Control Of Photovoltaic System Research Articles

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

Accurate, reliable, and efficient extraction of photovoltaic (PV) model parameters is an essential step towards PV system simulation, control, and optimization. Nevertheless, this problem is still facing great challenges because of its intrinsic nonlinear, multivariate, and multimodal properties. In this paper, a new variant of cuckoo search (CS), adaptive operator selection CS (AOSCS), is advanced for the PV model parameter extraction problems. AOSCS includes two major improvements: (1) an adaptive operator selection mechanism is developed to automatically assign the workloads of exploration and exploitation operators, and (2) the exploration and exploitation operators used in the original CS are modified to promote the exploration capability and reduce the blindness of search, respectively. The performance of AOSCS is firstly validated on CEC 2017 test suite and then it is utilized to solve the parameter extraction problems of five PV models. Moreover, further experiments on two commercial PV modules under distinct irradiance and temperature levels are also conducted to evaluate the practicality of the proposed algorithm. It is manifested that the results yielded by AOSCS are very competitive relative to other parameter extraction approaches. Accordingly, the proposed AOSCS is able to be served as an up-and-coming candidate algorithm for PV model parameter extraction problems.

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

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.

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

This article presents an innovative artificial nonlinear control technique for photovoltaic (PV) systems. A boost converter extracts maximum power from the PV system under different weather conditions. A fuzzy logic maximum power point (MPP) finding algorithm is used to determine the reference voltage of the PV system under various atmospheric conditions. It is assumed that the nonlinear dynamic model, parameters, and uncertainties of the converter are unknown. To estimate a precise model that contains nonlinearities, model uncertainties, and disturbances of the converter, proximal policy optimization (PPO) with a special reward function is applied. PPO is optimal in policy learning and sampling, fast to implement, and is a simple reinforcement learning (RL) algorithm. Then, estimations of dynamics and uncertainties are used to enhance the robustness of the suggested fractional order sliding mode controller. Furthermore, the controller coefficients are optimally adjusted by the PPO algorithm. The Lyapunov theorem is used for proving the control system's closed-loop stability. Also, the proposed methods can be generalized and used for other control systems. The simulation is conducted by MATLAB to evaluate the suggested controller's performance effectiveness. Robustness against the presence of parameter uncertainty is investigated. Moreover, the suggested technique is also compared with sliding mode controller.

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

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

FEATURES OF THE CONFIGURATION OF CONTROL SYSTEMS FOR PHOTOVOLTAIC INVERTERS

Today, the issue of energy supply and the implementation of ecological systems of electricity generation is relevant, including in the agricultural sector of the economy. This issue is important not only at the state level, but also at the level of the world community. The world's leading countries are actively implementing world-leading technologies for the decarbonization of the energy sector. One of the options for the introduction of alternative and renewable energy sources in the agricultural sector of the economy of the leading countries of the world is the use of photovoltaic systems. The use of modern sources of electrical energy is also inherent in the transport industry. One of the options for such implementation is the use of photovoltaic systems in electrical networks of electric transport and auxiliary systems and infrastructure complexes. Also, attention should be paid to modern global trends in the development of distributed energy systems, which are characterized to a large extent by the use of new progressive developments in power electronics. Also, attention should be paid to modern world trends in the development of distributed energy systems, which are characterized by the use of new progressive developments in power electronics, which are already used in various areas of the economy [1]. Therefore, network converters of electrical energy, which are operated as part of distributed generation systems and as part of complexes based on renewable sources of electrical energy, must meet the parameters of the network, and their control system must be adaptive to stochastic modes of network operation and modes of generation by renewable sources of electric energy. Meanwhile, attention should be paid to the synchronization of three-phase electrical energy converters with the electrical network to which they are connected, which must be ensured by a system for regulating their operation. It should be noted that the structures of control systems of network converters have a significant difference from the structures of control systems of autonomous converters, which directly concerns inverters. This state of affairs is connected with the peculiarity of the functioning of network inverters. Since the specified converters have a modulated sinusoidal voltage using PWM to form in accordance with the parameters of the network to which they are connected. Therefore, the issue of synthesizing a mathematical method that will simplify the development of an automatic control system and also optimize the computing resources of the power converter control controller for photovoltaic systems is relevant. On the basis of analytical data, it is possible to predict with full confidence the post-war development of Ukraine's economy based on the introduction of modern progressive technologies in the field of energy. To date, the sustainable development of the economic and energy component of Ukraine's economy is postponed. Meanwhile, electric power generation systems remain a priority in the context of not only implementation into existing power supply systems, but also relevant in the context of national energy security. The paper considers the use of Park's transformation in the development of control systems for network inverters of photovoltaic systems. The results of the research, which were conducted on inverters whose control systems were built using the proposed mathematical apparatus, confirm high control accuracy and efficiency, without spending a large amount of computing resources of the microcontroller. The proposed approach to building control systems for inverters of photovoltaic systems will allow to increase the efficiency of the operation of photovoltaic systems, which will make them more economically attractive for implementation in the agricultural sector of the economy.

A state‐space average model of a three‐level PV inverter for transient short‐circuit currents analysis

AbstractThis paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning.