Fuzzy Efficiency Enhancement of Induction Motor Drive

 

ABSTRACT:

Efficiency improvement of motor drives is important not only from the viewpoints of energy loss and hence cost saving, but also from the perspective of environmental pollution. Several efficiency optimization methods for induction motor (IM) drives have been introduced nowadays by researchers. Distinctively, artificial intelligence (AI)-based techniques, in particular Fuzzy Logic (FL) one, have been emerged as a powerful complement to conventional methods. Design objectives that are mathematically hard to express can be incorporated into a Fuzzy Logic Controller (FLC) using simple linguistic terms. The merit of FLC relies on its ability to express the amount of ambiguity in human reasoning. When the mathematical model of a process does not exist or exists with uncertainties, FLC has proven to be one of the best alternatives to move with unknown process. Even when the process model is well-known, there may still be parameter variation issues and power electronic systems, which are known to be often approximately defined. The purpose of this paper is to demonstrate that a great efficiency improvement of motor drive can be achieved and hence a significant amount of energy can be saved by adjusting the flux level according to the applied load of an induction motor by using an on-line fuzzy logic optimization controller based on a vector control scheme. An extensive simulation results highlight and confirm the efficiency improvement with the proposed algorithm.

KEYWORDS:

  1. Induction Motor Drive
  2. Indirect Field Oriented Control (IFOC)
  3. Efficiency Enhancement
  4. Losses Minimization
  5. Optimization
  6. Fuzzy Logic

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig.1. Block diagram of the optimization system

 EXPECTED SIMULATION RESULTS:

Fig.2. Motor Performances Comparison

Fig.3. Motor efficiency evolution with motor load

 CONCLUSION:

This paper aims to improve the induction motor drive efficiency that leads to a significant amount of energy saving. This efficiency enhancement is carried out by adjusting the flux level depending on the applied load of an induction motor by using an on-line fuzzy logic optimization controller based on a vector control scheme. A series of the induction motor drive performances are obtained with a variable load under this proposed algorithm. The application of the proposed algorithm yields to a series of simulation performances of the induction motor drive with a variable load. They present the IM drive efficiency evolution with a certain load profile with the suggested losses minimization strategy based on fuzzy control and the conventional field oriented control. The comparison between these two control schemes reveals that the achieved results are of a great interest. Indeed, the fuzzy control contributes with a great deal to the efficiency improvement for all operating speeds particularly in light load region. This contribution conducts to a paramount energy saving and hence to environment protection.

REFERENCES:

[1] I. Boldea, A. Nasser, The Induction Machine Design Handbook, CRC Press Inc; 2nd Revised Edition, 2009.

[2] Jinchuan. Li and all, “A new Optimization Method on Vector Control of Induction Motors”, Electric Machines and Drives, 2005 IEEE International Conference, 15-18 May 2005, pp.1995-2001.

[3] H. Sepahv and, Sh. Ferhangi, “Enhancing Performance of a Fuzzy Efficiency Optimizer for Induction Motor Drives”, Power Electronics Specialists Conference, 2006. PESC ’06. 37th IEEE, 18-22 June.2006, pp.1-5.

[4] Branko Blanusa and all, “An Improved Search Based Algorithm for Efficiency Optimization in the Induction Motor Drives”, XLII Konferencija- za ETRAN, Hercy-Novi, 2003.

[5] D. S. Kirischen, D. W. Novoty and T. A. Lipo, “Optimal Efficiency Control of an Induction Motor Drive”, IEEE Transaction on Energy Conversion, Vol. EC-2, N° 1, March 1987, pp.70-76.

Adaptive Speed Control of Brushless DC (BLDC) Motor Based on Interval Type-2 Fuzzy Logic

 

ABSTRACT:

To precisely control the speed of BLDC motors at high speed and with very good performance, an accurate motor model is required. As a result, the controller design can play an important role in the effectiveness of the system. The classic controllers such as PID are widely used in the BLDC motor controllers, but they are not appropriate due to non-linear model of the BLDC motor. To enhance the performance and speed of response, many studies were taken to improve the adjusting methods of PID controller gains by using fuzzy logic. Use of fuzzy logic considering approximately interpretation of the observations and determination of the approximate commands, provides a good platform for designing intelligent robust controller. Nowadays type-2 fuzzy logic is used because of more ability to model and reduce uncertainty effects in rule-based fuzzy systems. In this paper, an interval type-2 fuzzy logic-based proportional-integral-derivative controller (IT2FLPIDC) is proposed for speed control of brushless DC (BLDC) motor. The proposed controller performance is compared with the conventional PID and type-1 fuzzy logic-based PID controllers, respectively in MATLAB/Simulink environment. Simulation results show the superior IT2FLPIDC performance than two other ones.

KEYWORDS:

  1. Brushless DC (BLDC) Motor
  2. Invertal Type-2 Fuzzy Logic
  3. Speed Control
  4. Self-tuning PID Controller

  SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Block Diagram of speed control of BLDC Motor

EXPECTED SIMULATION RESULTS:

Figure 2. Speed Deviation of BLDC Motor

Figure 3. Load Deviation of BLDC Motor

Figure 4. Torque Deviation of BLDC Motor

 CONCLUSION:

In this paper, the speed control of the BLDC motor is studied and simulated in MATLAB/Simulink. In order to overcome uncertainties and variant working condition, the adjustment of PID gains through fuzzy logic is proposed. In this study, three controller types are considered and compared: conventional PID, type-1 and type-2 fuzzy-based self-tuning PID controllers. The simulation results show that type-2 fuzzy PID controller has superior performance and response than two other ones.

REFERENCES:

[1] A. Sathyan, N. Milivojevic, Y. J. Lee, M. Krishnamurthy, and A. Emadi, “An FPGA-based novel digital PWM control scheme for BLDC motor drives,” IEEE Trans. Ind. Electron., vol. 56, no. 8, pp. 3040–3049,Aug. 2009.

[2] F. Rodriguez and A. Emadi, “A novel digital control technique for brushless DC motor drives,” IEEE Trans. Ind. Electron., vol. 54, no. 5, pp. 2365–2373, Oct. 2007.

[3] Y. Liu, Z. Q. Zhu, and D. HoweDirect Torque Control of Brushless DC Drives With Reduced Torque RippleIEEE Trans. Ind. Appl., vol. 41, no. 2, pp. 599-608, March/April 2005.

[4] T. S. Kim, S. C. Ahn, and D. S. Hyun , “A New Current Control Algorithm for Torque Ripple Reduction of BLDC Motors,” in IECON’01, 27th Conf. IEEE Ind. Electron Society,2001

[5] W. A. Salah, D. Ishak, K. J. Hammadi, “PWM Switching Strategy for Torque Ripple Minimization in BLDC MotorFEI STU, Journal of Electrical Engineering, vol. 62, no. 3, 2011, 141–146.

A Novel Fuzzy Dynamic Observer for High Speed BLDC Motor

ABSTRACT:

In this paper, a high performance brushless DC (BLDC) motor drive based on a fuzzy dynamic observer (FDO) is investigated. The FDO acts on the motor current and its gains are corrected by estimating current, rotor position and speed by fuzzy logic control (FLC). FLC is correcting gain’s FDO via real time. A PI speed control was chosen due to its low processing time and fast control. In order to reduce the model complexity, the back-EMF is assumed as being trapezoidal in a simplified machine model. The presented drive has been simulated by the MATLAB/SIMULINK software on the high speed BLDC motor model. Simulation results show that the proposed drive is able to estimate the rotor position and speed with high precision when high speeds are considered. Simulation results also show the reliability, fast computation and excellent dynamic performance with using fuzzy logic for high speed BLDC motor.

KEYWORDS: 

  1. BLDC motor
  2. Fuzzy dynamic observer
  3. Fuzzy logic

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Block diagram of speed control of a three-phase BLDC motor.

EXPECTED SIMULATION RESULTS:

Fig. 2. Speed of rotor (N=30,000 rpm).

 Fig. 3. Back EMF three-phases (N=30,000 rpm).

Fig. 4. Total torque (N=30,000 rpm).

Fig. 5. Speed and estimated speed with FDO (N=30,000 rpm).

Fig. 6. Rotor position and estimated position with FDO (N=30,000 rpm).

Fig. 7. Speed estimated error with FDO (N=5,000 rpm, N=10,000 rpm and N=30,000 rpm).

CONCLUSION:

In this study, a fuzzy dynamic observer (FDO) scheme for a high speed BLDC motor drive is investigated. FDO micro gains are regulation by using fuzzy logic control (FLC) via real time. This approach has been simulated on a high speed BLDC motor nonlinear model. The FDO acts on the phase currents and also the micro gains will be quickly regulated real time according to error values by FLC. Also, the use of PI speed control accelerates the calculations of the rotor position estimation and speed. In this study, simulation results show that FDO are suitable for high speed BLDC motors and torque ripple is one of the indirect factors affecting the errors estimation. Nevertheless, these results show that the FDO is suitable for high speeds. In addition, as torque ripple is one of the main estimation error this parameter can be decrease by the torque ripple optimization.

 REFERENCES:

[1] Lei Hao, H. A. Toliat, “BLDC Motor Full-Speed Operating Using hybrid Sliding Mode Observer “Applied Power Electronics Conference and Exposition, APEC ’03. Eighteenth Annual IEEE, vol. 1, pp. 286 – 293, February 2003.

[2] S. M. M. Mirtalaei, J. S. Moghani, K. Malekian, B. Abdi, “A Novel Sensorless Control Strategy for BLDC Motor Drives Using a Fuzzy Logic-based Neural Network Observer “International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2008. IEEE, vol. 1, pp. 1491 – 1496, July 2008.

[3] Li Qiang, W. Ruixia, “Study on Rotor Position Detection Error in Sensorless BLDC Motor Drives “5th International Conference Power Electronics and Motion Control, IPEMC 2006. IEEE, vol. 3, pp. 1-5, August 2006.

[4] J. Lee, S. Sathiakumar, Y. Shrivastava, “A novel speed and position estimation of the brushless DC motor at low speed “Power Engineering Conference, AUPEC 2008. IEEE, vol. 3, pp. 1-6, December 2008.

[5] M. Divandari. R. Brazamini, A. Dadpour, M. Jazaeri, “A Novel Dynamic Observer and Torque Ripple Minimization via Fuzzy Logic for SRM Drives “International Symposium on Industrial Electronics, ISIE 2009. IEEE, vol. 1, pp. 847 – 852, July 2009.

A Comparative Study of Speed Control of D.C. Brushless Motor Using PI and Fuzzy Controller

 

ABSTRACT:

This paper presents an intelligent control architecture for a sensor based brushless DC motor. A BLDC motor is superior to a brushed DC motor, as it replaces the mechanical commutation unit with an electronic one; hence improving the dynamic characteristics, efficiency and reducing the noise level marginally. Conventionally a PI-controller is used for speed control purpose in many industrial BLDC motor drives. But the accuracy level obtained by the PI-controlled drive is insufficient for advanced sophisticated applications. So as a better choice, a fuzzy logic control technique is applied to this motor to achieve a greater accuracy in controlling the speed.

KEYWORDS:

  1. Intelligent control
  2. BLDC motor
  3. Dynamic characteristics
  4. Accuracy
  5. Fuzzy logic

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAMS:

Fig. 1. Block diagram for speed control of BLDCM using PI controller.

Fig. 2. Block diagram of a fuzzy logic controlled BLDC motor drive.

 EXPECTED SIMULATION RESULTS:

                  Fig. 3. Speed response of PI controlled BLDC motor drive(Nref=1500 r.p.m)

Fig. 4. Speed response of fuzzy logic controlled BLDC motor drive (Nref=1500 r.p.m)

Fig. 5. Speed response of PI controlled BLDC motor drive(transition from 1500 r.p.m to 1400 r.p.m)

                         Fig. 6. Speed response of fuzzy logic controlled BLDC motor drive (transition from 1500 r.p.m to 1400 r.p.m)

CONCLUSION:

In this paper we discussed the BLDC motor speed control using a fuzzy logic controller. A detailed analysis was done on fuzzification, fuzzy rules and defuzzification methods and lookup table was obtained by using fuzzy algorithm. The PI control scheme and fuzzy based PI scheme were simulated using MATLAB and compared. The dynamic response of speed in using FLC was better than only PI scheme. These results show that a PI based FLC technique is a better choice for BLDC motor drive and favors to widen its area of application in near future.

REFERENCES:

[1] Paul C. Krause, “Analysis of electric machinary”, McGraw-Hill, 1984.

[2] P.S. Bimbhra, “ Generalized Theory of Electrical Machines”, Khanna Publishers.

[3] P. Yedamale, Brushless DC (BLDC) Motor Fundamentals. Application Note 885, Microchip Technology Inc., Chandler, AZ,2003.

[4] Dutta, P.; Mahato, S.N., “Design of mathematical model and performance analysis of BLBLDC motor,” Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on , vol., no., pp.457,461, Jan. 31 2014-Feb. 2 2014

[5] Ko, J.S.; Jae Gyu Hwang; Myung-Joong Youn, “Robust position control of BLDD motors using integral-proportional plus fuzzy logic controller,” Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON ’93., International Conference on , vol., no., pp.213,218 vol.1, 15-19 Nov 1993

Fuzzy Efficiency Enhancement of Induction Motor Drive

 

ABSTRACT:

Efficiency improvement of motor drives is important not only from the viewpoints of energy loss and hence cost saving, but also from the perspective of environmental pollution. Several efficiency optimization methods for induction motor (IM) drives have been introduced nowadays by researchers. Distinctively, artificial intelligence (AI)-based techniques, in particular Fuzzy Logic (FL) one, have been emerged as a powerful complement to conventional methods. Design objectives that are mathematically hard to express can be incorporated into a Fuzzy Logic Controller (FLC) using simple linguistic terms. The merit of FLC relies on its ability to express the amount of ambiguity in human reasoning. When the mathematical model of a process does not exist or exists with uncertainties, FLC has proven to be one of the best alternatives to move with unknown process. Even when the process model is well-known, there may still be parameter variation issues and power electronic systems, which are known to be often approximately defined.

The purpose of this paper is to demonstrate that a great efficiency improvement of motor drive can be achieved and hence a significant amount of energy can be saved by adjusting the flux level according to the applied load of an induction motor by using an on-line fuzzy logic optimization controller based on a vector control scheme. An extensive simulation results highlight and confirm the efficiency improvement with the proposed algorithm.

KEYWORDS:

  1. Induction Motor Drive
  2. Indirect Field Oriented Control (IFOC)
  3. Efficiency Enhancement
  4. Losses Minimization
  5. Optimization
  6. Fuzzy Logic

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig.1, Block diagram of the optimization system

EXPECTED SIMULATION RESULTS:

 

 Fig.2, Motor Performances Comparison

Fig.3, Motor efficiency evolution with motor load

CONCLUSION:

This paper aims to improve the induction motor drive efficiency that leads to a significant amount of energy saving. This efficiency enhancement is carried out by adjusting the flux level depending on the applied load of an induction motor by using an on-line fuzzy logic optimization controller based on a vector control scheme. A series of the induction motor drive performances are obtained with a variable load under this proposed algorithm. The application of the proposed algorithm yields to a series of simulation performances of the induction motor drive with a variable load. They present the IM drive efficiency evolution with a certain load profile with the suggested losses minimization strategy based on fuzzy control and the conventional field oriented control. The comparison between these two control schemes reveals that the achieved results are of a great interest. Indeed, the fuzzy control contributes with a great deal to the efficiency improvement for all operating speeds particularly in light load region. This contribution conducts to a paramount energy saving and hence to environment protection.

REFERENCES:

[1] I. Boldea, A. Nasser, The Induction Machine Design Handbook, CRC Press Inc; 2nd Revised Edition, 2009.

[2] Jinchuan. Li and all, “A new Optimization Method on Vector Control of Induction Motors”, Electric Machines and Drives, 2005 IEEE International Conference, 15-18 May 2005, pp.1995-2001.

[3] H. Sepahv and, Sh. Ferhangi, “Enhancing Performance of a Fuzzy Efficiency Optimizer for Induction Motor Drives”, Power Electronics Specialists Conference, 2006. PESC ’06. 37th IEEE, 18-22 June.2006, pp.1-5.

[4] Branko Blanusa and all, “An Improved Search Based Algorithm for Efficiency Optimization in the Induction Motor Drives”, XLII Konferencija- za ETRAN, Hercy-Novi, 2003.

[5] D. S. Kirischen, D. W. Novoty and T. A. Lipo, “Optimal Efficiency Control of an Induction Motor Drive”, IEEE Transaction on Energy Conversion, Vol. EC-2, N° 1, March 1987, pp.70-76.

Designing of Multilevel DPFC to Improve Power Quality

 

ABSTRACT:

According to growth of electricity demand and the increased number of non-linear loads in power grids, providing a high quality electrical power should be considered. In this paper, Enhancement of power quality by using fuzzy based multilevel power flow controller (DPFC) is proposed. The DPFC is a new FACTS device, which its structure is similar to unified power flow controller (UPFC). In spite of UPFC, in DPFC the common dc-link between the shunt and series converters is eliminated and three-phase series converter is divided to several single-phase series distributed converters through the line. This eventually enables the DPFC to fully control all power system parameters. It, also, increases the reliability of the device and reduces its cost simultaneously. In recent years multi level inverters are used high power and high voltage applications .Multilevel inverter output voltage produce a staircase output waveform, this waveform look like a sinusoidal waveform leads to reduction in Harmonics. Fuzzy Logic is used for optimal designing of controller parameters. Application of Fuzzy Multilevel DPFC for reduction of Total Harmonic Distortion was presented. The simulation results show the improvement of power quality using DPFC with Fuzzy logic controller.

KEYWORDS:

  1. FACTS
  2. Power Quality
  3. Multi Level Inverters
  4. Fuzzy Logic
  5. Distributed Power Flow Controller component

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig.1: The DPFC Structure.

EXPECTED SIMULATION RESULTS:

 

 Fig.2: 5 Level Voltage Waveform

Fig.3: Three Phase output Voltage and Current Waveform

Fig.4: Supply Voltage and Current Waveform with unity PF

Fig.5: THD without fuzzy

Fig.6: THD with fuzzy

CONCLUSION:

In this paper Fuzzy Logic Controller technique based distributed power flow controller (DPFC) with multilevel voltage source converter (VSC) is proposed. The presented DPFC control system can regulate active and reactive power flow of the transmission line. We are reducing the THD value from 24.84% to 0.41% by using this technic as shown in fig’s (12) & (13).The series converter of the DPFC employs the DFACTS concept, which uses multiple 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 are low. Also results show the valid improvement in Power Quality using Fuzzy Logic based Multilevel DPFC.

 REFERENCES:

[1] K Chandrasekaran, P A Vengkatachalam, Mohd Noh Karsiti and K S Rama Rao, “Mitigation of Power Quality Disturbances”, Journal of Theoretical and Applied Information Technology, Vol.8, No.2, pp.105- 116, 2009

[2] Priyanka Chhabra, “Study of Different Methods for Enhancing Power Quality Problems”, International Journal of Current Engineering and Technology, Vol.3, No.2, pp.403-410, 2013

[3] Bindeshwar Singh, Indresh Yadav and Dilip Kumar, “Mitigation of Power Quality Problems Using FACTS Controllers in an Integrated Power System Environment: A Comprehensive Survey”, International Journal of Computer Science and Artificial Intelligence, Vol.1, No.1, pp.1-12, 2011

[4] Ganesh Prasad Reddy and K Ramesh Reddy, “Power Quality Improvement Using Neural Network Controller Based Cascaded HBridge Multilevel Inverter Type D-STATCOM”, International Conference on Computer Communication and Informatics, 2012

[5] Lin Xu and Yang Han, “Effective Controller Design for the Cascaded Hbridge Multilevel DSTATCOM for Reactive Compensation in Distribution Utilities”, Elektrotehniski Vestnik, Vol.78, No.4, pp.229- 235, 2011

Designing of Multilevel DPFC to Improve Power Quality

 

ABSTRACT:

According to growth of electricity demand and the increased number of non-linear loads in power grids, providing a high quality electrical power should be considered. In this paper, Enhancement of power quality by using fuzzy based multilevel power flow controller (DPFC) is proposed. The DPFC is a new FACTS device, which its structure is similar to unified power flow controller (UPFC). In spite of UPFC, in DPFC the common dc-link between the shunt and series converters is eliminated and three-phase series converter is divided to several single-phase series distributed converters through the line. This eventually enables the DPFC to fully control all power system parameters. It, also, increases the reliability of the device and reduces its cost simultaneously. In recent years multi level inverters are used high power and high voltage applications .Multilevel inverter output voltage produce a staircase output waveform, this waveform look like a sinusoidal waveform leads to reduction in Harmonics. Fuzzy Logic is used for optimal designing of controller parameters. Application of Fuzzy Multilevel DPFC for reduction of Total Harmonic Distortion was presented. The simulation results show the improvement of power quality using DPFC with Fuzzy logic controller.

KEYWORDS:

  1. FACTS
  2. Power Quality
  3. Multi Level Inverters
  4. Fuzzy Logic
  5. Distributed Power Flow Controller component

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig.1: The DPFC Structure.

EXPECTED SIMULATION RESULTS:

 Fig.2: 5 Level Voltage Waveform

Fig.3: Three Phase output Voltage and Current Waveform

Fig.4: Supply Voltage and Current Waveform with unity PF

Fig.5: THD without fuzzy

Fig.6: THD with fuzzy

CONCLUSION:

In this paper Fuzzy Logic Controller technique based distributed power flow controller (DPFC) with multilevel voltage source converter (VSC) is proposed. The presented DPFC control system can regulate active and reactive power flow of the transmission line. We are reducing the THD value from 24.84% to 0.41% by using this technic as shown in fig’s (12) & (13).The series converter of the DPFC employs the DFACTS concept, which uses multiple 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 are low. Also results show the valid improvement in Power Quality using Fuzzy Logic based Multilevel DPFC.

 REFERENCES:

[1] K Chandrasekaran, P A Vengkatachalam, Mohd Noh Karsiti and K S Rama Rao, “Mitigation of Power Quality Disturbances”, Journal of Theoretical and Applied Information Technology, Vol.8, No.2, pp.105- 116, 2009

[2] Priyanka Chhabra, “Study of Different Methods for Enhancing Power Quality Problems”, International Journal of Current Engineering and Technology, Vol.3, No.2, pp.403-410, 2013

[3] Bindeshwar Singh, Indresh Yadav and Dilip Kumar, “Mitigation of Power Quality Problems Using FACTS Controllers in an Integrated Power System Environment: A Comprehensive Survey”, International Journal of Computer Science and Artificial Intelligence, Vol.1, No.1, pp.1-12, 2011

[4] Ganesh Prasad Reddy and K Ramesh Reddy, “Power Quality Improvement Using Neural Network Controller Based Cascaded HBridge Multilevel Inverter Type D-STATCOM”, International Conference on Computer Communication and Informatics, 2012

[5] Lin Xu and Yang Han, “Effective Controller Design for the Cascaded Hbridge Multilevel DSTATCOM for Reactive Compensation in Distribution Utilities”, Elektrotehniski Vestnik, Vol.78, No.4, pp.229- 235, 2011

Smooth Shunt Control of a Fuzzy based Distributed Power Flow Controller to Improve Power Quality

 

ABSTRACT

Presently, the quality of power supplied is essential to many customers. Power quality (PQ) is a valued utility service where many customers are prepared to pay and get it. In the future, distribution system operators ought to decide, to provide their customers with distinct PQ ranges at different prices. Here, in this paper, a new control action to improve and maintain and enhance the power quality of an electrical power system is proposed in this paper. Fuzzy based distributed power flow controller (DPFC) is designed and put into action to compensate the voltage imbalances arising in a power system. This customized DPFC is an advanced FACTS device, which has its structure analogous to unified power flow controller (UPFC). DPFC comprises of both series and shunt converters, in which its three phase series converter is distributed over the transmission line as several single phase static converters ensuring high controllability and reliability at a low cost compared to an UPFC. A central controlling circuit is designed to supply reference signals to each of the individual controlling circuits of both series and shunt converters. This customized device is applied to a single machine infinite bus power system having nonlinear loads connected to it and is simulated in MATLAB/Simulink environment by using OPAL-RT 5600 Real-time digital Simulator. The results demonstrate the validation of proposed technique to enhance the power quality.

KEYWORDS

  1. Power quality
  2. Voltage fluctuations
  3. Harmonic analysis
  4. Power harmonic filters
  5. Voltage control
  6. Load flow Voltage Sag and Swell
  7. Fuzzy Logic

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Control network of DPFC

EXPECTED SIMULATION RESULTS

Fig. 2. Voltage waveform during fault condition

Fig. 3. Current waveform during fault condition

Fig. 4. Simulated results for Voltage by employing DPFC controller

Fig. 5. Simulated results for Current by employing DPFC controller

Fig. 6. THD of load voltage without Controller

Fig. 7. FFT Analysis for PI Controller

Fig. 8. FFT Analysis for Fuzzy Controller

CONCLUSION

The work is presented to provide a solution for maintaining Power Quality at the distribution end, compensation of harmonics in grid voltage and in load currents. In order to consummate specified intentions in this paper a new concept for controlling power quality problems was proposed and implemented. By putting the customized device into action, results were analyzed for voltage dips and their mitigations for a three phase source with non-linear loads. The DPFC is modeled by positioning three control circuits designed independently. In this paper we also proposed and implemented the concept of fuzzy logic controller for having better controlling action, which will help in minimization/elimination of harmonics in the system. As compared to all other facts devices the Fuzzy based DPFC converter effectively controls all power quality problems and with this technique we can put THD to 3.04% proving the effectiveness of the proposed controller.

REFERENCES

[1] D. Divan and H. Johal, “Distributed facts-A new concept for realizing grid power flow control,” in IEEE 36th Power Electron. Spec. Conf. (PESC), 2005, pp. 8–14.

[2] K K. Sen, “Sssc-static synchronous series compensator: Theory, modeling, and application”,IEEE Trans. Power Del., vol. 13, no. 1, pp. 241–246, Jan. 1998.

[3] L.Gyugyi, C.D. Schauder, S. L.Williams, T. R. Rietman, D. R. Torgerson, and A. Edris, “The unified power flow controller: A new approach to power transmission control”, IEEE Trans. Power Del., vol. 10, no. 2, pp. 1085– 1097, Apr. 1995.

[4] M. D. Deepak, E. B. William, S. S. Robert, K. Bill, W. G. Randal, T. B. Dale, R. I. Michael, and S. G. Ian, “A distributed static series compensator system for realizing active power flow control on existing power lines”, IEEE Trans. Power Del., vol. 22, no. 1, pp. 642–649, Jan.2007

[5] M. Mohaddes, A. M. Gole, and S. Elez, “Steady state frequency response of statcom”, IEEE Trans. Power Del., vol. 16, no. 1, pp. 18–23, Jan. 2001.

 

Novel Development of A Fuzzy Control Scheme with UPFC’s For Damping of Oscillations in Multi-Machine Power Systems

ABSTRACT:

This paper presents a novel development of a fuzzy logic controlled power system using UPFCs to damp the oscillations in a FACTS based integrated multi-machine power system consisting of 3 generators, 3 transformers, 9 buses, 4 loads & 2 UPFCs. Oscillations in power systems have to be taken a serious note of when the fault takes place in any part of the system, else this might lead to the instability mode & shutting down of the power system. UPFC based POD controllers can be used to suppress the oscillations upon the occurrence of a fault at the generator side or near the bus side. In order to improve the dynamic performance of the multi-machine power system, the behavior of the UPFC based POD controller should be coordinated, otherwise the power system performance might be deteriorated. In order to keep the advantages of the existing POD controller and to improve the UPFC-POD performance, a hybrid fuzzy coordination based controller can be used ahead of a UPFC based POD controller to increase the system dynamical performance & to coordinate the UPFC-POD combination. This paper depicts about this hybrid combination of a fuzzy with a UPFC & POD control strategy to damp the electro-mechanical oscillations. The amplification part of the conventional controller is modified by the fuzzy coordination controller. Simulink models are developed with & without the hybrid controller. The 3 phase to ground symmetrical fault is made to occur near the first generator for 200 ms. Simulations are performed with & without the controller. The digital simulation results show the effectiveness of the method presented in this paper.

KEYWORDS:

  1. UPFC
  2. POD
  3. Fuzzy logic
  4. Coordination
  5. Controller
  6. Oscillations
  7. Damping
  8. Stability
  9. SIMULINK
  10. State space model

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

image001

Fig. 1 : A 3-machine, 9-bus interconnected power system model with 4-loads without the controllers

image002

Fig. 2: A 3-machine, 9-bus interconnected power system model with 4-loads & 2 POD-UPFC & the fuzzy controller

 EXPECTED SIMULATION RESULTS:

 image003

Fig. 3 : Simulation result of power angle v/s time (without Fuzzy-POD-UPFC)

image004

Fig. 4 : Simulation result of power angle v/s time (with UPFC & fuzzy control)

image005

Fig. 5 : Comparison of the simulation results of power angle v/s time (without UPFC & with UPFC & fuzzy control)

 CONCLUSION:

AFACTS based multi-machine power system comprising of 3 generators, 9 buses, 3 loads with and without the 2 Fuzzy-POD-UPFC controllers was considered in this paper. SIMULINK models were developed in MATLAB 7 with & without the Fuzzy- POD-UPFC controllers for the considered multi machine model in order to damp out the oscillations. The control strategy was also developed by writing a set of fuzzy rules. The fuzzy control strategy was designed based on the conventional POD-UPFC controller & put before the POD-UPFC in the modeling.

The main advantage of putting the fuzzy coordination controller before the POD-UPFC in modeling is the amplification part of the conventional controller being modified by the fuzzy coordination unit, thus increasing the power system stability. Simulations were run in Matlab 7 & the results were observed on the scope. Graphs of power angle vs. time were observed with and without the controller. From the simulation results, it was observed that without the Fuzzy-POD-UPFC controller, the nine bus system will be having more disturbances, while we check the power angle on the first generator.

There are lot of ringing oscillations (overshoots / undershoots) & the output takes a lot of time to stabilize, which can be observed from the simulation results. But, from the incorporation of the Fuzzy- POD-UPFC coordination system in loop with the plant gave better results there by reducing the disturbances in the power angle and also the post fault settling time also got reduced a lot. The system stabilizes quickly, thus damping the local mode oscillations and reducing the settling time immediately after the occurrence of the fault.

The developed control strategy is not only simple, reliable, and may be easy to implement in real time applications. The performance of the developed method in this paper thus demonstrates the damping of the power system oscillations using the effectiveness of Fuzzy-POD-UPFC coordination concepts over the damping of power system oscillations without the Fuzzy-POD-UPFC coordination scheme.

REFERENCES:

[1]. L. Gyugi, “Unified Power flow concept for flexible AC transmission systems”, IEE Proc., Vol. 139, No. 4, pp. 323–332, 1992.

[2]. M. Noroozian, L. Angquist, M. Ghandari, and G. Anderson, “Use of UPFC for optimal power flow control”, IEEE Trans. on Power Systems, Vol. 12, No. 4, pp. 1629–1634, 1997.

[3]. Nabavi-Niaki and M.R. Iravani, “Steady-state and dynamic models of unified power flow controller (UPFC) for power system studies”, IEEE’96 Winter Meeting, Paper 96, 1996.

[4]. C.D. Schauder, D.M. Hamai, and A. Edris. “Operation of the Unified Power Flow Controller (UPFC) under Practical constraints”, IEEE Trans. On Power Delivery, Vol. 13, No. 2. pp. 630~639, Apr. 1998.

[5]. Gyugyi, L., “Unified power flow controller concept for flexible AC transmission systems”, IEE Proc. Gener. Transm. Distrib., No.139, pp. 323-331, 1992

 

Fuzzy Controller for Three Phases Induction Motor Drives

ABSTRACT:

Because of the low maintenance and robustness induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy logic controller. Induction motor is modeled in synchronous reference frame in terms of dq form. The speed control of induction motor is the main issue achieves maximum torque and efficiency. Two speed control techniques, Scalar Control and Indirect Field Oriented Control are used to compare the performance of the control system with fuzzy logic controller. Indirect field oriented control technique with fuzzy logic controller provides better speed control of induction motor especially with high dynamic disturbances. The model is carried out using Matlab/Simulink computer package. The simulation results show the superiority of the fuzzy logic controller in controlling three-phase induction motor with indirect field oriented control technique.

 KEYWORDS:

  1. Vector control
  2. Fuzzy logic
  3. Induction motor drive

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

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Fig. 1. Block diagram of scalar controller for IM.

image002

Fig. 2. Indirect Field Oriented Control of IM.

 EXPECTED SIMULATION RESULTS:

 image003

Fig. 3. Speed response of scalar and vector control

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Fig 4. Torque response of scalar and vector control.

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Fig. 5. Flux response of scalar control.

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Fig. 6. Flux response of vector control.

CONCLUSION:

Fuzzy logic controller shows fast control response with three-phase induction motor. Two different control techniques are used with Fuzzy logic controllers which are scalar and field oriented control techniques. Fuzzy logic controller system shows better response with these two techniques. Meanwhile, the scalar controller has a sluggish response than FOC because of the inherent coupling effect in field and torque components. However, the developed fuzzy logic control with FOC shows fast response, smooth performance, and high dynamic response with speed changing and transient conditions.

 REFERENCES:

 [1] A. Mechernene, M. Zerikat and M. Hachblef, “Fuzzy speed regulation for induction motor associated with field-oriented control”, IJ-STA, volume 2, pp. 804-817, 2008.

[2] Leonhard, W.,” Controlled AC drives, a successful transfer from ideas to industrial practice”, CETTI, pp: 1-12, 1995.

[3] M. Tacao, “Commandes numérique de machines asynchrones par lagique floue”, thése de PHD, Université de Lava- faculté des science et de génie Québec, 1997.

[4] Fitzgerald, A.E. et al., Electric Machinery, 5th Edn, McGraw-Hill, 1990.

[5] Marino, R., S. Peresada and P. Valigi, “Adaptive input-output linearizing control of induction motors”, IEEE Trans. Autom. Cont., 1993.