A FUZZY-BASED COMBINED MPPT TECHNIQUE FOR WIND AND SOLAR SYSTEM

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1 International Journal of Electrical Engineering & Technology (IJEET) Volume 9, Issue 4, July-August 2018, pp , Article ID: IJEET_09_04_004 Available online at ISSN Print: and ISSN Online: Journal Impact Factor (2016): (Calculated by GISI) IAEME Publication A FUZZY-BASED COMBINED MPPT TECHNIQUE FOR WIND AND SOLAR SYSTEM Dr Sudhir Sharma Associate Professor and Head of the Department, Electrical Engineering Department, DAV Institute of Engg. & Tech., Jalandhar, India ABSTRACT Sandeep Kaur Student, Department of Electrical Engineering, DAV Institute of Engg. & Tech., Jalandhar, India Maximum Power Point Tracking has been using to enhance the capability of the power generation systems. There are two different power generation systems such as solar and wind which have been used by several researchers. Alternatively, in this paper, hybridization of these systems is done which are controlled by the fuzzy logic. It has combined with the system to make a decision about the participation of MPPT in the model. The simulation analysis has performed using the MATLAB Simulink model where wind and solar both systems are operated. From the results analysis, it has shown that the proposed system concludes fewer fluctuations as well as less distortion in the system. Key words: Maximum Power Point Tracking, Solar energy, Wind energy, Total Harmonic Distortion. Cite this Article: Dr Sudhir Sharma and Sandeep Kaur, A Fuzzy-Based Combined MPPT Technique for Wind and Solar System. International Journal of Electrical Engineering & Technology, 9(4), 2018, pp INTRODUCTION 1 There is a variable source of energy that is available on earth such as coal, petroleum, natural gasses etc. These resources can be categorized into two types as follows: Renewable Sources of Energy Non-Renewable Sources of Energy Renewable Sources of Energy: The renewable sources of energy generation are solar, wind, rain, tides, geothermal heat etc [1]. These sources of energies generally opt the energy from natural resources editor@iaeme.com

2 A Fuzzy-Based Combined MPPT Technique for Wind and Solar System Nonrenewable Source of Energy: The nonrenewable resources are such as coal, oil, petroleum, etc. which can be exhausted totally if used in excess. There is a need to conserve nonrenewable sources of energy, therefore, these are also known as finite sources of energy [2]. The example of a non-renewable source of energy is minerals, ores that are extracted from the crust of earth by the time when the natural geographical process takes place which makes sources viable to extract for economical use. As the human being is overusing the energy sources such as petroleum, coal, a natural oil which will results in depletion of these resources within coming hundred years. Hence there is a need to shift the focus on the renewable source of energy in spite of nonrenewable sources of energy because; there is no risk of depletion of the renewable source of energy. In 2012, it was surveyed that 19% of energy was consumed in that year and this number observed to be increased in 2013 and 2014 as per renewable global status survey [3][4]. Given table depicts the summarized report generated by a survey that point to global rapid enhancement in renewable energy on the basis of last few years from Table 1 Necessary global indicators for renewable energy S. No. Energy Sources (Gw) Renewable power 1, installed capacity with hydro 2. Renewable power installed capacity without hydro 3. Solar PV installed capacity 4. Wind power installed capacity 5. Concentrating Solar Thermal Power installed capacity From the above-defined tables and points, it is observed that the natural resources are depleting day by day for that reason in order to fulfil the required need of energy the power sector is researching for substitute of these resources. By using the renewable energy, the carbon molecules that are available in the atmosphere can be decreased through which the issue of global warming can be overcome [5]. There are various renewable sources are available out of which solar PV systems or wind PV systems are most prominent sources due to its simple and easy implementable topology. PV system is a device which is made up of semiconductors which are in the solid state. These semiconductors are used to generate electric power when it comes in the touch with light [6]. A PV device can be created by interconnecting these solar cells either parallel or in a series. It is better to connect these solar cells in series to achieve the high output voltage. The features like long-lasting benefits and free of maintenance cost make PV systems more useful editor@iaeme.com

3 Dr Sudhir Sharma and Sandeep Kaur Figure 1 PV generation System PV devices are mainly divided into categories as per their functionalities and operations performed by the devices, modules, the configuration of the system [7]. Grid-connected and standalone systems are two main categories of PV systems. These systems are created to produce DC and AC power energy to operate grid on an individual basis which is connected with other storage devices and energy sources. 2. MPPT (MAXIMUM POWER POINT TRACKING) MPPT stands for maximum power point tracking. It is a control technique or method which is basically employed with energy resources in order to extract the maximum capable energy of PV modules [8][9]. The objective behind employing MPPT is to enhance the efficiency of PV system. A large number of algorithms is created for MPPT control Types of MPPT Algorithms Various MPPT techniques have been developed form last few years. These techniques are divided as follows: 1. Online methods a. VMPPT b. CMPPT c. Look up Table 2. Offline methods a. P & O b. Incremental c. Temperature d. 3 Point MPPT 3. Indirect methods a. ANN b. Load Parameter c. RCC Out of above-defined techniques, offline methods are solar cell dependable methods. Figure 2 depicts the categorization of MPPT techniques. In this the three main categorize are further subdivided into modules as online MPPT techniques have further 4 more techniques under its category, similarly, the offline technique consist 5 subcategories and indirect 35 editor@iaeme.com

4 A Fuzzy-Based Combined MPPT Technique for Wind and Solar System technique is divided into three more types. Most of the MPPT techniques suffer from the problem of slow tracking which directly degrades the utilization efficiency of the system [10] Need for MPPT The need for MPPT arises due to disadvantages of PV systems. By deploying PV system, if the system tries to extract more energy as compared to the amount of energy that is produced by the system then there is a requirement of extra cost which is not feasible to implement. The MPPT is an electrical DC to DC converter or device that optimizes the similarities among PV devices, in this way it enhances the efficiency of power system [11]. To enhance the capability of power generation PV devices modifies the high voltage output that is received from the renewable source of energy to lower voltage power which is required to run the batteries. In MPPT DSPs are used to handle the converters [12]. The working of MPPT starts by receiving the input from PV systems then converts this power to high voltage DC voltage power and then splits it to the various DC voltage currents 2.3. Fuzzy Logic Control based MPPT techniques. From last few years, fuzzy logic based MPPT mechanism is most popular among the research workers in this field. The fuzzy logic based MPPT technique operates on the basis of estimated inputs and it is also able to manage the non-linear functions and did not require an exact model or match for processing [13]. Above defined characteristics of the fuzzy logic system makes it more popular among the researchers in various fields. The working of the fuzzy logic system is divided into three various states such as Fuzzification, decision making and Defuzzification. Following figure 3 explains the working process of the fuzzy system in brief. Firstly a crisp value is added to the fuzzy system as an input. Then Fuzzification process is applied to the crisp fuzzy values. Fuzzification is a process which converts the crisp values into fuzzy sets [20]. Figure 2 Fuzzification [34] Then defined rules are applied to the fuzzy input set driven by applying fuzzification. On the basis of rules an intelligent decision is taken and then the fuzzy sets are converted to the crisp values back by applying the Defuzzification [14] editor@iaeme.com

5 Dr Sudhir Sharma and Sandeep Kaur Rules Crisp Input Fuzzy Input Fuzzification Defuzzification Crisp Outp ut Fuzzy Output Intelligence Figure 3 Working of Fuzzy Logic Based System Fuzzification: It is a process in which the numerical values are converted to linguistic variables. The input membership function is defined on the basis of any interval such as [-1 1]. In case of the power system, PV array and MPPT voltages are two inputs that are used to define the control values. Decision making phase: In this phase, the system generates output by using input functions on the basis of If-Then rules set. Fuzzy logic contains a fuzzy rule set which defines the rules in the form of If-Then. Defuzzification: It is a process in which to manage the power and to control the operating point to MPP analogue signals are generated. 3. PROBLEM FORMULATION Energy is important for the human life and economy. Consequently, due to the increase in the industrial revolution, the world energy demand has also increased. In the later years, irritation about the energy crisis has been increased. Fossil fuels have started to be gradually depleted. It is a global challenge to generate a secure, available, and reliable energy and at the same time reduce the greenhouse gas emission. Energy saving was suggested by the researchers to meet the worldwide energy demand. But this method is a cost-effective solution. One of the most effective and suitable solutions is the renewable energy supplies. Recently an MPPT strategy was proposed to extract maximum power from a variable speed wind turbine based on squirrel cage induction machine drive by using a fuzzy logic control. The main issue that was analyzed in the previous model was that the till date most of the researchers work on the Pv array based MPPT systems only a few did work on wind-based power source but no one worked on combining both sources. Also if it will be done there will need to decide when it is to be switched so further work can be done in the proposed model. 4. PROPOSED WORK As per the study, it was analyzed that no work was done on combining the multi-source models as PV that is solar and the wind source. So in the proposed model, it is proposed that the model will have both the solar and the wind as a source and a hybridized model. Also, a new thing will be used that the switching will not be done manually that will be done using a fuzzy model that which source will be switched on. Finally, the rest MPPT will work on the fuzzy control based simulation. The whole working will be done using the MATLAB Simulink editor@iaeme.com

6 A Fuzzy-Based Combined MPPT Technique for Wind and Solar System 5. MATHEMATICAL MODELLING AND SIMULINK MODEL Mathematical Model 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 the resistor. These components defined the internal resistance to the current flow. The figure depicts this idea below: Figure 4 Circuit diagram of the PV model The equation of solar cell is given below. This equation is of voltage-current characteristics such as: ( ( ) ) (1) In the above equation 1, 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 = 1.6 * C and K = 1.38* 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 1. The photocurrent is totally dependent upon the solar irradiance as well as on the cell temperature which can be derived in the below equation as: ( ( )) (2) In the equation 2, Iscr represents the cell s short current at a 25 0 C and 1 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-20) * ) + (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 38 editor@iaeme.com

7 Dr Sudhir Sharma and Sandeep Kaur variable N is considered as the ideality factor which is dependent on the PV technologies. Thus, the concluded behaviour 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. Mathematical Model of Wind 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:.. (5) (6) With λ (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. Mathematical Model of MPPT The mathematical model of the MPPT which has used in designing the proposed model has described in this section. The MPPT can be defined in terms of two different conditions shown as: ( ) (9) ( ) ( ) ( ) (10) In the above equation (10), (a) represents the condition at MPP, and (b) and (c) represents the condition on left and right respectively. Moreover, the beta method is used to approximate the point of maximum power using the equation of variable which is independent i.e. (11) ( ) (11) In the equation 11, c represents the (q/ (σ.k.t.ns)) which is constant depending on the electric charge q, 39 editor@iaeme.com

8 A Fuzzy-Based Combined MPPT Technique for Wind and Solar System σ= quality factor of the junction panel, K=Boltzmnn constant, T= Temperature and N s = amount of PV cells in series As the conditions of the operation can be change but the value of β remains constant this can be used to calculate the voltage as well as the current of the panel and further, be inserted into a conventional closed loop having a constant reference. Figure 5 Proposed model of MPPT The above method described the proposed model of MPPT where fuzzy has combined to it to generate an optimum solution and perform switching between two different power systems i.e. wind and solar. 6. RESULTS This section of the paper explained the results acquired after performing the proposed method. In the proposed method solar and wind have combined together. Moreover, it uses the fuzzy logic decision model to decide when the MPPT will be participating. The experiment analysis was performed using the MATLAB Simulink model to examine the results of the proposed method. Figure 6 Fuzzy model of the proposed work In this model wind and solar units are combined and an MPPT controller is designed for the system to extract maximum power from the resources. The MPPT is controlled by the fuzzy logic editor@iaeme.com

9 Degree of membership Degree of membership Degree of membership Dr Sudhir Sharma and Sandeep Kaur Figure 6 represents the fuzzy model of the proposed work. This fuzzy model is used to switch between two different power generation systems wind and solar. For the proposed model, two different inputs such as error and change error and one output i.e. Voltage reference has taken. Based on the given input, an output has produced. NB NS ZE PS PB Error Figure 7 Membership function of the first input parameter Figure 7 shows the membership function of the first input parameter such as Error. The above membership function ranges from 0 to 1. Likewise, figure 8 and 9 also depicts the membership function of another input i.e. change error and one output function i.e. voltage reference respectively. NB NS ZE PS PB ChangeError Figure 8 Membership function of the second input parameter NB NS ZE PS PB VoltageReference Figure 9 Membership function of the output parameter 41 editor@iaeme.com

10 Current THD Voltage A Fuzzy-Based Combined MPPT Technique for Wind and Solar System Time Figure 10 Voltage of the proposed model The figure 10 shows the voltage of the proposed model. From the result it has been cleared that the number of distortions in the voltage are less and the system is more stable Time Figure 11 Total Harmonic Distortion Figure 11 shows the THD i.e. total harmonic distortion of the proposed model. It calculates the power quality of the electronic power system. The lesser the distortion, more accurate the reproduction of the signal will be. The figure concludes that THD is quite less in the proposed model Time Figure 12 The current of the proposed model The figure 12 represents the current of the proposed model. It depicts the three-phase current which varies from the -6 to editor@iaeme.com

11 Dr Sudhir Sharma and Sandeep Kaur Figure 13 Analysis of the proposed system The figure 13 characterizes the analysis of the proposed system in terms of current, Total Harmonic Distortion and the voltage. The numbers of fluctuations in the proposed model are less which confirms that the system is a more stable 7. CONCLUSION AND FUTURE SCOPE From the experimental analysis using the Simulink model has shown that the proposed model is efficient. It has been explored over three different system analysis parameters such as Current, Voltage and the THD. The three-phase current of the proposed model varies from -6 to 6. And the voltage acquired from the system has less number of fluctuations which concludes more stability. Lastly, the less distortion of the system confirms high accuracy. Consequently, the proposed model using the fuzzy logic 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 [1] A. Yaakoubi et al. 2016, "A MPPT Strategy Based on Fuzzy Control for a Wind Energy Conversion System", Procedia Technology, vol. 22, pp [2] Ahmed Saidi and Benachaiba Chellali, 2017, Simulation and control of Solar Wind hybrid renewable power system, Systems and Control (ICSC), th International Conference. [3] Anil Kumar Kashyap and Mr. Amit Agrawal, 2017, A Review On Pv Wind Based Hybrid Power System With MPPT Controllers, International Journal Of Engineering Sciences & Research Technology, Vol. 6, No. 4, Pp editor@iaeme.com

12 A Fuzzy-Based Combined MPPT Technique for Wind and Solar System [4] Ameni Kadri, Hajer Marzougui and Faouzi Bacha, 2017, MPPT control methods in wind energy conversion system using DFIG, Control Engineering & Information Technology (CEIT), th International Conference. [5] Arfaoui Jouda et al. 2017, Optimization of Scaling Factors of Fuzzy MPPT Controller for Stand-alone Photovoltaic System by Particle Swarm Optimization, Energy Procedia Vol. 111, Pp [6] Gabriel Merino et al. 2015, Power converter with maximum power point tracking MPPT for small wind-electric pumping systems, Energy Conversion and Management, Vol. 97, Pp [7] M. Nabipouret al. 2017, "A new MPPT scheme based on a novel fuzzy approach", Renewable and Sustainable Energy Reviews, vol. 74, pp [8] M. M. H. Prodhan et al. 2016, Design, Analysis and Performance Study of PV-Wind- Diesel Generator Hybrid Power System for a Hilly Region Khagrachari of Bangladesh, Journal Of Scientific Research, Vol. 9, No. 1, Pp [9] Majid Ali, Fuad Usman and Adnan Yousaf, 2017, Design and Simulation of Power Electronic Controller for Grid Connected PV Array with maximum power point tracking (MPPT), Renewable Energy Congress (IREC), th International. [10] Muhammad Ibrahim Munir et al. 2017, Control of Grid Connected PV Array Using P&O MPPT Algorithm, Green Technologies Conference (GreenTech), 2017 Ninth Annual IEEE. [11] Nuno Miguel Martins da Rocha and Julio Cesar Passos, 2016, MPPT method based on temperature control of the photovoltaic cells, Industry Applications (INDUSCON), th IEEE International Conference. [12] S. Marmouh, M. Boutoubat and L. Mokrani, 2017, MPPT fuzzy logic controller of a wind energy conversion system based on a PMSG, Modelling, Identification and Control (ICMIC), th International Conference. [13] Sabir Messalti et al. 2017, A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation, Renewable and Sustainable Energy Reviews, Vol. 68, No.1, Pp [14] Sakshi Gupta and Neha Sharma 2016, 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. 1, Pp [15] Saleh A. M. Saleh 2017, 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 [16] Samir Ladjouzi et al. 2017, A neural MPPT approach for a wind turbine, Systems and Control (ICSC), th International Conference. [17] Soedibyo, T. Hadi et al. 2016, "Integration of Hybrid PV/Wind Generation System Using Fuzzy MPPT in Grid Connected System for Remote Area", MATEC Web of Conferences, vol. 59, p editor@iaeme.com

13 Dr Sudhir Sharma and Sandeep Kaur [18] Yousef Mahmoud and Ehab F. El-Saadany, 2017, A Novel MPPT Technique Based on an Image of PV Modules, IEEE Transactions on Energy Conversion, Vol. 32, No. 1, Pp [19] Zakariae Jai Andaloussi et al. 2017, Toward an approach to improve MPPT efficiency for PV system, Wireless Technologies, Embedded and Intelligent Systems (WITS), 2017 International Conference. [20] Zouhaira Ben Mahmoud et al. 2017, A comparative study of four widely-adopted MPPT techniques for PV power systems, Control Engineering & Information Technology (CEIT), th International Conference editor@iaeme.com