Comparison of Solar P&O and FLC-based MPPT Controllers & Analysis under Dynamic Conditions

Increase in electricity generation is caused due to population increase, which leads to the depletion of fossil fuels, and increased pollution. This leads to focusing on alternate renewable energy, mainly solar photovoltaic generation, due to the abundant availability. The maximum power generated by a PV module depends on the temperature and irradiance because the P-V and V-I natures are non-linear. Various DC-DC boost converters are used along with the MPPT techniques because the conversion efficiency of the PV system is low [1][2]. In this paper, comparative analysis between Perturb and Observe (P&O) and Fuzzy Logic-based Maximum Power Point Tracking (MPPT) systems along with modified SEPIC are done using MATLAB/ SIMULINK software. Simulations are done at different irradiations to observe its tracking speed towards MPP. From the obtained output (simulation), it is observed that the Fuzzy Logic Converter (FLC)-based MPPT controllers have good dynamic performance, reduced oscillation, high tracking speed, maximum power, etc...[3].


Introduction
Energy consumption by humans is increased due to increased loads and most of the energy is generated from conventional resources like fossil fuels, which results in heavy environmental pollution.Alternative energy resources like wind, solar, biomass, hydropower generation, and geothermal are considered to meet the energy demands.Among all the alternate sources, PV power generation is popular due to the abundant availability of solar power.
Solar power generation reached 179 TWh in 2022 and made a 22 % increase in power generation compared to the previous year.But it has a few limitations, low conversion efficiency and unstable output power due to weather conditions like changes in temperature and irradiance in a day and it also depends on the connected load.An MPPT is used to recognize the efficient utilization of solar cells because P-V and V-I curves are nonlinear, which results in non-linear power production [4].
Many MPPT techniques have evolved from the conventional MPPT algorithms like hill climbing, Perturb & Observe, IC, etc., and other advanced soft computing-based algorithms like Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC), Particle Swarm Optimization (PSO), and hybrid algorithms are also available [5][6].This paper focuses on the comparison of conventional P&O and soft computing based FLC algorithms based on the results.The MPPT controller senses the voltage and current at that instant time and calculates the power.The present power value and previous power value is compared [7].If power is equal to the previous power value, then there is no change in the duty cycle.But if there is a difference in power, the MPPT controller adjusts the duty cycle of the modified SEPIC controller so that the converter is extracting the maximum power output of the PV system [8][9].The proposed modified SEPIC converter is a combination of a conventional SEPIC converter with two circuits.The split inductor circuit is the first part, and the second part has 2 diodes and 2 capacitors [10][11].The SEPIC converter allows either buck operation or boost operation, allowing output voltages that are greater or less than the input voltage and that have identical polarity.With this modified SEPIC converter, the gain can be raised to 7.5 and the duty cycle is decreased to 0.5 [12].This circuit reduces the ripple of output current and voltage, also the limited input ripple leads to reduced switch stress [13][14].

Perturb and Observe Controller:
This MPPT algorithm will often change the switching cycle in a modified SEPIC converter by analyzing the voltage and power of current values with previously measured values [15].

Duty cycle changes:
1.The duty cycle will be increased to reach MPP if the current sensed voltage and power are higher than the previous value.2. The duty cycle will be lowered to reach MPP if the current sensed voltage and power are less than the preceding value.3. The duty cycle will be boosted if the voltage is less than the preceding value and the current sensed power is higher.4. The duty cycle will be decreased if the voltage is greater than the previous value and the current sensed power is lower [16][17].

Fuzzy Logic-based MPPT Controller:
The FLC-based MPPT controller is widely used nowadays because it effectively handles ambiguity and can handle linear and non-linear variation in the parameter with multiple rule-based systems [18].Fuzzification, a rule-based inference system, and defuzzification are its three main components.The inputs for the solar panel in the abovedescribed picture, which depicts the simulated circuit design of the FLC, are the irradiance and temperature [22].The FLC controller maintains track of the MPP in accordance with a set of regulations.These regulations are contrasted, and the duty cycle is established based on the voltage, thereby boosting the voltage.However, it takes longer to reach the MPP using this method [23].converter output Power at variable irradiance i.e., 400,600,800,1000W/2.The output power is maximum for specified irradiance i.e., 490W, 990W, 1700W, 2125W.

Conclusion:
In this paper, P-V and V-I characteristics of P&O, FLC-based controllers are designed with variable irradiance level using MATLAB/Simulink.Controllers work in improving the steady-state accuracy.This is done by decreasing the steady state error.With better steady-state accuracy, we acquire better stability [24].Controllers also help with unwanted offsets produced by the system by reducing them.From the charts and result it observed the FLC-based controllers has maximum output power, voltage and current of 2123 W, 392.3 V, and 5.412 I.A reduced oscillation, less fluctuation, and high tracking speed makes FLC controller better than the P&O controller.To improve the output power several other hybrid soft computing algorithms will be used for future enhancement [25].

Figure 4 .
Figure 4. Modified SEPIC Converter The inference system, where the output is turned, has 25 if-else-based rules and has five membership functions: Negative big (NB), Negative small (NS), Zero (Z), Positive small (PS), and Positive big (PB).It also has a defuzzification module, transforming linguistic variables into real ones [19][20].

Figure 7 .
Figure 7. Simulink model of solar PV with FL MPPT controller.

Figure 8 .
Figure 8. Solar PV simulation of P&O Controller with DC-DC modified SEPIC converter output Voltage at variable irradiance

Figure 10 .
Figure 10.Solar PV simulation of P&O Controller with DC-DC modified SEPIC converter output Power at variable irradiance Fig.10 indicates the Solar PV simulation with P&O Controller with DC-DC modified SEPIC

Table 1 :
Comparison output of P&O, FLCbased MPPT controllerFrom Table.1, it is observed that the Fuzzy Logic Controller has improved output at all irradiance levels of 400 W/m2, 600 W/m2, 800 W/m2, and 1000 W/m2 with power level of