Photovoltaic Energy Conversion System Research Articles

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.

GA and PSO Techniques Based Optimal Tuning of Power System Stabilizers in a PV Integrated Power Network

This paper involves the employment of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) methods for the optimal tuning of gain and time constant parameters of power system stabilizers in a power network with integrated Photovoltaic (PV) energy conversion systems. To examine the critical modes of the PV integrated system and the impact of the PV integration on various modes of oscillations, eigenvalue analysis is carried out under various PV penetration levels. The investigations indicate that the rotor angle oscillations following a disturbance have less damping as the PV penetration level increases. To enhance the small signal stability, the excitation systems are provided with the signal from power system stabilizers (PSS). The gain and time constant parameters of the PSS are tuned utilizing the GA and PSO methods and compared with the conventional proportional-integral (PI) PSS. The preliminary investigations on the PV integrated standard IEEE power system reveal that the PSO and GA-based PSS are significantly better than the PI-PSS, and further, PSO-PSS is marginally more effective than the GA-PSS in damping the rotor angle oscillations.

Towards more reliable photovoltaic energy conversion systems: A weakly-supervised learning perspective on anomaly detection

With the increasing popularity of photovoltaic (PV) systems, both academia and industry have been paying growing attention to fault prediction and health management. Although deep learning has achieved remarkable results in this field, the high cost of labeled data acquisition has become a bottleneck for its application. While unsupervised methods can reduce this cost, they fail to effectively utilize the prior knowledge of anomalous samples. To address this issue, this paper innovatively proposes a weakly-supervised anomaly detection network based on feature map conversion and hypersphere transformation (FMC-HT). Firstly, PV electroluminescence (EL) images are input into the feature extraction layer based on feature map conversion, which can reduce redundancy, enhance information density, and achieve efficient feature extraction through feature map conversion. Subsequently, inverse squared norm loss is set to utilize the knowledge of unlabeled sample features and labeled anomalous sample features, and backpropagation is performed separately to train the hypersphere transformation network, ultimately achieving effective distinction between normal and anomalous samples. On real PV EL datasets, this model exhibits impressive performance and remains stable under different prior knowledge and anomaly rates. This study not only provides a new solution for anomaly detection in PV systems but also expands new directions for the application of deep learning in scenarios with limited labeled data.

A Survey on the Integration of Solar Photovoltaic Energy Conversion Systems into Commercial and Industrial Electrical Energy Usage

A Survey on the Integration of Solar Photovoltaic Energy Conversion Systems into Commercial and Industrial Electrical Energy Usage

Artificial Intelligence with Metaheuristic Algorithm Based Optimization for a Hybrid Energy Storage System

This paper presents an optimization framework for a hybrid energy storage system (HESS) integrating photovoltaic (PV) and wind energy conversion systems (WECS). The PV system voltage is stepped up using a Cuk converter, controlled by a Particle Swarm Optimization (PSO) tuned Artificial Neural Network (ANN) controller to enhance dynamic performance and ensure maximum power point tracking (MPPT). The Doubly Fed Induction Generator (DFIG) based WECS is interfaced through a Pulse Width Modulation (PWM) rectifier regulated by a Proportional Integral (PI) controller, maintaining stable and efficient power conversion. Both converter outputs converge at a common DC link, whose voltage is subsequently applied to a single phase Voltage Source Inverter (VSI). This VSI is connected to the grid through an LC filter, ensuring smooth power delivery with minimal harmonics. The proposed system combines AI and metaheuristic algorithms for optimized control, enhancing overall efficiency, reliability and stability of the renewable energy integration into the grid. Simulation results demonstrate the effectiveness of the PSO ANN and PI control strategies in managing the dynamic interactions and power quality of the hybrid system. The proposed hybrid controller shows 92.47% tracking efficiency and the proposed converter shows 89.6% efficiency.

An improved regression‐based perturb and observation global maximum power point tracker methods

AbstractSolar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently.

Harmonic Elimination Using Grid Integrated Hybrid Renewable Energy System with Optimized Control for Nano Scale Based 31-Level MLI

This research focuses integration of grid integrated hybrid renewable energy system (HRES) with optimized control techniques for harmonic elimination using Nano Scale Based 31-Level Multi-Level Inverter (MLI). The proposed system combines Renewable Energy Sources (RES), such as Solar Photovoltaic (PV) and Wind Energy Conversion System (WECS) assisted with Doubly Fed Induction Generator (DFIG) to generate power for grid integration. Interleaved KY converter with novel Rain Optimized Algorithm assisted Artificial Neural Network (ROA-ANN) based Maximum Power Point Tracking (MPPT) is established, which is a hybridized approach extracting maximum PV power and optimizes power output for utilization. Excess energy generated from hybrid power sources is utilized to charge battery. The DC supply from hybrid sources is transferred to silicon carbide switches of 31 MLI for elimination of harmonics using Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM). The 31-Level MLI facilitates conversion of DC voltage to AC with assistance of Particle Swarm Optimized Proportional Integral (PSO-PI) controller. Finally, controlled AC supply is transferred to LC filter, resulting in effective grid synchronization with reduced harmonics. The proposed system’s performance is validated using MATLAB Simulink, and obtained results demonstrate effectiveness with reduced total harmonic distortion (THD) of 0.66% in contrast to state-of-art approaches

Multi-Objective Cascaded Control Strategy for Grid-Connected Photovoltaic Energy Conversion System Using a Five-Level ANPC Inverter

Multi-Objective Cascaded Control Strategy for Grid-Connected Photovoltaic Energy Conversion System Using a Five-Level ANPC Inverter

Simulation based study of Maximum Power Point Tracking and Frequency Regulation for Stand-alone Solar Photovoltaic Systems

The main objective of this paper is to develop a simulation model of stand-alone solar photovoltaic (PV) system based on mathematical models. A maximum power point tracking (MPPT) algorithm based on incremental conductance method is applied to extract the maximum power from solar PV energy conversion system. A DC/DC boost converter allows the MPPT to control the intermediate voltage. To regulate to voltage at load side a voltage source converter (VSC) based controller is applied to generate the pulse signal for three-phase inverter. The frequency regulation controller is developed based on conventional phase locked loop (PLL) system along with resistive dumping loads. A simulation model based on mathematical model Matlab®/Simulink®/SimPower® is constructed interface. Results using from simulation are stated to demonstrate the behaviour of stand alone solar PV energy conversion system.

Switched Z-Source Boost Converter in Hybrid Renewable Energy System for Grid-Tied Applications

The world's focus is shifting toward Renewable Energy Sources (RES) as a result of the sharp rise in energy demand and the depletion of conventional energy sources. Distributed generation (DG) units developed on RES are easily and reliably integrated into grid due to microgrid technology. Inverters are typically used to integrate DGs into microgrid. At present, the fastestgrowing renewable sources of electricity in the world is photovoltaic (PV) and wind energy conversion system (WECS), which evolved in response to growing environmental concerns about the risks of climate change connected with power generation. Recent technological developments have made it a top priority for researchers to find ways to enhance the efficiency of hybrid systems and the power converter stage. In order to achieve transmission losses and ensure energy savings, a new 13-bus system is developed in this work for regulating the output voltage in distribution networks. Switched Z-source Boost converter along with Proportional Integral (PI) controller is employed to boost the voltage obtained from PV system. A battery converter along with a bi-directional battery is connected to the DC link, to store energy generated by Hybrid Renewable Energy System (HRES) in excess amount. The obtained DC link voltage is transferred to three-phase voltage source inverter (VSI) for the conversion of DC to AC voltage. Effective harmonic reduction is attained with the aid of LC filter coupled to Three phase grid, and the PI controller associated to the grid supports in achieving effective grid synchronization. The proposed research is validated using MATLAB Simulink by considering the 13-bus system and an efficiency of 96.8 % is obtained with 0.93% Total Harmonic Distortion (THD).

Enhanced Standalone Photovoltaic System with Novel Multi-Level Inverter and Nonlinear Control for Improved THD and Converter Efficiency

This article presents a control approach for a photovoltaic system connected to an AC load, aiming to optimize the use of energy generated by solar panels to directly power the load, independent of the electricity grid. The studied system is an autonomous photovoltaic energy conversion system without storage. A new topology of multilevel inverter is utilized to take advantage of its simplicity of control and high efficiency. Additionally, to convert DC to AC with reduced harmonic distortion, the inverter employs a 7-level pulse width modulation (LS-PWM) technique, minimizing harmonic distortion and demonstrating reliability. A sliding mode control strategy is applied to the boost converter to achieve stable voltage and frequency, improve the total harmonic distortion (THD), and minimize losses in the power converter. By efficiently managing power flow and maintaining voltage stability, the control system ensures optimal performance of the photovoltaic system connected to the variable load. The proposed approach and the performance of the controlled system are validated through Matlab Simulink simulations.

Optimization of silicon solar cell design for use under concentrated solar irradiation

The task of reducing the cost of a unit of photoelectric-generated electricity is still relevant today. One of the most effective ways to do that is to use concentrator photovoltaic energy conversion systems with cheaper, small-area silicon solar cells (SC), because they require significantly less semiconductor material to make. In this study, the authors develop solutions to optimize the design and improve the manufacturing technology of silicon SCs of the combined diffusion-field type. Such SCs are used to concentrate solar irradiation. The authors propose design and technological solutions for the development and manufacture of a concentrator unit based on the Fresnel lens, which is designed to measure the photoelectric characteristics of SCs when concentrating solar irradiation. Next, the photoelectric characteristics of the combined diffusion-field type SCs were investigated under concentrated solar irradiation in natural sun conditions. The degree of concentration varied from 1X to 100X. Measuring the light I –V characteristics allowed determining photoelectric parameters of the cells — shortcircuit current, open-circuit voltage, fill factor and photoconversion efficiency. The obtained experimental results are in good agreement with the results of theoretical modeling. It is shown that due to the minimization of the specific series resistance, the samples of such SCs have high values of operational parameters in the range of natural solar irradiation concentration K=1X–100X.

Hybrid optimized-ANFIS based MPPT for hybrid microgrid using zebra optimization algorithm and artificial gorilla troops optimizer

This paper presents an effective hybrid renewable energy system. This system utilizes primary energy sources represented by both photovoltaic (PV) and wind energy conversion system (WECS). In addition, integrating the battery energy storage system (BESS), the hydrogen energy storage system (HESS), and the supercapacitor energy storage system (SESS), as backup sources which providing reliable simultaneous power supply and loading. A new design for a wind energy conversion system based on a hybrid excitation permanent magnet synchronous generator (HEPMSG) is proposed, and then artificial intelligence strategies are utilized to determine the optimal flow of energy when the system is operating immediately. Moreover, new hybrid maximum power point tracking (MPPT) techniques have been proposed including zebra optimization algorithm (ZOA)-ANFIS-based MPPT and gorilla troops optimizer (GTO)-ANFIS-based MPPT to obtain the maximum power of solar panels and wind turbines, which leads to enhancing the performance of these energy sources. The implemented power flow management (PFM) model is designed by MATLAB/Simulink, considering three various operating cases to evaluate the performance and effectiveness of various backup system configurations under different operating scenarios. According to the results, power can be generated, and load requirements can be efficiently met by applying the proposed PFM strategy, and this leads to the system operating at optimal performance. When comparing two optimization techniques, it will become clear that both give robust performance, but ZOA technique significantly outperforms the GTO technique in the computation time (26.17 % reduction) for the photovoltaic system. On the other hand, in the case of the wind energy conversion system, the ZOA technique achieves a significant reduction in calculation time by 35.5 %. The ZOA technique achieves computation times of 1616.80 s and 672.43 s for the photovoltaic and wind energy conversion system, respectively.

Multi-objective control and optimization of a stand-alone photovoltaic power conversion system with battery storage energy management

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Hybrid solar photovoltaic and salinity-gradient based osmotic energy conversion system with synergistic performance enhancement

Solar photovoltaic has become essential for generating renewable electricity to overcome global crisis of fossil energy. However, solar photovoltaic suffers from a dramatic decrement on its output power when panel surface temperature is extremely high. In addition, osmotic energy conversion is a fascinating route for utilizing energy existed between ionic aqueous solutions with salinity-gradient. Nevertheless, osmotic energy conversion also confronts a drawback of insufficient power density. When the ionic aqueous solution temperature increases, the osmotic power generation under salinity-gradient is improved owing to the strengthened ion selective diffusion. Under these circumstances, we present a hybrid solar photovoltaic and osmotic energy conversion system, and their power generation performance can be synergistically improved via heat transfer from solar photovoltaic panel to ionic aqueous solution using high thermal conductive copper heat pipes. The theoretical modelling indicates that output power of solar photovoltaic is increased by 22.8 % with a decreased panel surface temperature of 35.6 °C by using 10 copper heat pipes, while the corresponding osmotic power with a salt concentration ratio of 50-fold is simultaneously increased by 36.8 %. Then, a hybrid solar photovoltaic and osmotic energy conversion system is experimentally constructed. Due to heat transfer from photovoltaic panel to ionic aqueous solution through 10 copper heat pipes, the maximum surface temperature of photovoltaic panel is dropped from 67.3 °C to 43.4 °C, and its output power is consolidated by 35.2 %. Besides, the osmotic power density under 50-time salinity-gradient ratio is synergistically ameliorated from 1.39 W/m2 to 2.72 W/m2 by 95.7 %. The current research designs a hybrid solar photovoltaic and osmotic energy conversion system, paving a fetching way for synergistic utilization of solar energy and salinity-gradient energy.

New Approach-based MPP Tracking Design for Standalone PV Energy Conversion Systems

In searching for a maximum power point (MPP) using a DC boost converter for photovoltaic (PV) energy conversion systems, we realised that the fast and accurate way to find the suitable duty ratio value is the core problem to enhance the energy conversion efficiency of the PV system. Under uniform irradiation, the panels will generate the same values, so they have only one peak on the P-V curve; conventional MPP tracking methods easily obtain this MPP. However, under partial shading conditions, many peaks are created, traditional MPP tracking methods can fall into the local MPP, and this issue will cause energy loss and reduce PV energy conversion efficiency. To avoid this disadvantage, this paper proposes a hybrid method (HM) by combining the improved chicken swarm optimisation (CSO) method and the incremental conductance (InC) algorithm for a DC standalone PV energy conversion system. In this hybrid method, the improved CSO modified approach is used to search the global region, and the InC algorithm is responsible for capturing the top of this global region. MATLAB simulation and experimental results were performed to demonstrate that the proposed method has achieved the global MPP under uniform solar irradiance and partial shadow effects.

Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study

Solar radiation constitutes the Earth’s primary energy source and is critical in regulating surface radiation equilibrium, vegetation photosynthesis, hydrological cycles, and extreme atmospheric. On the other hand, the depletion of global fossil fuel reserves mandates the power sector to adopt renewable energy-based sources, including photovoltaic and wind energy conversion systems. Therefore, the precise solar radiation prediction is imperative for climate research and the solar industry. This paper illustrates the use of two machine-learning approaches: random forest (RF) and support vector machine (SVM), to predict surface solar radiation in the Diyala governorate of Iraq for one step ahead, utilizing only lagged monthly time series data of the factor as input predictors. The findings were evaluated using three performance measures: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that using 10 monthly lags time series as input predictors leads to the best prediction performance. Furthermore, in terms of the RMSE, the prediction performance of the RF algorithm was better than that of the SVM algorithm (RF's RMSE, MAE, and R2 were 181.398, 129.522, and 0.979, while for SVM were 240.149, 184.802, and 0.978, respectively).

PV and Wind Energy Conversion Exploration based on Grid Integrated Hybrid Generation Using the Cuttlefish Algorithm

This paper focuses on the exploration of grid-integrated hybrid generation comprising Photovoltaic (PV) and Wind Energy Conversion Systems (WECSs) using the Cuttlefish Algorithm (CFA). PV and wind energies are opposite because sunny days are normally calm, and strong winds frequently occur on cloudy days or at night. Hence, a hybrid PV and wind power system is more reliable than either individual source in terms of delivering uninterrupted power. The regulation of the DC voltage is the key issue in this configuration. The conventional PI controller is inaccurate in regulating the DC voltage. The PI controller gain tuning using optimization techniques provides good DC voltage regulation and less Total Harmonic Distortion (THD). Hence, CFA is proposed for the tuning of PI gains. The performance parameters such as DC voltage regulation and THD of CFA are analyzed.

Voltage Sag Assessment and Mitigation Techniques of a Solar PV Integrated 110 kV/13.8 kV Saudi Substation

The point of common coupling of a photovoltaic energy conversion system (PVES) to the utility grid is the most critical interface point where voltage sag issues arise. This paper involves a comparative evaluation of Dynamic Voltage Restorer (DVR) and Distribution Static Compensator (D-STATCOM) in mitigating the voltage sag at the point of common coupling of a photovoltaic energy conversion system integrated onto AC auxiliary distribution system in a 110 kV/13.8 kV transmission substation in the national power grid in Saudi Arabia. The voltage sag due to a three-phase short circuit at the point of common coupling of the photovoltaic system with the AC auxiliary distribution system is investigated. The results indicate that the DVR is better than the D-STATCOM in mitigating the voltage sag issue.

Photovoltaic Energy Conversion System Integrated Into Unbalanced Distribution Electrical Networks Through Hardware in the Loop

In this article, a real-time, hardware in the loop (HIL) and experimental photovoltaic energy conversion system (PVECS) integrated into unbalanced distribution electrical networks is presented. Commonly, the three-phase voltages are not balanced, because the input–output of single-phase loads in low and medium voltage networks. In this context, the photovoltaic (PV) systems integration under the dq0-Frame control operation scheme, tend to generate current deformations. In contrast, a new control technique for PVECS interconnection is validated in a HIL scheme, even in the presence of unbalanced voltage. This new technique is considered simple and easy to implement, since it consists of a single PI control loop, guaranteeing reliable operation under unbalances voltage events. Thus, preserving favorable characteristics, such as: 1) always balanced currents; 2) low harmonic distortion; 3) unit power factor; and 4) Compliance with the rules of the network code. The PVECS effectiveness is assessed by complete mathematical model, the simulation results are evaluated using MATLAB-Simulink (MATLAB r2018, Mathworks, Natick, MA, USA), and the experimental results are validated with a small-scale prototype operating in a HIL environment and the real-time simulator Opal-RT Technologies (Montreal, QC, Canada); integrating a power capacity of 15 kW in distribution networks.