Modelling and Simulation of Standalone PV Systems with Battery supercapacitor Hybrid Energy Storage System for a Rural Household

ABSTRACT:  

This paper presents the comparison between the standalone photovoltaic (PV) system with battery-supercapacitor hybrid energy storage system (BS-HESS) and the conventional standalone PV system with battery-only storage system for a rural household. Standalone PV system with passive BS-HESS and semi-active BS-HESS are presented in this study. Two control strategies, Rule Based Controller (RBC) and Filtration Based Controller (FBC), are developed for the standalone PV system with semi-active BS-HESS with the aim to reduce the battery stress and to extend the battery lifespan. The simulation results show that the system with semi-active BS-HESS prolongs the battery lifespan by significantly reducing the battery peak current up to 8.607% and  improving the average SOC of the battery up to 0.34% as compared to the system with battery only system.

KEYWORDS:

1.     Renewable energy

2.     PV

3.     Hybrid energy storage system

4.     Supercapacitor

5.     Battery

6.     Control strategy

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Simulink Models. (a) Standalone PV system with Battery-only Storage. (b) Standalone PV System with Passive BS-HESS. (c) Standalone PV system with Semi-Active BS-HESS.

EXPERIMENTAL RESULTS:

Fig. 2. 24-hours Profiles. (a) Solar Irradiation Profile. (b) Load Demand (c) PV Power Output.

Fig. 3. Battery Current. (a) Battery-only (b) Passive BS-HESS. (c) Semi-active BS-HESS (RBC). (d) Semi-active BS-HESS (Moving Average).

Fig. 4. Supercapacitor Current. (a) Passive BS-HESS. (b) Semi-active BS-HESS (RBC). (c) Semi-active BS-HESS (Moving Average).

CONCLUSION:

The BS-HESS shows the positive impact to the battery and the overall system. The passive BS HESS is easy to be implemented, but the improvement is not significant as it cannot be controlled. Therefore, semi-active BS-HESS is a better configuration that improves the battery lifespan and maximizes the level of utilization of the supercapacitor. The system with semi-active BS-HESS (moving average filter) has significantly smoothened the battery current. The system with semi-active BS-HESS (RBC) shows a great capability in battery peak current reduction and the prevention of battery deep discharge by reducing the peak power demand by 8.607% and improving the average SOC of the battery by 0.34% as compared to the system with battery-only system.

REFERENCES:

[1] Kan SY, Verwaal M, and Broekhuizen H, The use of battery-capacitor combinations in photovoltaic powered products, J. Power Sources 2006, 162: 971–974.

[2] Chong LW, Wong YW, Rajkumar RK, Rajkumar RK, and Isa D, Hybrid energy storage systems and control strategies for stand-alone renewable energy power systems, Renew. Sustain. Energy Rev. 2016, 66, pp: 174–189.

[3] Kuperman A and Aharon I, Battery-ultracapacitor hybrids for pulsed current loads: A review, Renew. Sustain. Energy Rev. 2011, 15: 981– 992.

[4] Dougal RA, Liu S, and White RE, Power and life extension of battery-ultracapacitor hybrids, IEEE Trans. Components Packag. Technol 2002., 25: 120–131.

[5] Kuperman A, Aharon I, Malki S, and Kara A, Design of a semiactive battery-ultracapacitor hybrid energy source, IEEE Trans. Power Electron.2013, 28: 806–815.

A Unity Power Factor Converter with Isolation for Electric Vehicle Battery Charger

ABSTRACT:  

This paper deals with a unity power factor (UPF) Cuk converter EV (Electric Vehicle) battery charger having a high frequency transformer isolation instead of only a single phase front end converter used in vehicle's conventional battery chargers. The operation of the proposed converter is defined in various modes of the converter components i.e. DCM  (Discontinuous Conduction Mode) or CCM (Continuous Conduction Mode) along with the optimum design equations. In this way, this isolated PFC converter makes the input current sinusoidal in shape and improves input power factor to unity. Simulation results for the proposed converter are shown for charging a lead acid EV battery in constant current constant voltage (CC-CV) mode. The rated full load and varying input supply conditions have been considered to show the improved power quality indices as compared to conventional battery chargers. These indices follow the international IEC 61000-3-2 standard to give harmonic free input parameters for the proposed circuit.

KEYWORDS:

1.      UPF Cuk Converter

2.      Battery Charger

3.      Front end converter

4.      CC-CV mode

5.      IEC 61000-3-2 standard

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

Fig. 1 General Schematic of an EV Battery Charger with PFC CUK Converter

EXPERIMENTAL RESULTS:

Fig.2 Simulated performance of the isolated Cuk converter in rated condition

(a) rated input side and output side quantities (b-c) harmonic analysis of the

current at source end

Fig.3 Simulated performance of the isolated Cuk converter while input is

varied to 270V (a) rated input side and output side quantities (b-c) harmonic

analysis of the current at source end

Fig.4 Simulated performance of the isolated Cuk converter while input is

reduced to 270V (a) rated input side and output side quantities (b-c) harmonic

analysis of the current at source end

Fig.5 Simulated performance of the isolated Cuk converter at light load

condition (a) rated input side and output side quantities (b-c) harmonic analysis

of the current at source end

CONCLUSION:

An isolated Cuk converter based battery charger for EV with remarkably improved PQ indices along with wellregulated battery charging voltage and current has been designed and simulated. The converter performance has been found satisfactory and well within standard for rated as well as different varying input rms value of supply voltages. The considerably improved THD in the current at the source end makes the proposed system an attractive solution for efficient charging of EVs at low cost.

The proposed UPF converter performance has been tested to show its suitability for improved power quality based charging of an EV battery in CC-CV mode. Moreover, the cascaded dual loop PI controllers are tuned to have the smooth charging characteristics along with maintaining the low THD in mains current. The proposed UPF converter topology have the inherent advantage of low ripples in input and output side due to the added input and output side inductors. Therefore, the life cycle of the battery is increased. MATLAB based simulation shows the performance assessment of the proposed charger for the steady state and dynamics condition which clearly state that the proposed charger can sustain the sudden disturbances in supply for charging the rated EV battery load. Moreover, during whole disturbances in supply voltage, thepower quality parameters at the input side, are maintained within the IEC 61000-3-2 standard and THD is also very low.

REFERENCES:

[1] Limits for Harmonics Current Emissions (Equipment current ≤ 16A per Phase), International standards IEC 61000-3-2, 2000.

[2] Muhammad H. Rashid, “Power Electronics Handbook, Devices, Circuits, and Applications”, Butterworth-Heinemann, third edition, 2011.

[3] N. Mohan, T. M. Undeland, and W. P. Robbins, Power Electronics: Converters, Applications and Design. Hoboken, NJ, USA: Wiley, 2009.

[4] B. Singh, S. Singh, A. Chandra and K. Al-Haddad, “Comprehensive Study of Single-Phase AC-DC Power Factor Corrected Converters With High-Frequency Isolation”, IEEE Trans. Industrial Informatics, vol. 7, no. 4, pp. 540-556, Nov. 2011.

[5] A. Abramovitz K. M. Smedley "Analysis and design of a tapped-inductor buck–boost PFC rectifier with low bus voltage" IEEE Trans. Power Electron., vol. 26 no. 9 pp. 2637-2649 Sep. 2011.

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:

1.      P Controller

2.      PI Controller and Fuzzy Logic Controller

3.      Switched Reluctance Motor

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig. 1 Block diagram of SRM speed control.

 EXPERIMENTAL RESULTS:

Figure 2. Output flux.

Figure 3. Output current.

Figure 4. Output torque.

Figure 5. Speed.

Figure 6. Output flux.

Figure 7. Output current.

Figure 8. Output torque

Figure 9. Speed.

Figure 10. Output flux.

Figure 11. Output current.

Figure 12. Output torque.

Figure 13. 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 modelling 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.

Design and Control of SR Drive System using ANFIS

ABSTRACT:  

This paper presents the modeling and simulation of an adaptive neuro-fuzzy inference strategy (ANFIS) to control the speed of the switched Reluctance motor .The SRM control is thus a  difficult to be in use in the nonlinear applications, particularly in the control of speed in automobiles. The Neuro-fuzzy system incorporates the advantages of both neural-network and fuzzy system. This controller is great additional effectual than Fuzzy  logic and neural network based controller, while it has the ability of  self-learning the gain values and acclimatizes accordingly to situations, thus accumulating more flexibility to the controller. A complete simulation, well-designed to the nonlinear model of Switched Reluctance Drive was premeditated using MATLAB /SIMULINK.

KEYWORDS:

1.     SR Drive

2.     ANFIS

3.     ANN

4.     FLC

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig.1.Block Diagram of ANFIS Controller for SRM Plant

EXPERIMENTAL RESULTS:

Fig.2: Response of the speed control of SRM using FUZZY, ANN and ANFIS with speed Command 3000 RPM under no load conditions.

Fig.3: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 3000 Rpm under no load conditions.

Fig.4: Response of The Speed and Torque Control of SRM using Fuzzy, ANN and ANFIS with Speed command 4000 rpm.

Fig.5: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 4000 rpm.

Fig.6: Response of the speed control of SRM using FUZZY, ANN and ANFIS with speed Command 3000 RPM under load Conditions

Fig.7: Response of the speed and torque control of SRM using ANFIS with speed Command 3000 RPM under load conditions

CONCLUSION:

In this paper, ANFIS-based controller was presented for SR drives. The speed and torque control method existing in this  paper and comparing with the previous control schemes(fuzzy &ANN), while it can be used in both no load and load operating speeds and conditions including speed and torque transients, zero-speed standstill, and startup, and does not suppose the linear characteristics of the SR motor. Moreover, the proposed technique does not need of complex calculations to be carried out during the real-time operation, and no complex mathematical model of the SR motor is required. A main thought in the research was the robustness and reliability of the speed controlling method.

 REFERENCES:

[1] J. P. Lyons, S. R. MacMinn, and M. A. Preston, “Flux/current methods for SRM rotor position estimation,” in Proc. IEEE Industry Application Soc. Annu. Meeting, vol. 1, 1991, pp. 482–487.

[2]S. R. MacMinn, C. M. Steplins, and P. M. Szaresny, “Switched reluctance motor drive system and laundering apparatus employing same,” U.S. Patent 4 959 596, 1989.

[3] M. Ehsani, I. Husain, S. Mahajan, and K. R. Ramani, “New modulation encoding techniques for indirect rotor position sensing in switched reluctance motors,” IEEE Trans. Ind. Applicat., vol. 30, pp. 85–91, Jan./Feb. 1994.

[4] G. R. Dunlop and J. D. Marvelly, “Evaluation of a self commuted switched reluctance motor,” in Proc. Electric Energy Conf., 1987, pp. 317– 320.

[5] Ramesh.Palakeerthi,Subbaiah.P ,2014, ‘High Speed Charging and Discharging Current Controller Circuit to Reduce Back EMF by NeuroFuzzy Logic ‘, International Journal of Applied Engineering Research,vol. 9, no.22