Maximum Power Point Tracking Performance Research Articles

Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Hybrid photovoltaic-thermoelectric generator (PV-TEG) system combines two types of energy conversion which is an important innovation to advance the development of renewable energy technologies. Hybrid system in practice needs to track the best operating point in real-time with the help of maximum power point tracking (MPPT) technology to ensure the system outputs maximum energy and optimizes performance. To improve the energy conversion efficiency and utilization of hybrid PV-TEG systems, as well as to effectively combat the negative impacts under partial shading conditions (PSC) and non-uniform temperature distribution (NTD), this paper proposes a hybrid system MPPT technique based on the bi-directional long and short-term neural networks (Bi-LSTM). The Bi-LSTM can consider both past and future data information, which will enhance the training process of the network and the accuracy of the model, making the results more comprehensive. It approaches the global maximum power point by searching for the maximum power down in the forward layer and up in the backward layer. The algorithm can effectively prevent from falling into local optimum under PSC and NTD. The case study section tests and explores the feasibility of utilizing Bi-LSTM to obtain the maximum power from a hybrid PV-TEG system under five case study scenarios, namely, start-up test, step change in solar irradiance, stochastic variation, Hong Kong four-season real case, and uncertainty analysis. Comparative analysis with the other five algorithms are also conducted to thoroughly verify the superiority of the proposed technique for hybrid system MPPT applications. The simulation results indicate that the hybrid PV-TEG system with Bi-LSTM achieves the optimal MPPT performance stably and efficiently under different operating conditions. In particular, low irradiate condition in spring, the energy generated by Bi-LSTM exceeds the INC, SAO and P&O energy outputs by 80.51%, 61.22%, and 31.04%, respectively.

Experimental test of optimizing maximum power point tracking performance in solar photovoltaic arrays based on backstepping control and optimized by genetic algorithm

Maximum Power Point Tracking (MPPT) is a key component of modern solar photovoltaic (PV) systems. It improves the efficiency of PV power generation systems by inevitably reducing power losses. The most developed MPPT methods adopted like hybrid approaches combine algorithms with nonlinear controllers, such as backstepping controller (BSC). Despite their prevalence, there is still a lack of standardized procedures for tuning the parameters of these nonlinear controllers. In response to this challenge, a proposed method is adopted to identify the optimal gains using Genetic Algorithm (GA) for an efficient power tracking. The advantage of this strategy is established by real-time testing with the GA-BSC technique, which validates its capacity to generate appropriate controller gains for improved tracking performance. Experimental comparisons reveal that the modified GA-BSC approach significantly outperforms the conventional GA-BSC strategy. The conventional GA-BSC method falls short due to its use of non-optimal gains, leading to less effective reference voltage tracking and lower overall performance. In contrast, the proposed method benefits from optimal gains, resulting in enhanced stability and faster response times—achieving maximum power point tracking in 0.2 s compared to 0.6 s for the P&O-BSC method. Another experiment was conducted to test the robustness of the proposed method under variable irradiation profile. The results demonstrated the efficiency of the improved GA-BSC approach compared to other methods, which was more stable and faster. These improvements highlight the capability of the proposed method to enhance hybrid method efficiency, thereby leading to increased efficiency and reliability in photovoltaic systems.

Optimizing photovoltaic systems: A meta-optimization approach with GWO-Enhanced PSO algorithm for improving MPPT controllers

Environmental factors and load conditions influence the efficiency of power converters - key elements in Photovoltaic (PV) systems. This study employs optimization algorithms to fine-tune the converter's operation, focusing on metaoptimization, an algorithm increasing attention in recent research. The analysis introduces the Grey Wolf Optimizer (GWO) to enhance the Particle Swarm Optimization (PSO) algorithm. The optimized PSO algorithm is integrated into a PV system's Maximum Power Point Tracking (MPPT) controller. Implemented in MATLAB/Simulink, this approach is validated by combining measured data and simulation scenarios: 1. staggered vs 2. real irradiation changes. The results underscore the efficacy of the GWO-optimized PSO MPPT algorithm in enhancing the MPPT controller's performance. In Scenario 1, the GWO-optimized PSO algorithm demonstrated 9.1 % higher energy generation than the Incremental Conductance MPPT, 19.8 % more than the PSO MPPT, and 20.7 % more than the Perturb and Observe MPPT. Scenario 2 showed the superior performance of the GWO-optimized PSO MPPT, showcasing a 15.36 % increase in generation over the PSO and a 21.62 % improvement compared to the Perturb and Observe MPPT, with a 4.74 % advantage over the Incremental Conductance MPPT. The results highlight the GWO-optimized PSO MPPT's robustness under diverse conditions, emphasizing its potential PV technologies by optimizing MPPT controllers.

A deep recurrent reinforcement learning approach for enhanced MPPT in PV systems

Under partial shading condition (PSC), for efficient usage, solar photovoltaic (PV) systems are required to operate at global maximum power point (GMPP). A maximum power point tracking (MPPT) controller is usually employed for this task. In this work, for MPPT under PSC, a recurrent deep reinforcement learning (DRL) approach has been developed and investigated. The optimized neural network, augmented by long short-term memory (LSTM), is trained using the proximal policy optimization (PPO) DRL algorithm. The MPPT performance of the proposed scheme was assessed using the GMPP attainment accuracy (%), on several realistic static and dynamic test cases. The presented investigations revealed the proposed controller's average MPPT accuracy of 97.79 % for the considered random static PSCs. Similarly, average MPPT accuracies of 95.69 % under changing irradiance, 95.70 % under change in temperature and 95.77 % under changes in both irradiance and temperature were recorded. These accuracies are found to be significantly superior to recent reported works. The resulting performance enhancement is attributed to the inclusion of LSTM, which allowed the agent to retain information about past states and actions, enabling it to make informed decisions. Therefore, based on the presented investigations, it is concluded that the proposed PPO-LSTM approach is an excellent MPPT alternative for PV systems.

Design and simulation of an off-grid marine current-powered seawater desalination and hydrogen production system

The use of marine current energy to provide a stable supply of energy and fresh water to islands and remote areas is a promising prospect. In this study, an integrated marine current-powered seawater desalination and hydrogen production system was proposed. For higher integration and efficiency, the generator and seawater pump were integrated into the nacelle and mechanically connected to the main shaft. Four operating modes and the corresponding start-stop and maximum power point tracking (MPPT) control strategy were designed to achieve energy distribution between the two loads. Considering the frequent start-stop caused by the sinusoidal characteristic of marine current energy, a soft start-up process was designed for the RO elements, including dual hydraulic accumulators and a smooth transition process to avoid abrupt changes in feed pressure and feed flow rate. Finally, the performance was obtained by simulation. During the 6 h of the ebb tide, the desalination system produced a total of 841.77 L of fresh water, while the electrolyzer produced 1038.5 SL of hydrogen and consumed 0.95 L of water. The average power coefficient of the turbine was 0.434, and the specific energy consumption of the desalination system was 2.03 kWh/m3, indicating good MPPT performance and high efficiency.

A novel approach to evaluate dynamic performance for photovoltaic system using software platform

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

An efficient MPPT techniques for inter-harmonic reduction in grid connected hybrid wind and solar energy systems

In this work, the operation of photovoltaic system, wind turbine driven doubly fed induction generator along with battery has been observed. Also, a searching space minimization-based artificial bee colony scheme is developed for tracking the maximum power in a doubly fed induction generator-based system. To track maximum power in solar systems, an improved adaptive reference voltage approach has been presented. Several conventional and optimization-based techniques are used by DFIG and photovoltaic systems to get around the non-linearity features in the output parameters. Regarding DFIG, the artificial bee colony method based on searching space minimization can be used to solve the shortcomings of the perturb and observe algorithm. Because of its weather-sensitive nature, it can withstand sudden changes in wind speed. The suggested searching space minimization based artificial bee colony strategy uses a mechanism for determining the range of optimal rotor speed in order to track the maximum power point more quickly. The maximum power point tracking performance of the adaptive reference voltage technique is superior to that of current perturb and observed-based systems. However, a huge processing memory is required in order to track the maximum possible power point. This paper proposes an enhanced maximum power point tracking technique based on adaptive reference voltage that does not require a memory unit. Additionally, despite sudden changes in irradiation conditions, improved adaptive reference voltage can drift-free and reliably monitor the maximum power point. The new adaptive reference voltage technique uses temperature and radiation sensors to identify the region nearest to the maximum power point. This helps the system respond more quickly. The proposed system with searching space minimization based artificial bee colony and improved adaptive reference voltage schemes displays lower inter-harmonic content in grid current compared to perturb and observe scheme. The proposed scheme has been implemented in MATLAB & simulink atmosphere and OPAL-RT displayed satisfactory results.

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

This study proposes a novel honey bee dancing (HBD) maximum power point tracking (MPPT) algorithm inspired by the foraging behavior of honey bees. When a bee finds nectar, it returns to the honeycomb and dances to inform others about the location of the nectar. Other bees then fly towards the location and gather the nectar. The proposed HBD algorithm uses five bees searching for the nectar who communicate with each other about the location and the quantity of nectar by dancing. Finally, the five bees found the location of the most nectar which is represented by the maximum power point. The proposed HBD algorithm applies to uniform irradiance condition (UIC) and partial shaded condition (PSC). It is then compared with the PV panel output power and load relationship (OPLR) algorithm and perturb and observe (P&O) algorithm. Experimental verification has been performed under both the UIC and PSC. At the UIC's irradiance level of 200 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 120 W, 118 W, and 94.5 W, respectively, with efficiencies of 99 %, 97 %, and 78 %. Additionally, under the PSC with an irradiance level of 600 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 218 W, 189.2 W, and 74.8 W, respectively, with efficiencies of 99 %, 86 %, and 34 %, and convergence times of 4.7 ms, 6.5 ms, and 6 ms, respectively. It is evident from the results that the solar MPPT performance of the proposed HBD algorithm is better than the OPLR algorithm and P&O algorithm. This method ingeniously combines the foraging behavior of bees with solar power generation to produce the maximum natural power. This approach does not require the development of photovoltaic (PV) panel specification data, complex calculations, and additional temperature meters and heliographs. It is highly efficient and has significant economic benefits.

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 & Observation (P&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.

Enhancing MPPT performance for partially shaded photovoltaic arrays through backstepping control with Genetic Algorithm-optimized gains

As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua’s dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua’s solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.

A Modified Perturb-and-Observe Control for Improved Maximum Power Point Tracking Performance on Grid-Connected Photovoltaic System

A Modified Perturb-and-Observe Control for Improved Maximum Power Point Tracking Performance on Grid-Connected Photovoltaic System

Revolutionary building approach for maximal photovoltaic system results to improve maximum power point tracking in solar inverter

Due to the inherent frequency ripple in single-phase photovoltaic (PV) grid-connected solar inverters, the Maximum Power Point Tracking (MPPT) will inevitably be affected. To improve the MPPT performances, a passive LC power decoupling circuit with a Robust Sliding-Mode Control (RSMC) is proposed in this article. The frequency pulsation on the DC link is effectively canceled with the passive LC decoupling path. Thus, the MPPT accuracy is significantly enhanced, and the utilization of a small DC-link capacitor becomes possible. The resonance between the LC circuit and the main DC-link capacitor appears, which can be damped through an active damping method. The proposed RSMC offers good steady-state, dynamic performance (voltage fluctuation and settling time), and the robustness of the DC-link voltage, which is also beneficial to MPPT control in terms of high accuracy and fast dynamics. The systematic design of RSMC is presented, and a detailed parameter optimization design of the LC decoupling circuit is discussed. Experimental tests are performed on a 2.5-kW single-phase grid-connected solar inverter, and the results validate the effectiveness of the proposed strategy.

A New MPPT-Based Extended Grey Wolf Optimizer for Stand-Alone PV System: A Performance Evaluation versus Four Smart MPPT Techniques in Diverse Scenarios

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications.

Maximum Power Point Tracking Charge Controller Using Modified Perturb and Observe Algorithm for Lead Acid Battery

A study was undertaken on development of a modified perturb and observe algorithm for maximum power point tracking (MPPT) charge controller. This MPPT algorithm was developed in Matlab/Simulink environment, using three-state charging method for improved battery charging with higher efficiency. A buck topology was utilised as a DC-DC converter for charge controller implementation. The maximum power of the photovoltaic panel was tracked by a modified perturb and observe algorithm. The battery charge controller charged a lead-acid battery using a three- stage charging strategy including the bulk charge, constant voltage (absorption charge), and float charge stage. The performance analysis of Simulink model was carried out by maximum power point tracking performance, battery charging performance, and overall charge controller efficiency performance. The performance results indicated that the maximum power point tracking was capable to track to the maximum power of PV panel at any solar irradiance variation with power tracking efficiency up to 98 per cent. The efficiency of overall charge controller was up to 98.3%, which matched many high-end commercial charge controller product specifications.

Conventional and Switched Capacitor Boost Converters for Solar PV Integration: Dynamic MPPT Enhancement and Performance Evaluation

This paper designs two DC-DC converter configurations integrated with solar PV renewable energy resource. Its focuses on comparing two converter topologies: the conventional boost converter and the switched capacitor boost converter. The Perturb and Observe (P&O), Incremental Conductance (INC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) algorithms are employed to dynamically enhance the Maximum Power Point Tracking (MPPT) performance for both converters. The simulation results demonstrate that both converter topologies, when integrated with appropriate MPPT algorithms, can effectively harvest maximum power from the solar PV. However, the switched capacitor topology converter exhibits advantages in terms of current capabilities and voltage performance. In addition, combing the switched capacitor boost converter with the GA-MPPT algorithm improved the output voltage profile. The switched capacitor topology demonstrates distinct advantages by exhibiting enhanced current control, enabling improved handling of dynamic load changes and varying irradiance conditions. It shows voltage regulation, resulting in reduced output voltage fluctuations and enhanced stability, thereby optimizing energy extraction. The GA-MPPT simulation demonstrates a substantial increase in maximized output current for the switched capacitor boost configuration (70 A) when compared to the conventional type (10 A). The validation and implementation of the system models are carried out using MATLAB/Simulink.

Fuzzy-based maximum power point tracking (MPPT) control system for photovoltaic power generation system

The ability of the Maximum Power Point Tracking (MPPT) technology to prevent losses by stabilizing power fluctuations during severe weather conditions is critical in improving photovoltaic power generation systems. Overall system stability is improved by carefully tracing the maximum power point (MPP). This research focuses on improving MPPT performance in solar systems by employing the "Fuzzy Logic" control method. The simulation, which is run in MATLAB/Simulink, includes a detailed model of the entire system. The primary circuit is designed with a DC-DC Boost architecture and a single MOSFET transistor. The Fuzzy Logic Controller (FLC) unit in MATLAB/Simulink generates an output variable led by two input variables via the Fuzzy Logic Controller unit. The Fuzzy Logic Controller (FLC) unit generates an output variable led by two input variables in MATLAB/Simulink. The simulation model, called "Fuzzy Disturbance," combines Perturb & Observe with Fuzzy Logic and runs for 3 s. The results show an essential increase in system efficiency to 97%. The simulation results show that the suggested approach tracks MPP, reduces output power fluctuations, and improves system efficiency.

Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach

Photovoltaic (PV) systems suffer from partial shade and nonuniform irradiance conditions. Meanwhile, each PV module has a bypass shunt diode (BSD) to prevent hotspots. BSD also causes a series of a peak in the power-voltage characteristics of the PV array, trapping traditional maximum Power Point Tracking (MPPT) methods in local peaks. This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. The results compared the yield power by Tracking the MPP using only GWO, CS, or PSO MPPT techniques and combining them. Results show that in four cases: in case 1) Uniform Irradiation in three patterns (High, Medium, and Low), In case 2) Fixed Nonuniform Irradiation, While In case 3) Slow Dynamic Nonuniform Irradiation and case 4) ) Fast Dynamic nonuniform irradiation. The efficiency (PSO + CS) 97.86%, (PSO + GWO) 97.74%, and (GWO + CS) 98.55% were the highest performers in the case 1 results in (high, medium, and low), respectively. In Case 2, the efficiency (GWO + CS) is 98.62%, and it operates more effectively under fixed nonuniform irradiance. It has the highest efficiency in both Cases 3 and 4, even though its respective PSO + GWO efficiencies are 97.45% and 97.26%. Based on these results, a hybrid mode of merging algorithms based on weather radiation conditions is proposed.

Atomic Orbital Search Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems

The efficient extraction of solar PV power is crucial to maximize utilization, even in rapidly changing environmental conditions. The increasing energy demands highlight the importance of solar photovoltaic (PV) systems for cost-effective energy production. However, traditional PV systems with bypass diodes at their output terminals often produce multiple power peaks, leading to significant power losses if the optimal combination of voltage and current is not achieved. To address this issue, algorithms capable of finding the highest value of a function are employed. Since the PV power output is a complex function with multiple local maximum power points (LMPPs), conventional algorithms struggle to handle partial shading conditions (PSC). As a result, nature-inspired algorithms, also known as metaheuristic algorithms, are used to maximize the power output of solar PV arrays. In this study, we introduced a novel metaheuristic algorithm called atomic orbital search for maximum power point tracking (MPPT) under PSC. The primary motivation behind this research is to enhance the efficiency and effectiveness of MPPT techniques in challenging scenarios. The proposed algorithm offers several advantages, including higher efficiency, shorter tracking time, reduced output variations, and improved duty ratios, resulting in faster convergence to the maximum power point (MPP). To evaluate the algorithm’s performance, we conducted extensive experiments using Typhoon HIL and compared it with other existing algorithms commonly employed for MPPT. The results clearly demonstrated that the proposed atomic orbital search algorithm outperformed the alternatives in terms of rapid convergence and efficient MPP tracking, particularly for complex shading patterns. This makes it a suitable choice for developing an MPP tracker applicable in various settings, such as industrial, commercial, and residential applications. In conclusion, our research addresses the pressing need for effective MPPT methods in solar PV systems operating under challenging conditions. The atomic orbital search algorithm showcases its potential in significantly improving the efficiency and performance of MPPT, ultimately contributing to the optimization of solar energy extraction and utilization.

Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

Rapidly Determine the Maximum Power Point in the Parallel Configuration of the Photovoltaic System.

The maximum power point tracking (MPPT) solutions improve power generation efficiency, quickly stabilizing the output waveform of photovoltaic (PV) systems under variable operating conditions. Along with new algorithms, improved and adjusted methods to exploit energy from PV systems are increasingly being researched and proposed. However, the proposed solutions based on the traditional algorithms and their improvements have poor performance, while the advanced algorithms or hybrid methods bring high performance but need to be simplified, and the response speed is higher. Moreover, a suitable PV configuration makes choosing a simple but highly efficient algorithm, especially in low-power PV system applications such as rooftop solar power, traffic lights, and moving vehicles…where the number of PV panels is insufficient to implement flexible configurations. This paper proposes a modified version of the Perturb and Observe (MPO) algorithm to improve MPPT performance and increase convergence speed in the parallel structure of PV panels. The Short-Circuit Current (Isc) and Open-Circuit Voltage (Voc) are calculated directly at specific operating conditions to quickly determine the potential maximum power point (MPP) that will reduce power interruptions and increase power generation efficiency compared to periodic updates. Therefore, the proposed solution converges faster, with higher efficiency, and the output signal in static and dynamic MPPT situations is more stable. The results show that the highest efficiency in simulation and experiment is 99.99% and 99.93%, respectively, while the convergence speed is 0.01 s and 0.03 s, respectively. They are better than the traditional Perturb and Observe (P&O) algorithm, the Variable Step Size Perturb and Observe (VSSP&O) method, and the Particle Swarm Optimization (PSO) technique under the same operating conditions. In addition, its performance and convergence speed are also compared with the latest introduced algorithms. The results show that it is valuable and reliable for parallel PV configuration.