See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/267324714 Development of MATLAB/SIMULINK Models for PV and Wind Systems and Review on Control Strategies for Hybrid Energy Systems Article in International Review on Modelling and Simulations · August 2012 CITATION READS 1 4,727 1 author: Bogaraj Thirumalaisamy PSG College of Technology 6 PUBLICATIONS 27 CITATIONS SEE PROFILE All content following this page was uploaded by Bogaraj Thirumalaisamy on 24 October 2014. The user has requested enhancement of the downloaded file. International Review on Modelling and Simulations (I.RE.MO.S.), Vol. 5, N. 4 ISSN 1974-9821 August 2012 Development of MATLAB/SIMULINK Models for PV and Wind Systems and Review on Control Strategies for Hybrid Energy Systems T. Bogaraj, J. Kanakaraj _____________________________________________________________________________ Abstract – Depletion of fossil fuel resources and environmental pollution caused by them necessitates alternate energy sources for electricity production. In the recent past renewable energy sources are considered as alternative for the fossil fuel energy sources. Photovoltaic and Wind are the more promising renewable energy sources and are widely used in many countries for standalone applications or connected to Utility Grid. The unpredictable pattern of natural resources requires combined utilization of these sources for providing uninterrupted and reliable power supply to the consumers. A MATLAB/SIMULINK model for 10 kW Solar PV system has been developed and its characteristics are presented. The characteristics of Wind turbine is also simulated and results are presented. Further this paper presents the review of current state of control strategies for hybrid energy system in standalone mode and grid connected mode. Also the future developments of such system and their recognition by the industrial and home users are discussed. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved. Keywords: Energy Management, Hybrid Power Systems, MPPT, Power Conversion, Solar Energy, Wind Energy ______________________________________________________________________________ Nomenclature a IPV, cell I0,cell Isc Voc q k T Tn Ipv I0 Nss Npp Rs Rp Pmax,m, Pmax,e Vt Vmp Imp Ipv,n Isc,n Voc,n T Ideality or completion factor Current generated by the incident light [A] Reverse saturation/leakage current of the diode [A] Short circuit current of PV array[A] Open circuit voltage of PV array [V] Electron charge (1.60217646 × 1019 C) Boltzmann constant (1.3806503 × 1023 J/K) Temperature of the p–n junction [K] Nominal temperatures [K] Photovoltaic (PV) [A] Saturation current [A] Number of cells connected in series Number of cells connected in parallel Series resistance of PV cell [ ] Parallel resistance of PV cell [ ] Maximum power calculated by the I–V model of PV array [W] Maximum experimental power o f PV array from the datasheet [W] Thermal voltage of the array [V] Voltage at maximum power point [V] Current at maximum power point [A] Light generated current at the nominal condition (usually 250C and 1000 W/m2) Short circuit current of PV array at the nominal condition [A] Open circuit voltage of PV array at the nominal condition [V] Manuscript received and revised July 2012, accepted August 2012 G Gn R Vw ref T – Tn Irradiation on the device surface [W/m2] Nominal irradiation [W/m2] Air density ( =1.225 kg/m3) Blade length [m] Wind speed [m/s] Power coefficient Maximum value of power coefficient Pitch angle [Mechanical Degrees] Tip speed ratio Optimal value of the tip speed ratio Rotational speed of the wind turbine [rad/s] Reference speed of the wind turbine [rad/s] I. Introduction A Hybrid power system is one in which the electrical load is served by two or more power sources. Because renewable energy sources, such as Solar and Wind, are inherently intermittent, frequently they are combined with dispatchable power sources such as utility grid and diesel generators, to ensure the continuous supply of power. Energy storage is often included in hybrid systems that can deliver certain amount of energy on power demand until its stored energy is exhausted, but it must be recharged at that point. Around the world, thousands of communities and industrial sites are not powered by utility grid. The traditional means of electrification in such locations is Diesel generation, because the initial cost is low and Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved 1701 T. Bogaraj, J. Kanakaraj there are many people trained in operation and maintenance. As fuel cost rise and environmental concern mounts, there is increasing interest in the design and usage of hybrid power systems during last two decades such as PV/Diesel, Wind/Diesel, PV/Wind/Diesel, PV/Fuel cell, Wind/Fuel cell, PV/Wind/Fuel cell etc. Recent research and developments in Solar PV and Wind energy Technologies [1] and increase in the cost of conventional energy sources make the renewable hybrid energy systems cost effective in the near future. Since they are clean and green, the application of such systems will increase and accepted worldwide. Hybrid energy systems can significantly reduce the total life cycle cost of stand-alone power supplies in many situations, while at the same time providing a more reliable supply of electricity through the combination of energy sources [2]. Hybrid Systems Performance Assessment is one of the key issues for the development of Hybrid systems [3]. Lack of confidence of reliability is a major barrier for market development. Hybrid power systems [4] are being operated by multi-sources and decentralized distribution of subsystems means to achieve better energy management and control is one of hot topics of research. In Literature many different control strategies have been considered. Hybrid power systems could be considered for distributed generation using renewable energy sources. Such systems address the following issues: Reduce the peak demand Greatly reduce the transmission losses and improve the reliability of the grid network. To Remote and inaccessible areas of the country extension of the grid may not be economically feasible. In such cases distributed generation can play a major role. The modular nature of distributed generation system coupled with low gestation period enables the easy capacity additions when required. Disruptions such as grid failure, etc., can be prevented as electricity is produced close to the consumer. The quality of power (voltage and frequency) can also be maintained easily. Distributed generation systems offer the possibility of combining energy storage and management systems. Inadequacies in distribution network have been one of the major reasons for poor supply of power. Distributed generation facilitates an optimal use of the grid that improves the reliability of the grid network and reduces the congestion. The configuration of the Hybrid power system considered in this paper is shown in the Fig.1. For the stand alone mode the grid connection is avoided in the figure. In its structure, it consists of energy conversion devices, MPPT controllers, power electronic converters, and Energy management controller. The advances in power electronic devices and systems, communication technologies, software and computer technologies led to the development of research in the following areas of Hybrid Power system: Improvement of efficiency of photovoltaic cells Development of models for PV, Wind and/or Hybrid Power Systems Design of MPPT control strategies for Hybrid Power Systems Development of Energy management control strategies to utilize the available power, control the power flow between sources, reliable supply of power and power quality Finding the size (capacity) of sources for particular application to reduce the initial cost and running cost Integration with fuel cell and ultra capacitor to improve the system reliability and power quality [30] WG AC DC BUS DC LOAD MPPT PV DC AC GRID DC DC MPPT BATTERY ENERGY MANAGEMENT CONTROLLER Fig. 1. Configuration of PV/Wind Hybrid power system This paper aims to present the current state of technologies for Hybrid Power System in the context of modeling, energy management, and Maximum Power Point Tracking. II. Modeling of System Components II.1. PV Module A simulation must be performed for system analysis and parameter settings during design process of PV powered systems. Therefore an efficient user friendly simulation model of the PV Arrays is always needed. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved Fig. 2. Generalized model of a PV cell International Review on Modelling and Simulations, Vol. 5, N. 4 1702 T. Bogaraj, J. Kanakaraj The model is developed by using equivalent circuit of PV cell. From the theory of Photovoltaic system the basic equation that mathematically describes I-V characteristic of the ideal photovoltaic cell shown in Fig. 2 [5]-[7] is: Imp) point of the I -V curve, i.e. the maximum power calculated by the I -V model of (2), Pmax,m, is equal to the maximum experimental power from the datasheet, Pmax,e, at maximum power point (MPP). The Rs and Rp may be found by making Pmax,m= Pmax,e to get the I-V and P-V curve model fits with the experimental data: (1) where Ipv,cell is the current generated by the incident light, Id is the Shockley diode equation, I0,cell is the reverse saturation or leakage current of the diode, q is the electron charge (1.60217646 × 10-19 C), k is the Boltzmann constant (1.3806503 × 10-23 J/K), T (in Kelvin) is the temperature of the p–n junction, and a is the diode ideality constant. Practical arrays are composed of several photovoltaic cells that are connected in series and parallel. Cells connected in parallel increase the output current and cells connected in series increase output voltage. The observation of the characteristics at the terminals of the photovoltaic array requires [5] additional parameters to the basic equation (1). So it can be expressed as [8]: (2) where Ipv and I0 are the photovoltaic (PV) and saturation currents, respectively, of the array and Vt= NsskT/q is the thermal voltage of the array with Nss cells connected in series and Npp is number of cells connected in parallel. The I-V characteristic of the photovoltaic device depends on the internal characteristics of the device (Rs, Rp) and on external influences such as irradiation level and temperature: (5) (6) It shows that for any value of Rs there will be a value of Rp that makes the mathematical I-V curve across the experimental (Vmp, Imp) point. This equation must be solved by a numerical method. In this paper NewtonRaphson method is used. In the iterative process Rs must be incremented from Rs = 0. Adjusting the P-V curve to match the experimental data in the data sheet requires finding the curve for several values of R s and R p . This iterations are done until P ma x,m = P ma x,e . By increasing Rs the peak power (Pmax, m) moves towards the Maximum Power Point of the P-V curve. For every P-V curve, there is a corresponding I-V curve that crosses the experimental MPP at (Vmp, Imp). Further improvement of a model can be done by introducing the equation: (3) It is difficult to determine the light-generated current (Ipv) of the elementary cells, without the influence of the series and parallel resistances. The diode saturation current I0 to improve photovoltaic model is described by: (7) Every iteration updates the Rs and Rp towards the best solution. In order to get the best solutions Rs and Rp values are initialized. Initial value of Rs may be zero: (8) (4) Similarly the initial value of Rp may be given by: This equation matches the open-circuit voltages of the model with the experimental data for a very large range of temperatures. II.2. Adjusting the Model Parameters (Rs and Rp) This paper proposes a method for adjusting Rs and Rp based on the fact that there is an only pair {Rs, Rp} that ensures [5] that Pmax,m=Pmax,e=Vmp. Imp at the (Vmp. (9) It determines the minimum value of Rp, which is the slope of the line segment between points of short-circuit and the maximum-power in the I-V curve of the solar panel. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 4 1703 T. Bogaraj, J. Kanakaraj The TITANS6_60 specifications are tabulated in Table I. The values of Rs and Rp found from the iterated method are given in Table II. The photovoltaic array has been simulated with an equivalent circuit model of a photovoltaic system using MATLAB/SIMULINK environment which is shown in Fig. 3. Fig. 5. P-V curve for TITANS6_60 Fig. 3. Generalized Simulation model of Photovoltaic system TABLE I PARAMETERS OF THE TITANS6_60 SOLAR ARRAY AT 250C, 1000 W/M2 REFERRED FROM DATA SHEET Iscn 8.21A Vocn 37.0V Imp 7.44A Vmp 28.9V Pmax,e 215.016W Kv -0.123V/K Ki 3.18x10-3 A/K Ns 60 TABLE II PARAMETERS OF T HE MODEL OF THE TITANS6_60 SOLAR ARRAY AT NOMINAL OPERATING CONDITION OBTAINED FROM NUMERICAL METHOD Iscn 8.21A V ocn 37.0V Imp 7.44A Vmp 28.9V Pmax,e 215.016W Ion 2.90204x10-10A Ipv 8.264950A a 1 Rs 0.438800 Rp 65.570456 Fig. 6. I-V Curve for different Irradiation Levels at 25oC Fig. 7. P-V Curve for different Irradiation Levels at 25oC Fig. 4. I-V curve for TITANS6_60 Fig. 4 and Fig. 5 shows the P-V and I-V curve of one module of TITANS6_60 solar array for the Rs and Rp values found. This model is used to study the model for a 10 kW solar PV system installed at PSG College of Technology, Coimbatore, India which consists of two parallel combinations of 6 series and 4 parallel arrays. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 4 1704 T. Bogaraj, J. Kanakaraj II.3. Wind Energy System The aerodynamic power converted by the wind turbine is depending on the power coefficient such as [9],[10]: (10) where is the air density ( =1.225 kg/m3), R is the blade length and Vw is the wind speed.The power coefficient is depending on the pitch angle and the tip speed ratio given by: (11) Fig. 8. I-V curve for Different Temperature Levels at 1000W/m2 where is the rotational speed of the wind turbine and generator. The power coefficient is given by the expression: (12) with: The characteristics of the wind turbine for different pitch angles were obtained from the simulation in Matlab and is shown in Fig. 10. Fig. 9. P-V curve for Different Temperature Levels at 1000W/m2 Simulated results for 10 kW PV system is shown in Fig. 6 to Fig. 9. I-V and P-V curve are shown in Fig. 6 and Fig. 7 for the various irradiation conditions at 25 0C. I-V and P-V curves are shown in Fig. 8 and Fig. 9 obtained for various temperature conditions at irradiation level of 1000W/m2. These results fit with the experimental data shown in Table III obtained from the installed 10 kW PV system. This model is fairly accurate and is used for design Hybrid system in the further research work. TABLE III EXPERIMENTAL RESULTS OF 10 KW PV SYSTEM USING TITANS6_60 Temp (0C) Irradiation (W/m2) 26.3 27.2 28.6 28.9 29.0 29.8 30.2 31.6 250 520 630 640 650 570 480 310 Solar Power (kWh ) 3.3 5.2 6.2 6.4 6.0 5.6 4.5 2.9 Voltage (V) 177 152 140 130 133 136 137 136 Current (A) 18.6 34.4 45.6 49.0 45.0 41.2 32.8 21.3 Fig. 10. Tip speed ratio Vs Power coefficient at different pitch angles From the characteristics it is perceived that there exist the maximal value of power coefficient corresponding to the optimal value of the tip speed ratio for each value of pitch angle . The maximal power can be expressed as: (13) The speed reference for maximizing the power captured by the wind (MPPT strategy) is given by: (14) Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 4 1705 T. Bogaraj, J. Kanakaraj From the Fig.11 it is clear that the maximum power can be achieved at different rotor speeds for different power curves. The rotor speed is adjusted to achieve the Maximum Power Point operation of the Wind Turbine. Fig. 11. Turbine speed Vs Power at different wind velocities III. Control Strategies Hybrid Energy Systems has the problem of controlling energy flow between sources and loads, in spite of nonlinear variation of natural resources and continuously changing loads [11]. The Control system should meet the load demand as well as maintain power quality and stability. The main requirements of power management strategy [2] for hybrid energy system are to satisfy the load demand under variable weather conditions and to manage the power flow while ensuring efficient operation of the different energy systems. In Literature there are several control strategies for MPPT and Energy management between sources and load. Some of the methods are discussed in this section. III.1. Energy Management Control Strategies P. J. R. Pinto and C. M. Rangel have developed a power management strategy [2] for a PV/FC hybrid energy system that optimizes the overall system performance. It was stated that simulation results shows that the control strategy is effective in protecting the battery bank from overutilization and properly regulating the operating pattern of the Fuel cell and electrolyzer. Further simulation studies need to be performed in order to optimize the dc bus voltage thresholds of the hysteresis band on this system [30]. Guangming LI et al. have designed a hardware Controller [4] which is composed of PLC, HMI, gridconnected control module, ac multi-function electric power meters, dc electric power meters, RS485/TCP converter etc. This is applicable for small and Medium HPS and can run under grid connection or stand alone mode. M. Uzunoglu, O.C. Onar, M.S. Alam [11] have developed model of a stand-alone PV/FC/UC hybrid power system for residential micro-grid users with appropriate power flow controllers in MATLAB and Simpowersystems environment. The dynamic behavior of the hybrid system is tested under varying solar radiation and load demand conditions where the solar radiation and power demand data are based on real-world records. The developed system and its control strategy exhibit excellent performance for the simulation of a complete day or longer periods of time. Shuyun Jia, Jiang Chang [12] established a Multiagent model (MACMWSHPS) in Wind-solar Energy Hybrid Power System, which includes data collecting and processing module, control module, infomanagement module, resource-management module, wind and solar energy power generation agent modules, inverter agent etc. This technology is the application of Artificial Intelligence combined with network technology. The multi-agent technology can solve some control difficulties such as low reliability, weak maintainability resulting from the system’s nonlinear, distributing and random features. The authors reported that it can optimize the system and make the system’s output more stable, performance better, antijamming ability stronger. Yi-Feng Wang, et al. [13] defines a Hierarchical Fuzzy logic control for energy management is less complex and fast. The three state battery charging by using Fuzzy-PID control [14] was discussed, in which Grouping of batteries into two, made the possibility of charging and discharging simultaneously ie, one group supplying the load and the other is charging when the SOC is minimum. Caisheng Wang and M. Hashem Nehrir [15] have developed the simulation model of ac-linked PV/Wind/Fuelcell hybrid system for stand-alone applications using MATLAB/Simulink. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources and the storage unit in the system. Simulation studies have been carried out to verify the system performance under different scenarios using the practical load profile in the Pacific Northwest regions and the real weather data collected at Deer Lodge, MT. A. M. Sharaf et al. [16] proposed a novel control system for control of hybrid wind/photovoltaic (PV) farm utilization with alternative power source for DC type loads which have been simulated using MATLAB/Simulink/Simpower systems. The controller consisting of six different loop controllers is mainly used to regulate the DC-DC converter to reduce a weighted total sum of all loop errors, to mainly track a given speed reference trajectory depicting the demand for discharge or flow and also to ensure power quality, reliability and stability. The control strategy is based on source-load matching that is fully suitable for hybrid wind/photovoltaic farm with alternative interface connection to the local electric grid. The BP algorithm based ANN model [17] has been proposed for the energy management in PV/Wind Hybrid system. Training patterns are presented repeatedly to the Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 4 1706 T. Bogaraj, J. Kanakaraj ANN model and the adjustment is performed after each iteration whenever the network’s computed output is different from the desired output. This process continues until weights converge to the desired error level or the output reaches an acceptable level. Tamer T.N. et al. [18] proposed the control strategy for Renewable Energy Hybrid Source (HRES), which is decomposed into two parts, a local control depending on the power structure of each source and a global control deduced from global considerations and power objectives. The complete control device, playing the role of supervisor developed in this work, was qualified to control and coordinate the operation of the different subsets. The simulation results showed the effectiveness of the adopted control strategy. A grid connected PV/Wind operated Induction Generator system [19] has been simulated in Simpower of MATLAB. The proposed Average Current Mode Controller (ACMC) for the inverter connecting PV to the grid makes it possible for supplying reactive power to the wind generator and to act as real power source. The model for small stand-alone AC system with fuel cell and solar panel [20] was implemented and simulated with MATLAB/Simulink. The results show the feasibility of including dynamic models of the renewable energy resources in the analysis of the system performance. In order to increase the reliability of Hybrid power system, Majid Nayeripour and Mohammad Hoseintabar [21] included Fuel Cell and Super capacitor for energy storage. Proposed system was simulated for a day and real weather data. The slow dynamics of Fuel Cell system actuator makes the Fuel Cell sluggish and it cannot change its power to desired value as fast as the load variation [29], [30]. The dynamic response of fuel valve was simulated as first lead-lag transfer function and this delay time was applied to the power reference to keep the utilization factor of Fuel Cell system at its optimal value to improve Fuel Cell system lifetime. A new control strategy based on optimal usage of Fuel Cell system was proposed and it can prolong FC system lifetime and can improve its performance. III.2. MPPT Control Methods for Solar PV The solar cell V-I characteristics is nonlinear and varies with irradiation and temperature. But there is a unique point on the V-I and P-V curves, called as the maximum power point (MPP), at which the PV system is said to operate with maximum efficiency and produces its maximum power output. The location of the MPP is not known but can be traced by either through calculation models or search algorithms. Thus, maximum power point tracking (MPPT) techniques are needed to maintain the PV array’s operating point at its MPP. Many MPPT techniques have been proposed in the literature in which the techniques vary in many aspects, including simplicity, convergence speed, hardware implementation and range of effectiveness. However, the most widely used MPPT technique is the perturbation and observation (P&O) method [22], [31]. The proposed new controller scheme [22] for the standalone PV system controls both the boost converter and the charge controller. The simple MPPT algorithm is based on the measurement of load voltage and the PV voltage. Depending on these two observations, the controller will generate a suitable duty cycle for the Converter which is given by: (15) Also a PWM based charge controller which controls the charging current is used to protect the batteries. Fuzzy logic control [23] is employed to achieve maximum power tracking for both PV and wind energies and to deliver this maximum power to a fixed dc voltage bus from the considered site data at Al-Hammam. YuenHaw Chang and Chia-Yu Chang [24] described a Scaling Fuzzy Logic Control for MPPT, which is modified fuzzy logic control. The control algorithm was implemented in OrCAD Pspice for the cases of steady-state response, dynamic response to the variation of solar-cell current or load (RL). The simulation results were illustrated to show the efficacy of the proposed scheme. The authors have designed hardware for Scaling Fuzzy Logic Control and the results have not been verified at that time. III.3. MPPT Control Methods for Wind Turbine The maximum power output [25] can be manipulated upon the control of the rotor speed of wind power generator. As wind power is varying with wind velocity, the power acquired by the wind generator is maximized by either using Blade Pitch Control or Power Converters on generator side. The power converters change the duty cycle to vary the output power of the generator according to the wind speed. Variation of duty cycle is achieved by conventional methods such as Tip Speed Ratio Control, Hill climb Search method or Power Signal Feedback control. Nowadays more researchers are employing the techniques such as Fuzzy, Neural Network Genetic Algorithm and Particle Swarm Optimization. Chun-Yao Lee et al. proposed a method [25] based on Artificial Neural Network (ANN) and particle swarm optimization (PSO) for tracking the maximum power point of wind power generator. The estimated wind speed not only replaces the measurement of anemometer but also solves the problems such as aging anemometer and moved position. ANN and PSO were applied to estimate and control the optimal rotor speed so as to obtain the maximum power output of wind power generator by considering simultaneous variation of load and wind speed. The problem of maximizing [26] the WG output power using the converter duty cycle as a control variable was effectively solved using the steepest ascent method according to the following control law: Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 4 1707 T. Bogaraj, J. Kanakaraj (16) where Dk and Dk-1 are the duty-cycle values at iterations k and k - 1, respectively (0 < Dk < 1); Pk-1/ D k-1 is the WG power gradient at step k - 1; and C1 is the step change. Maximum-power-point tracking (MPPT) system presented, consisting of a high efficiency buck-type dc/dc converter and a microcontroller-based control unit running the MPPT functions. MPPT control algorithms for optimal wind energy capture using radial basis function network (RBFN) and torque observer MPPT algorithm [27] have been proposed by the authors for WECS which uses PMSG as prime mover. It was found that the PI method with torque observer can almost operate at the optimal C p. Torque observer is very efficient, especially for an optimization problem that the objective function cannot be explicitly expressed. This technique can maintain the system stability and reach the desired performance even with parameter uncertainties. The hybrid algorithm [28] developed, taking the generator output voltage value as the control measure and calculating power variations, which is given to the duty cycle of the converter. The duty cycle of the buck converter is varied according to the wind speed which varies the generator output power. This method is always operating at MPOP (Maximum Power Operating Point) under varying load and environmental conditions and is easy to implement. This algorithm overcomes the disadvantages of Perturbation & Observation method and Incremental conductance method. The average output power is increased when compared to the above said methods. IV. Conclusion improvements in renewable energy technologies provide more potential for wider applications of Hybrid Energy Systems. To reduce the generation cost of electricity, certain R&D improvements in solar PV and wind generator technologies are required. Many Researchers have simulated the control strategies using Artificial Intelligence Methods. Such methods have to be implemented in real time applications of PV and Wind energy systems. This may improve the ease of implementation & integration with the system and reduction in cost of the control system. References [1] [2] [3] [4] [5] [6] [7] [8] Very Recent Literatures were considered for the survey of MPPT methods for Solar PV and Wind Energy systems separately and as a Hybrid Energy System the Energy management control methods. A 10 kW Solar PV system and Wind generator systems have been simulated in MATLAB/Simulink and the simulated results are presented. These systems will be integrated to form a PV/wind Hybrid Energy System for further research. Hybrid Energy systems consisting of PV and Variable speed Wind generator are very much useful for the remote communities, telecommunication stations, water pumping system and industries which are far away from the Grid, even though it is not cost effective when compare to fossil fuels at present. Energy management controller and MPPT control system for energy sources may be designed and integrated to achieve efficient energy management and Maximum power point operation respectively. This also ensures continuous and reliable power to the consumers and more available energy extraction in natural resources. Need for pollution free energy sources, ever increasing cost of fossil fuels and comparable [9] [10] [11] [12] [13] [14] Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved Pragya Nema, R.K. Nema, Saroj Rangnekar, A current and future state of art development of hybrid energy system using wind and PV-solar: A review, Renewable and Sustainable Energy Reviews, vol. 13, pp. 2096-2103, 2009. P. J. R. Pinto, C. M. Rangel, A Power Management Strategy for a Stand-Alone Photovoltaic/Fuel Cell Energy System for a 1kW Application, Hydrogen Energy and Sustainability- Advances in Fuel Cells and Hydrogen Workshop, Apr.2010. 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Chandra Has, P.Ajay-D-Vimalraj, G.R.K.D.Satya Prasad, Power Enhancement of Wind Energy Conversion System Using Hybrid Method, International Journal of Advanced Engineering Sciences and Technologies, Vol. No. 7, No. 2, pp. 248 – 253, 2011. Meo, S., Perfetto, A., Esposito, F., Fuel-cell based inverter for residential systems, 5th IASTED International Conference on POWER AND ENERGY SYSTEMS (EuroPES 2005), June 15-17, 2005, Benalmádena, Spain, art. no. 468-149, pp. 49-54. Meo, S., Pagano, M., Paparo, G., Velotto, G., Integration of fuel cell electrical source in renewable energy power system supply, (2004) International Conference on Probabilistic Methods Applied to Power Systems, September 12-16, Ames, Iowa, Iowa State University, pp. 445-450. Meo, S., Perfetto, A., Piegari, L., Esposito, F., A ZVS current fed dc/dc converter oriented for applications fuel-cell-based, (2004) IECON Proceedings (Industrial Electronics Conference), 1, 30th Annual Conference of the IEEE Industrial Electronics Society, November 2 - 6, 2004, Busan, Korea, art. no. TA7-71, pp. 932937. Authors’ information T. Bogaraj completed his BE degree from Government College of Technology, Coimbatore during 1995. He worked as maintenance engineer for three years in a Paper Industry. Then he received M.Tech. degree in Process Control and Instrumentation from National Institute of Technology, Trichirapalli, Tamilnadu, India. Since 2003 he is working as Assistant Professor in Electrical and Electronics Department, PSG College of Technology, Coimbatore, India. His fields of interests are Control systems, Renewable energy systems, Hybrid power systems, Virtual Instrumentation. Dr. J. Kanakaraj received his B.E degree in Electrical and Electronics Engineering from Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India and M.E. Degree in Electrical Machines from PSG College of Technology, Coimbatore, Tamil Nadu, India. He received his Ph.D in Electrical and Electronics Engineering from Bharathiar Univerity, Coimbatore, Tamil Nadu, India. He is currently working as Associate Professor in Electrical and Electronics Department, at PSG College of Technology, Coimbatore, India. His research interests include in the areas of Control systems, Measurements and Control systems, Special Electrical machines, and Renewable energy systems. He has published several International and National Journals. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved International Review on Modelling and Simulations, Vol. 5, N. 4 1709 View publication stats