Maximum Power Point Tracking Algorithm Research Articles

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.

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.

Evaluation and deployment of a unified MPPT controller for hybrid Luo converter in combined PV and wind energy systems

This work emphasizes the development and examination of a Hybrid Luo Converter integrated with a unified Maximum Power Point Tracking (MPPT) for both grid and independent hybrid systems. The primary objectives of this hybrid system are to efficiently harness power from intermittent and variable renewable sources while elevating low-voltage energy inputs to utility-grade levels. Unlike previous studies employing specific MPPT algorithms for solar and wind sources, this work aims to simplify the control system by utilizing a unified MPPT controller. This research also introduces a novel approach involving dual-lift hybrid Luo converters to create hybrid systems, operating exclusively or concurrently based on the availability of renewable resources. To maximize power generation from all renewable sources, a unified MPPT algorithm is developed. The hybrid system, incorporates 500 W wind and 560 W PV systems, the innovative Luo converter, and the unified MPPT controller. A comprehensive comparative analysis is presented, comparing the hybrid system's performance with that of traditional control algorithms, such as the Perturb & Observe, and Radial Basis Function Network controllers. The successful prototype of the converter validates the practicality of the proposed approach.

A Novel Hybrid Whale Optimization Algorithm Differential Evolution Algorithm-Based Maximum Power Point Tracking Employed Wind Energy Conversion Systems for Water Pumping Applications: Practical Realization

This article describes the implementation of a proposed hybrid whale optimization algorithm differential evolution (WOADE) as a maximum power point tracking (MPPT) for wind energy conversion systems (WECS) employed for water pumping applications. The proposed hybrid MPPT algorithm provides rapid and zero oscillation power tracking to achieve the global maximum power point (GMPP) within a limited number of iterations. In this work, the exploitation capability of the whale optimization algorithm (WOA) is integrated with the exploration behavior of differential evolution (DE) to improve the tracking ability over a steady and dynamic operating climate. The performance of a permanent magnet synchronous generator (PMSG) driven centrifugal pump for water pumping applications with wind energy conversion systems has also been investigated. A modified Ćuk converter provides a higher output voltage with an enhanced voltage conversion ratio and is employed for optimal wind power tracking. The practical realization is done to substantiate the productiveness of the introduced hybrid WOADE based MPPT algorithm for WECS applied for water pumping under varying and sudden changes in wind velocity.

A novel maximum power point tracking with hybrid control algorithm for automotive thermoelectric generator system.

To dynamically track the maximum power of an automotive thermoelectric generator (ATEG) system in real-time, this study introduces a novel maximum power point tracking (MPPT) algorithm that integrates Kalman filtering and fuzzy control. Employing a two-phase interleaved parallel DC-DC boost converter in the MPPT controller effectively reduces current ripple and switch loss. Results demonstrated a significant improvement in tracking time compared to the traditional incremental conductance algorithm, attributed to the elimination of high-frequency components in output power by the Kalman filter. The novel algorithm exhibits enhanced tracking stability through the application of fuzzy control. Ultimately, the tracking accuracy of the novel algorithm surpasses that of the incremental conductance algorithm by 5.2%, achieving an impressive 94.9%. This study, therefore, presents a valuable contribution to a novel MPPT algorithm for precisely and rapidly tracking the global maximum power points of the ATEG system throughout the entire vehicle driving cycle.

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.

Maximum Power Point Extraction in PV Array Under Partial Shading Conditions Using GWO-Assisted Beta Method

This paper presents a hybrid maximum power point tracking (MPPT) algorithm, which combines the grey wolf optimization (GWO) technique and Beta method for extraction of maximum power from a photovoltaic (PV) system operating under partial shading conditions. The proposed algorithm named GWO-Beta-based MPPT uses the GWO technique to achieve the global maximum power point (GMPP), while, simultaneously, the MPPT reference using Beta method is calculated according to the PV voltage and current at GMPP. After that, the MPPT algorithm is switched to operate only with the Beta method that keeps tracked on the MPP reference, guarantying the GMPP in that specific operation point. By means of computational simulation results, the performance of the proposed MPPT algorithm is evaluated taking into account the convergence speed and power oscillations to achieve the GMPP.

Simulation of Photovoltaic Generators and Comparison of two common Maximum Power Point trackers.

Maximum power point tracker algorithms play an important role in the optimization of the power and the efficiency of a photovoltaic generator ‘PVG’. We made the comparison between two algorithms currently implemented for the power optimization of PVG. These algorithms are based on the Perturb & Observe and the Conductance-Increment methods allowing the Maximum Power Point Tracking, ‘MPPT’, principle. The study leads us to conclude that these algorithms are not well adapted for PVG exposures in very unfavorable but realistic external conditions.

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.

An adaptive fuzzy maximum power point tracking for PV systems by a mutant gray wolf optimization algorithm

Abstract An innovative and adaptive maximum power point tracking (MPPT) based on a fuzzy controller is proposed in this article. The proposed controller is optimized by the mutant gray wolf optimization (MGWO) algorithm, and the objective function uses an integral squared time squared error (ISTSE) index. To indicate the efficiency of the proposed MPPT algorithm (MGWO-AF-MPPT), the simulations for several irradiation conditions are modeled in MATLAB. In addition, two fair comparisons are performed between the simulation results of the proposed MPPT method with an adaptive fuzzy controller that optimized with particle swarm optimization (PSO-AF-MPPT), as well as a Perturbation and Observation method (P&O-MPPT). The comparison results illustrate that the amount of generated electrical power from the PV system for all adaptive MPPT systems is almost close to each other, indicating the high accuracy of both fuzzy-based controllers. However, the PV power generation of the proposed MGWO-AF-MPPT is higher, and also its ripple is lower than PSO-AF-MPPT. Note that, the calculated total harmonic distortion (THD) for the P&O-MPPT, PSO-AF-MPPT, and the proposed MGWO-AF-MPPT methods are calculated at 3.4%, 2.3%, and 2.1%, respectively. A lower THD indicates better power quality generated by the PV system employing the MGWO-AF-MPPT method.

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&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.

Improved maximum power point tracking algorithms by using numerical analysis techniques for photovoltaic systems

Solar photovoltaic (PV) panels generate optimal electricity when operating at the maximum power point (MPP). This study introduces a novel MPP tracking algorithm that leverages the numerical prowess of the predictor-corrector method, tailored to accommodate voltage and current fluctuations in PV panels resulting from variable environmental factors like solar irradiation and temperature. This paper delves into the intricate dynamics of solar panels, presenting a comprehensive mathematical model capturing the interdependencies between current, voltage, power, solar irradiation, and temperature. Existing numerical MPPT techniques are explored to provide their advantages and disadvantages. The proposed algorithm, formulated in MATLAB, encapsulates essential solar panel variables and undergoes rigorous dynamic testing in the Simulink® environment under diverse solar irradiation and temperature scenarios. These results are visually represented through graphs and tabulations. A subsequent section offers a simulation-driven comparative review of the proposed algorithm against established methodologies. The article culminates with conclusions drawn from the empirical findings and outlines promising avenues for future research.

Novel design and adaptive coordinated energy management of hybrid fuel‐cells/tidal/wind/PV array energy systems with battery storage for microgrids

AbstractThis paper proposes a comprehensive solution to the challenges of managing a hybrid microgrid that generates electricity from multiple sustainable energy sources by proposing a coordinated energy management strategy and storage system. As renewable energy generation becomes increasingly popular, it introduces greater intermittency and stochasticity in energy management. To address this issue, a coordinated energy management strategy and storage system is proposed, which includes a fuzzy logic modified super twisting algorithm (MSTA). The objectives are to optimize the design and operation of microgrid including electrical based energy conversion systems such as photovoltaic and wind turbines, fuel cells, tidal energy, electric vehicle charging stations, and main grid. The second objective is to develop an energy management system for hybrid energy storage systems (HESS) and renewable energy sources (RESs) to maximize power production and ensure service continuity and smooth output energy of the microgrid, while also providing optimal benefits. To maintain cost‐effectiveness, an On/Off maximum power point tracking (MPPT) algorithm is also proposed. The contribution of this paper is to provide a solution to the intermittent and stochastic nature of renewable energy management and to improve the efficiency and durability of the energy conversion systems. The proposed management unit provides consistent output power and long‐term service.

Fuzzy Logic Based Adaptive P&O MPPT Control of a DC-DC Boost Converter Used in a Wind Energy Conversion System with a Permanent Magnet Synchronous Generator

In this paper, we present a Maximum Power Point Tracking (MPPT) algorithm employing Perturb and Observe (P&O) using Fuzzy Logic (FL) control to overcome limitations associated with the conventional P&O MPPT method in order to improve transient response and reduce steady-state terminal voltage oscillations. In particular, the FL approach was applied to define the perturbation amplitude for the P&O MPPT algorithm, according to the ratio between the incremental power and incremental voltage in the input of a DC-DC boost converter. The approach was a variable speed wind power conversion chain based on the permanent magnet synchronous machine. The MPPT control continuously adjusts the duty cycle of the DC-DC converter to maintain the wind turbine at the maximum power point. MATLAB-SIMULINK environment is used to simulate the entire system demonstrating the extraction of the maximum available power from the wind turbine under a varying wind speed.

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A Real-Time Deterministic Peak Hopping Maximum Power Point Tracking Algorithm for Complex Partial Shading Condition

A Real-Time Deterministic Peak Hopping Maximum Power Point Tracking Algorithm for Complex Partial Shading Condition

A Comparative Study of PSO, GWO, and HOA Algorithms for Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems

Abstract Solar energy harnessed through photovoltaic technology plays a crucial role in generating electrical energy. Maximising the power output of solar modules requires optimal solar radiation. However, challenges arise due to obstacles such as stationary objects, buildings, and sand-laden winds, resulting in multiple points of maximum power on the P–V curve. This problem requires the use of maximum power point tracking algorithms, especially in unstable climatic conditions and partial shading scenarios. In this study, we propose a comparative analysis of three MPPT methods: particle swarm optimisation (PSO), grey wolf optimisation (GWO) and Horse Herd Optimization Algorithm (HOA) under dynamic partial shading conditions. We evaluate the accuracy of these methods using Matlab / Simulink simulations. The results show that all three methods solve partial shading problems effectively and with high precision. Furthermore, the Horse Herd Optimization approach has superior tracking accuracy and faster convergence compared with the other proposed methods.

An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this paper brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions

In this paper, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.