A HYBRID CONTROLLER TO COORDINATE THE ENERGY PATHS OF HYBRID (WIND_ PHOTOVOLTAIC) SYSTEM

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 13, December 2018, pp , Article ID: IJMET_09_13_121 Available online at aeme.com/ijmet/issues.asp?jtype=ijmet&vtype= =9&IType=13 ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed A HYBRID CONTROLLER TO COORDINATE THE ENERGY PATHS OF HYBRID (WIND_ PHOTOVOLTAIC) SYSTEM Sadiq Muhsin Ihmood, Ali Hameed Mechanical Engineering Department, Collegee of Engineering, ThiQar University, Iraq ABSTRACT In this paper, a hybrid controller consists of neural network and fuzzy logic are proposed in order to coordinate the paths of energies obtained from different sources such as wind turbine and photovoltaic cells. PV system needs to apply the (MPPT) algorithm due to the instability of external circumstances like solar radiation and temperature. An artificial neural network applied to perform the optimization process and get the MPP value of PV system. This paper also presents a control strategy for power management in a grid -connected photovoltaic and wind turbine systems based on fuzzy logic technique. The control strategy was established to manage the flow of the power and select the optimum operating mode to ensuring continuous supply of the power. Results showed that the suggested control strategy for the hybrid system gives a greater reliability in terms of power generation and distribution compared to a stand- alone system with single source. The power distribution between the sources are % of power from photovoltaic cells only, % from wind turbine and % of power are provided by wind turbine and photovoltaic cells, the remaining % of the power is supplied through the grid. The complete system is analyzed through simulation in MATLAB/Simulink. Keywords: Maximum Power Point Tracking (MPPT), Photovoltaic (PV) System, Wind Turbine, Neural Network, Fuzzy Logic, Control Strategy. Cite this Article: Sadiq Muhsin Ihmood and Ali Hameed, A Hybrid Controller To Coordinate The Energy Paths of Hybrid (Wind_ Photovoltaic) System, International Journal of Mechanical Engineering and Technology, 9(13), 2018, pp et/issues.asp?jtype=ijmet&vtype=9&itype e= editor@iaeme.com

2 Sadiq Muhsin Ihmood and Ali Hameed 1. INTRODUCTION Because fossil fuels are exhaustible and they are not environmentally friendly, countries have avoided using them and rely on new and renewable sources of energy. [1]One of the most famous sources of new and renewable energy are wind energy and solar energy. In the first type, the wind kinetic energy is converted into electrical energy by wind turbine. In the second type, the sunlight is used to generate photovoltaic energy. Photovoltaic cells transform solar radiation into electrical energy [2]. Wind turbines have a horizontal rotation axis and the rotor with three blades. In addition to high speed electric asynchronous generator (induction generator) with gearbox. Asynchronous generators are entered to work in wind turbines because of their many advantages, including lower investment costs, possibility to work in different work conditions and simplicity of installation.[3] Photovoltaic energy is an energy obtained from sunlight. Through the system of photovoltaic cells that directly transform solar radiation into electricity. Photovoltaic cell has the primary role in this conversion process. Set of photovoltaic cells connected together called Panel. In General, panels are a number of solar cells linked to form a series cells for getting high voltage. Either for the purpose getting high currents, this is done by increasing the surface area of cells or by connecting cells in parallel. Photovoltaic system contains one or more panel; they are connecting to either parallel or a series. [2] there are multitude of studies about hybrid systems, many of them used the neural networkas a power management controller[4,5,6]. Some of the researchers used the fuzzy logic and genetic algorithm to develop controllers for the hybrid system [7,8,9,10].Other studies were presented about the general hybrid configurations. In this work, a hybrid controller consists of fuzzy logic and genetic algorithm were built to manage the power distribution in a grid connected hybrid system contain two sources of energy wind turbine and photovoltaic cells. 2. MAXIMUM POWER POINT TRACKING ALGORITHM. This algorithm is commonly used in photovoltaic systems for process optimization. Helped to reduce fluctuations in the MPPT, get a quick response and ease of operation, in addition to tracking the MPP when rapid changes in solar radiation conditions. [11] The MPPT algorithms is very essential for photovoltaic systems applications, because the point of the MPP are constantly changing and are unstable with the change of temperature and solar radiation. Therefore, it's important to use these algorithms to get the maximum power point from photovoltaic system. [12] Neural network used to perform the MPPT algorithm, the network fed with data arranged as input-output patterns, the network trained using Levenberg-Marquardt algorithm until each input produces the appropriate output. The inputs to the network are PV current and PV voltage while the output was Duty cycle. The network consists of input layer with two nodes, two hidden layers, 37 nodes in the first hidden layer and 36 nodes in the second hidden layer finally output layer of one node as demonstrated in figure (1). After training, testing and validation process for the network, the weight and baises of the network are extracted and used in Simulation editor@iaeme.com

3 A Hybrid Controller To Coordinate The Energy Paths of Hybrid (Wind_ Photovoltaic) System Figure (1) MPP based on neural network technique. 3. SYSTEM MODELING AND CONTROLLER The whole hybrid system implemented via MATLAB/ Simulink consists of two parts, the first represent a doubly-fed induction generator (DFIG) wind turbine of power 1.5 MW. The input for the wind turbine is the wind velocity profile shown in figure (2). Figure (2) wind speed profile m/s The second part of the model represent a 100 kw PV array, the inputs for this part are temperature and irradiance profiles shown in figure (3). The hybrid system illustrated in figure (4) implemented in order to provide the power demand of one day, the profile of this power demand illustrated in figure (5) editor@iaeme.com

4 Sadiq Muhsin Ihmood and Ali Hameed Figure (3) Sun irradiance profile in W/m2 and temperature profile in degrees C Figure (4) Hybrid system model Figure (5) Power demand profile kw editor@iaeme.com

5 A Hybrid Controller To Coordinate The Energy Paths of Hybrid (Wind_ Photovoltaic) System The purpose of fuzzy logic controller is to control the electric power generated from solar, wind turbine and grid sources. FL controller depends on the electrical load required (Power demand) and electric power produced from the hybrid system. When designing the FLC by MATLAB program power demand data must be entered with power generated from photovoltaic cells and wind turbine. In this study design each of fuzzy control system of photovoltaic and wind turbine in separately modes. In the FLC power demand compares with electrical power generated to produce an error signal which is used as input to FLC, in addition to the power demand signal as second input to FLC. Membership functions are linguistic variables represent an important part in the design of the FLC. Membership function values are assigned to the linguistic variables. There are four fuzzy subsets: S (small). 2- M (medium). 3- H (high). 4- VH (very high). The FLC deals with linguistic variables while the input and output are values. Inputs to FLC of PV are p-demand, error (difference between p-demand and PV power) e_pv.inputs for FLC of wind turbineare p-demand, error (difference between p-demand and wind turbine power) e_pw. The shape of membership functions is compound from triangular and trapezoidal. The method used in the stage of fuzzification is min-max method. In the inference mechanism stage use mamdani method. In the last stage (Defuzzification) use center of gravity method which is simple and fast. When implementing stage defuzzification by mamdani must specify the Rule base. 32rule bases on this controller as described in the tables (1,2). Table (1) Rule base of PVfuzzy logic controller e-pv p-demand S M H VH S Z Z VH Z M Z S M Z H Z Z Z M VH Z Z Z Z Table (2) Rule base of fuzzy logic controller for wind turbine e_pw p-demand S M H VH S S H VH VH M S M VH VH H S S H VH VH S S M VH The membership function plots used in this controller for PV and wind turbine are shown in the figures (6-11) editor@iaeme.com

6 Sadiq Muhsin Ihmood and Ali Hameed Figure (6) Membership function plot for power demand as input to PV Figure (7) Membership function plot for error signal e_pv as input to PV Figure (8) Membership function plot for power generated as output from PV

7 A Hybrid Controller To Coordinate The Energy Paths of Hybrid (Wind_ Photovoltaic) System Figure (9) Membership function plot for power demand as input to wind turbine Figure (10) Membership function plot for error signal e_pw as input to wind turbine Figure (11) Membership function plot for power generated as output from wind turbine. 4. CONTROL STRATEGY The objective of the control strategy was developed to coordinate the power flow and choose the optimum operating mode to ensuring continuous supply of the power without relying as much as possible on national grid. The basic principle to implement the control strategy use logical phrases such as if-else if. According to the following modes:

8 Sadiq Muhsin Ihmood and Ali Hameed Photovoltaic (PV) mode: When the power of the photovoltaic (PV) cells is sufficient to provide the power while the wind turbine power (PW) is not enough to provide electricity. In this mode, the photovoltaic cells will meet the power demand. PV>=P-demand and PW< P-demand wind turbine mode: When a wind turbine generate power enough to meet the power demand while the photovoltaic power is less than the power demand. The wind turbine will provide the electricity. PW>=P-demand and PV< P-demand When both of the wind turbine and photovoltaic cells power is sufficient to supply electricity to the load in this case provide load with electricity via photovoltaic. PW>=P-demand and PV>= P-demand Hybrid (PV-wind) mode: When the sum of power generated by photovoltaic cells and wind turbine sufficient to supply the electrical load. In this mode, both the photovoltaic cells and wind turbine will provide the power demand. PW + PV >=P-demand Grid mode: In the case of hybrid system is shutdown, national grid must supply the load. PW=0 and PV=0 Hybrid-grid mode: When the summation of power generated by photovoltaic cells and wind turbine are not enough to supply the electrical load. Here all the sources (Wind turbine, photovoltaic cells and grid) will share to provide the power demand. PW + PV < P-demand 5. RESULTS AND DISCUSSION Neural network architecture is very important to find the right solution for complex systems with non-linear relations or which owns random variable. The training process is the basis for the neural network. There are multiple algorithms to perform the training process but the famous type is the back propagation (or feed-forward), it is widely used. The common form of neural network contains three layers. Input layer receives external data, hidden layer get data from input layer and sent to the output layer. The neural network was used to get a proper duty cycle to reach near the maximum power point ofa photovoltaic cell. Figure (12) shows the curve of duty cycle produced from solar cells using different control methods. The curve on the right represents the duty cycle using a smart algorithm neural network technology, the purpose is to reach as close as possible to the maximum power point, while the curve on the left is the duty cycle using the classic algorithm Perturb & Observe (P & O) editor@iaeme.com

9 A Hybrid Controller To Coordinate The Energy Paths of Hybrid (Wind_ Photovoltaic) System Figure [12a]: Duty cycle of MPPT Controller Figure [12b]: Duty cycle of MPPT Using perturb & Observe technique. Controller using neural network. In order to validate the results of neural networks, their results were compared with the classical method (P &O), the results of the comparison are shown in figure (13) the error between 3% and 12%, these results showed that there is a considerable correlation between the two methods as clear in figure (14).The regression value was R= this high regression value confirm the accuracy of the results. Figure (13) errors values of neural network editor@iaeme.com

10 Sadiq Muhsin Ihmood and Ali Hameed R= Fit.Y=T Figure (14 ) The regression of neural network. 1. Wind turbine mode: In this mode, the wind turbine is the main source to provide power to the load when the wind turbine generating electric power greater than or equal to the power demand. This mode performs a conditional format tool (if condition) within the MATLAB function. If the condition is true, the power supply to the load from the wind turbine only with stop other sources (pv and grid). Figure(15) shows the time periods during which implementation processes where the wind turbine supply power to the load. Figure (15) Power from wind turbine only (wind turbine power mode) 2. PV-mode:pv model supply the power to the load if generating power greater than or equal to the power demand, this concept implemented by MATLAB function. The entrance condition (if) in MATLAB function turns on the pv system only while stop other sources(wind turbine, grid). Figure (16) shows the time periods in which the pv system is the primary source editor@iaeme.com

11 A Hybrid Controller To Coordinate The Energy Paths of Hybrid (Wind_ Photovoltaic) System Figure (16) Power from PV only (PV power mode) 3. hybrid mode (wind turbine-pv mode): This mode use if the system of photovoltaic cells alone is insufficient to provide the power demand as well as wind turbine power is insufficient to providethe power demand. In the MATLAB function add another condition combining both sources (pv+pw). The resulting power from photovoltaic cells combined with power from wind turbine and compare with the power demand if greater than or equal to the power demand, the hybrid system is the main supplier without need the grid. In this case should use logical elseif again to give more flexibility to the control system to implement different situations. Figure (17) shows the power generated during periods of photovoltaic cells and wind turbine together. Figure (17) Power from wind turbine and PV (wind turbine-pv mode) 4. hybrid-grid mode: This mode is applied if the hybrid system power(pv+pw) does not meet the electrical load. In this case the electrical grid is outsourced as additional power with the hybrid system. Logical expression (else) is representing the last condition to supply electricity to load. Figure (18) shows this mode

12 Sadiq Muhsin Ihmood and Ali Hameed Figure (18) Power from wind turbine, PV and grid (hybrid-grid mode) The final results of the above modes represents in figure (19) which agree with power demand as shown in figure (5), this means that the hybrid controller has succeeded in performing the task required from it. Figure (19) Power from all modes Finally, the power distribution between the sources are % of power from photovoltaic cells only, % of power from wind turbine only, % of power are provided by wind turbine and photovoltaic cells, the remaining % of the power is supplied from the grid as shown in figure (20). In each mode any access power from wind turbine or photovoltaic cells will send to grid

13 A Hybrid Controller To Coordinate The Energy Paths of Hybrid (Wind_ Photovoltaic) System Figure (20) Power distribution of all sources 6. CONCLUSIONS In this paper, a hybrid controller consists of neural network and fuzzy logic were built to coordinate the distribution of energies of hybrid system include two sources photovoltaic and wind turbine. The main task of the neural network was finding the duty cycle of photovoltaic, while the task of fuzzy logic was organizing the energies between the sources which reduces dependence on the electrical grid. The use of the suggested control strategy led to a reduction in costs as the reliance on the grid reduced to %.Finally this control strategy and fuzzy logic controller (FLC) has succeeded in organizing the distribution of power between multiple renewable sources to provide the power demand. REFERENCES [1] Omid Nematollahi, HadiHoghooghi, Mehdi Rasti, Ahmad Sedaghat, "Energy Demands and Renewable Energy Resources in the Middle East", Renewable and Sustainable Energy Reviews, 54(2016), [2] CemalZeray. "Renewable Energy Sources". MSc Thesis, University of Cukurova, Institute of natural and applied sciences. Department of electrical electronic engineering. January [3] Jasmin Martinez. "Modelling and control of wind turbines ". MSc Thesis, Imperial college London, Department of Chemical Engineering and Chemical Technology,September 21, [4] ChandrakantJaiswal, Dharmendra Kumar Singh, Analysis of Hybrid System using Feed Forward Neural Network, International Journal for Innovative Research in Multisciplinary Field, Volume 2, Issue 9 (2016). [5] KerimKarabacak, Numan Cetin, Artificial Neural Networks for Controlling Wind-PV Power Systems a Review, Renewable and Sustainable Energy Reviews, Volume 29 (2014), [6] Cong Hui Huang, Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems, Journal of Applied Research and Technology, Volume 12, Issue 6, (2014), editor@iaeme.com

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