Maximum Power Point Tracking Method Research Articles

Enhancing PV Systems with Intelligent MPPT and Improved control strategy of Z-Source Inverter

Abstract The Improved Z-Source Inverter (IZSI) has gained attention in the photovoltaic industry for its ability to boost PV voltage with a single-stage topology, simplifying system design and reducing costs. However, research on integrating IZSI into PV systems, particularly regarding the Maximum Power Point Tracker (MPPT) and IZSI control strategy, is limited. This study proposes an Intelligent Improved Particle Swarm Optimization (IPSO) algorithm as an MPPT method for PV systems under constant and varying irradiance conditions. The IPSO algorithm is compared to the FPA, CSA, and traditional MPPT algorithm (PSO), and the results demonstrate that IPSO outperforms all algorithms in terms of speed, efficiency, and convergence in finding the Maximum Power Point (MPP). Two methods, Simple Boost Control (SBC) and Maximum Constant Boost Control with Third Harmonic Injection (THIMCBC), are employed to control IZSI. Simulation results using MATLAB-Simulink show that both strategies successfully find and track the MPP, but THIMCBC exhibits superior voltage-boosting performance compared to SBC. Overall, the proposed IZSI topology with the IPSO MPPT method and THIMCBC IZSI control strategy offers several advantages, including improved voltage boost ability, reduced z-source capacitor voltage stress, inherent inrush current limitation, and cost-effectiveness. These advantages make the proposed system a promising solution for photovoltaic systems.

Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm

PV systems currently generate 4% of the world's energy needs, and their share is growing quickly. The maximum power point tracking (MPPT) is a complex non-convex optimization problem because the electrical characteristics of the PV model are nonlinear. Changes in temperature, partial shading (PS), and irradiance levels can all affect the amount of power that can be extracted from the solar system. Therefore, in this work, a novel energy valley optimizer (EVO) based MPPT algorithm is suggested to extract maximum power from solar. The classical perturb and observe (P&O), whale optimizer algorithm (WOA), cuckoo search algorithm (CSA), and particle swarm optimization (PSO) algorithms are all compared to EVO. Five case studies, including a field atmospheric data study, partial shading, variable temperature, and irradiance, are used to conduct in-depth analytical and statistical analysis. Furthermore, the successful verification of the MPPT control algorithm on the real microcontroller (Arduino MKRZERO board) through the PIL test is a critical milestone in this research. Quantitative, comparative, statistical and experimental results indicate that the proposed EVO-based MPPT achieves superior performance through 30% quicker tracking time and 80% faster settling time, which result in 4–8% higher power efficiency. The results indicate that the suggested MPPT controller successfully addresses the shortcomings of the current MPPT methods.

Metaheuristics-based maximum power point tracking for PV systems: a review

<span lang="EN-US">Over the years, numerous maximum power point tracking (MPPT) methods have been developed to extract the maximum available power from PV arrays. They are generally categorized as conventional or metaheuristic methods. The most employed conventional methods include perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC), due to their simplicity and ease of implementation. However, under partial shading condition (PSC), none of them can effectively locate a global maximum power point (GMPP) out of many local maximum power points (LMPPs). This results in significant power loss during PSC, prompting the development of various metaheuristic-based MPPT methods to address the problem. This paper reviews 38 existing metaheuristic-based MPPTs and 27 metaheuristic methods that have not yet been applied to any MPPT operation up to date. Metaphorically, these methods are divided into four categories: (i) evolutionary-based, (ii) physics-based, (iii) swarm-based, and (iv) human-based. The different MPPTs are compared in terms of complexity, converter topology, and PSC tracking capability. This paper is intended to serve as a one-stop resource for any researcher, practitioner, or advanced student seeking to develop a new metaheuristic-based MPPT method.</span>

Power Control Energy of a Pumping System (Photovoltaic- Diesel)

Abstract Future Algerian agricultural projects are oriented to the Sahara. Knowing that generally electrical energy is supplied largely by Generator Diesel (DGs). Due to the difficulty and lack of land roads for these desert areas, which creates fluctuations in the fuel supply process for farmers. In order to find a solution to mitigate this fluctuation requires the use of modern technology such as the hybridization of local renewable energy such as solar energy. Our system consists of the integration of a photogenerator (PVG) with a DG to feed a synchronous motor coupled to a central water pump controlled by means of transformers. This work presents an intelligent approach for improving and optimizing the control performance of a system using the Fuzzy Logic Control (FLC) based maximum power point tracking (MPPT) method. In this article, we will analyze a contribution to the use of a hybrid energy system (PVG-DG) according to a management strategy whose purpose is to reduce energy costs and reduce the emission of greenhouse gases (CO2). Our simulation results presented at the end of the article have demonstrated the efficiency and flexibility of the Matlab simulated techniques system based on the actual geographic information of the study area to validate the performance of the proposed system. Which can be used in other practical studies in the future.

A new approach to solve the problem of partial shading in a photovoltaic system

<p>This paper introduces a novel global maximum power point (GMPP) tracking method that addresses the challenges of efficiency and power quality degradation in photovoltaic (PV) systems caused by inadequate tracking of the GMPP. The proposed approach employs a cuckoo search algorithm with proportional, integral, and derivative (CSPID). A bio-inspired optimization technique, to effectively track the GMPP under varying weather conditions. To demonstrate its effectiveness, the CSPID algorithm is comprehensively evaluated against two well-established methods, particle swarm optimization (PSO), and cuckoo search algorithm traditional (CSA). The evaluation includes three different scenarios with gradual changes in irradiance and temperature, these tests show the ability of the algorithm to handle the condition of partial shading. The results reveal that the CSPID method achieves an average tracking time of 0.098s and an average tracking efficiency of 99.62%, thereby significantly improving the efficiency and quality of photovoltaic energy production.</p>

Maximum Power Point Tracking Based on Wind Speed Prediction

A control method for MPPT (maximum power point tracking) based on wind speed prediction is proposed to address the issue of slow attainment of optimal speed values by wind turbines in variable wind speed conditions in the wind power system. Initially, the wind speed variation value is accurately estimated using the SSA-BP neural network based on historical wind speed data. Subsequently, the MPP (maximum power point) is calculated considering the optimal blade-tip speed ratio, thereby enhancing the tracking speed of the system. Simulation results demonstrate that this method enables real-time estimation of wind speed variation, improves wind energy utilization rate, and maximizes the utilization of wind energy.

Assessment of hybrid MPPT for SPV based high gain cubic boost converter interfaced with reduced switch multilevel inverter for power quality enhancement

A high-gain cubic boost converter (HG-CBC) with hybrid-based maximum power point tracking (MPPT) through a neural network (NN) aided by the P&O technique (HNN-PO MPPT) has been suggested to acquire optimum power from a solar photovoltaic (SPV) model under varying climatic conditions. The SPV’s output is enhanced using the suggested HG-CBC as per the requirement. A detailed comparison of different conventional boost converters (BC) with the suggested HG-CBC is presented, mainly highlighting part count and boost factor (B). Using the MATLAB tool, the functionality of the developed HNN-PO MPPT technique has been examined for constant and different irradiation (G) levels. The hybrid-based MPPT helps quickly attain maximum power point (MPP) with minimum oscillations at the output. The convergence period is very short with high precision in comparison with P&O and NN MPPT. The results are examined between the suggested and traditional MPPT methods in relation to the percentage of oscillations and rise time. The Reduced Switch Multilevel Inverter (RSMLI) is proposed to integrate the SPV with the RL load. The RSMLI is compared with the conventional standard five-level MLI in relation to the quantity of DC sources, diodes, switches, capacitors, and other parts utilized. The suggested MLI involves a reduced switch count, which mitigates the overall losses during switching and hence improves the efficiency of an inverter. MLI switches are controlled using a sine Pulse Width Modulation (PWM) technique. The THD of the output current of a five-level RSMLI is 4.47%, and it falls within the IEEE 519 norm. Hence, output power quality is enhanced.

Maximum Power Point Tracking Constraint Conditions and Two Control Methods for Isolated Photovoltaic Systems

A maximum power point (MPP) always exists in photovoltaic (PV) cells, but a mismatch between PV system circuit parameters, weather conditions and system structure leads to the possibility that the MPP may not be tracked successfully. In addition, the introduction of an isolation transformer into a basic PV system allows for moderate values of the converter duty cycle and electrical isolation. However, there is no comprehensive research on MPPT (maximum power point tracking) constraint conditions for different isolated PV systems, which seriously hinders the application of isolated PV systems and the development of a related linear control theory. Therefore, in this paper, the overall mathematical models of different isolated PV systems are first established based on the PV cell engineering model and the MPP linear model, and then, two sets of constraint conditions are found for the successful realization of MPPT. These MPPT constraint conditions (MCCs) describe in detail the direct mathematical relationships between PV cell parameters, weather conditions and circuit parameters. Finally, based on the MPP linear model and MCCs, two new MPPT methods are designed for isolated PV systems. Considering the MCCs proposed in this paper, a suitable range of load and transformer ratios can be estimated from the measured data of irradiance and temperature in a certain area, and the range of MPPs existing in PV systems with different structures can be estimated, which is a good guide for circuit design, theoretical derivation and product selection for PV systems. Meanwhile, comparative experiments confirm the rapidity and accuracy of the two proposed MPPT methods, with the MPPT time improving from 0.23 s to 0.03 s, and they have the advantages of a simple program, small computational volume and low hardware cost.

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.

A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

A comparison of FLC MPPT techniques and HC MPPT techniques for photovoltaic systems

This study aims to examine the behaviour of various maximum power point tracking (MPPT) methods used with PV systems. The methodologies of hill climbing (HC) and fuzzy logic controller (FLC) are assessed in this paper. PV module and DC/DC boost converter models were simulated using PSIM and Simulink software with various MPPT strategies. The PSIM and Simulink software programmes were founder throughout the FLC MPPT method’s development. To employ each software for specific system components, the co-simulation was conducted. The effectiveness of the various MPPT approaches was assessed in response to rapidly varying weather conditions. The results show that for most of the tested MPPT approaches’ usual working ranges, FLC outperforms the HC MPPT approach in transient behavior and constant.

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.

An Innovative MPPT Method Based on the Estimated Model of the Photovoltaic Cell considering the Environmental Conditions

In this paper, a novel adaptive photovoltaic maximum power point tracking (MPPT) circuit is proposed, called AT2MPPT. The AT2MPPT circuit is based on a type two fuzzy logic set and utilizes a detailed two-diode model of a solar cell, as well as an innovative hybrid algorithm for designing the MPPT circuit. The effects of environmental conditions such as azimuth, wind speed, wind direction, and irradiation, as well as the uncertainty of radiation intensity, are taken into account in the solar cell model. It is demonstrated that changes in environmental conditions can cause up to a 20% change in the PV parameters. To optimize the AT2MPPT parameters and fuzzy membership functions (MFs), a hybrid lightning search algorithm and whale optimization algorithm (hLSA-WOA) are proposed. The accuracy of the hLSA-WOA is evaluated by comparing its results with those of other optimization algorithms on 10 standard benchmarks. The simulation results indicate that the hLSA-WOA has high accuracy and is nontrapping in local optima. Finally, the proposed AT2MPPT circuit is used to track the MPPT of a PV array in partial shade, and its performance is compared with that of P&O and fuzzy MPPT circuits. The results confirm the superior performance of the AT2MPPT circuit, which produces 1.1% more power than the other two circuits. Additionally, the AT2MPPT circuit has lower power ripple and tracking time compared to perturb, observation, and fuzzy MPPT circuits.

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.

Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions

During partial shading conditions (PSCs), the efficiency of power transfer in a Photovoltaic (PV) system decreases significantly, which can result in the formation of hotspots in the PV array. Although the insertion of bypass diodes can alleviate this issue, it can result in multiple power peaks on the power-voltage (P–V) characteristics and complicate the process of maximum power tracking. To tackle this problem, using metaheuristic algorithms for Maximum Power Point Tracking (MPPT) can yield favourable results by avoiding convergence to local power peaks and reducing computation stress on the microcontroller. However, continuous research is required in this area due to variations in the performance of metaheuristic algorithms. Therefore, this work introduces a novel MPPT approach based on the Archimedes Optimization Algorithm (AOA) for the successful capture of the Maximum Power Point (MPP) under various PS scenarios. The performance of AOA is evaluated against the other state-of-the-art algorithms such as particle swarm optimization (PSO), Jaya, and the newly proposed variant of Jaya called the adaptive Jaya (A-Jaya). The proposed MPPT method is analysed in MATLAB/Simulink software and validated using real-time results obtained from Typhoon Hardware-in-the-loop (HIL)-402 emulator. The proposed algorithm outperforms the previous algorithms, as evidenced by a comparison of the results in terms of power tracking efficiency, tracking time, and the quantity of power fluctuations.

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.

An improved standalone photovoltaic system with hybrid dual integral sliding mode and model predictive control for MPPT

AbstractIn this research paper, a double diode model‐based photovoltaic (PV) system with a proposed hybrid dual integral sliding mode control (DISMC) and model predictive control (MPC) based maximum power point tracker (MPPT) has been started for a standalone power system. Compared with the single diode model, the advanced model dual diode provides accurate maximum power point (MPP) estimation with high velocity and more searching efficiency. It also delivers the extrapolation of current‐voltage characteristics of the photovoltaic system under any weather situation. Compared to classical sliding mode control (SMC) and dual integral sliding mode controller (DISMC) based MPPT, the proposed hybrid dual integral sliding mode controller (DISMC) and model predictive control (MPC) MPPT provide high, robust behavior with minimized steady‐state error. A high gain multilevel boost converter is employed, which provides an output voltage directly proportional to the level number. Moreover, the predictive model controller extracts peak power from the PV module by predicting errors in further sampling periods and switching pulses. The proposed hybrid MPPT method provides a fixed switching frequency of a high gain multilevel boost converter under varying environmental and loading conditions. Practical responses justify the capability of advanced MPPT control using the dSPACEDS1104 platform.

Connecting multiple vehicular PEM fuel cells to electrical power grid as alternative energy sources: A Case Study

Advances in fuel cell (FC) technology make them an alternative energy source in several applications such as static, portable, and transportation. Recently, the energy needs of hydrogen electric vehicles, which have developed as an alternative to battery electric vehicles (BEVs) and have significant potential in the future, are met by vehicular hydrogen FCs. In this regard, fuel cell electric vehicles (FCEVs), which have started to take their place in the transportation sector, can also be used as an additional energy source with their energy unit for electrical power grid-connected systems. In grid-vehicle integration, vehicular FCs can meet a significant part of the electricity consumption when parked and help stabilize the grid. In this study, multiple vehicular PEM fuel cells (PEMFCs), the most widely used in FCEVs, support the residential grid by injecting electrical power into the connecting system. For this, a multi-input dc-dc boost converter interface is designed and utilized to manage electrical energy from vehicular PEMFCs to the electrical power grid instead of a conventional single-input dc-dc boost converter. In the dc-dc converter control, Incremental-Conductance (InC) maximum power point tracking (MPPT) method is utilized to extract maximum power from PEMFCs. Subsequently, the extracted electrical energy is supplied to the grid through a full-bridge inverter controlled by hysteresis pulse width modulation. The designed grid-support system supplied by vehicular PEMFCs in the ratings of 75 kW, 100 kW, and 125 kW are connected to local dynamic loads and analyzed for a 24-h operation. The minimum load consumption value is 84.6 kWh at midnight while load consumes at most 396.2 kWh during the daytime. Various connection situations of vehicular PEMFCs are considered to examine the energy flow analysis between vehicles and the grid/consumer side. A detailed economic analysis has been carried out by using hourly energy values in terms of load profile and generated energy to show the profit amount on the user side. While the cost of the energy consumed by the loads is calculated as €1004.8 without connection to the grid of FCEVs, the amount of profit obtained on the load side is measured as €943.9 with the connection.

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.

Modified grey wolf optimizer based MPPT design and experimentally performance evaluations for wind energy systems

The method that aims to operate the wind energy system (WES) at the maximum power point (MPP) is called the maximum power point tracking (MPPT) method in the literature. The grey wolf optimization (GWO) is one of the newest population-based meta-heuristic methods, and its performance as an MPPT algorithm in WESs has not been extensively studied yet. In this study, the standard GWO algorithm has been modified considering the requirements of WES, so that the system can reach the MPP quickly and stably, thereby improving the system’s efficiency. Moreover, the performance of the proposed method is examined comparatively with the well-known MPPT methods via simulation and experimental studies for many possible scenarios. It is demonstrated that the proposed modified GWO (MGWO) performance is better than the classic and modified perturb and observe methods. The results have also been compared with the Fibonacci Search (FS) and Golden Section (GS) Search-based MPPT algorithms newly presented in the literature for WES. Although the results of FS, GS, and MGWO-based MPPT algorithms are very close to each other, it has been observed that FS has a slightly better performance.