Power Optimization of Partially Shaded PV System Using Interleaved Boost Converter-Based Fuzzy Logic Method

Abstract

Partial shading condition (PSC) for photovoltaic (PV) arrays complicates the operation of PV systems at peak power due to the existence of multiple peak points on the power–voltage (P–V) characteristic curve. Identifying the global peak among multiple peaks presents challenges, as the system may become trapped at a local peak, potentially resulting in significant power loss. Power generation is reduced, and hot-spot issues might arise, which can cause shaded modules to fail, under the partly shaded case. In this paper, instead of focusing on local peaks, several effective, precise, and dependable maximum power point tracker (MPPT) systems monitor the global peak using a fuzzy logic controller. The suggested method can monitor the total of all PV array peaks using an interleaved boost converter DC/DC (IBC), not only the global peaks. A DC/DC class boost converter (CBC), the current gold standard for traditional control methods, is pitted against the suggested converter. Four PSC-PV systems employ three-phase inverters to connect their converters to the power grid.

1. Introduction

Solar energy, in contrast to fossil fuels such as coal, oil, and natural gas, remains inexhaustible and does not result in environmental contamination [1]. It is proficient in generating both heat and electrical energy using photovoltaic (PV) systems, which encounter issues with partial shade [2,3,4]. The partial shading condition (PSC) occurs when aging, damage, dust collection, temporary clouds, or nearby buildings and structures shade some photovoltaic (PV) cells or modules. After solar energy system installation, some circumstances may cause performance concerns. Shaded solar modules reduce output power and efficiency by 70% [5,6]. Hot-spot and thermal breakdown concerns may need bypass diodes, multilevel inverters, and a smart algorithm to safeguard PV modules [7,8,9,10,11]. PSC causes one global peak (GP) and multiple local peaks (LPs) in P-V. Recent research has monitored GPs rather than LPs using soft computing methodologies to maximize power extraction and efficiency. Traditional MPPT techniques like incremental conductance (IC), Hill Climbing (HC), and perturb and observe [12,13] are easy to use to detect the MPP under uniform conditions. These approaches vary in simplicity, design, cost, tracking speed, performance, efficiency, resilience, and accuracy. MPPT ant colony optimization (ACO) was evaluated, investigated, and compared to traditional MPPT perturb and observe methods and AI-based MPPT methods including ANN, fuzzy logic controllers, ANFIS, and FL-GA. It was compared to particle swarm optimization (PSO) and colony optimization [14]. Intelligence techniques like colony optimization and PSO performed well in accuracy, convergence speed, stability, and robustness [15]. Cooperation and competition enable IPSO [12,16,17], an upgraded particle swarm optimization technique, to track GPs precisely with minimum oscillation. Sending the optimal duty cycle to the DC-DC converter updates the particle’s velocity and position to monitor the GP [18]. AI methods like artificial neural networks (ANN), fuzzy logic controllers, (GA) genetic algorithms, and hybrid methods (for example, neuro-fuzzy systems or ANFIS with the GA) were shown to be accurate, fast, adaptable, energy-efficient, and easy to implement in [19]. Modern MPPT systems can monitor the global peak, while the PSCPV system can monitor all individual peaks better. This paper describes a novel method for reducing partial shadowing (PSC) impacts and maximizing power extraction from a photovoltaic solar power (PSC-PV) system using an interleaved boost converter (IBC) combined to numerous PV arrays. IBC improves PV system power quality, reliability, efficiency, and DC-link voltage stability. Several industrial industries utilize it for high voltage, current, and power. Additionally, IBC has several enticing features: (i) decreased input current fluctuations; (ii) minimized power switch and inductance burden, reducing passive component size; (iii) boosted voltage output. A dc/dc class converter is not recommended for PSPV systems; use an IBC since each PV module, string, or array in this IBC DC-DC converter has a different irradiance level, therefore a different output voltage. For DC-link voltage stability, a fixed IBC is recommended. Many evaluations in the literature [20,21,22,23,24,25,26,27,28,29] have shown that interleaved DC-DC converters are reliable, efficient, effective, electromagnetic emission-free, and ripple-free compared to a class boost converter (CBC) with continuous input power. Researchers in [20,24] showed how IBC may be integrated with PV to evaluate reliability, reduce ripple, and accelerate the transient response. However, unlike PSC, their study concentrated on PV systems running independently of PS to establish the IBC effectiveness in a constant-input power situation. Ref. [21] compared the DC-DC IBC topologies for energy renewable applications based on cost, reliability, flexibility, and efficiency [6]; however, Ref. [26] indicated that IBC might replace CBC for high-voltage applications.

In this article, an interleaved boost converter (IBC) in a PSC-based fuzzy MPPT approach may be the first to increase PSPV system performance. This method is economical and effective since it solves PS effects and becomes more power from each PV array branch than the PSPV system with a class boost converter (CBC). This research will examine a novel application that combines utility grid-connected multi-partially shaded solar arrays with interleaved boost converters. We compared the proposed IBC PSC-PV system to an existing CBC system that employed fuzzy controller logic based on maximum power point tracking to see how it varied in efficiency, power quality, and reliability. Thus, we provide the optimum IBC design that manages DC-link voltage and maximizes power extraction from the utility grid-linked PSPV system.

This system uses multiple PV arrays, which are integrated with an interleaved boost converter (IBC) that features its own MPPT control and separate DC-link for each PV system to solve the PSC problem. This structure aims to lower ripple current, regulate voltage DC-link, enhance power quality, and provide each PV array with its own MPPT control. This configuration, featuring a fuzzy MPPT, aims to achieve high output power and improve overall efficiency.

2. Materials and Methods

2.1. Modeling of Class and Interleaved Boost Converter

The solar array’s output voltage is not high enough to satisfy the needs of the load or inverter. In order to increase the voltage output of the system, a step-up DC/DC converter is integral to PV system applications and is included in the PV panel. The class DC/DC converter, as seen in Figure 1, only needs one MPPT controller.

The interleaved boost converter (IBC) has the added benefit of using a multi-MPPT controller as shown in Figure 2. In this research, a fuzzy MPPT controller is used in conjunction with a step-up interleaved DC/DC boost converter to obtain the best possible voltage output from the PV array. In order to maximize the power production of the solar panels, the duty cycle of the switch regulates the boost converter. Some designs for IBCs use continuous conduction mode as their foundation [30,31]. The input inductor current is not allowed to drop to zero in this mode. For the IBCs’ design,

Table 1. Specifications of PV panels and simulated system and parameters of IBCs.

Parameter	Value
A10 Green technology A10J-S72-175 panel	1 Series × 1 Parallel
Maximum Power	175.09 W
Open circuit voltage Voc	43.99 V
Short-circuit current Isc	5.71 A
Voltage at maximum power point Vmp	36.63 V
Current at maximum power point Imp	4.78A
Inductor L	0.1 mH
Output capacitance $C_{out}$	12,000 μF
Duty ratio $d$	0.4
Input voltage converter each PV	38 V
Vin total input voltage boost converter	152 V
Vout output voltage boost converter	250 V

shows the specifications of the PV panels and simulated system.

The design input inductor (L) and output capacitor (C dc) of the IBC are derived from the following formulas:

V_out = m * V_in

(1)

d = (V_out − V_in)/V_out = (m * V_in − V_in)/(m * V_in)

(2)

d = (m − 1)/m

(3)

L_b = (V_in * d)/(f_s * ΔI_Lb)

(4)

C_dc = (V_out * d)/(R * f_s * ΔV_out)

(5)

where the voltage gain is (m); (V_in) and (V_out), the input and output of the IBC; (d), the cycle duty; (fs), the switching frequency;$∆{\mathrm{I}}_{\mathrm{L}\mathrm{b}}$, the inductor ripple current; and $∆{\mathrm{V}}_{\mathrm{o}\mathrm{u}\mathrm{t}}$, the ripple value in the output voltage.

2.2. Analyzed PV Systems with CBC and IBC

2.2.1. PV Energy System Integrated CBC

Figure 3 depicts a photovoltaic (PV) energy transformation system, with a four-phase inverter and class boost converter (CBC) linking the PV array to the utility grid. This figure shows a four-PV group-type panel A10 Green technology A10J-S72-175, connected between each of the two PV groups with a bypass diode, integrated with a class boost converter with one MPPT controller to solve the PSC problem. This bypass diode is used to safeguard the photovoltaic module against hot-spot phenomena and potential thermal breakdowns (refer to Figure 1). A substantial reverse voltage will be generated across any modules or shaded cells without protection from bypass diodes. As a result, both the power output and the efficiency of the photovoltaic system diminish owing to heightened power losses.

The solar array receives its inputs from the sun’s irradiance (W/m²) and the temperature of the cells (°C). To symbolize the PSCs, three PV arrays are used, each with its own unique radiation. Multiple peaks, namely three, are produced by PSCs, and the GP occurs at various points along the P-V curve.

2.2.2. PV Energy System Integrating IBC

A multi-PV array integrated IBC converter-based fuzzy MPPT controller, as shown in Figure 2. Figure 4 shows a multi-PV array that is part of a system that is linked to the utility grid using an IBC and a PWM-VSC. The objective behind integrating intelligent battery controllers with multiple photovoltaic systems is to lower ripple current, enhance power quality, manage the voltage DC-link, and allow each PV array to be independently controlled using MPPT, all of which will lead to a higher output produced power and more efficiency. Regarding the PS effects associated with the system’s maximum power and efficiency, this suggested PV system also fixes and reduces them.

A distinct MPP tracker keeps tabs on each PV array’s performance by monitoring the duty cycle of its own boost converter. Figure 4 shows that each IBC branch has its own distinct duty cycle, and that each PV array uses its own MPP tracker fuzzy controllers to monitor its own MPP. So, as will be seen in the section on simulation results, the suggested multi-PV integrated with an IBC operates at its maximum power point, and the aggregated peak power supplied by this partial shading PV system is higher than the global power recorded by the prior PSC-PV system with a CBC.

2.3. Fuzzy Controller Design

2.3.1. Fuzzy Control

Fuzzy logic control is a technique that enables the development of nonlinear controllers utilizing heuristic information derived from the expertise of an expert [32,33,34,35,36,37,38,39,40,41,42]. The input signals are processed by the fuzzification block, which assigns them an imprecise value. This set of rules is predicated on the process’s understanding and enables the regulation of the variables’ linguistic descriptions. The inference mechanism interprets the data according to their membership functions and rules. The defuzzification block transforms fuzzy data produced by the inference method into precise, fuzzy non-information advantageous for process management For designing a fuzzy controller, use the input current and voltage PV as shown in Figure 5. Result in a new duty cycle (dD) after using a fuzzy controller by changing the duty cycle (E&DE) according to the changing voltage as shown in Figure 6.

2.3.2. Structure of Fuzzy Logic

(a)

Two inputs: Error (E) and change in error (DE); range: [−1, 1]; Gaussian membership functions.

(b)

Control fuzzy logic: A total of 25 fuzzy rules are applied using Mamdani inference, as shown

Table 2. Fuzzy rules for system.

Error (E)/dE	NB	NS	Z	PS	PB
NB (Negative Big)	NB	NB	NB	NS	Z
NS (Negative Small)	NB	NB	NS	Z	PS
Z (Zero)	NB	NS	Z	PS	PB
PS (Positive Small)	NS	Z	PS	PB	PB
PB (Positive Big)	Z	PS	PB	PB	PB

. Each rule connects two inputs and one output, as shown in the table, using the Inference Engine method (AND Method (min), OR Method (max)) and then converting the results into an actual control output signal using the centroid method. This method computes the centroid of the fuzzy form.

(c)

One output (dD): Range: [−1, 1]; the correction signal controls for changes in duty cycle (dD) using centroid-based defuzzification over Gaussian double-output membership functions.

2.3.3. Summary of Flow Chat Implementation Steps for Fuzzy Logic Controller

Step 1.

Input Variables (E, dE):

The error (E) and change in error (dE) as inputs to the fuzzy logic controller.

Step 2.

Fuzzification:

The crisp input values (E and dE) are converted into fuzzy values using membership functions (MFs). Here, you use Gaussian MFs to represent the fuzziness of the input data.

Step 3.

Rule Evaluation:

The fuzzy rules (“If E is NB and dE is NB, then dD is NB”) are applied to evaluate the fuzzy outputs. You have 25 rules to determine the fuzzy control action.

Step 4.

Aggregation:

The outputs of all the active rules are combined to create an aggregated fuzzy output. This step combines the contributions of each rule based on the fuzzified inputs.

Step 5.

Defuzzification:

The centroid technique is used to transform the fuzzy output back into a crisp value. This method calculates the weighted average of the fuzzy set to produce a crisp output.

Step 6.

Control Signal (dD):

The crisp output (dD) is used to adjust the system. This is the control action that your FLC generates. Fuzzy logic offers faster response, adapts to nonlinearity, is noise-resistant, and reduces oscillation at MPP through comparisons with incremental conductance and perturb and observe methods. Table 3 shows the advantages and disadvantages of these three different methods of MPPT.

Table 3. Advantages and disadvantages of three different methods of MPPT [43,44,45,46,47,48].

Method of MPPT	Advantages	Disadvantages
Fuzzy controller	Fast response with fast irradiance and temperature variations. Adaptive and responsive to nonlinear and unpredictable systems. Lower oscillation related to the maximum power point. Resilient to noise, does not need exact measurements. Design-independent, operates without an integrated PV model.	Higher intricacy in design and execution. Demands adjustment of membership functions and rule basis. The quality of the fuzzy logic design has significant influence on its effectiveness.
Incremental Conductance	Precise monitoring of the MPP only in constant state. Implementation is easy, utilizing fundamental mathematical formulas. Appropriate for embedded systems and devices with limited resources.	High sensitivity to noise in measurements of current and voltage. High oscillations about MPP. Slow responses to abrupt variations in irradiance or temperature. Needs precise, real-time current and voltage readings. Incorrectly selecting the step size might cause instability.
Perturb and Observe	A straightforward algorithm—clear and easy to apply. Minimal computational expense, appropriate for low-power systems.	Near MPP oscillations, particularly in constant state. Slow tracking under rapidly fluctuating circumstances (irradiance/temperature). Less accurate than fuzzy or IC algorithms.

Table 3. Advantages and disadvantages of three different methods of MPPT [43,44,45,46,47,48].

Method of MPPT	Advantages	Disadvantages
Fuzzy controller	Fast response with fast irradiance and temperature variations. Adaptive and responsive to nonlinear and unpredictable systems. Lower oscillation related to the maximum power point. Resilient to noise, does not need exact measurements. Design-independent, operates without an integrated PV model.	Higher intricacy in design and execution. Demands adjustment of membership functions and rule basis. The quality of the fuzzy logic design has significant influence on its effectiveness.
Incremental Conductance	Precise monitoring of the MPP only in constant state. Implementation is easy, utilizing fundamental mathematical formulas. Appropriate for embedded systems and devices with limited resources.	High sensitivity to noise in measurements of current and voltage. High oscillations about MPP. Slow responses to abrupt variations in irradiance or temperature. Needs precise, real-time current and voltage readings. Incorrectly selecting the step size might cause instability.
Perturb and Observe	A straightforward algorithm—clear and easy to apply. Minimal computational expense, appropriate for low-power systems.	Near MPP oscillations, particularly in constant state. Slow tracking under rapidly fluctuating circumstances (irradiance/temperature). Less accurate than fuzzy or IC algorithms.

2.4. Control Strategy of Voltage Source Converter

Inverter topology operates as a converter, specifically transforming direct current (DC) to alternating current (AC) during the process of transferring electrical power to the load. The control system is a voltage source converter (VSC) for grid-connected applications, as shown in Figure 7. The control method initiates with the PLL (phase-locked loop) block, which transforms voltage and current into dq components and provides the angular frequency. The VDC regulator maintains voltage DC bus by producing current reference on d-axis. The reference, in conjunction with a constant q-axis current reference, is transmitted to the current regulator. This component compares the received references with the observed dq currents and produces the required dq voltage references. The references are transformed back into voltages and transmitted to the PWM generator, which creates switching pulses to control the converter. The system ensures accurate power management and stable DC voltage output.

3. Results

The performance of the suggested fuzzy MPPT interleaved boost converter was compared to that of the class boost converter under normal weather and partial shading using Matlab/Simulink software R2023A. The PV system configuration shown in Figure 8 is a converter-integrated array of four modules. Figure 8a presents the current and voltage power for each PV group under normal weather (constant irradiation) with a stable duty cycle, as shown in Figure 9a. Figure 8b presents the current, voltage, and power for each PV group under partial shading (variable irradiation) with a change in duty cycle, as shown in Figure 9b.

3.1. Performance of Interleaved Boost Converter (IBC)-Based Fuzzy Logic MPPT

3.1.1. Performance of IBC for Constant-Level Irradiance (CLI)

The efficacy of the proposed four-PV string boost converter using a fuzzy MPPT controller was evaluated under constant irradiance and temperature conditions, namely S = 1000 W/m² and T = 25 °C. The whole array’s maximum power (P_PV = 740 W), consisting of four arrays, each rated at 185 W, was achieved under stable weather circumstances. The MPPT eliminated the steady-state oscillation around the MPP, hence enhancing PV efficiency. Figure 10, Figure 11 and Figure 12 illustrate the output DC voltage, DC, and DC power of the proposed IBC-based fuzzy MPPT under stable weather circumstances.

The voltage inverter output, following LCL filtering (L = 40 mH, C = 50 mF), given to the load, is shown in Figure 13. THD in the output voltage is 0.76% at a high frequency of 10 kHz, as seen in Figure 14. A commendable total harmonic distortion (THD) rating of 0.76% was observed, according to the IEEE standard limit of ≤5%.

3.1.2. Performance of IBC Under Partial Shading Conditions (PSCs)

This part evaluates the performance of the suggested IBC-based MPPT fuzzy logic under rapid variations in irradiance. The irradiance levels are adjusted to PV1 at 1000 W/m², PV2 at 800 W/m², PV3 at 1000 W/m², and PV4 at 200 W/m², as seen in Figure 8. Figure 15, Figure 16 and Figure 17 depict the output DC voltage, DC, and DC power of the proposed IBC-based fuzzy MPPT under variable irradiance conditions. The voltage inverter output, after LCL filtering and load provision, is shown in Figure 18. The THD observed in the output voltage is 0.93% at a high frequency of 10 kHz, as seen in Figure 19. Figure 18 shows that, in comparison to the converter CBC, this IBC demonstrated better quality for partial shading.

3.2. Performance Class Boost Converter (CBC)-Based Fuzzy Logic MPPT

3.2.1. Performance of CBC with Constant Irradiance Level (CLI)

The efficacy of the proposed four-boost converter-based fuzzy MPPT controller was evaluated under constant irradiance and temperature conditions, namely S = 1000 W/m² and T = 25 °C. Figure 20, Figure 21 and Figure 22 illustrate the output DC voltage, DC, and DC power of the proposed IBC-based fuzzy MPPT under constant weather circumstances. The filtered voltage from the inverter, given to the load, is shown in Figure 23. The THD in the output voltage is 1.35% at a high frequency of 10 kHz, as seen in Figure 24.

3.2.2. Performance of CBC Under Partial Shading Conditions

In this part, the performance of the proposed IBC was evaluated under rapid variations in irradiance, in comparison to the CBC. As illustrated in Figure 8, the irradiance levels were varied as follows: PV1 (1000 W/m²), PV2 (800 W/m²), PV3 (1000 W/m²), and PV4 (200 W/m²). Figure 25, Figure 26 and Figure 27 depict the output DC voltage, DC, and DC power of the proposed IBC-based fuzzy MPPT under constant weather conditions. The output current and voltage of the inverter, post-filtering through the LCL, delivered to the load, are presented in Figure 28. The THD in the output voltage is 1.87% at a high frequency of 10 kHz, as seen in Figure 29.

4. Discussion

In this section,

Table 4. Output voltage, current, power, and THD for each converter under CLI and PSC.

Converter	CLI					PSC
	Output Current DC	Output Voltage DC	Output Power DC	THD Voltage AC	Efficiency	Output Current DC	Output Voltage DC	Output Power DC	THD Voltage AC	Efficiency
IBC	2.96	250	738	0.76%	99.72%	2.3	200	450	0.93%	97.82%
CBC	3.4	220	700	1.35%	93.58%	2.2	160	380	1.87%	92.63%

provides a summary of the output voltage, current, power, and THD for the IBC converter under PSCs, compared with those of the CBC converter. The efficiency of 99.72% under normal conditions and 97.82% under partial shading for the IBC is greater than that of the CBC converter. In addition, there is less THD because each independent MPPT controller PV panel string integrates separate DC links, while the opposite converter, CBC that has one DC link input, one capacitor, and one MPPT controller. therefore, high-power-quality AC voltage for the IBC, as shown in Figure 13, Figure 18, Figure 23 and Figure 28.

5. Comparative Analysis with Comparable Studies

This

Table 5. Output voltage and power, THD, efficiency, and MPPT technique for different studies.

Reference	Output Power	Input Voltage	Output Voltage	MPPT Technique	THD	Efficiency	Converter Type	Applications
Hassan M. H. [6]	150 KW	120	220 V	PSO P&O	-	-	Interleaved Boost Converter (IBC)	Grid-Connected PV
Girish Ganesan [49]	100 W	280	174	-	-	92%	Hybrid IBC	Renewable Energy Applications
Ersan Kabalci [50]	3 kW	200	250	Kalman Filter (EKF)	-	Nor:99% PSC:96%	IBC	Under PSC
Sen-Tung Wu [51]	1.5 kW	20–125 V	400 V	-	-	93.57%	Interleaved Boost Serial Resonant Converter	Fuel Cell Battery
Shin-Ju Chen [28]	1 kW	36 V	400 V	-	-	95.69%	IBC	Renewable Energy Applications
K. Krishnaram [52]	2 kW	150	416 V	ANN- P&O	-	99.80%	IBC	PV system
Hasan Uzmus [53]	400 W	135 V	400 V	modified P&O	-	95%	Hybrid IBC	PV System
Farhan Mumtaz [54]	1.26 kW	20 V	400 V	P&O	3.22%		IBC	Fuel Cell Power in a Microgrid
Radhia G [55]	642 W	-	140 V	ANN-DISM		99.84%	IBC	Under Partial Shading
Avinash M [56]	-	25 V	50 V	FO-PID controller	-	-	IBC	Fuel Cell-Based Electric Cars
Fahrul Indra [57]	-	50 V	55 V	-	3.3%	-	IBC	Renewable Energy
Proposal IBC for normal	735 W	152 V	250 V	Fuzzy logic MPPT	0.76%	99.72%	IBC	Under Normal PV Conditions
Proposal IBC for PSC	450 W	152 V	220 V	Fuzzy logic MPPT	0.93%	97.82%	IBC	Under Partial Shading PV Conditions

contrasts various IBC designs for diverse energy systems. The choice of MPPT technology, converter type, and design parameters depends on the specific application, whether it involves large-scale PV systems, fuel cell integration, or systems operating under partial shading. The proposed IBC systems demonstrate enhanced performance, mostly due to the integration with fuzzy logic MPPT independent of a separate dc-link for each PV, which enhances dynamic responsiveness and maintains high efficiency under partial shade conditions. Compared to in many previous studies, these systems offer improved harmonic performance, increased reliability, and broader applicability, making them suitable for photovoltaic systems.

6. Conclusions

This paper proposes a fuzzy logic controller MPPT method with an interleaved boost converter PV system and grid topology. The performance analysis and comparison involved two proposed fuzzy logic controller MPPT methods with the following PV system and dc/dc boost configurations: interleaved (IBC) and class (CBC) converters. This comparison between partial shading and normal conditions was performed by modeling the circuitry in MATLAB/SIMULINK. The partial shading conditions (PSC) significantly decrease the power output of photovoltaic (PV) systems in comparison to optimal generation conditions. An independent MPPT controller with PV panel string on separate DC links addresses the issue of partial shading conditions (PSC) and safeguards the PV system from damage caused by the hot-spot effect for IBCs. In addition, to track the maximum power through the MPPT, the use of effective soft computing methods makes sure that the maximum power from the partial shading PV system compared with a CBC. The primary objective of this study is to suggest a solution that is both effective and efficient through the use of an IBC. The results demonstrate the effectiveness of IBCs in managing PSC using a fuzzy MPPT controller, primarily through its role in extracting the maximum power from the entire PV system under PSCs. Furthermore, it improves the stability of voltage DC-links, as well as the quality, reliability, efficiency, and flexibility of the power system. The multisystem PV IBC configuration is determined to be effective for managing PSCs and is applicable for large-scale PV applications. Furthermore, it provides an excellent option for mitigating the impacts of PSCs while outperforming alternative PV system designs in terms of output power and complexity.