able to inject controllable active and reactive power at the fundamental frequency .Also the performance of DPFC is simulated using two mechanisms i.e., with PI and Fuzzy Logic controllers.The results prove that the DPFC with Fuzzy controller gives better voltage compensation than DPFC with PI controller.

REFERENCES:

[1] Zhihui Yuan, Sjoerd W.H de Haan, Braham Frreira and Dalibor Cevoric “A FACTS Device: Distributed Power Flow Controller (DPFC)” IEEE Transaction on Power Electronics, vol.25, no.10,October 2010.

[2] Krishna Mohan Tatikonda,N.Swathi,K.Vijay Kumar”A Fuzzy Control scheme for damping of oscillations in multi machine system using UPFC” International trends for emerging trends in engineering and development on September 2012

[3] Y. H. Song and A. Johns. Flexible ac transmission systems (FACTS). Institution of Electrical Engineers, 1999.

[4] ” Power quality improvement and Mitigation case study using Distributed Power Flow Controller “Ahmad Jamshidi ,S.Masoud Barakati and Mohammad Moradi Ghahderijani,IEEE Transactions on,2012

[5] N.G.Hingorani and L.Gyugyi, Understanding FACTS, Concepts and Technology of Flexible AC Transmission Systems. Piscataway, NJ: IEEE Press 2000

Speed Control of Induction Motor Using New Sliding Mode Control Technique

ABSTRACT

Induction Motors have been used as the workhorse in the industry for a long time due to its easy build, high robustness, and generally satisfactory efficiency. However, they are significantly more difficult to control than DC motors. One of the problems which might cause unsuccessful attempts for designing a proper controller would be the time varying nature of parameters and variables which might be changed while working with the motion systems. One of the best suggested solutions to solve this problem would be the use of Sliding Mode Control (SMC). This paper presents the design of a new controller for a vector control induction motor drive that employs an outer loop speed controller using SMC. Several tests were performed to evaluate the performance of the new controller method, and two other sliding mode controller techniques. From the comparative simulation results, one can conclude that the new controller law provides high performance dynamic characteristics and is robust with regard to plant parameter variations.

 

KEYWORDS:

  1. Induction Motor
  2. Sliding Mode Control
  3. DC Motors
  4. PI Controller

 

SOFTWARE: MATLAB/SIMULINK

 

BLOCK DIAGRAM:

Induction motor drive system with sliding mode controller

Fig. 1 Induction motor drive system with sliding mode controller

EXPECTED SIMULATION RESULTS:

                           Rotor speed tracking performance (b)Rotor speed tracking error (c)Control effort Rotor speed tracking performance (b)Rotor speed tracking error (c)Control effort Rotor speed tracking performance (b)Rotor speed tracking error (c)Control effort

Fig.2 (a)Rotor speed tracking performance  (b)Rotor speed tracking error   (c)Control effort

image005 image006 image007

Fig.3 (a)Rotor speed tracking performance  (b)Rotor speed tracking error   (c)Control effort

image008 image009 image010

Fig.4 (a)Rotor speed tracking performance  (b)Rotor speed tracking error   (c)Control effort

 

CONCLUSION

In this paper, new technique to reduced chattering for sliding mode control is submitted to design the rotor speed control of induction motor. To validate the performances of the new proposed control law, we provided a series of simulations and a comparative study between the performances of the new proposed sliding mode controller strategy and those of the Pseudo and Saturation sliding mode controller techniques. The sliding mode controller algorithms are capable of high precision rotor speed tracking. From the comparative simulation results, one can conclude that the three sliding mode controller techniques demonstrate nearly the same dynamic behavior under nominal condition. Also, from the simulation results, it can be seen obviously that the control performance of the new sliding mode controller strategy in the rotor speed tracking, robustness to parameter variations is superior to that of the other sliding mode controller techniques.

 

REFERENCES

  1. Wade, M.W.Dunnigan, B.W.Williams, X.Yu, ‘Position control of a vector controlled induction machine using slotine’s sliding mode control’, IEE Proceeding Electronics Power Application, Vol. 145, No.3, pp.231-238, 1998.
  2. I.Utkin, ‘Sliding mode control design principles and applications to electric drives’, IEEE Transactions on Industrial Electronics, Vol.40, No.1, pp. 23-36, February 1993.
  3. K.Namdam, P.C.Sen, ‘Accessible states based sliding mode control of a variable speed drive system’, IEEE Transactions Industry Application, Vol.30, August 1995, pp.373-381.
  4. Krishnan, ‘Electric motor drives: modelling, analysis, and control’, Prentice-Hall, New-Jersey, 2001.
  5. J.Wai, K.H.Su, C.Y.Tu, ‘Implementation of adaptive enhanced fuzzy sliding mode control for indirect field oriented induction motor drive’, IEEE International Conference on Fuzzy Systems, pp.1440-1445, 2003.

 

Speed Controller of Switched Reluctance Motor

ABSTRACT

Fuzzy logic control has become an important methodology in control engineering. The paper proposes a Fuzzy Logic Controller (FLC) for controlling a speed of SRM drive. The objective of this work is to compare the operation of P& PI based conventional controller and Artificial Intelligence (AI) based fuzzy logic controller to highlight the performances of the effective controller. The present work concentrates on the design of a fuzzy logic controller for SRM speed control. The result of applying fuzzy logic controller to a SRM drive gives the best performance and high robustness than a conventional P & PI controller. Simulation is carried out using Matlab/Simulink.

 

KEYWORDS: P Controller, PI Controller, Fuzzy Logic Controller, Switched Reluctance Motor

 

SOFTWARE: MATLAB/SIMULINK

 

BLOCK DIAGRAM

Block diagram of SRM speed control

Figure 1. Block diagram of SRM speed control

 

 SIMULATION MODELS

Simulation model using P controller

Figure 2. Simulation model using P controller

Simulation model using PI controller.

Figure 3. Simulation model using PI controller.

Simulink model using FLC.

Figure 4. Simulink model using FLC.

 

SIMULATION RESULTS

Output flux.

Figure 5. Output flux.

Output current

Figure 6. Output current

Output torque

Figure 7. Output torque.

Speed

Figure 8. Speed.

 

CONCLUSION

Thus the SRM dynamic performance is forecasted and by using MATLAB/simulink the model is simulated. SRM has been designed and implemented for its speed control by using P, PI controller and AI based fuzzy logic controller. We can conclude from the simulation results that when compared with P & PI controller, the fuzzy Logic Controller meet the required output. This paper presents a fuzzy logic controller to ensure excellent reference tracking of switched reluctance motor drives. The fuzzy logic controller gives a perfect speed tracking without overshoot and enchances the speed regulation. The SRM response when controlled by FLC is more advantaged than the conventional P& PI controller.

 

REFERENCES

  1. Susitra D, Jebaseeli EAE, Paramasivam S. Switched reluctance generator – modeling, design, simulation, analysis and control -a comprehensive review. Int J Comput Appl. 2010; 1(210):975–8887.
  2. Susitra D., Paramasivam S. Non-linear flux linkage modeling of switched reluctance machine using MVNLR and ANFIS. Journal of Intelligent and Fuzzy Systems. 2014; 26(2):759–768.
  3. Susitra D, Paramasivam S. Rotor position estimation for a switched reluctance machine from phase flux linkage. IOSR–JEEE. 2012 Nov–Dec; 3(2):7.
  4. Susitra D, Paramasivam S. Non-linear inductance modeling of switched reluctance machine using multivariate non- linear regression technique and adaptive neuro fuzzy inference system. CiiT International Journal of Artificial Intelligent Systems and Machine Learning. 2011 Jun; 3(6).
  5. Ramya A, Dhivya G, Bharathi PD, Dhyaneshwaran R, Ramakrishnan P. Comparative study of speed control of 8/6 switched reluctance motor using pi and fuzzy logic controller. IJRTE; 2012

 

 

 

Indirect Vector Control of Induction Motor Using Sliding-Mode Controller

 

ABSTRACT:

The paper presents a sliding-mode speed control system for an indirect vector controlled induction motor drive for high performance. The analysis, design and simulation of the sliding-mode controller for indirect vector control induction motor are carried out. The proposed sliding-mode controller is compared with PI controller with no load and various load condition. The result demonstrates the robustness and effectiveness of the proposed sliding-mode control for high performance of induction motor drive system.

 KEYWORDS:

  1. Indirect vector control
  2. Sliding mode control
  3. PI controller
  4. Induction motor
  5. Speed control

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

image001

Figure 1: Indirect vector controlled induction motor drive

EXPECTED SIMULATION RESULTS:

 image002

Figure 2: Speed response of PI controller at no load

image003

Figure 3:Speed response of Sliding-mode controller at no load

image004

Figure 4: Speed response of PI controller at load

image005

Figure 5: Speed response of Sliding- mode controller at load

image006

Figure 6:X-Y plot of Rotor flux of PI controller

image007

Figure 7: x-v plot of Rotor flux of Sliding-mode controller

CONCLUSION:

In this paper sliding-mode controller for the control of an indirect vector-controlled induction motor was described. The drive system was simulated with sliding-mode controller and PI controller and their performance was compared. Here simulation results shows that the designed sliding-mode controller realises a good dynamic behaviour of the motor with a rapid settling time, no overshoot and has better performance than PI controller. Sliding-mode control has more robust during change in load condition.

.REFERENCES:

[1] B.K Bose “Modern power electronics and ac drives “Prentice-Hall OJ India, New Delhi, 2008.

[2] M.Masiala;B.Vafakhah,;A.Knght,;J.Salmon,;”Performa nce of PI and fuzzy logic speed control of field-oriented induction motor drive,” CCECE , jul. 2007, pp. 397-400.

[3] F.Barrero;A.Gonzalez;A.Torralba,E.Galvan,;L.G.Franqu elo; “Speed control of induction motors using a novel Fuzzy-sliding mode structure,”IEEE Transaction on Fuzzy system, vol. 10, no.3, pp. 375-383, Jun 2002.

[4] H.F.Ho,K.W.E.Cheng, “position control of induction motor using indirect adaptive fuzzy sliding mode control,” P ESA, , Sep. 2009, pp. 1-5.

[5] RKumar,R.A.Gupta,S.V.Bhangale, “indirect vector controlled induction motor drive with fuzzy logic based intelligent controller,” IETECH Journals of Electrical Analysis, vol. 2, no. 4, pp. 211-216, 2008.