Conventional Maximum Power Point Tracking Research Articles

Optimal initial duty for improved MPPT under change in irradiances and partial shading conditions

Abstract Solar photovoltaic cells are widely used in renewable energy power generation. The efficient operation of these photovoltaic (PV) system is based on maximum power point tracking (MPPT). Various MPPT methods have been proposed to increase the output of a PV system. But confusion arises when choosing the optimal MPPT algorithm and its parameters such as perturbation time and initial duty. This paper examines the optimal initial duty and perturbation time to improve the performance of conventional MPPT techniques under uniform shading and partial shading. During the change in irradiances, at the initial irradiance the Modified P&O tracked MPPT faster because the modified P&O had less settling time during changes in irradiance for different initial duty ratios. During partial shading conditions, the TCT generates more power than the S and SP topologies and modified P&O tracked the MPPT in less time. However, at the high duty ratio, the P&O and INC gave similar results, which were better than the modified P&O. It was also observed that the initial duty ratio (Di) in the MPPT algorithm had a negligible effect on TCT configuration under partial shading conditions. These findings help in various applications for PV systems.

Simplified control algorithm for stable and efficient standalone PV systems: An assessment based on real climatic conditions

This research addresses the critical challenge of optimizing efficiency and performance in solar photovoltaic (PV) systems. It focuses on duty cycle optimization of DC/DC converters for maximum power point tracking (MPPT). The study introduces an innovative hybrid MPPT solution that integrates a novel temperature-based single-sensor approach with an integral backstepping controller. This method precisely achieves the PV array's maximum power point (MPP) under varying weather conditions while optimizing the converter's duty cycle to best align with the MPP voltage. Comprehensive simulations in MATLAB/Simulink reveal that the hybrid method surpasses conventional MPPT systems in key metrics, achieving faster convergence time (below 0.66 ms), reduced power ripple (<0.3 W), and enhanced tracking efficiency (exceeding 97.67 %). Notably, experimental data validates the practical efficacy of the proposed system under real-world conditions in the context of El Jadida, Morocco. This integrated approach advances the theoretical understanding of MPPT optimization and demonstrates tangible improvements in PV system performance, offering significant potential for enhancing solar energy utilization in variable climate regions.

Nonlinear MPPT techniques to control hybrid power systems

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC–DC boost converter (DC–DC–BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC–DC–BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

Research on MPPT control strategy based on CCAOA algorithm

ABSTRACT Photovoltaic arrays operating under partial shading conditions (PSCs) may exhibit multiple peaks in the power-voltage curve of the PV system output. The conventional maximum power point tracking (MPPT) algorithm cannot address the multipeak problem and demonstrates slow convergence speed, often falling into a local optimum. Consequently, this study proposes a collaborative and cosine arithmetic optimisation algorithm (CCAOA). First, a cosine factor is introduced into the mathematical optimisation. Circle chaotic mapping and cross-variance strategy were incorporated to boost the diversity and randomness within the algorithm population followed by a cooperative search strategy involving addition and subtraction to enhance the algorithm’s local search capability, thereby accelerating its convergence speed. We assessed the effectiveness of the CCAOA using six IEEE standard test functions. The CCAOA outperforms other algorithms in terms of convergence speed and accuracy. Furthermore, when applied to the MPPT control strategy, the CCAOA’s performance is validated through simulations and experiments. MPPT can be performed within approximately 0.3 to 0.4 s under static conditions, with 99.98% efficiency. Under dynamic conditions, only approximately 0.4 s is required for MPPT, thereby achieving a maximum efficiency of 99.99%. Conversely, the AOA, TSO, and PSO algorithms require over 0.4 s for MPPT and can be trapped in local optima under these two conditions. Experiments were conducted using a PV simulator and DC/DC converter, and the algorithm achieved efficiencies of 99.22%, 99.33%, and 99.54% under uniform irradiation and two PSCs, respectively.

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&amp;O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&amp;O method. The AP&amp;O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&amp;O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.

Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms.

Control strategies for grid-connected hybrid renewable energy systems: Integrating modified direct torque control based doubly fed induction generator and ANFIS based maximum power point tracking for solar PV generation

This research article introduces advanced control strategies for grid-connected hybrid renewable energy systems, focusing on a doubly fed induction machine (DFIM) based wind power plant and a photovoltaic based solar power plant. The proposed approach integrates Direct Torque Control (DTC) with modified filters and a fractional Proportional-Integral (PI) controller for the DFIM wind power plant. The modified DTC utilizes estimated torque and flux, employing a modified filter to minimize deviations, and a fractional PI controller for improved vector selection. This results in an enhanced duty cycle for the rotor side converter, optimizing power output to the grid. Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Maximum Power Point Tracking (MPPT) algorithm is implemented for the solar power plant, dynamically adjusting the duty cycle for increased power output compared to conventional MPPT methods. Simulation results in MATLAB and experimental verification demonstrates the stability and feasibility of the proposed controllers, showcasing superior performance compared to conventional counterparts under various operating conditions.

Implementation of modified P&O and an adaptive fuzzy logic controller based MPPT tracking system under partial shading and variable environmental conditions

Most of the conventional Maximum Power Point Tracking (MPPT) Algorithms provide the finest efficiency during uniform irradiation conditions, but under variable irradiation or partial shading condition (PSC), the performance deteriorates and the solar PV system is unable to provide the maximum electricity out of the PV Modules. This degradation in performance occurs due to the presence of numerous local maximum power points (LMPP) and a single global maximum power point (GMPP) in the power versus voltage (P-V) characteristics and the perfect tracking of these LMPP is not possible with the prevailing MPPT algorithms. In order to eradicate this shortcoming, we have proposed the implementation of an adaptive Fuzzy Logic Controller (FLC) based on Perturb and Observe (P&O) technique by employing a boost converter with variable resistive load under uniform irradiance condition (UIC), dynamic atmospheric conditions (DAC) and PSC in this article. We also demonstrated the usage of a simple and improved P&O algorithm by employing a buck boost converter for faster tracking time. These two suggested approaches of FLC and P&O incorporate a variable load scenario and a stable load scenario respectively to address the problem of output voltage oscillation in a PV-based system. The FLC is designed to adjust the dynamic change in step size based on the rate of change of the yield power of the PV panel to reduce oscillations in output of the boost converter and the PV system. According to the results of this analysis, the recommended FLC-based P&O (FLC-P&O) algorithm outperforms the proposed P&O algorithm under DAC and PSC with respect to tracking speed, steady-state error, and dynamic response. Apparently, it can be concluded that the proposed FLC-P&O technique can be applicable to real-time systems for reliable and efficient operation of the boost converter and add to the stability and simplicity of the contemporary PV-based systems.

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

Design and development of energy management system for solar electric vehicle with dynamic power split approach

ABSTRACT The integration of on-roof solar photovoltaic (PV) systems in electric vehicles (EVs) represents a groundbreaking advancement in the evolution of sustainable transportation. The objective of the research is to design and validate an integrated system for on-roof solar PV integration in EVs, optimising energy utilisation and reducing reliance on grid electricity. This paper explores the implementation of an Adaptive Maximum Power Point Tracking (MPPT) system, which maintains the output of the PV system at a constant level, leading to a significant improvement of approximately 10.20% in energy output compared to conventional MPPT systems. Additionally, a dynamic power split approach (DPAS) is employed to intelligently manage the allocation of power between the solar PV system and the vehicle battery, ensuring continuous fulfilment of energy demand while simultaneously recharging the battery. The proposed system reduces 65% of energy demand from grid and hence, reliance on grid. Furthermore, the dynamic power split approach enable substantial reductions in battery capacity requirements, with a potential decrease of 25–35% observed. These advancements not only enhance the performance and efficiency of EVs but also contribute to cost savings and the transition towards a cleaner and more sustainable transportation ecosystem.

A novel on high voltage gain boost converter with cuckoo search optimization based MPPTController for solar PV system

Traditionally, isolated and non-isolated boost converters are used for solar photovoltaic systems (SPV). These converters have limitations such as low voltage gain, less voltage ripples, temperature dependence, high voltage stress across the switches, and being bulky in size. Besides, the solar PV system also has non-linear characteristics between I–V and P–V, and the energy yield potential is affected by partial shading phenomena. Therefore, maximum power point tracking (MPPT) is being added to the SPV system to get the maximum output power under steady and dynamic climate conditions. Although the conventional MPPT has drawbacks such as less accuracy in predicting the MPP under partial shading conditions, low tracking speed, and more ripples, Hence, the research proposes a stackable single switch boost converter (SSBC) with a Cuckoo search MPPT controller for the SPV system. The efficiency of the proposed circuit topology has been compared with conventional boost converters with various MPPTs. Subsequently, the accuracy of tracking true MPPT by CSO is compared with that of PSO and FPNA. The results show, that the CMPPT with CBC has produced more ripples, whereas the BMPPT with SSBC produces ripple-free power under steady conditions. It is also observed that SSBC with BMPPT produces more power than SSBC with TMPPT. The efficiency of SSBC with BMPPT is better than other combinations. Finally, a prototype model has been developed and verified.

A comprehensive comparison of the two MPPT techniques (P&amp;O, SMC) for photovoltaic systems

This paper aims to study the behavior of different maximum power point tracking (MPPT) techniques applied to PV systems. In this work, we evaluate and compare perturbation and observation (P&amp;O) and sliding mode control (SMC) techniques. a DC/DC boost converter is used between the PV system and load to transfer the maximum possible power. the conventional MPPT methods Perturb and Observe (P&amp;O) cannot detect and track the highest peak. Thus, this causes an important loss of power, so a technique is suggested sliding mode control, which offers many benefits such as durability against parameter changes, minimal current output distortion, and excellent reference tracking. The simulation results shown in MATLAB confirm that the sliding mode control method allows to have a faster response compared to P&amp;O, to reduce the steady-state fluctuations and better track the maximum power point with fewer power losses.

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Maximum power point tracking improvement using type-2 fuzzy controller for wind system based on the double fed induction generator

Introduction. In this paper, to maximize energy transmission in wind power system, various Maximum Power Point Tracking (MPPT) approaches are available. Among these techniques, we have proposed the one based on typical fuzzy logic. Despite the somewhat reduced performance of fuzzy MPPT. For a number of reasons, fuzzy MPPT can replace conventional optimization techniques. In practice, the effectiveness of conventional MPPT methods depends mainly on the accuracy of the information given and the wind speed or knowledge of the aerodynamic properties of the wind system. Novelty. Our new MPPT for monitoring the maximum power point has been proposed. We developed an algorithm to improve control performance and govern the stator’s developed active and reactive power using the typical fuzzy logic 2 and enable robust control of a grid-connected, doubly fed induction generator. Purpose. MPPT which implies the wind turbine’s rotating speed should be modified in real time to capture the most wind energy, is necessary to achieve high efficiency for wind energy conversion, according to the aerodynamic characteristics of the wind turbine. Methods. Developing a mathematical model for a wind energy production system is complex, can be strongly affected by wind variation and is a non-linear problem. Thanks to these characteristics, thus, the Lyapunov technique is combined with a sliding mode control to ensure overall asymptotic stability and robustness with regard to parametric fluctuations in order to accomplish this goal. We contrasted our fuzzy type-2 algorithm’s performance with that of the fuzzy type-1 and Perturbation &amp; Observation (P&amp;O) suggested in the literature. Practical value. The simulation results demonstrate that the control performance is satisfactory when using the fuzzy logic technique. From these results, it can be said for the optimization of energy conversion in wind systems, the fuzzy type-2 technique may offer a workable option. Since it presents a great possibility to avoid problems either technical or economics linked to conventional strategies.

Performance optimization of photovoltaic system under real climatic conditions using a novel MPPT approach

ABSTRACT In order to mitigate the impact of temperature and irradiation variations on photovoltaic (PV) panels, it is crucial to employ the maximum power point tracking (MPPT) technique. This paper introduces a novel MPPT approach that provides a reference current, denoted as Iref, to extract the maximum power from the PV array. The difference between the input current Ipv, and Iref is then fed into a PID controller, which adjusts the duty cycle accordingly to control the Quadratic Boost Converter (QBC). The proposed MPPT approach is based on the analysis of measurement data obtained from PV panels. From this, a linear mathematical function between Iref and solar irradiation is achieved. To assess the performance of this approach, a comparative study is being carried out not only with conventional MPPT techniques such as P&O and INC-COND but also with modern techniques such as ANN, PSO, FLC, and improved P&O (IMP-P&O). The simulation results clearly demonstrate enhanced tracking performance under various test conditions, including the Step & Ropp test, as well as real profiles for both sunny and cloudy days. Certainly, the novel approach shows its ability to outperform both conventional and modern methods with a faster convergence speed (less than 2 milliseconds), the lowest steady-state oscillations (less than 0.25 Watts), and the highest tracking efficiency (greater than 99.79%).

Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems

Background: One of the key challenges in maximizing the performance of PV systems is the efficient tracking of the maximum power point (MPP) under varying operational conditions, including changes in solar irradiance and temperature. Accurate MPP tracking is essential for achieving optimal energy conversion efficiency and maximizing the electricity generation potential of the PV array, even during partial shading conditions. Traditional maximum power point tracking (MPPT) algorithms, such as the incremental conductance (INC) method, often struggle to efficiently handle partial shading conditions. As a result, there is a need for more sophisticated and robust optimization techniques that can effectively address this challenge. This study presents a novel and innovative Giza Pyramid Construction (GPC) algorithm to solve the partial shadinginduced MPP tracking problem. Objective: This study aims to apply the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in photovoltaic systems under partial shading conditions, aiming to enhance energy conversion efficiency and overall system performance. Methods: The methodology involves implementing the Giza Pyramid Construction (GPC) algorithm as the core optimization technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The GPC algorithms are utilized to iteratively adjust the duty cycle of the boost converter, enabling efficient power extraction from the PV array under varying shading conditions. The performance of the GPC algorithm is evaluated through simulations in MATLAB/SIMULINK and compared against conventional MPPT methods like INC and DGO techniques. Results: The successful application of the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in PV systems under partial shading led to significantly reduced optimization time, faster settling times, and minimized output ripples. With the proposed GPC MPPT, optimization time is reduced to 41ms, settling time is reduced to 93ms, and ripples are minimized to 0.092%. Conclusion: the Giza Pyramid Construction (GPC) algorithm demonstrates its effectiveness as a robust and efficient maximum power point tracking method in photovoltaic systems, particularly under partial shading conditions. The improved optimization speed, reduced settling times, and minimized output ripples underscore the GPC algorithm's potential to enhance the overall efficiency and reliability of PV systems, paving the way for its practical implementation in real-world renewable energy applications.

A Coronavirus Optimization (CVO) algorithm to harvest maximum power from PV systems under partial and complex partial shading conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions

IAs the utilization of solar photovoltaic (PV) energy systems continues to expand, the efficient extraction of energy under non-linear operational conditions becomes paramount. This research focuses on the development and enhancement of a Maximum Power Point Tracking (MPPT) algorithm, specifically tailored for solar PV systems, through the integration of Improved Grey Wolf Optimization (IGWO) techniques. The study utilizes mathematical modeling and statistical analysis to evaluate the performance of the proposed IGWO-based MPPT algorithm. This research, we first establish a comprehensive mathematical model of a solar PV energy system that accurately represents its non-linear operational characteristics, taking into account factors such as temperature variations, shading effects, and changing environmental conditions. Subsequently, we introduce the Improved Grey Wolf Optimization algorithm to optimize the MPPT process, aiming to enhance energy extraction efficiency by dynamically adapting to varying conditions. The statistical analysis includes the comparison of the IGWO-based MPPT algorithm with conventional MPPT methods, such as Perturb and Observe (P&amp;O) and Incremental Conductance (IncCond), under various non-linear operational scenarios. Key performance metrics, including energy conversion efficiency, response time, and tracking accuracy, are thoroughly evaluated to assess the algorithm's effectiveness in real-world conditions. The results of this study demonstrate the superior performance of the IGWO-based MPPT algorithm in enhancing the energy harvesting capabilities of solar PV systems under non-linear operational conditions. The proposed approach not only improves the overall energy conversion efficiency but also reduces the adverse effects of environmental variables on the system's performance. In conclusion, the integration of Improved Grey Wolf Optimization into the MPPT process represents a promising advancement in the field of solar photovoltaic energy systems. The mathematical modeling and statistical analysis conducted in this research provide valuable insights into the practical benefits of this approach, paving the way for more efficient and reliable solar energy utilization in the future.

New ARMO-based MPPT Technique to Minimize Tracking Time and Fluctuation at Output of PV Systems under Rapidly Changing Shading Conditions

The presence of bypass diodes that mitigate the negative effects of partial shading (PS) conditions produces multiple peak characteristics at the output of a photovoltaic (PV) array. Conventional maximum power point tracking (MPPT) methods develop errors under certain circumstances and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP). Several artificial intelligence (AI)-based methods have been used to modify the performance of conventional controllers. However, these methods have either not completely solved the PS problem or resulted in considerably complicated and unreliable methodologies, as well as require further development to be used in high-speed applications. This study aims to design, develop, and verify a novel rapid, reliable, and cost-effective method called adaptive radial movement optimization (ARMO) to diminish the effect of the PS problem in the MPP detection for PV systems with additional dynamic applications. The main advantages of ARMO are its improved tracking speed and significant reduction in output fluctuations during the tracking period. An extensive experimental verification has been conducted to provide a fair evaluation of the proposed method compared with conventional and recently developed methods under similar conditions while being applied to a unique PV system and DC/DC converter.