Maximum Power Point Tracking Efficiency Research Articles

Study, simulation and realization of a fuzzy logic-based MPPT controller in an isolated DC microgrid

This study presents a pioneering methodology for implementing the maximum power point tracking (MPPT) controller, based on fuzzy logic. Through a comprehensive performance analysis, we evaluate its effectiveness compared to the widely used perturb and observe (P&O) algorithm, which is a common MPPT technique. The main objective of our proposed MPPT approach is to improve the performance of a photovoltaic (PV) system. To evaluate the performance of the proposed MPPT controller and compare it with the P&O algorithm, we designed and simulated both controllers using MATLAB/Simulink. We also implemented a prototype of the controllers using an Arduino Mega board, and evaluated their performance under real operating conditions. The experimental results unequivocally confirm that the fuzzy logic-based MPPT controller outperforms the P&O algorithm in terms of performance, speed and accuracy. The fuzzy logic controller offers greater accuracy in tracking the maximum power point under various environmental circumstances, including variations in solar irradiation and connected load. Overall, this work contributes to the development of efficient and reliable MPPT controllers for PV systems, and provides a comparison of the performances of two popular MPPT techniques. Future research could explore other MPPT techniques and evaluate their performance using similar experimental setups.

Piezoelectric energy harvesting interface with fast open-circuit voltage sampling

This paper presents a piezoelectric energy harvesting interface with fast open-circuit voltage (VOC) sampling and a wide operating frequency range. The fractional open-circuit voltage (FOCV) method is the primary method for maximum power point tracking (MPPT) in energy harvesting systems, due to its easy implementation and relatively low cost. For this method to be efficient, it is necessary to shorten the time required for VOC sampling. To minimize power loss due to VOC sampling, a novel technique is proposed that is capable of sampling the VOC within a time shorter than half a cycle by using an adaptive tracking pulse instead of conventional fixed ones. We also present a peak detector design technique that can operate across a broad frequency spectrum and adapt to diverse vibration scenarios. The proposed technique reduces the duty cycle of the tracking pulse to 0.42%, which is 3.7 times smaller than the conventional 1.56%. The proposed circuit, designed using a 0.35μm complementary metal oxide semiconductor (CMOS) process, consumes just 94nA at 100Hz, 3V VOC, and a 1kΩ load. In a 2~4V VOC range and a 15~500Hz frequency range, the MPPT efficiency exceeds 95%, peaking at 99.9%, and the power efficiency remains over 93%, reaching a maximum of 97.7%.

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.

A Sensorless MPPT Approach For PV Pumb System Used BLDC Motor

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

Experimental study on modified GOA-MPPT for PV system under mismatch conditions

This paper presents a modified grasshopper optimization algorithm (GOA) tailored for optimizing the power extraction capability of a solar photovoltaic (PV) system. The algorithm`s focus is on addressing one of the issues associated with mismatch loss (MML), particularly the mismatch (MM) in solar irradiance conditions, to attain maximum output power. The core strategy of the GOA involves optimizing the duty cycles of the converter to achieve the maximum power point (MPP) for the PV system. The PV system configuration comprises three PV modules connected in series and a SEPIC converter. To facilitate efficient maximum power point tracking (MPPT), the paper proposes using the GOA as a controlling mechanism. The study employs a comparative approach, contrasting the performance of the proposed system against established algorithms, such as PSO and GWO. The results of these evaluations exhibit the superior performance of the proposed GOA when compared to other optimization techniques. The GOA exhibits exceptional MPPT tracking characteristics, characterized by rapid tracking speed, heightened efficiency, and minimal oscillations within the PV system. Consequently, the GOA effectively addresses one of the MML issues.

MPPT efficiency enhancement of a grid connected solar PV system using Finite Control set model predictive controller

Maximum power point tracking (MPPT) is required to get the highest possible power generated from a photovoltaic (PV) cell. Numerous researchers have proposed different MPPT strategies to be able to collect maximum generated electricity from the photovoltaic cells.In this research paper, a MPPT model predictive control strategy for a grid-connected PV system is presented. Model predictive control (MPC) was used to develop and model the AC load energy tracking efficiency for the PV systems with a power rate of 20 kW at standard test conditions. For the purpose of obtaining the power tracking performance, a DC-DC boost converter, DC-AC two level three phase inverter, and control mechanism for a grid connected AC load system was examined and presented in this paper. To approximate the actual PV array properties, the PV model is used, and the MPPT approach is suggested as a way to regulate the DC-DC boost converter and get the most power possible from the PV array when compared to P&O and model predictive control system. A three-phase, two-level VSI is employed in this study that is controlled by a model predictive control system with SVPWM. The inverter's control structure is developed using a model predictive control system (inner loop current controller) with reference frame transformation (abc to dq) coordinates by utilizing PLL. The PLL is used to obtain critical information about the grid voltage. A RL filter is then used to lower the total harmonic distortion of the output and connect the inverter's output to the grid. The MATLAB R2019a environment is used to create the system model. The overall performance of the system for conventional perturb and observer is around 97.72%, while for Finite Control Set Model Predictive Controller is 99.80%, which is better than previous similar research with faster time response and less oscillation around maximum power point.

Highly Efficient Maximum Power Point Tracking Control Technique for PV System Using Different Controller and Converter with Modular Multilevel Inverter

In order to operate photovoltaic (PV) systems using maximum power point tracking (MPPT), three distinct combinations of controllers and converters are proposed in this research and compared. Using MATLAB/Simulink simulation, these strategies are assessed based on the output parameters of time, power, and current. The demand on power production has increased manifold in recent years and on the other hand, the conventional resources utilized for it will be vanished in near future. The requirement of PV based generation is getting increased. The procedure of getting solar energy from a solar panel is common. With MPPT, here the output obtained must be the same quantity of energy even when the source of that energy is partially available. Climate change and other issues could be to blame for this inefficiency. In this project three distinct converters and three distinct controllers have been compared. All three converters are linked to each controller individually, and measurements of current, voltage, and power are analysed. By which the result is obtained. After the comparison of nine outputs, the most powerful and efficient combination is identified. By doing this, the converters and controllers produce high D.C voltage. Direct voltage transmission to the MMI. A.C. voltage is created by converting D.C. voltage. Increase the MMI's output by doing this. The voltage generated by the MMI is sent to the grid for domestic usage. Even when the source is not readily available, the solar panel's voltage can still be used.

Providing Low Current Ripple and MPPT Requirements in PV Panels with IBC Operating in Critical Current Mode

As energy demand increases, the investment cost of photovoltaic (PV) systems decreases due to mass production. That results the usage of PV panels to become widespread. Changes in environmental conditions result in a change in the power produced by PV panels. Maximum power point tracking (MPPT) is proposed to handle this problem. In addition to changing environmental conditions, the current ripple of the PV panel is another significant problem for the power produced. High current ripples affect the dynamic response of the PV panels and make it difficult to maintain the system to operate at maximum power point. Using an interleaved boost converter (IBC) and driving the converters in critical conduction mode (CRM) is a well-known solution for reducing high current ripple and switching losses in power supplies. In this study, an IBC operating in CRM with MPPT and ensuring low current ripple from the panels is proposed. To verify the theoretical approach, experimental and simulation studies were conducted. A 30 W prototype is designed and tested under various conditions. The MPPT efficiency of the prototype circuit is measured as 98.1%. It has been seen that the real-time and simulation results are in accordance with the theoretical ones.

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Visual Re-Initialization Model Development Methodology for Solving Problems Regarding Metaheuristic Algorithm-Based MPPT Applications

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and “Embedded Coder Support Package for TI C2000 Processors” allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency.

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.

Ultra-Fast and Accurate MPPT Control Structure for Mobile PV System Under Fast-Changing Atmospheric Conditions

Photovoltaic (PV) system efficiency is highly dependent on atmospheric conditions, which are continuously varying. The presence of an efficient maximum power point tracking (MPPT) method suitable for fast-changing environmental conditions, even under partial shading conditions (PSC) is required to optimize the PV system efficiency. This paper proposes a different MPPT control structure, where the MPPT is nested in the proportional-integral (PI) voltage controller routine. By using the proposed control structure. The Analog-to-Digital Converter (ADC) samples are shared between the MPPT algorithm and PI controller. As a result, extreme dynamic tracking improvements while a cost-effective implementation is ensured. Using the proposed control structure, a high-speed MPPT is achieved, making it suitable for mobile PV systems subjected to fast-changing atmospheric conditions. As the tracking accuracy is high, the P&O subroutine usually used in GMPPT methods to maintain the GMPP is eliminated. Results revealed that with the proposed implementation scheme, the tracking speed and the transient energy losses can be four times better than the traditional scheme.

Design of Fast Intelligence Hybrid Fuzzy Logic and Improved Incremental Conductance Based MPPT Technique

Presently, there has been a significant growth in the solar PV technology due to various factors such as clean technology, zero carbon emission and low maintenance. However, it becomes significant to extract the power in abundance from solar PV system due to its intermittent nature. To extract the significant power, a suitable maximum power point tracking (MPPT) technique has been presented. The MPPT technique is developed based on the hybrid fuzzy logic (FL) and improved incremental and conductance (IInC) method. The aim of developing hybrid FL-IInC technique for MPPT is to regulate duty cycle so that maximum power point (MPP) is achieved with stable and accurate outcomes during dynamic and steady state conditions. The IInC method was developed by regulating the duty cycle and step-size. of It was observed that proposed hybrid FL-IInC delivers accurate and stable results as compared with IInC method. During the model testing at the irradiance with 1000w/m2, the achieved MPPT efficiency was 99.96% as compared with the IInC with an outcome of 98.47% for the improved technique. Additionally, the settling time for the proposed hybrid FL-IInC was less compared with the IInC method. All the simulations were conducted with the MATLAB Simulink Platform. In future, the hybrid FL-IInC method can be employed with other conditions such as partial shading for better applicability

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.

A novel optimized dynamic fractional-order MPPT controller using hunter pray optimizer for alleviating the tracking oscillation with changing environmental conditions

Introducing new control strategies in the photovoltaic (PV) system to continuously harvest the maximum power with the changes in environmental conditions is a crucial issue. Therefore, this paper proposes an efficient maximum power point tracker (MPPT) using the perspective of fractional calculus to provide an accurate dynamic response to the rapid changes in environmental conditions. The proposed control scheme is an integration between the fractional proportional–integral (FPI) controller and dynamic variable fractional-order perturb and observe (P&O) MPPT. To optimally identify the proposed MPPT controller parameters, a novel hunter-pray optimizer (HPO) is implemented as it is featured by its efficient balance between exploration and exploitation capacity. The proposed MPPT controller is examined with a series of experiments under dynamically changed environmental conditions. Furthermore, a detailed comparison is conducted versus a set of state-of-the-art including; incremental conductance (INC), basic P&O, MPPT-based particle swarm optimizer(PSO), MPPT-based Grey Wolf Optimizer(GWO), and MPPT-based cuckoo search algorithm (CSA). The results prove that the proposed MPPT is capable to track the global maximum generated power with a notable steady-state response and is almost free of oscillations which ensures an optimal adaptive dynamic performance in response to the rapid variation in the environmental conditions. Moreover, the proposed approach affirms its superiority compared to the set of state-of-the-art techniques in providing the highest maximum power levels in the shortest conversion time. The outcomes provide proof of the remarkable impacts of integrating fractional calculus in enhancing the dynamic response of the proposed MPPT because of the extra degree of freedom that enhance the flexibility of the MPPT.

Enhanced Hybrid PSO-CSA Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems: Modeling and Analysis

This research paper presents a mathematical model and analysis of an Improved Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) hybrid Maximum Power Point Tracking (MPPT) system for partially shaded solar PV systems. The proposed hybrid MPPT system aims to overcome challenges in tracking the global maximum power point (MPP) under partial shading conditions. The paper discusses the significance of MPPT techniques in solar PV systems, particularly under partial shading conditions, and the motivation for developing an improved MPPT algorithm using the hybrid PSO-CSA approach. The performance of the proposed hybrid MPPT system is assessed through simulation and experimentation, demonstrating its robustness and superior performance compared to conventional MPPT techniques. The results validate the efficiency and practicality of the proposed hybrid approach, emphasizing its potential for real-world implementation and advancement in renewable energy systems.

Adaptive Controller PI-Fuzzy Logic Speed for Brushless DC Motor Drive Supplied by PEMFC Cell Optimized by P&O

Brushless Direct Current (BLDC) motors have recently gained momentum. In this study, a fuel cell stack, namely, a Proton-Exchange Membrane Fuel Cell (PEMFC), one of the promising renewable energy technologies, is chosen for a brushless DC motor. To improve the performance of PEMFC, an efficient maximum power point tracking (MPPT) algorithm was applied to the DC/DC boost converter. To this end, the perturbation and observation (P&O) algorithm were developed. This work proposes an adaptive controller proportional-integral (PI)-fuzzy logic speed for the BLDC. To evaluate its performance, the proposed controller was simulated under several conditions: load disturbance and reference speed variation. This controller is analyzed and compared with the classical PI controller. Therefore, the control performance parameters, such as rise time, settling time, steady-state error, and overshoot, were determined and compared. This system is analyzed and simulated using MATLAB/Simulink software.

Adaptive energy harvesting approach for smart wearables towards human-induced stochastic oscillations

Harvesting diverse renewable energy sources from humans offers an effective approach to achieving cleaner production and sustainable development. The integration of self-powered smart wearables enables continuous power supply for long-term operation of outdoor civilian and military electronics, especially in situations with limited access to conventional power sources. Resonant energy harvesting schemes working at the dominant frequencies of human movement can trigger large mechanical oscillations and significantly enhance the generation of electrical power. This study presents the development of a nonlinear two-degree-of-freedom (2DOF) resonant energy harvesting system with a maximum power point tracking (MPPT) algorithm that adapts to individual human differences and stochastic motion patterns. Through an analysis of the system characteristics based on equivalent circuit modeling, it is found that the electrical damping of the harvester plays a crucial role in determining the actual power output, and the relationship between its optimal value and the oscillation frequency is unique. Thereby, an energy harvesting circuit with an adaptive MPPT algorithm is developed to optimize the electrical damping in real time by applying a novel Perturb and Observe technique based on the average power over each time period. In addition, a variable-step search method and a special convergence judgment are proposed to enhance the tracking speed. The sensitivity analysis of algorithm parameters indicates that the time period and power tolerance need to be reasonably selected to strike a balance between search speed and accuracy. The results of constant motion pattern tests show that the proposed algorithm with a variable-step search method exhibits superior search speed and accuracy over fixed-step configurations. The results of variable motion pattern tests reveal an increase in the actual system power output of up to 194% compared to the conventional fractional open-circuit voltage (FOCV) algorithm. Finally, the system adaptability test is conducted to validate the MPPT efficiency in four resonant systems. The results showcase an impressive MPPT efficiency of 93%–99%, demonstrating its reliability and robustness in various application scenarios.

Coupling Analysis of Differential Power Processing-Based PV System and Its Decoupling Implementation of Synchronous MPPT Control

Differential power processing (DPP) is considered to be a highly competitive architecture for photovoltaic (PV) applications due to its characteristics, such as high energy efficiency and mitigation of mismatch issues. However, the complexity of coupling between DPP converters poses challenges in terms of maximum power point tracking (MPPT) in DPP-based PV systems. To address the disadvantage of the asynchronous MPPT commonly used in existing DPP topologies, which is inefficient and difficult to extend to large-scale systems, this paper presents a method that makes the application of synchronous MPPT in DPP topology feasible based on a simple and easy partial decoupling method. The innovation of the proposed decoupling method is that the dominant part of the system coupling is obtained by decomposition of the control system, to facilitate more targeted decoupling and reduce the difficulty and complexity of decoupling. Unlike other decoupling methods, the proposed approach does not become more complex with an increase in the size of the system, and thus has good scalability. In addition, the efficiency of MPPT is improved, as synchronous MPPT control can significantly reduce the MPPT time of the PV system. The results of simulations and experiments validate the effectiveness of the proposed decoupling method and the high performance of synchronous MPPT in DPP-based PV systems.

A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition

Particle swarm optimization (PSO) is envisioned as potential solution to overcome maximum power point tracking (MPPT) problems. Nevertheless, conventional PSO suffers from large transient oscillation, slow convergence and tedious parameter tuning when tracking global MPP (GMPP) under partial shading conditions (PSC), leading to poor efficiency and significant power loss. Therefore, a modified PSO hybridized with adaptive local search (MPSO-HALS) is designed as a robust, real-time MPPT algorithm. A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. Additionally, a rank-based selection scheme is adopted to choose best half of population for subsequent global and local search modes. A modified global search method with fewer parameters is devised to rapidly identify approximated location of GMPP. Finally, a modified local search method using Perturb and Observe with adaptive step size method (P&O-ASM) is proposed to refine the near-optimal duty cycle and track GMPP with negligible oscillations. MPSO-HALS is implemented into low-cost microcontroller for real-time application. Extensive studies prove the proposed algorithm outperforms bat algorithm (BA), improved grey wolf optimizer (IGWO), conventional PSO and P&O, with convergence time shorter than 0.3s and tracking accuracy above 99% under different complex PSCs.