AUTOMATIC SWITCHING CONTROL OF A HYBRID SOLAR-WIND SYSTEM USING FUZZY LOGIC

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1 International Journal of Electrical Engineering & Technology (IJEET) Volume 9, Issue 4, July-August 28, pp , Article ID: IJEET_9_4_5 Available online at ISSN Print: and ISSN Online: Journal Impact Factor (26): 8.89 (Calculated by GISI) IAEME Publication AUTOMATIC SWITCHING CONTROL OF A HYBRID SOLAR-WIND SYSTEM USING FUZZY LOGIC Dr. Sudhir Sharma Associate Professor and Head of the Department, Electrical Engineering Department, DAV Institute of Engg. & Tech., Jalandhar, India Sandeep Kaur Department of Electrical Engineering, DAV Institute of Engg. & Tech., Jalandhar, India ABSTRACT Modeling of hybrid system that utilizes both solar and wind energy source to provide power without any kind of disturbances is a powerful system. Considering, a hybrid approach has been introduced in this paper that makes use of both energy sources and drive accordingly depending upon the requirement. For the switching of one source to another or vice-versa, Fuzzy Logic Algorithm has been initiated. Fuzzy Logic utilizes the defined rules based upon which a particular energy source either wind or PV has selected and switch turns on or Off. The experimental analysis has performed using MATLAB simulink model to authenticate the performance of the proposed system. The results concluded that hybrid system can derive maximum power. Key words: Maximum Power Point Tracking, Solar energy, Wind energy, Total Harmonic Distortion. Cite this Article: Dr Sudhir Sharma and Sandeep Kaur, Automatic Switching Control of a Hybrid Solar-Wind System Using Fuzzy Logic. International Journal of Electrical Engineering & Technology, 9(4), 28, pp INTRODUCTION Fuzzy logic is not particularly used for Binary s and s. It can also be used to obtain several degrees of truth. Mainly, Fuzzy logic examines the truthiness of certain experiment while including binary value and or extreme False and extreme truth. In order to evaluate the truthiness of a particular experiment, it aggregates earlier results and supposed to produce a partial truth. The process continues for further higher truths. Considering, a truth is evaluated and resembles to the human brain. Moreover, another definition used to conclude the fuzzy logic is that it considers as a special way of expressing Boolean logic. The above 46 editor@iaeme.com

2 Dr. Sudhir Sharma and Sandeep Kaur discussion concludes that fuzzy logic is not only described binary and but takes several steps in between binary and. Binary value represents false whereas binary shows completely true. The steps taken in between and are described through user or designer. The entire control can be made gradually in all the degrees as well as directions. Fuzzy logic in this paper is treated as a key algorithm. The values received through wind sources and solar sources are continuously varying as the atmospheric conditions do not remain same for long period of time. Consequently, the acquired values are computed through fuzzy logic and based on which power sources are switched on and off. For instance, if sun is at its peak then solar panel is used to charge the battery alone and on the other side, wind turbine will be switched off using fuzzy logic. Alternatively, if wind is more, in that case solar panel will not be involved in charging of the battery. This paper involved switching of both sources such as solar and wind using Fuzzy Logic Controller. It has been seen that, in existing work, switching was done manually which resultant into less efficiency. Therefore, a fuzzy logic algorithm based switching has been introduced in this paper. Fuzzy logic has the capability of organizing the problem in terms of human operators can understand. Moreover, it is an effective tool in managing uncertainties as well as nonlinearities of the system. 2. SYSTEM ARCHITECTURE OF PV ARRAY In the proposed work, both source models have been utilized. The general mathematical model of PV array has been studied over past years. This equivalent circuit consists of different models such as a photocurrent, a diode, a parallel resistor expressing a leakage current and a series of resistor. These components defined the internal resistance to the current flow. The figure depicts this idea below: Figure Circuit diagram of the PV model The equation of solar cell is given below. This equation is of voltage current characteristics such as: ( ( ) ) () In the above equation, Iph is considered as the light generated current or photocurrent. Io is the cell saturation of dark current. The electron charge is considered as q =.6 * -9 C and K =.38* -23 J/K is the Boltzmann s constant. Other parameters such as T, N and Rsh are considered as the cell s working temperature, ideal factor and shunt resistance respectively. And Rs is a series resistance in the equation. Photo current is totally dependent upon the solar irradiance as well as on the cell temperature which can be derived in the below equation as: 47 editor@iaeme.com

3 Automatic Switching Control of a Hybrid Solar-Wind System Using Fuzzy Logic ( ( )) (2) In the equation 2, Iscr represents the cell s short current at a 25 C and Kw/m 2. Ki symbolizes the cell s short circuit current temperature coefficient. Tr is the cell s reference temperature and G is the solar irradiance in Km/m 2. With the variation in the temperature of the cell, the saturation current of the cell also varies. The equation to describe this variation is discussed below: ( ) ( ( ) ) (3) Tc= ((NOCT-2) * ) + (Ta) (4) In the equation 3, I OR signifies the cell reverse saturation current at a reference temperature as well as a solar irradiance. The Nominal operating cell temperature symbolizes through the NOCT. The band gap energy of the semiconductor in the cell shows as eg. The variable N is considered as the ideality factor which is dependent on the PV technologies. Thus, the concluded behavior of the PV cells are described using five parameters such as Iph, N, Is, Rsh. This model is used to represent the physical PV cell/module. The five parameters mentioned are related to the two different environmental parameters such as solar irradiance and the temperature. 3. SYSTEM ARCHITECTURE OF WIND TURBINE The method which is used to extract the amount of power from the wind is Wind Turbine. And the expression which is used for such purpose is given as: With (7) C p in the equation 5 shows the ability of a wind turbine in order to extract the power from the wind. This parameter is a complex function of λ and. The C p parameter is considered as the power coefficient. This coefficient parameter is used to represents the fraction of a power in the wind which is captured by the wind turbine. The power coefficient can be represented in the following formula as: ( ) (8) Where the equation 2 shows the pitch angle of the blade in degrees with the symbol β and tip speed ratio of the turbine is shown with γ. 4. EXPERIMENTAL RESULTS This section of the paper explained the results acquired after performing the proposed method. In the proposed method solar and wind has combined together. Moreover, it uses the fuzzy logic decision model to decide when to switch on which source. The experiment analysis has performed using the MATLAB simulink model to examine the results of proposed method. (5) (6) 48 editor@iaeme.com

4 Dr. Sudhir Sharma and Sandeep Kaur In this objective, fuzzy based decision capability of switching between the wind and solar systems has been implemented. The experimental results acquired from this objective are shown as: PV (5) Sw itchingfis (mamdani) State (2) 25 rules Wind (5) State 2 (2) System Sw itchingfis: 2 inputs, 2 outputs, 25 rules Figure 2 Fuzzy inference system of proposed model The above figure illustrates fuzzy inference system that takes two inputs i.e. PV and Wind that produces two outputs i.e. State and State 2 correspondingly. This system generates output based upon 25 different rules. The proposed fuzzy system used for switching defined five different fuzzy sets such as V_Low (Very Low), Low, Medium, High and V_High (Very High). These rules defined the output of switch and switch 2 either ON or OFF. Table Rules for the proposed FLC: Rule No. IF (PV) And (Wind) Then (State ) (State 2) V.Low V.Low On Off 2 V.Low Low Off On 3 V.Low Medium Off On 4 V.Low High Off On 5 V.Low V.High Off On 6 Low V.Low On Off 7 Low Low On Off 8 Low Medium Off On 9 Low High Off On Low V.High Off On Medium V.Low On Off 2 Medium Low On Off 3 Medium Medium On Off 4 Medium High Off On 5 Medium V.High Off On 6 High V.Low On Off 7 High Low On Off 8 High Medium On Off 9 High High On Off 2 High V.High Off On 2 V.High V.Low On Off 22 V.High Low On Off 23 V.High Medium On Off 24 V.High High On Off 25 V.High V.High On Off 49 editor@iaeme.com

5 Degree of membership Degree of membership Degree of membership Automatic Switching Control of a Hybrid Solar-Wind System Using Fuzzy Logic Membership functions of individual parameters are shown as: V.Low Low Medium High V.High PV Figure 3 Membership function of PV array input parameter V.Low Low Medium High V.High Wind Figure 4 Membership function of wind input parameter The membership function of PV array and Wind input parameters are shown in the figure 3 and 4. Each input parameter has five different membership functions such as Very Low, Low, Medium, High and Very High that varies from to (degree of membership) with respect to varied solar and wind values such as to 7 with the difference of. off on State Figure 5 Membership function of State output parameter 5 editor@iaeme.com

6 Degree of membership Dr. Sudhir Sharma and Sandeep Kaur off on State 2 Figure 6 Membership function of State 2 output parameter The figure 5 and figure 6 depicts the output parameter of State and State 2. In this work, state and state 2 depicts the ON and OFF state whereas confirms ON state and confirms OFF state..8 State Wind 2 2 PV 4 6 Figure 7 Surface Viewer of State.8 State Wind 2 2 PV 4 6 Figure 8 Surface viewer of State 2 The figure 7 and 8 depicts surface viewer of state and state 2. This graphical representation of proposed work shows output surface of input and output parameters. On X and Y axis, two input parameters such as PV and Wind are used and for Z axis, output parameter state and state 2 are evaluated. 5 editor@iaeme.com

7 Automatic Switching Control of a Hybrid Solar-Wind System Using Fuzzy Logic Figure 9 Rule Viewer of Objective 3 (Proposed model) The figure above exemplifies the rule viewer for proposed model where two parameters are input and two parameters are output. This viewer exemplifies the output value that depends upon the input values and shows at which value the particular value will be acquired. Considering the example, where value of PV and Wind system places at 35 for individual that produces State and State 2 output at.989 and.6 respectively. Figure Output Power of Solar and Wind Power system The figure above shows the Solar system power and Wind system power individually and then Output power is assessed. From the figure, it has been shown that initially solar system s power can be considered that shows variation and at the power is reduced. Alternatively, wind power system generates power at and shows variations. Consequently, output power shows the modest variations from initial point and continues till processing editor@iaeme.com

8 Power Power Power Dr. Sudhir Sharma and Sandeep Kaur 7 Output Power of Wind System 5 Output Power of Solor System (a) (b) Figure Output Power of (a) Wind and (b) solar system 7 Output Power Figure 2 Output Power The figure and 2 depicts the output power of solar and wind power system. In figure (a) output power of wind has shown where initially till time there is no power and then fluctuations in power has occurred varies from 3 to 62. Similarly, power from solar system generates at with modest fluctuations varies from to 5. Thus, generated output power varies from to 62 with variations in power from initial. Figure 3 Voltage of solar and wind system 53 editor@iaeme.com

9 Current Automatic Switching Control of a Hybrid Solar-Wind System Using Fuzzy Logic The figure 3 illustrates the voltage of solar and wind system. Each power system generates power accordingly. Considering the figure below, solar system generates voltage and with the passage of time it has been decreasing; similarly, in wind system, voltage has been generated with a peak and gradually decreased with time. Figure 4 Switching of solar and wind power system The figure 4 depicts the switching between solar and wind power systems. The time when solar system generates enough power, the switch regarding solar system switched ON. Likewise, the wind system switches ON when the wind power system generates enough power. However, when switch of solar system is ON the alternative wind power system switch is OFF and vice- versa. 4 3-Phase Output Current Figure 5 Three Phase Output Current 54 editor@iaeme.com

10 Switching Switching Voltage Voltage Dr. Sudhir Sharma and Sandeep Kaur 8 3-Phase Output Voltage Figure 6 Three Phase Output Voltage Figure 5 and 6 shows 3-phase output current and voltage. This simulation is shown in MATLAB software. At time to 3, the current and voltage is generated and varies. 4 Output Voltage of Solor System Figure 7 Output voltage of solar system The figure 7 exemplifies the output voltage of solar system. Initially, from time to, voltage of solar system varies and from that time onwards, the voltage of solar system start decreasing. The voltage of solar system gradually decreases till time Output Switch of Solor Syatem Output Switch of Wind Syatem Figure 9 Output switch of Wind system 55 editor@iaeme.com

11 Automatic Switching Control of a Hybrid Solar-Wind System Using Fuzzy Logic The figure 8 and 9 depicts the switching performed by proposed model. There are two output switch such as solar and wind system. If the switch is at then it is OFF and if the value is then switch is ON. Initially solar system is ON and at time, it if OFF. Alternatively, at time, the wind switch system is ON and then it gets OFF. 5. CONCLUSION AND FUTURESCOPE The proposed system performs switching in between two power sources such as wind and solar. In order to perform switching, fuzzy logic algorithm has used due to its efficiency. The primary factor of proposing hybrid approach is to accomplish the requirement of maximum power. This power can be acquired through either solar or wind or combined. For both systems, two switches are introduced i.e. one for solar and one for wind. Fuzzy logic pay wise attention on the power and turns switch on or off of both systems according to the requirement or defined rules. The simulation analysis has been concluded that maximum power can be acquired using hybrid system. Consequently, the proposed model using the fuzzy logics with hybridization of two different power generation systems ensures more stability and accuracy. The proposed model combines the energy power of both the system for the utilization. Thus, in case if the energy of both the system goes down, the demanding energy will not be fulfilled. Therefore, in future, diesel generator can be introduced to accomplish the desired requirement of the energy. REFERENCES [] A. Yaakoubi, A. Asselman, A. Djebli and E. Aroudam, "A MPPT Strategy Based on Fuzzy Control for a Wind Energy Conversion System", Procedia Technology, vol. 22, pp , 26. [2] M. Nabipour, M. Razaz, S. Seifossadat and S. Mortazavi, "A new MPPT scheme based on a novel fuzzy approach", Renewable and Sustainable Energy Reviews, vol. 74, pp , 27. [3] S. Marmouh, M. Boutoubat and L. Mokrani, MPPT fuzzy logic controller of a wind energy conversion system based on a PMSG, Modelling, Identification and Control (ICMIC), 26 8th International Conference on, January 27. [4] Nuno Miguel Martins da Rocha, Julio Cesar Passos, MPPT method based on temperature control of the photovoltaic cells, Industry Applications (INDUSCON), 26 2th IEEE International Conference on, March 27. [5] Arfaoui Jouda, Feki Elyes, Abdelhamid Rabhi, Mami Abdelkader, Optimization of Scaling Factors of Fuzzy MPPT Controller for Stand-alone Photovoltaic System by Particle Swarm Optimization, Energy Procedia Vol., Pp , March 27. [6] Sabir Messalti, Abdelghani Harrag, Abdelhamid Loukriz, A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation, Renewable and Sustainable Energy Reviews, Vol. 68, No., Pp , February 27. [7] Anil Kumar Kashyap,Mr. Amit Agrawal, A Review On Pv Wind Based Hybrid Power System With MPPT Controllers, International Journal Of Engineering Sciences & Research Technology, Vol. 6, No. 4, Pp , April 27 [8] Muhammad Ibrahim Munir, Tasneim Aldhanhani, Khalifa Hasan Al Hosani, Control of Grid Connected PV Array Using P&O MPPT Algorithm, Green Technologies Conference (GreenTech), 27 Ninth Annual IEEE, May editor@iaeme.com

12 Dr. Sudhir Sharma and Sandeep Kaur [9] Ameni Kadri, Hajer Marzougui and Faouzi Bacha, MPPT control methods in wind energy conversion system using DFIG, Control Engineering & Information Technology (CEIT), 26 4th International Conference on, May 27 [] Yousef Mahmoud and Ehab F. El-Saadany, A Novel MPPT Technique Based on an Image of PV Modules, IEEE Transactions on Energy Conversion, Vol. 32, No., Pp , March 27 [] Zouhaira Ben Mahmoud, Mahmoud Hamouda and Adel Khedherm, A comparative study of four widely-adopted MPPT techniques for PV power systems, Control Engineering & Information Technology (CEIT), 26 4th International Conference on, May 27 [2] Saleh A. M. Saleh, Testing the Performance of a Resolution-Level MPPT Controller for PMG-Based Wind Energy Conversion Systems, IEEE Transactions on Industry Applications, Vol. 53, No. 3, Pp , June 27 [3] Majid Ali, Fuad Usman and Adnan Yousaf, Design and Simulation of Power Electronic Controller for Grid Connected PV Array with maximum power point tracking (MPPT), Renewable Energy Congress (IREC), 27 8th International, May 27 [4] Muhammad Ibrahim Munir, Tasneim Aldhanhani and Khalifa Hasan Al Hosani, Control of Grid Connected PV Array Using P&O MPPT Algorithm, Green Technologies Conference (GreenTech), 27 Ninth Annual IEEE, May 27 [5] Zakariae Jai Andaloussi, Abdelhadi Raihani, Abdelmounaim Elmagri and Omar Bouattane, Toward an approach to improve MPPT efficiency for PV system, Wireless Technologies, Embedded and Intelligent Systems (WITS), 27 International Conference on, May 27 [6] Yousef Mahmoud and Ehab F. El-Saadany, A Novel MPPT Technique Based on an Image of PV Modules, IEEE Transactions on Energy Conversion, Vol. 32, No., Pp , March 27 [7] Samir Ladjouzi, Said Grouni, Mustapha Djebiri and Youcef Soufi, A neural MPPT approach for a wind turbine, Systems and Control (ICSC), 27 6th International Conference on, June 27 [8] Ahmed Saidi and Benachaiba Chellali, Simulation and control of Solar Wind hybrid renewable power system, Systems and Control (ICSC), 27 6th International Conference on, June 27 [9] Soedibyo, T. Hadi, H. Putra, H. Suryoatmojo and M. Ashari, "Integration of Hybrid PV/Wind Generation System Using Fuzzy MPPT in Grid Connected System for Remote Area", MATEC Web of Conferences, vol. 59, p. 2, 26. [2] Sakshi Gupta, Neha Sharma, A Literature Review of Maximum Power Point tracking from a PV array with high Efficiency, International Journal of Engineering Development and Research, Vol. 4, No., Pp. 57-6, editor@iaeme.com