Control Of Photovoltaic System Research Articles

Maximum power point tracking control of photovoltaic systems using a hybrid improved whale particle swarm optimization algorithm

ABSTRACT Maximum power point tracking (MPPT) control techniques are commonly employed to maximize the benefits and enhance the operational efficiency of photovoltaic systems in challenging outdoor environments. However, when the photovoltaic array is subjected to partial shading conditions, the conventional MPPT methods exhibit inadequate global maximum power point (GMPP) tracking performance. To this end, in this study, an effective MPPT control method for photovoltaic systems is proposed based on a hybrid improved whale particle swarm optimization algorithm (IWPOA). The control method integrates the advantages of the Improved Whale Optimization Algorithm (IWOA) and the Particle Swarm Algorithm (PSO) with additional control and stochastic elements. Validation analysis under a variety of conditions reveals that IWPOA significantly improves MPPT, achieving a tracking speed of approximately 0.13 s, which represents a 18.75–53.57% enhancement over other comparative algorithms. The mean tracking accuracy of IWPOA is 99.21%, and it offers superior tracking stability, making it suitable for diverse applications in MPPT.

A comprehensive review of multi-level inverters, modulation, and control for grid-interfaced solar PV systems

With the significant development in photovoltaic (PV) systems, focus has been placed on inexpensive, efficient, and innovative power converter solutions, leading to a high diversity within power converters and new system configurations for grid-connected PV (GCPV) systems. During the last decade, multilevel inverter (MLI) designs have gained popularity in GCPV applications. This article provides a wide-ranging investigation of the common MLI topology in contrast to other existing MLI topologies for PV applications. Furthermore, the various modulation techniques used in MLI switching are elucidated and contrasted. The modulation strategies are reviewed with particular regard to their comparative suitability for the modulation of MLIs for PV applications. This article also provides a comparative analysis of available MLI control techniques and controllers for GCPV applications in recent times.

An enhanced LVRT control for photovoltaic systems with supercapacitor and crowbar

An enhanced LVRT control for photovoltaic systems with supercapacitor and crowbar

Enhancing Grid Stability and Efficiency: Cost-Effective Hardware Implementation for Advanced Control of Grid-Connected PV Systems

Enhancing Grid Stability and Efficiency: Cost-Effective Hardware Implementation for Advanced Control of Grid-Connected PV Systems

Identifying the parameters of photovoltaic cells using Gaussian bare-bone imperialist competitive algorithm with opposition-based learning mechanism

Extracting the precise parameters of photovoltaic (PV) cells has become very important for simulation, evaluation, control, and optimization of PV systems. However, it is still a challenging task to accurately and reliably extract the parameters of PV cells. To solve this difficult problem, in this paper, a new meta-heuristic algorithm called Gaussian bare-bone imperialist competitive algorithm with opposition-based learning (OBL-GBBICA) is proposed to extract the parameters of PV cells. To strengthen the exploring ability and speed up the convergence, opposition-based learning (OBL) is introduced into an imperialist competitive algorithm (ICA) for two considerations. First, OBL is adopted in the population initialization to produce a high-quality population. Second, the OBL is introduced into the assimilation step to guide ICA to explore more promising regions. The above improvements not only speed up the convergence of ICA but also enhance its searchability, which is beneficial to improving the accuracy and reliability of identification results. Experimental results show that OBL-GBBICA exhibits great superiority in extracting the PV cells parameters, compared with other methods in the literature.

Global MPPT based on Fennec Fox algorithm for control of photovoltaic systems

Global MPPT based on Fennec Fox algorithm for control of photovoltaic systems

UV PHOTODETECTOR MODULE BASED ON THIN FILM ZINC OXIDE

An analysis of the work of the created photodetector module for ultraviolet (UV) radiation based on the zinc oxide/silicon heterostructure was carried out. A wireless online UV photodetector network capable of providing real-time data has been developed and tested. The Wi-Fi protocol is configured to maximize spatial coverage and have high bandwidth to meet radiation monitoring requirements. The availability of low-latency information makes the monitoring system a sufficient tool for a wide range of photovoltaic system control.

Maximum power point tracking controller for photovoltaic system based on chaos quantum particle swarm optimization – moth-flame optimization hybrid model

To convert photovoltaic arrays to solar energy in a more efficient way, this paper has proposed a maximum power point tracking controller model based on the chaotic quantum particle swarm-mothballing hybrid algorithm. First, the optimization of the particle swarm algorithm is designed to solve defects, such as premature maturity by using the quantum and chaotic strategies. The mothballing algorithm is introduced to help the model find global optimization-seeking more quickly. After that, further optimization was made to operate the tracking model in both offline and real-time parameters. The conductivity increment method and the perturbation observation method were adopted to effectively track the model under different temperatures and light intensities. Finally, the simulation and analysis experiments were carried out on the Simulink platform. The study’s proposed maximum power point tracking controller achieved a steady-state accuracy η2 of 99.84%. In summary, the study has proposed a hybrid intelligent algorithm with extraction of internal parameters. The maximum power point tracker based on the proposed method is proved to be both effective and accurate.

Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%.

An improved takagi - sugeno variable step size perturb and observe MPPT algorithm based lead acid battery charge controller for photovoltaic system

The main objective of photovoltaic system controller is to reduce the price of the kWh compared to traditional fuel-based electricity. Thus, the improvement of simple and low – cost controllers is a major challenge. Perturb and Observe (P&O) is the commonly used algorithm in order to ensure the power maximization of the photovoltaic system, due to its simplicity and effectiveness. However, on the main drawbacks of that algorithm is the fixed step size of perturbation, due to working environmental changes, which require dynamic tuning of the controller parameter. In order to overcome the cited disadvantage, a new Takagi - Sugeno variable step P&O is proposed. Based on the real-time measurements of the solar radiation and temperature, the proposed fuzzy system schedules the appropriate step size based on human expertise. A zero order Takagi – Sugeno with singleton output is proposed. The fuzzification were carried out using triangular and trapezoidal membership functions. The centroid defuzzification method based 25 If -Then rules are adopted. In order to verify the effectiveness of the improved P&O, three scenarios were used. The obtained results show the effectiveness of the proposed controller under shaded conditions and various temperatures.

An adaptive operator selection cuckoo search for parameter extraction of photovoltaic models

An adaptive operator selection cuckoo search for parameter extraction of photovoltaic models

Advanced controller design for D-FACTS device in grid-connected photovoltaic system controller

Advanced controller design for D-FACTS device in grid-connected photovoltaic system controller

Deep reinforcement learning-based robust nonlinear controller for photovoltaic systems

Deep reinforcement learning-based robust nonlinear controller for photovoltaic systems

Novel Intelligent control of photovoltaic system using ANFIS Gravitational Search for MPPT controller

Novel Intelligent control of photovoltaic system using ANFIS Gravitational Search for MPPT controller

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

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

Modified Supertwisting Sliding Mode MPPT for PV system

This paper focuses on the control of photovoltaic systems using sliding mode control (SMC). Considering the oscillation problems that appear in the classical sliding mode control, which can affect the output power generated by the photovoltaic panel, many studies have proposed a super twisting sliding mode control (STSMC) to minimize the chattering phenomena associated with classical SMC. However, to further reduce oscillations, this work proposes a modified super twisting SMCthat incorporates a fractional integrator to increase its flexibility and performance. To demonstrate the enhancements achieved with the proposed controller, a comparative simulation is implemented using Matlab/Simulink software. The results obtained highlight a significant reduction in oscillations, and clearly indicate the superiority of the modified STSMC controller over its classical counterpart.

Comprehensive Analysis of the Integration and Advanced Control of Photovoltaic Systems for Grid-Connected Applications

In-depth examination of photovoltaic (PV) system integration and enhanced control for grid-connected applications is presented in this study. It examines the difficulties, methods of control, and cutting-edge technologies needed to raise the dependability and effectiveness of PV systems in contemporary power networks. The study explores innovative Maximum Power Point Tracking (MPPT) algorithms, dynamic modeling methodologies, and different adaptive control strategies, emphasizing their importance in PV system performance optimization. It also looks at how high solar PV penetration affects grid stability and talks about creative ways to deal with these issues so that we can have robust and sustainable energy sources in the future. Key Words: photovoltaic Systems, pv integration, pv control strategies, energy storage systems, smart grid technologies, MPPT.

Reinforcement learning based fractional fuzzy controller for photovoltaic systems

Reinforcement learning based fractional fuzzy controller for photovoltaic systems

Distributed Cooperative Reactive Power Control of PV Systems With Dynamic Leader

Distributed Cooperative Reactive Power Control of PV Systems With Dynamic Leader

An enhanced control strategy for photovoltaic system control based on sliding mode-PI regulator

An enhanced control strategy for photovoltaic system control based on sliding mode-PI regulator