Maximum Power Point Tracking Control Research Articles

Optimal predictive voltage control of a wind driven five phase PMSG system feeding an isolated load

Wind energy conversion systems (WECS) are considered to be a promising alternative to traditional power generation systems. The principal purpose of this study is to enhance the performance of a stand-alone wind-driven five-phase PMSG system. The initial step involves providing a clear explanation of the system model, which includes the wind turbine, five-phase PMSG, and a battery storage system. Then, the maximum power point tracking (MPPT) and pitch angle control (PAC) technique is employed to optimize the power delivered to an isolated load. Furthermore, the system incorporates the machine-side converter (MSC) and battery converter, which require control to efficiently regulate the power delivered from them to an isolated load. In addition, the MSC is regulated by two predictive control algorithms: predictive torque control (PTC) and a newly formulated predictive voltage control (PVC). The PTC faces limitations such as considerable ripple, significant load commutation, and the weighting factor of its cost functions. The proposed prediction technique aims to overcome these constraints by using a simple cost function and eliminating the need for weighting factors to address stability issues also another study's objective revolves around proposing a PVC controller that achieves an optimal design. The results demonstrate that the newly proposed predictive controller outperforms the conventional control method for the wind generator. The developed controller produces consistently distributed sinusoidal currents with significantly fewer ripples and current harmonics. This fact is verified mathematically as the total harmonic distortion (THD) is reduced to 1.168 % as an average value.

Super-twisting MPPT control for grid-connected PV/battery system using higher order sliding mode observer

In recent times, photovoltaic (PV) power generation has been growing due to increase in energy demand. In grid-connected mode, achieving maximum power (MP) from the PV array is difficult by using conventional techniques due to various reasons like low tracking efficiency, stability issues, etc. This motivates the design of an appropriate control strategy to obtain the maximum power point tracking (MPPT) to harvest MP from the PV array. This paper proposes a combined higher order sliding mode observer (HOSMO)–super-twisting control (STC) for a grid-connected scenario. A perturb and observe (P &O) technique is employed to generate reference voltage, and a HOSMO is proposed to drive the STC by estimating the inductor current of the PV boost converter. The proposed controller performance is evaluated based on response time across various scenarios, including generation changes, dynamic faults, islanding and resynchronization, and load variations in comparison to other existing controllers. These microgrid test cases have been thoroughly simulated, and their effectiveness has been validated in real-time using OPAL-RT (OP4510).

Backpropagation artificial neural network-based maximum power point tracking controller with image encryption inspired solar photovoltaic array reconfiguration

The enhancement of photovoltaic (PV) arrays through reconfiguration presents a promising avenue for increasing the global maximum power (GMP) and improving overall array performance. This enhancement is achieved by minimizing differences between rows, thereby reducing the computational load on Maximum Power Point Tracking (MPPT) systems. However, many existing reconfiguration methods face various challenges, including scalability issues, inadequate shading dispersion, distortion of array characteristics, emergence of multiple power peaks, increased mismatch, and more. In order to overcome these obstacles, this study presents a novel method for array reconfiguration that is modelled after the widely used Kolakoski Sequence Transform in picture encryption. The suggested approach is assessed in eight different scenarios with 9 × 9 and 5 × 5 PV arrays shaded differently. Its performance is compared against seven established techniques. Due to its intelligent reconfiguration aimed at minimizing shade dispersion, the suggested approach consistently outperforms alternative methods. It results in substantial improvements in GMP, enhancing it by 32.79%, 14.98%, 10.15%, and 4.13% for 9 × 9 arrays, and 37.10%, 14.36%, and 9.88% for 5 × 5 arrays across diverse conditions. Furthermore, this study comprehensively investigates three separate Artificial Neural Network algorithms, specifically the Levenberg-Marquardt (LMB), Scaled Conjugate Gradient, and Bayesian Regularization algorithms for MPPT. A Levenberg-Marquardt Backpropagation-based MPPT controller for a 250Wp standalone PV system is used to validate the effectiveness of the recommended configuration. This integrated approach, which combines reconfiguration and LMB-based MPPT utilizes only two sensors regardless of array size. It achieves accelerated convergence tracking within a short 0.13 s, displaying minimal steady-state oscillations.

A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions

In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).

Dynamic PI-PD cascaded MPPT controller for SPV system with battery charging circuit

This paper proposes a dynamic maximum power point tracking (MPPT) controller for a solar photovoltaic (SPV) system with a battery charging circuit. The voltage and current, and consequently the maximum available power of SPV panels vary based on environmental conditions. To operate SPV system at maximum power point under different weather conditions, a cascaded (PI-PD) controller with PSO gain scheduling is suggested in this paper. Also, the FOPI control is applied to an accurate dynamic model of the buck converter to function as a charge controller. For tuning the FOPI controller parameters, a stochastic inertia weight GWO algorithm is employed which maintains an appropriate balance between detection and hunting strategies, and gives the fittest wolf position during iterations. The proposed algorithm is compared with the original GWO algorithm to show its superiority. The accuracy of the proposed cascaded controller used in the SPV system to find MPP ranges from 96.05% to 98.87%. The goal of this study is to operate the SPV panel at maximum power point under variable atmospheric conditions to increase efficiency at a lower cost. It also provides appropriate current and voltage for faster battery charging, thereby increasing the life span of the battery. The system is implemented and analyzed in MATLAB/Simulink, and results are validated.

Research on Maximum Power Point Tracking Control in Omnidirectional Wireless Power Transfer System

Research on Maximum Power Point Tracking Control in Omnidirectional Wireless Power Transfer System

Control of wind energy conversion systems with permanent magnet synchronous generator for isolated green hydrogen production

This paper addresses the design and analysis of the control system for a Wind Energy Conversion System (WECS) with a Permanent Magnet Synchronous Generator (PMSG) and its application for isolated green hydrogen production. The designed control maximizes the wind turbine (WT) power generation by regulating the electrolyzer current consumption, where the electrolyzer operates as a controlled load, ensuring power balance in the system and enabling the generation of maximum power from the WECS without the use of any energy storage system. The system involves directly integrating a WECS with an alkaline electrolyzer (AEL), and its configuration includes a WT with a PMSG, an AC/DC voltage source converter (VSC) acting as the generator side converter (GSC), and a DC/DC converter acting as the electrolyzer side converter (ESC) with Maximum Power Point Tracking (MPPT) control for the WT. The proposed control system has been fully modeled and tested through simulations in Matlab/Simulink, demonstrating its reliable performance under variable wind speeds. Simulation results illustrate how, over rated wind speeds, the pitch control limits the rotational speed and power to their maximum values, and at under rated wind speeds, the ESC control regulates the AEL current following the MPPT of the WT, while the GSC control maintains the DC bus voltage at its designated nominal value. Overall, the proposed control system shows robust and effective performance across the entire range of possible operational points, ensuring no wind generation power losses by following the maximum power point.

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

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

Maximum Power Point Tracking Controller of PV System Based on Two Hidden Layer Recurrent Neural Network

Maximum Power Point Tracking Controller of PV System Based on Two Hidden Layer Recurrent Neural Network

MPPT constraint conditions based on four-parameter cell model for some usual photovoltaic systems

Hitherto there is no work to specially analyze in detail the question when the maximum power point (MPP) of the photovoltaic (PV) system exists and when it disappears. It has caused the difficulty in the hardware and software designs of the PV system when the maximum power point tracking (MPPT) control must be implemented all the time. To solve this question, in this paper, the MPPT constraint conditions (MPPTCC) of the 12 usual PV systems are proposed under ideal conditions and expressed by the model parameters of the four-parameter cell model in the engineering application. They are the necessary and sufficient conditions to guarantee the existence of the MPPs of these 12 PV systems. Meanwhile, they also disclose the inherent relationships between load (or bus voltage) and cell parameters when the MPP of the PV system always exists. In addition, to apply them in practice, their expressions in practical application are also presented. Finally, some simulation experiments verify the reasonableness and accuracy of these MPPTCC in practical application, and test the influence of the varying weather to the MPPT range. By this work, the possible failure of the MPPT control arising from the occasional disappearance of the MPP of the PV system can be completely avoided. Therefore, the findings in this work can be used as the theory foundation for the hardware design, theoretical research and product installation of the PV system.

Research on MPPT control strategy based on CCAOA algorithm

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

Comparative Assessment of P&O, PSO Sliding Mode, and PSO-ANFIS Controller MPPT for Microgrid Dynamics

This paper compares different maximum power point tracking (MPPT) control strategies in microgrid dynamics, focussing on perturb and observe (P&O), adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimisation (PSO), and PSO sliding mode controller techniques. The study investigates their performance under varying microgrid conditions, considering factors like weather and load variations. The simulation results provide a detailed comparative analysis of the power at the point of common coupling (PCC) for MPPT techniques at different time intervals. Both the P&O and PSO sliding mode recorded a power output of 287 kW, while PSO-ANFIS achieved a slightly higher power output of 294 kW. At 2.5 seconds, the P&O method recorded a power output of 712 kW, while the PSO sliding mode and the PSO-ANFIS techniques achieved 717 kW and 738 kW, respectively. Overall, the PSO-ANFIS technique consistently outperformed the other methods in terms of power output, demonstrating its effectiveness in maximising energy extraction and adaptability to dynamic conditions. These findings provide valuable insights for designing and implementing MPPT controllers in microgrid systems, emphasising the efficiency of the hybrid PSO-ANFIS technique in enhancing the overall performance and stability of renewable energy systems.

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

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

Combining dynamic adaptive snake algorithm with perturbation and observation for MPPT in PV systems under shading conditions

Photovoltaic (PV) systems play an increasingly vital role in the global new energy landscape. However, when PV arrays are affected by partial shading, their power-voltage (P-U) characteristic curves exhibit dynamic multi-peak features, posing challenges to traditional Maximum Power Point Tracking (MPPT) techniques, including slow convergence speed, low tracking efficiency, and oscillations around the Maximum Power Point (MPP), leading to unnecessary power losses. Therefore, this study proposes a dual-layer control MPPT technology that integrates dynamic adaptive snake optimization algorithm with variable step perturbation and observation (ISO-IP&O) to address the MPP extraction problem of PV systems under complex weather conditions. This study compares and analyzes the proposed ISO-IP&O controller with current renowned MPPT techniques, including Grey Wolf Optimization combined with P&O (GWO-P&O), Improved Squirrel Search Algorithm (ISSA), Original Snake Optimization (SO), Horse Herd Optimization (HHO), and Adaptive Factor Selection Multi-Swarm Particle Swarm Optimization (FMSPSO), validating the effectiveness of the proposed ISO-IP&O controller. Simulation results demonstrate that the ISO-IP&O technology achieves an average tracking efficiency of 99.91 % under various shading conditions, with an average convergence speed of only 0.07 seconds. Compared with other existing methods, the ISO-IP&O technology exhibits superior performance in terms of GMPP tracking speed, maximum power tracking efficiency, and stability. Experimental validation on the HIL+RCP physical experimental platform shows that under eight partial shading conditions(PSC), the proposed method still achieves the highest average tracking efficiency of 99.68 %, with the fastest average tracking speed of 0.66 seconds. The ISO-IP&O controller achieves rapid, robust, and efficient GMPP tracking under various complex shading conditions, contributing to the enhancement of PV system performance.

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

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.

The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions

Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (PO), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and PO MPPT controllers in terms of tracking maximum power.

Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic controller

The power generated by photovoltaic (PV) systems is influenced by environmental factors. This variability hampers the control and utilization of solar cells' peak output. In this study, a single-stage grid-connected PV system is designed to enhance power quality. Our approach employs fuzzy logic in the direct power control (DPC) of a three-phase voltage source inverter (VSI), enabling seamless integration of the PV connected to the grid. Additionally, a fuzzy logic-based maximum power point tracking (MPPT) controller is adopted, which outperforms traditional methods like incremental conductance (INC) in enhancing solar cell efficiency and minimizing the response time. Moreover, the inverter's real-time active and reactive power is directly managed to achieve a unity power factor (UPF). The system's performance is assessed through MATLAB/Simulink implementation, showing marked improvement over conventional methods, particularly in steady-state and varying weather conditions. For solar irradiances of 500 and 1,000 W/m<sup>2</sup>, the results show that the proposed method reduces the total harmonic distortion (THD) of the injected current to the grid by approximately 46% and 38% compared to conventional methods, respectively. Furthermore, we compare the simulation results with IEEE standards to evaluate the system's grid compatibility.

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

This work aimed to develop and evaluate a maximum power point tracking (MPPT) control system for a wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). PMSG is commonly used to generate direct-drive and variable-speed wind energy. Initially, the generator and converter on the DC load side are controlled to follow the wind speed reference set by the MPPT algorithm. The paper presents the optimization problem formulation, including the optimization space, constraints, and objectives. The genetic algorithm (GA) is used to extract the maximum power from the WECS in this design improvement. In this study, to control and stabilize the maximum power point (MPP) of the wind turbine, a proportional integral (PI) controller and a GA heuristic approach were utilized. The GA approach was employed to determine the best settings (Kp, Ki) using MATLAB/Simulink with a 12.3 kW PMSG to model and simulate the proposed system. Based on four performance indicators-integrated squared error (ISE), integrated absolute error (IAE), integrated time absolute error (ITAE), and integrated time squared error (ITSE), the GA approach was used to optimize the controller settings. The results of the simulation show that the wind turbine (WT) can effectively track the necessary MPP. The simulation's output also includes generated power, DC bus voltage, electromagnetic torque, and currents.

Design and Performance Analysis of Photovoltaic Power Generation Light Emitting Diode Device

This research focuses on an independent photovoltaic power generation system with supercapacitor energy storage as the study subject. The model is simplified, and the photovoltaic power generation light emitting diode (LED) device is designed based on the given parameters. This system selects a Boost-type step-up converter for the supercapacitor charging circuit, capable of achieving Maximum Power Point Tracking (MPPT). The supercapacitor discharge circuit employs an LM2596 series regulator to power the rated load LED (12 V, 1 W), providing good linear regulation capability. In designing other hardware components of the device, the characteristics of a photoresistor are utilized to implement an automatic load switch circuit. An efficient single-chip integrated circuit LM2575 series is used as a regulator to convert the voltage across the supercapacitor terminals to +15 V and +5 V. Considering the need to collect the output voltage of the photovoltaic cell array and the voltage across the supercapacitor terminals, a resistor divider method is used to sample these two voltages. The sampling circuit includes a resistor divider circuit and a linear optocoupler isolation circuit. An HNC-25LTS series Hall current sensor is used for current sampling to measure AC, DC, and pulse signals under electrical isolation conditions. The supercapacitor, solar photovoltaic panel, control unit, and the main circuit for supercapacitor charging and discharging have been assembled in the experiment. Connecting the charging and discharging circuit with the photovoltaic panel and LED, the system provides power to the LED during the night. Under varying light intensity and temperature conditions, the photovoltaic output voltage waveform, PWM waveform, and Boost circuit output voltage waveform remain stable when reaching the MPPT point. The output power with added MPPT control at different times is compared. The results indicate that the designed system has effectively achieved the functionality of MPPT for solar energy.