Enhancing Photovoltaic-Powered DC Shunt Motor Performance for Water Pumping through Fuzzy Logic Optimization

Abstract

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios.

1. Introduction

In the wake of global climate change, the use of renewable energy (RE) sources is on the rise. Several countries have invested heavily in the development of renewable energy parks to reduce their carbon footprints and to generate electricity [1]. Among all the RE sources, solar PV is considered a viable option for remote electrification. This is mainly because of the ease of development of civil infrastructure for a PV plant, and the easy deployment of PV system components using ordinary devices [2]. While the PV system has many advantages, it suffers from its dependence on sunlight and its nonlinear characteristics. Owing to these two problems, the solar PV system output poses special challenges for designers to harness maximum power under a given environmental condition [3]. Failing to do this, the investment return or payback time increases, which ultimately results in an uncompetitive per unit price of electricity. Furthermore, the nonlinear characteristics of a PV module make it difficult to couple the load directly with a PV module [4]. In practical scenarios, a DC-DC converter is used to match the impedance offered by the PV module under a given environmental condition with that of a load. This impedance match occurs by setting the duty ratio of the DC-DC converter at the optimum point. The algorithm that ensures that the duty ratio is optimal is called the maximum power point tracking (MPPT) algorithm. The MPPT algorithms not only ensure the maximum output power under all environmental conditions but also create a narrow band of PV operating voltage. This narrow band of PV voltage is of great help to maintain a stable output voltage [5]. Various MPPT algorithms have been designed and evaluated by the research community [6,7]. These methods vary in their functioning capability, complexity, implementation cost, tracking speed, and efficiency. Nowadays, the use of heuristic and meta heuristic methods is very common to enhance the system performance [8,9].

The fuzzy logic controller (FLC) has emerged as a popular solution for tackling various technological challenges, including the maximum power point tracking (MPPT) of PV modules [10,11]. This intelligent control strategy mimics human-like logical reasoning [12], incorporating internal components such as fuzzy rules and fuzzification and defuzzification blocks to structure its operation. One of the key advantages of the fuzzy logic controller is its independence from the traditional induction machine paradigm, allowing it to effectively handle nonlinear control commands. Its applicability extends to complex and imprecise systems where conventional mathematical models may be inadequate [13]. Consequently, the design and implementation of FLC-based systems are straightforward, making it an attractive choice for practical applications.

In recent studies, fuzzy logic controllers (FLC) have been extensively employed in photovoltaic systems to optimize their operation at the maximum power point, as demonstrated in [14,15]. Typically, such FLC-based systems utilize a set of rules to determine the appropriate duty ratio for efficient power extraction. Furthermore, authors in [16] introduced an FLC approach to enhance the control and optimization of photovoltaic water pumping systems (PWPS), integrating it with the conventional direct torque control (DTC) method. By incorporating a variable-step size perturb and observe (P&O) algorithm, maximum power extraction from the PV panel was achieved. The effectiveness of this approach was investigated using MATLAB/Simulink. Similarly, in [17], a proportional–integral (PI) controller-based maximum power point tracking (MPPT) algorithm, combined with an adaptive neuro-fuzzy inference system (ANFIS), was proposed for solar-powered brushless DC motor applications in water pumping. This method adjusted the duty cycle of the Zeta converter using an ANFIS with a PI controller to maximize power extraction from the PV array, although its evaluation was limited to varying irradiance conditions. Furthermore, ref. [18] discussed the utilization of a photovoltaic (PV) water pumping system and proposed an MPPT algorithm based on the Grey Wolf Optimizer (GWO) and a modified LUO (M-LUO) converter. This approach, while ensuring effective power extraction from the PV array, facilitated the smooth starting of a brushless DC (BLDC) motor. Moreover, authors in [19] focused on designing and modeling an efficient photovoltaic water pumping system incorporating a fuzzy logic controller (FLC) and proportional–integral (PI) techniques. They implemented the maximum power point tracking (MPPT) using incremental conductance and perturb and observe (P&O) algorithms, evaluating the proposed method under various weather conditions using MATLAB/Simulink. Lastly, [20] presented a two-stage control strategy for a solar photovoltaic (PV) system coupled with a brushless DC motor through a DC-to-DC zeta converter.

In the quest to optimize power extraction from the PV system and expedite the tracking of maximum power, a fuzzy rule-based maximum power point tracking (MPPT) algorithm is introduced in the initial stage. Subsequently, in the second stage, precise control of the brushless DC motor is achieved through a trapezoidal control strategy employing electronic commutation. The efficacy of the proposed algorithm is demonstrated through simulation results conducted on the MATLAB platform. In [21], the authors present a PV generator integrated with a shunt motor operating on direct current. Employing a fractional open-circuit voltage approach, the generator efficiently monitors power, allowing for the generation of significant power output. Various isolation rates are employed to analyze the dynamic, transient, and steady-state behavior of the direct current shunt motor powered by the PV module generator. Although multiple maximum power point tracking (MPPT) techniques are incorporated to enhance the performance of the DC shunt motor utilizing PV panels, their responses lack the ability to achieve dynamic performance effectively in the face of load torque changes. Furthermore, in [22], a novel approach for the adaptive neuro-fuzzy inference system (ANFIS) controller in a photovoltaic (PV)-powered three-phase induction motor water pumping system is proposed. The ANFIS controller is utilized to determine optimal inverter output (V/F) patterns, optimizing various control objectives including maximizing the power harvested from PV systems through MPPT, as well as maximizing both the pump flow rate and induction motor efficiency. Serving as an adaptive controller, the ANFIS controller rapidly adjusts modulation indices for diverse operating points based on variations in temperature, irradiance, and insolation levels.

However, to our best knowledge, a lot of the previous studies about photovoltaic water pumping systems were implemented only through simulation in MATLAB and did not test the effectiveness of the proposed methods in the presence of load variation. The novelty and contributions of this paper are as follows:

• Introducing a novel intelligent fuzzy logic controller-based MPPT algorithm aimed at enhancing the efficiency of PV systems.
• Designing the proposed FLC-MPPT system to accommodate load variations, ranging from constant resistive loads to dynamic loads.
• Utilizing input parameters such as changes in PV voltage and power, requiring only a single current sensor and voltage divider, thus ensuring a cost-effective solution.
• Implementing an accumulation technique at a specific interval (100 µs) to derive the necessary duty cycle from the change in reference voltage, eliminating the need for a proportional–integral–derivative (PID) controller to regulate PV voltage.
• Addressing oscillation issues around the maximum power point (MPP) and enhancing response speed through temporal adjustments, particularly in comparison to the widely used perturb and observe (P&O) method.
• Conducting rigorous simulation assessments using MATLAB/Simulink, benchmarking against the conventional P&O method, showcasing reduced oscillations and improved response rates with the FLC-based MPPT algorithm.
• Validating the algorithm’s real-world effectiveness through experimental verification, employing a 120 W laboratory hardware prototype and assessing performance under resistive and water pump loads. Results obtained using a DS1104 embedded solution confirm the efficacy of the proposed FLC-based MPPT algorithm in optimizing PV module operation across diverse load conditions.

This study contributes to the scholarly discourse on advanced MPPT techniques, offering empirical evidence of the proposed algorithm’s effectiveness in enhancing the operational efficiency of PV systems.

The rest of this paper is organized as follows: Section 2 includes the simulation setup that involves the PV module, DC-DC buck-boost converter, and the proposed fuzzy logic-based MPPT. Section 3 presents the simulation results and discussion. Section 4 presents the experimental setup. Finally, a conclusion is presented in Section 5.

2. Simulation Setup

Figure 1 illustrates the conceptual validation setup employed for validating the proposed MPPT algorithm. The schematic encompasses essential components, including a PV module, a DC-DC converter, and the load. Moreover, the model incorporates sensors dedicated to the measurement of photovoltaic current, voltage, and load current. The output generated by the FLC is directed to the DC-DC converter through the pulse width modulation (PWM) block. A detailed exposition of the simulation model is provided subsequently.

2.1. PV Module

Figure 2 depicts the equivalent circuit of a practical PV module, employing a one-dimensional (1D) model that strikes a commendable balance between accuracy and computational complexity. The model is characterized by two resistances: series resistance and parallel resistance. The series resistance accommodates losses in the current path arising from factors such as the metal grid, contacts, and the current-collecting bus within the PV module. Conversely, the parallel resistance characterizes losses associated with the intrinsic device’s current leakage. In the case of a well-engineered PV module, the parallel resistance tends to be large, thereby minimizing its influence on the output characteristics.

The open-circuit voltage ($V_{oc}$) and short-circuit current ($I_{sc}$) of the PV model shown in Figure 2 are determined under various weather conditions. The $I_{sc}$ and the $V_{oc}$ at standard weather conditions ($G_{st}=1000$ W/m² and $T_{st}=$ 25 °C) can be obtained from the PV module’s nameplate.

The current generated by the incident light ($I_{PV})$, which is called the short-circuit current (I_sc), at a given cell temperature (T_a) [23,24] is as follows:

$$I_{PV}=I_{scn}(1+a\left(T_a-T_n\right))\frac{G}{G_n}$$

(1)

where:

• $I_{scn}$: Short circuit current at normal conditions (25 °C, 1000 W/m²).
• $T_a$: Given cell temperature (°C).
• a: Temperature coefficient of I_sc in percent change per degree temperature.
• $G_n$: Nominal value of irradiance, which is normally 1000 W/m².

As seen from Figure 1, the output current delivered ($I$) to the load can be expressed as [23,24]:

$$I=I_{PV}-\underset{I_D}{\underbrace{I_o\left(e^{\frac{q\left(V+I{R}_s\right)}{nK{T}_a}}-1\right)}}-\underset{I_P}{\underbrace{\frac{V_D}{R_P}}}$$

(2)

where $V_D$ is the voltage across the diode, R_P is the parallel resistance, and R_s is the series resistance.

The reverse saturation current of diode (I_on) at the reference temperature (T_n) is given as [23,24].

$$I_{on}=\frac{I_{scn}}{e^{\frac{{qV}_{ocn}}{nk{T}_n}}-1}$$

(3)

where $V_{ocn}$ is the open circuit voltage at normal conditions. The reverse saturation current (I_o) at T_a is given as [23]:

$$I_o=I_{on}{\left(\frac{T_a}{T_n}\right)}^{\left(\frac{3}{n}\right)}{e}^{\frac{-q{E}_g}{nK}(\frac{1}{T_a} - \frac{1}{T_n})}$$

(4)

The series resistance (R_s) of the PV module has a large impact on the slope of the I–V curve near the V_oc.

The output voltage of the PV cell is given as:

$$V=V_D-{IR}_s$$

(5)

For this study, the BP3115 PV module serves as the designated PV source, and its key performance parameters under standard testing conditions are systematically outlined in

Table 1. The PV module parameters.

Parameters	Value
Maximum Power (Pmax)	115 W
Voltage at Pmax (Vmp)	17.1 V
Current at Pmax (Imp)	6.7 A
Open Circuit Voltage (Voc)	21.8 V
Short Circuit Current (Isc)	7.5 A
Temperature Coefficient of Isc	0.065 ± 0.015%/°C

. Notably, the module’s characteristics are delineated under varying irradiance and temperature conditions in Figure 3 and Figure 4, respectively.

2.2. DC-DC Buck-Boost Converter

DC-DC switching converters play a pivotal role in electrical and electronic systems, serving as a crucial component for impedance matching between the PV module and the connected load. In this context, the buck-boost converter emerges as a particularly valuable solution, representing a cascaded combination of the fundamental buck converter and boost converter. This unique configuration endows the buck-boost converter with the capability to traverse the entirety of the PV curve [25]. The schematic representation of the buck-boost converter circuit is presented in Figure 5.

During the on-state period, the switch is turned on, the diode is reverse-biased, and it is blocking. The transistor is in one state during the 0 < t < DT_s interval. The voltage across the inductor is given as [26,27]:

$$V_L=V_{in}$$

(6)

$$V_L=L\frac{{di}_L}{dt}$$

(7)

where $V_L$ is the voltage across the inductor, $V_{in}$ is the source voltage, and L is the inductance.

And the capacitor current is given as [26,27]:

$$i_c\left(t\right)=-\frac{v_{out}\left(t\right)}{R_L}\approx -\frac{V_{out}}{R_L}$$

(8)

where $i_c\left(t\right)$ is the capacitor current, $V_{out}$ is the output voltage, and $R_L$ is the load resistor.

However, during the off state, the switch is turned off and the diode is turned on. Turning off the transistor is accomplished during the DT_S < t < T_S interval. The voltage across the inductor is given as [26,27]:

$$V_L=V_{out}$$

(9)

And the capacitor voltage is given as [26,27]:

$$i_c\left(t\right)=i_L\left(t\right)-\frac{v_{out}\left(t\right)}{R_o}\approx i_L-\frac{V_{out}}{R_o}$$

(10)

where $i_L\left(t\right)$ is the inductor current.

Assuming that the buck-boost converter is operating at a steady state, Figure 6 shows the inductor voltage and inductor current during the switching period. The average voltage $<v_L>$ across the inductor is equal to zero. Therefore [26,27]:

$${<v_L>=V}_{in}D+V_{out}\left(1-D\right)=0$$

(11)

where D is the duty cycle of the switch.

From (11), the mathematical expression governing the output voltage is articulated as [26,27]:

$$V_{out}=\frac{-D}{1-D}{V}_{in}$$

(12)

where D is the ratio of on time to the total switching time.

The design and simulation of the buck-boost converter have been executed through MATLAB/Simulink 2016. The key components employed in both the simulation and practical experimentation are enumerated in

Table 2. Parameters of buck-boost converter.

Buck-Boost Converter Parameters
Inductor L	1 mH
Input Capacitor C1	1000 $\mathsf{\mu }\mathrm{F}$
Output Capacitor C2	330 $\mathsf{\mu }\mathrm{F}$
Switching frequency	40 kHz
Resistive Load	5 Ω
MOSFET Type: IRF3710 Diode Type: BYV32-200
Components Used in the Experimental Setup
Controller Type: dSPACE 1104 DSP
Current Transducer	LTS 25-NP
Voltage Divider	Two 120 KΩ and 39 KΩ resistors are connected in series. The voltage is taken across the 39 KΩ resistor.

. This table provides a comprehensive overview of the converter components that have been utilized to ensure consistency between the simulated and real-world implementation of the buck-boost converter.

2.3. The Proposed Fuzzy Logic-Based MPPT Method

2.3.1. The Fuzzy Logic Controller

Artificial intelligence control has been extensively utilized across various domains, including machine drive control and power electronics control. The utilization of fuzzy logic controllers has experienced a notable rise in the past decade due to its inherent simplicity, ability to handle imprecise inputs, independence from an accurate mathematical model, and capacity to manage nonlinearity [28]. Fuzzy logic control (FLC) can be employed as a regulator to achieve the most power output that can be generated by the photovoltaic (PV) modules, even when faced with varying weather conditions.

The process of fuzzy logic control (FLC) can be categorized into three distinct stages: fuzzification, rule evaluation, and defuzzification [29]. The stages and overall structure of an FLC are depicted in Figure 7. The fuzzification stage entails the process of mixing a crisp input, such as the change in the voltage measurement, with a stored membership function in order to generate fuzzy inputs. In order to convert the precise inputs into imprecise inputs, it is necessary to initially assign a membership function for each input. After assigning the membership functions, the process of fuzzification involves taking real-time inputs and comparing them with the stored membership function information in order to generate fuzzy input values. In the fuzzy logic processing, the second stage involves rule evaluation.

During this step, the fuzzy processor utilizes linguistic rules to ascertain the appropriate control action that should be taken in response to a specific set of input values. Each form of consequent action yields a fuzzy output as a result of rule evaluation. The last stage of fuzzy logic processing involves determining the anticipated value of an output variable by extracting a precise value from the universe of discourse of the output fuzzy sets. During this process, each of the imprecise output values actively alters its corresponding output membership function. The center of gravity (COG) or centroid approach is a frequently employed defuzzification technique.

2.3.2. The Proposed FLC-MPPT Algorithm

The input variables are explicitly defined in Equations (13) and (14). In the fuzzification process, numerical input variables undergo conversion into linguistic variables based on predefined membership functions. Figure 8, Figure 9 and Figure 10 visually represent the membership functions corresponding to the change in PV power (ΔP), change in PV voltage (ΔV), and change in reference voltage (ΔV_ref), respectively. These membership functions are characterized by five fuzzy levels, denoted as NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big). The surface viewer is shown in Figure 11. Theoretical foundations of the proposed fuzzy rules stem from the principle that if a change in voltage leads to an increase in power, subsequent changes are retained in the same direction; otherwise, the change is reversed.

Table 3. Rule base used in the fuzzy logic controller.

	$\Delta \mathit{P}$	NB	NS	Z	PS	PB
$\Delta \mathit{V}$
NB		NB	NB	Z	NS	NS
NS		NS	NS	Z	NS	NB
Z		NB	NS	Z	PB	PB
PS		NB	NB	Z	PB	PB
PB		PB	PB	Z	Z	Z

shows the rule base used in the fuzzy logic controller. This table summarizes the relationship between the input parameters (ΔP and ΔV) and the output parameter (ΔV_ref). As an example, if ΔP is NB and ΔV is NB, then the output parameter ΔV_ref should be NB, and so on.

These fuzzy rules, crafted in this study, are designed to effectively track the maximum power point of the photovoltaic system amid dynamically changing weather conditions. The formulation of these rules is geared toward enhancing the adaptability and responsiveness of the fuzzy logic controller, ensuring optimal performance under varying environmental parameters.

$$\Delta V=V\left(K\right)-V(K-1)$$

(13)

$$\Delta P=P\left(K\right)-P(K-1)$$

(14)

3. Simulation Results

The efficacy of the proposed FLC-MPPT method is subjected to rigorous testing under dynamically changing weather conditions, as illustrated in Figure 12. This irradiance scenario presents a formidable challenge, encompassing not only rapid step changes but also gradual ramp changes in irradiance. Within this weather condition, the temperature is initially set at 50 °C, abruptly decreasing to 25 °C at 0.07 s. The tracking performance of the proposed FLC-MPPT method, depicted in Figure 13, attests to its effective and precise maximization of power output. The blue line in the power plot in Figure 13 represents the theoretical maximum power while the red line is the exact PV power. The controller consistently achieves maximum power tracking across diverse ambient conditions, resulting in a calculated tracking efficiency of 99.43%.

A comparative analysis is presented in Figure 14, showcasing the performance of the P&O MPPT method. The blue line in the power plot in Figure 14 represents the theoretical maximum power while the red line is the exact PV power. The tracking efficiency for the P&O method is calculated as 97.83%. Furthermore, a discernible advantage of the FLC-MPPT method is observed in the mitigation of power oscillations around the MPP when juxtaposed with the P&O MPPT method. The negligible power oscillation achieved through the FLC method underscores its superior capability in maintaining stability and precision in power tracking under challenging and dynamic environmental conditions.

Moreover, the proposed FLC-MPPT is compared with the conventional MPPT in terms of the tracking efficiency. The tracking efficiency can be given by dividing the actual power of the PV module by the theoretical PV power during a specific time. Therefore, the tracking efficiency can be given as:

$${\zeta }_{MPPT}=\frac{Actual Energy }{Theoretical Energy} = \frac{\int p_{PV\_actual}\left(t\right)dt}{\int P_{MPP-Theor. }\left(t\right)dt}$$

(15)

Form (15), the tracking efficiency of the PV powers of Figure 13 and Figure 14 are calculated. The obtained tracking efficiency of the proposed FLC-MPPT is 99.43%, while the tracking efficiency of the conventional MPPT is 97.83%. This proves the effectiveness and the accuracy of the proposed topology as compared to the conventional MPPT algorithm.

4. Experimental Setup

The practical implementation of the MPPT hardware setup is conducted through the utilization of the dSPACE real-time prototype system. Illustrated in Figure 15 is the block diagram encapsulating the essential components of the MPPT system’s hardware setup. The hardware configuration aligns closely with the parameters employed in simulation studies. Control system implementation and data acquisition are facilitated through the dSPACE 1104 software interfacing with a digital signal processor card on a personal computer. The measurement of the PV module voltage is achieved via a voltage divider circuit, while the PV current is quantified using a Hall effect LTS 25-NP current sensor. The real-time values of PV voltage and PV current, obtained through these sensors, are fed into the A/D converter channels of the dSPACE for subsequent utilization in the MATLAB/Simulink MPPT control block. The instantaneous voltage and current readings are multiplied to derive the instantaneous power, and these values are then input into the MPPT algorithm to determine the necessary duty cycle. The output signal from the MPPT algorithm is transmitted to the hardware through the dSPACE PWM block, steering the gate driver circuit. The gate driver circuit, integral to the hardware setup, incorporates optical isolation and a gate driver IC for expedited switching of the MOSFET. The experimental prototype’s hardware arrangement is visually depicted in Figure 16, emphasizing the practical realization of the MPPT system’s functionality through meticulous integration and interfacing of the components.

4.1. Case 1: Pure Resistive Load

A resistive load, characterized by a power factor of unity, serves as an exemplary illustration of a power factor-corrected load. In this context, a resistive load is represented by a 5-ohm 120 W resistor bank designed to interface with the DC-DC converter. For Case 1, experimental data spanning nearly 3.75 h were meticulously recorded, as illustrated in Figure 17. The resultant waveforms, depicted in Figure 18, provide a comprehensive insight into the performance of the proposed FLC-based MPPT method under these experimental conditions. These waveforms serve as visual indicators of the method’s effectiveness in optimizing power output with a resistive load configuration.

An additional case study evaluating the performance of the fuzzy logic controller (FLC) maximum power point tracking (MPPT) method is presented in Figure 19 and Figure 20. The FLC-MPPT method underwent testing under specific ambient conditions as depicted in Figure 19. Figure 20 visually represents the tracking performance of the FLC method under these specified conditions. Once again, the results affirm the accuracy and effectiveness of the FLC-based MPPT method in precisely tracking the maximum power point, showcasing its robust performance under the given environmental parameters.

Conversely, the MPPT system is subjected to testing under rapidly changing solar radiation conditions, simulated by covering the PV module with an opaque cloth to induce a step change in irradiance. The resultant variations in power, voltage, and current of the PV system are illustrated in Figure 21. This experimental setup aims to assess the adaptability and responsiveness of the MPPT algorithm to sudden alterations in solar radiation, providing insights into the system’s dynamic performance under challenging environmental conditions.

4.2. Case 2: Dynamic Load

Water pumping, especially prevalent in rural areas abundant with solar irradiation and located far from national grids, underscores the importance of efficient energy solutions. In this context, an effective solution necessitates the optimization of the PV system to operate consistently at the MPP while concurrently ensuring the motor operates at its optimal efficiency level. This dual objective addresses the specific energy needs of rural communities, leveraging solar power to drive water pumping systems in a manner that is both sustainable and economically viable. The integration of PV technology in water pumping applications serves as a practical and environmentally friendly alternative, catering to the energy requirements of remote areas with limited access to conventional power sources.

In the initial configuration, the unloaded water pump (with a rated power of 190 W) is linked in parallel with the resistive load. Figure 22a explains the performance of the MPPT system under dynamic load conditions. To rigorously evaluate the MPPT algorithm, the water pump is cyclically activated and deactivated. During the deactivation phase, the load voltage increases, whereas it decreases upon motor activation. Remarkably, the maximum power, voltage at maximum power, and current at maximum power remain unaffected by these dynamic load variations. The MPPT algorithm adeptly tracks the maximum power point, demonstrating resilience to fluctuations induced by the dynamic load changes. Subsequently, another tracking scenario is presented in Figure 22b, wherein the water pump is continuously connected to the DC-DC converter without intermittent activation and deactivation cycles. This configuration represents an additional test condition, further substantiating the robust performance of the MPPT algorithm under various operational scenarios.

In the subsequent configuration, two small water tanks were incorporated into the experimental setup. One tank, filled with water, was intricately connected to the pump’s inlet, while the other tank, left empty, was linked to the pump’s outlet. Numerous test conditions were systematically implemented to assess the efficacy of the proposed MPPT method. The water pump was systematically integrated into the system, both with and without the concurrent presence of the resistive load, as visually represented in Figure 23. Notably, across all tested conditions, the FLC method consistently excelled in accurately tracking the maximum power, showcasing remarkable performance. Figure 24 showcases an additional set of test conditions, providing further evidence of the MPPT system’s outstanding performance under varying scenarios, encompassing configurations involving both resistive load and water pump systems. The demonstrated effectiveness of the MPPT algorithm across these diverse conditions underscores its versatility and reliability in optimizing power output, solidifying its suitability for applications involving water pump systems confronted with a spectrum of operational scenarios.

5. Discussion

The proposed FLC-MPPT algorithm was simulated under variations of solar radiation and temperature. The results show that the proposed algorithm accurately tracked the maximum power of the PV module under the all-weather conditions. The oscillations in MPP are less than the oscillations when the conventional MPPT is used. Moreover, the tracking efficiency of the proposed MPPT is 99.43% which is higher than the tracking efficiency of the conventional MPPT (97.83%).

The proposed MPPT algorithm is practically examined under real weather conditions as discussed in Section 4. The proposed MPPT is examined under different cases of loading variations.

• In the first case, it is tested under a fixed resistive load for two different days, each for four hours (the measured temperature and solar radiation are seen in Figure 17 and Figure 19). In these two scenarios, the proposed MPPT accurately tracked the maximum power with low oscillations around the MPP. Moreover, it was examined under a step change in the solar radiation. To test the MPPT under a step change of solar radiation, the PV module is covered by an opaque cover and then removed. Even under this step change in the solar radiation, the proposed MPPT keeps tracking the maximum power as seen in Figure 21.
• In the second case, the MPPT is examined under load variation:
  • The water pump is connected in parallel with the fixed resistive load. That is, the load is a dynamic load. The water pump is connected and disconnected many times as seen in Figure 22a. The proposed MPPT keeps tracking the maximum power point, while the load voltage is changed due to the loading variation.
  • Moreover, the proposed MPPT is tested under different loading conditions in which the resistive load is only connected, then the motor is connected in parallel with the resistive load, and finally the resistive load is isolated. Figure 23 shows the robustness of the proposed MPPT algorithm under this hard loading variation.

Finally, the simulation and the experimental results show that the proposed MPPT is working effectively and accurately with high robustness under weather variations and under loading variations.

6. Conclusions

This study presents a comprehensive investigation into the design, implementation, and evaluation of a photovoltaic (PV) model integrated with a DC-DC buck-boost converter featuring maximum power point tracking (MPPT) capabilities, all developed using MATLAB/Simulink. The research proposes a novel MPPT method based on fuzzy logic control and compares it with the conventional P&O MPPT approach. Rigorous testing under varying solar irradiance and PV temperature conditions has been conducted, with simulation results conclusively illustrating the efficacy of the proposed fuzzy logic controller in consistently tracking the maximum power point (MPP) across diverse environmental circumstances. Comparative analysis highlights the superior performance of the fuzzy logic-based MPPT method, showcasing reduced oscillations around the MPP and faster response times compared to traditional methods. Furthermore, an assessment of tracking efficiency underscores the method’s higher effectiveness, particularly in dynamically changing atmospheric conditions. Real-world implementation of the MPPT system under constant and dynamic load scenarios validates its ability to accurately and swiftly track the MPP, thus affirming its potential to enhance overall PV system efficiency, especially in environments with fluctuating operational conditions. In summary, this research provides valuable insights into the development and evaluation of MPPT systems, demonstrating the effectiveness of the proposed fuzzy logic-based approach in optimizing PV module operation. The combination of simulation and experimental results enhances the credibility of the designed MPPT system, paving the way for its potential application in real-world photovoltaic setups subject to varying environmental conditions.