Maximum Power Point Tracker Controller Using Fuzzy Logic Control with Battery Load for Photovoltaics Systems

The target of this paper is to model a Maximum Power Point Tracker (MPPT) using a Fuzzy Logic Control (FLC) algorithm and to investigate its behavior with a battery load. The advantage of this study over other studies in this field is that it considers a battery load rather than the commonly used re-sistive load especially when we deal with the relationship between MPPT and system load. The system is about 60 kW which is simulated under various environmental conditions by Matlab/Simulink program. For this type of non-linear application, FLC naturally offers a superior controller for the real load case. The artificial intelligence approach also benefits from this method for overcoming the complexity of nonlinear system modelling. The results show that FLC provides high performance for MPPT of PV system with battery load due to its low settling time and limited oscillation around the steady state value. These are assistant factors for increasing battery life.


Introduction
PV systems need special control techniques to ensure the taking out of the utmost available power, otherwise, the system may not be sustainable. As there be learn the PV generator which also known as PV array, generate DC power. The output energy of PV modules had greatly influenced by environmental conditions or factors. Effects on external performance are characterized by photovoltaic modules from ambient environmental influences such as irradiation, the temperature of module and outer humidity. The standard test condition (STC) is In spite of attractive features of the PV cells, their energy efficiency is still very low. The PV cell has non-linear current-voltage and power-voltage properties (i.e. I-V and P-V, respectively) which vary greatly with the ambient environmental conditions (i.e. irradiation and temperature) that be mentioned before.     [5].
The PV output also depends on operating point imposed by the load which was not indicated before [6]. As a result, an advanced control strategy is needed to maximize efficiency and generate and transfer as much power as possible from the photovoltaic cell to a load. The strategy matches the load resistance to the source (PV cell) resistance, forcing the PV cell to work on the MPP and ensuring optimum power extraction independent of ambient atmospheric conditions or load. This strategy called Maximum Power Point Tracking (MPPT). In addition, the PV system's operation using the MPPT method leads to supply high power output and therefore decreases the total number of PV cells needed, lowering the overall cost [3].
Many studies have carried out the field of the MPPT and especially the FLC method used in the low power of PV systems. Additionally, usually the MPPT is usually tested performance with the resistant load. In this study, the system will be a little different so that the PV system with about 60 KW of power and the load will be Lithium-Ion battery. Furthermore the process of adding the output crisp value of the FLC to the previous value of duty ratio (D) before transfer to pulse width modulation (PWM) will be ignored.
The rest of this paper is organized as follows: Section 2 gives an introduction of MPPT and the most famous various existing methods available in the literature with their features, advantages and shortcomings. Section 3 demonstrates basic operation principles of previous and introduces the flowchart of the proposed FLC technique for MPPT controller. Section 4 describes how the Simulink model and software simulation can be implemented for a battery load fed from the PV system through MPPT based on FLC. Simulation results based on specific parameters of The PV system have been elaborated in Section 5. Finally, conclusions and a summary of the research work are presented in Section 6.

MPPT Controller Methods
The  [9]. These methods, on the other hand, have flaws such as difficulty, high cost, complexity, and instability.
In [10] and [11] IC algorithm is able to pursue a quick oscillate of the solar irradiance with a great grade of accuracy. The complication and the high price of implementing this method are solely harmful factors. In the other classical P&O algorithm the uncomplicatedness of its structure and simplicity of implementation are the good things. But this algorithm is not deprived of its particular downsides. Main downside happening is the failure of the algorithm to acclimate to fast varying atmospheric environments and there is moreover a losing power because of the constant perturbation variations entered. The last one of conventional methods (RCC) which is a more flexible, robust and high rate of convergence but not suitable for converters with dc loads, three-phase loads, or perhaps even noisy single-phase loads. It also needs a larger inductor to decrease the fluctuation of the voltage and power. If the fluctuation and gain are very high, the control can saturate and exhibition limit cycle performance. In additionally [12] [13] IC, P&O and RCC methods usually require two sensors to measure module voltage and current which leads to increased losses of power.
With regard to intelligent or advanced groups which are more efficient than conventional methods, both ANN and FLC methods are faster convergence and show no oscillation at MMP. Those methods perform well under changing atmospheric conditions. But FLC is distinguished more efficient than ANN, robust, able to detect global MPP and do not need the knowledge of exact model. Demerits of ANN and FLC complexity, costs and requires periodic tuning. SMC method has high accuracy, steady state, simple and strong, but sluggish transient response and chattering are main drawbacks [11] [12]. Now some studies discussing comparisons between different MPPT methods will be presented briefly in the next paragraph.
In [14] study provides an analytical comparison for RCC, P&O and IC at different irradiations. The simulation results show that P&O is greatly affected by slow tracking and fluctuations, and IC achieves better than P&O in terms of tracking, but not for ripples. On the other hand, the RCC is able to solve both

MPPT Using Fuzzy Logic Controller
The maximum power point monitoring devices are used by the dc to dc converters to indemnity the output voltage of the solar panel in order to maintain the voltage at the value that maximizes the output power. The conceptual fuzzy MPP controller calculates the power from the equation (P = V × I) to extract the controller inputs with voltage after measuring the voltage and current at the solar panel output. The waveform of the duty cycle of the PWM used to toggle the dc to dc converter is represented by the fuzzy controller's wavy output [18].
The concept principal of MPPT Fuzzy Logic Controller as follows. The output power of PV is examined by the FLC in each sample (Time_k) and then defines the change in power with respect to voltage (dp/dv). If this value (dp/dv) is big-Smart Grid and Renewable Energy ger than zero, the controller modifies the pulse width modulation (PWM) duty cycle to increase the voltage until the power is maximum or the value (dp/dv) = 0, if this value is lower than zero the controller changes the PWM service cycle to reduce the voltage until the power is maximum and so on. Figure 4 illustrated that.
FLC has two inputs that are: error and change of error, as well as an output feeding the modulation of pulse width to control the DC-DC converter. The input signals depend on the instantaneous power and output voltage values which in turn effected by ambient atmospheric like radiation and temperature. The two FLC input variables error (E(k)) and change of error (∆E(k)) at sampled times k are calculated by [20]: where P(k) and V(k) are the instant power and voltage respectively of the PV generator.
The input E(k) point to whether the load running point in moment k is to the left or right of power-voltage characteristic's maximum power point for the PV module, whereas the input ∆E(k) specifies the trend of movement of that point.
Mamdani process of FLC for the MPPT, is utilized to perform the fuzzy inference. Fuzzification, inference engine, and defuzzification are the three essential components for FLC as shown in Figure 5

Fuzzification
In Fuzzification process, the error E(k) and change of error ∆E(k) input va-  and D1…D7 for output that proposed for FLC in this work.

Inference Engine
Mandamni's method, which implements a rule to the fuzzy input to define the fuzzy output, is used to determine the fuzzy inference engine. To get an acceptable linguistic value, the actual input value must be fuzzified before the rule can be evaluated. Table 1 displays the fuzzy controller rules list, with all matrix entries being fuzzy sets of the inputs E(k), ∆E(k) and the output the duty ratio (D) to converter.

Defuzzification
In [22] the fuzzy controller output must be transformed from fuzzy information to peremptory information because the buck converter needs a specific D-control signal when it enters. Defuzzification is the term for this transforma-

PV Modelling for Simulation
The PV modeling system to generate power by PV array block are by Matlab/Simulink which has several parameters, a graphical user interface (GUI) has been created to insert the data of any array model by entering parameters from its datasheet or by selecting available PV modules in GUI as shown in Figure 8.
Module PV of SunPower SPR-343J-WHT-D was chosen for this study [25]. Smart Grid and Renewable Energy

Fuzzy Logic Controller for MPPT Simulation
The two inputs are the Error (E) and Change in Error (CE) signals, which are processed to calculate the duty ratio output (D) of this FLC. The fuzzy logic algorithm was simulated using the fuzzy logic toolbox in Simulink/Matlab, and the rules were fine-tuned. In Figure 10, the basic window of the fuzzy designer is shown, with the controller based on Mamdani's fuzzy inference method and the centroid method as a defuzzification mechanism.

Membership Functions of the Proposed Fuzzy System
Fuzzy sets for each input and output variable that is defined triangular and ge-

Batteries
Most PV systems during non-daylight hours, times of heavy cloud cover or elec-Smart Grid and Renewable Energy trical vehicle fast charge station that need some form of battery to power the system. So the battery use as the load for simulation (see Figure 12).

Computer Simulation and Results
The values of the parameters used for simulation are presented in Table 2.
The entire system was combined and tested in Simulink/Matlab for change values of solar irradiation and temperature by using signal builder with ramp function carve (as shown in Figure 13).
The simulation model shown in Figure 14 was implemented at Simulink/ Matlab at different changes of irradiance and temperature use signal builder with ramp function carve. In order to check the fuzzy controller performance and the efficiency of the converter the readings of input and output power of the MPPT were taken at solar irradiance (1000 w/m 2 , 600 w/m 2 ) and temperature (25˚C, 45˚C) and also other measurements.
Figures from (Figures 15-19) show the curves of the result simulation of PV          system based maximum power point tracking of photovoltaic using FLC. Table   3 illustrates details about simulation results.

Conclusion
This paper suggested a PV modeling system with a fuzzy controller for tracking the maximum power point of a photovoltaic source, which was then simulated