A comparison of FLC MPPT techniques and HC MPPT techniques for photovoltaic systems

This study aims to examine the behaviour of various maximum power point tracking (MPPT) methods used with PV systems. The methodologies of hill climbing (HC) and fuzzy logic controller (FLC) are assessed in this paper. PV module and DC/DC boost converter models were simulated using PSIM and Simulink software with various MPPT strategies. The PSIM and Simulink software programmes were founder throughout the FLC MPPT method’s development. To employ each software for specific system components, the co-simulation was conducted. The effectiveness of the various MPPT approaches was assessed in response to rapidly varying weather conditions. The results show that for most of the tested MPPT approaches’ usual working ranges, FLC outperforms the HC MPPT approach in transient behavior and constant.


Introduction
Photovoltaics and other materials of renewable power are crucial in the production of power.We will need to develop a new kind of power system in the future to minimise carbon emissions owing relating to the depletion of fossil fuels and the harm they cause to the environment, and solar energy will become even more crucial because it is renewable and non-polluting.Photovoltaic systems will produce more than 45% of the world's required electricity [1].Unfortunately, there are two fundamental issues with PV systems: the electricity generated has a low conversion efficiency, and the electricity generated by solar panels fluctuates a lot depending on the weather.[2].Additionally, since PV systems' I-V and P-V characteristics are non-linear, factors like solar energy, Temp. of the air, and the kind of the connected load always affect their output power [3].To guarantee optimal use of the solar cells, maximum power point tracking (MPPT) is necessary since there may be a discrepancy with relation to stress parameterss and the maximum power point (MPP) of the PV module [4].Utilizing MPPTs can lower the price of power produced by solar panels [5].The comparison of the Hill Climbing (HC) technique with the Fuzzy Logic Controller will be the main emphasis of this study (FLC).One method is more conventional, and the other is a more contemporary approach that makes use of artificial intelligence.Investigated are the most effective algorithms and if artificial intelligence-based algorithms outperform more conventional ones while taking into account solar radiation variance and quickly changing weather conditions.This work is significant because it forecasts future developments in solar energy and may be used as a guide for selecting algorithms for some potential photovoltaic energy sources.

PV system modeling
According to Figure 1, the solar PV system consists of batteries, DC/DC boost converters, and PV modules in total.The PV module receives incoming radiation (R).It generates a current (I) and a voltage (V) (I).The module and ground are both linked to the battery's negative terminal.

DC/DC boost converter
The maximum power is transmitted to the demand from the senderwhen the reactance and supply impedance are equal, according to the principle of maximum power transfer (load matching).By changing the DC/DC converter's work cycle, load matching may be achieved.Duty cycle is the proportion of a switch's switch-on time to its switch-switching duration.To track the MPP,It transforms must operate at the correct work cycle.When the meteorological conditions change, to maximise the power output of the DC/DC converter's phase voltage, and the Pv system must be changed [6].Circuits for DC/DC converters can be set up in a variety of ways for this usage.The boost setup was employed in these experimental results due of its broad application and great durability in comparison to other more sophisticated configurations [7]. Figure 1 depicts the whole power device architecture.The PV module is shielded from potentially harmful negative currents by diode D1.To reduce the amount of distortion at specific rate components, C1 is positioned upon increase feed [7].

MMPT technology 2.2.1. Hill climbing (HC) technique.
When executing the maximum power point tracking job,The pulse width of the bridge rectifier serves like the evaluation criterion in the hill climbing (HC) method.The maximum power point has been tracked after the criterion dP/dD = 0 is satisfied [8]. Figure 2 depicts the HC algorithm's flowchart.By contrasting the power at the present moment with the power at the prior time, the duty cycle for each sample interval is calculated.The recommended load current is raised to make dD > 0 if the incremental power dP > 0. If dP<0, It is necessary to lessen the power level till dD<0.To avoid excessive fluctuations in power close to the maximum power point and so lessen the amount of energy drawn from the PV, periods of steady radiation call for a very modest duty cycle variation value, or D. To quickly track the maximum power, however, rapidly shifting radiation need larger duty cycle values.Figure 2 depicts the flow chart for this approach.Figure 3 illustrates how the method has been simulated using the PSIM programme.

Fuzzy logic controller (FLC) technique for PV MPPT.
As illustrated in figure, the error signal may be determined (1). the formula used to get the value of E is (2).The software applications PSIM and Simulink have jointly simulated the suggested system model.The purpose of the combined simulation is to make it simple to model the power supply circuit in PSIM and the FLC in Simulink.
E and ΔE are calculated using the PV system's output power and voltage in accordance with (2) and (3).E and E are frequently used as liquid chromatography's inputs.On the basis of several testing, the ranges of E and ΔE intelligently established.The basic fuzzy subset is used to express these variables as linguistic variables or labels, such as PB (positive large), PM (positive medium), PS (positive small), ZE (zero), NS (negative small), NM (negative medium), and NB (negative big).According to Figure 4, the mathematical affiliation function MF may be used to characterise each of these acronyms.The FLC output, which is often the duty cycle variation, or the energy converter's D, searchable in the regulation database shown in Table 1once E and E have been determined and transformed to MF-based linguistic variables.The trigonometric affiliation function may be used to both input and output variables since a digital control system can quickly and easily apply it.The linguistic variables allocated to D for various combinations of E and ΔE depend on the power converter in use as well as the user's understanding.

𝛥𝐸(𝑖) = 𝐸(𝑖) − 𝐸(𝑖 − 1)
(2) where ΔD=change of duty cycle; c(k)=peak value of each output; Wk=height of rule k.These nominal terms of the input and output MFs are then compared to a collection of predetermined values during the aggregation process.The FLC system must use the proper If-then rules or fuzzy inference in order to respond in a suitable manner.Table 1 is a list of the conclusions utilised in this study.These variables have been scaled in some experiments to five fuzzy subset functions [9].To explain the control knowledge, Table 2  To generate a number, a fuzzy fraction of the regulation type inference is defuzzied.A defuzzification phase is necessary because the plant often requires a non-fuzzy control value.The height approach is used to fuzzify this system.The height approach is an extremely quick and easy procedure.In formal terms, the height fuzzification approach in rule systems is provided by (4).
The FLC's output is changed from a linguistic to a digital variable during the defuzzification stage.The boost converter's D signal, an analogue signal, is provided by this.To determine the new value of D, this value is deducted from its prior value.
Thus according Figure 5, the PSIM model displays the computed E and E as well as the input to Simulink.The Simulink simulation model shows the platform's changing logic as well as the output signal from the FLC to the PSIM, as illustrated in Figure 6. Figure 7 depicts the Simulink simulation the suggested system's design, with SIMULINK1 being the name of the simulation used as input to the PSIM part of Simulink.

Compare HC and FLC
In terms of dynamic responsiveness and % energy savings, MPPT approaches are contrasted.That subsequent expression may being utilized to compute the % energy decrease.
where E represents the power that the PV system provides throughout the simulation period and Emax is the maximum amount of energy that can be produced from PV.
The simulations are run under identical circumstances to examine how well various MPPT approaches work when the duty cycle of the boost converter varies.The reciprocal of sampling frequency is sampling time.The sampling period for the MPPT is set to 0.01 seconds, and the sampling frequency determines how many samples are taken each second.Therefore, the pulse width is updated per 0.01 seconds.The following describes the PV's changing reaction component's produced energy when employing the HC MPPT method: D = 2%, 4%, 6%, 8%, and 10%.Figures 8 and 9 depict, respectively, the HC MPPT's dynamic reaction at startup and steady state., 6, and 7 illustrate how the FLC simulation for MPPT was acted upon using co-simulation between the PSIM and Simulink simulation tools.Co-simulation is used because Simulink handles fuzzy logic control better than PSIM and PSIM is more powerful and straightforward for developing power electronic PV components.For several duty cycle increments of ΔD = 2%, 4%, 6%, 8%, and 10%, the FLC approach was simulated.Figures 10 and 11 depict, respectively, the FLC MPPT technique's dynamic response during startup and steady state.Figure 10 demonstrates the inverse relationship between the start-up time and the increasing duty cycle value.Given that when D dynamics alter based on the desired circumstance,The simulation findings of this strategy show that, in terms of and stable responsiveness, it outperforms HC solutions.

Conclusion
This paper investigates which algorithms have the best performance and whether algorithms with the help of artificial intelligence are superior to traditional algorithms, taking into account the variability of solar radiation and rapidly changing weather conditions.The results that may be derived are as follows: The simplicity of the MPPT approach for hill climbing is a benefit.FLC's benefits include dealing with highly nonlinear, incorrect inputs, and the lack of a clear mathematical model.
However Technique FLC delivers superior results.than technique HC along with relation to the reaction dynamic and steady-state.Therefore, the use of AI-assisted algorithms will be more relevant in the future than traditional algorithms, as they will save more money and capture more energy.
The perturb and observe approach is one example of an artificial intelligence-based algorithm that is not compared in this research.In order to accomplish further optimization, future research should instead focus on improving the stability of AI algorithms in transients.

Figure 1 .
Figure 1.Block diagram of a general PV system.

Figure 2 .
Figure 2. Diagram of the phase transition for the HC MPPT technique.

Figure 3 .
Figure 3. application of the HC MPPT control method with PSIM.

Figure 4 .
Figure 4. a fuzzy system that has two inputs, one output, and seven MFs per input.

Figure 5 .
Figure 5. Simulink inputs and the calculation of E and ΔE are displayed in a PSIM model.

Figure 6 .
Figure 6.Simulink simulation of the switching circuit and FLC output signal for PSIM.

Figures 5
Figures 5,6, and 7 illustrate how the FLC simulation for MPPT was acted upon using co-simulation between the PSIM and Simulink simulation tools.Co-simulation is used because Simulink handles fuzzy logic control better than PSIM and PSIM is more powerful and straightforward for developing power electronic PV components.For several duty cycle increments of ΔD = 2%, 4%, 6%, 8%, and 10%, the FLC approach was simulated.Figures 10 and 11 depict, respectively, the FLC MPPT technique's dynamic response during startup and steady state.Figure10demonstrates the inverse relationship between the start-up time and the increasing duty cycle value.

Table 1 .
Guidelines for fuzzy systems.