Output Power Of Photovoltaic System Research Articles

Study on the optimal layout of roof vents and rooftop photovoltaic of the industrial workshop

Roof vents are crucial for facilitating natural ventilation within industrial workshops. However, their substantial size and height can limit available roof space and create extensive shadow areas, thereby impeding the installation of photovoltaic (PV) systems on industrial roofs. This study quantitatively assesses the influence of roof vent positioning, morphology, and roof architecture on the natural ventilation efficiency of workshops with varying indoor heat sources. Subsequently, the study evaluates how different vent configurations affect the installable area for rooftop solar panels. Findings suggest that shifting from traditional roof ridge vents directly above a heat source to north-side vents inclined towards the roof can marginally decrease the ventilation flow rate by approximately 5%, translating to a minor temperature rise of around 0.46°C within the occupied zone. Nonetheless, the Predicted Mean Vote (PMV) thermal comfort indices remain consistent across scenarios. Notably, adopting north-side vents, either flush with the roof or inclined, can enhance the annual energy yield of the rooftop PV system by 15.49% and 22.67%, respectively, under equivalent solar irradiation. Thus, strategic adjustments to vent positioning, form, and roof design can substantially boost the photovoltaic system's power output without compromising the workshop's natural ventilation performance.

Analysing the Effects of Thin Object Shading on PV Sources: A Dual Approach Combining Outdoor and Laboratory Solar Simulator Experimentation

The impact of shading has been a prominent subject of discourse within the realm of photovoltaic (PV) energy harvesting and is recognized as a significant detriment to the system’s overall efficiency. Nevertheless, prevailing investigations, which predominantly focus on the hard shading originating from building structures and vegetation, singularly address the umbra shadow phenomenon while overlooking the complexity of shadow properties and their varying intensities. In this context, this present research aims to analyze the impact of shading caused by thin objects, wherein shadow formation deviates from a singular-intensity umbra to a blend of umbra and penumbra, exhibiting diverse intensities. In the initial experimental approach, outdoor trials produced statistically significant findings, identifying both the distance and thickness of shading objects as primary determinants influencing the impact of thin object shading on the power output of PV systems. Furthermore, the analysis of the results revealed that under the specified parameters and assumptions, when considering a thin object with a thickness-to-distance ratio of 2.3 mm/225 cm, the resulting power loss of 1.65% is statistically insignificant. Remarkably, laboratory investigations unveiled a notable correlation between penumbra and power loss, contrasting with outdoor experimentation results. The findings highlight the distinction between indoor and outdoor methodologies, stemming from discrepancies in shadow formation characteristics, thereby emphasizing the necessity of acknowledging and comprehending these variations.

Intelligent control for solar-electric vehicles: integrating gannet optimization algorithm and quantum neural network for power tracking and high gain conversion

This paper presents a hybrid approach for optimizing the maximum power point tracking of photovoltaic (PV) systems in electric vehicles. The hybrid technique involves the simultaneous utilization of the Gannet Optimization Algorithm (GOA) and Quantum Neural Network (QNN), collectively referred to as the GOA-QNN technique. The primary aim is to enhance the efficiency and maximize the power output of PV systems. The proposed hybrid methodology boosts the performance of the photovoltaic system by managing the power interface. A high step-up DC/DC converter is employed to adjust the photovoltaic source power and load, ensuring optimal power transfer under various operating conditions. The proposed method optimally determines the duty cycle of the converter. Subsequently, the model is implemented in the MATLAB/Simulink platform, and its execution is evaluated using established procedures. The results clearly demonstrate the superiority of the proposed method over existing approaches in terms of power quality, settling time, and controller stability. The proposed technique achieves an impressive efficiency level of 95%, exceeding the efficiency of other existing techniques.

Novel reference condition independent method for estimating performance for PV modules based on double-diode model

The traditional methods for performance estimation mainly include two steps: determine the model parameters under reference condition and calculate the model parameters at varying operating conditions through transformation equations. These methods have the disadvantages of being influenced by the selection of reference condition. A novel reference condition independent method is proposed based on double-diode model (DDM) for parameter calculation and performance estimation at varying operating conditions for photovoltaic (PV) modules. In the proposed method, a new set of transformation equations is reconstructed according to the dependence of physical parameters in DDM on irradiance and temperature. All coefficients in the proposed transformation equations are independent of the information under reference condition and applicable to all varying operating conditions, which eliminates the influence of reference condition. The coefficients are optimized by fitting measured I–V data at varying operating conditions using guaranteed convergence particle swarm optimization technology, and used for model parameters calculation at varying operating conditions. Then the output property of PV module under any operating condition can be determined independently, first against the reference condition and then against the model parameters under reference condition. The accuracy and applicability of proposed method is validated by massive experimental data for six different types of PV modules under wide range of operating conditions. Moreover, three traditional methods are selected to compare with the proposed method. The proposed method shows better accuracy in I–V, P–V curve and maximum power point estimation under different irradiance and temperature conditions, which can be further used to predict the output power of PV system.

A renewable approach to electric vehicle charging through solar energy storage.

Developing novel EV chargers is crucial for accelerating Electric Vehicle (EV) adoption, mitigating range anxiety, and fostering technological advancements that enhance charging efficiency and grid integration. These advancements address current challenges and contribute to a more sustainable and convenient future of electric mobility. This paper explores the performance dynamics of a solar-integrated charging system. It outlines a simulation study on harnessing solar energy as the primary Direct Current (DC) EV charging source. The approach incorporates an Energy Storage System (ESS) to address solar intermittencies and mitigate photovoltaic (PV) mismatch losses. Executed through MATLAB, the system integrates key components, including solar PV panels, the ESS, a DC charger, and an EV battery. The study finds that a change in solar irradiance from 400 W/m2 to 1000 W/m2 resulted in a substantial 47% increase in the output power of the solar PV system. Simultaneously, the ESS shows a 38% boost in output power under similar conditions, with the assessments conducted at a room temperature of 25°C. The results emphasize that optimal solar panel placement with higher irradiance levels is essential to leverage integrated solar energy EV chargers. The research also illuminates the positive correlation between elevated irradiance levels and the EV battery's State of Charge (SOC). This correlation underscores the efficiency gains achievable through enhanced solar power absorption, facilitating more effective and expedited EV charging.

Integration of hybrid PV-wind system for electric vehicle charging: Towards a sustainable future

This research addresses the pressing need for sustainable energy solutions in the context of Electric Vehicle (EV) charging. It focuses on the integration of Hybrid Renewable Energy Sources (HRES) such as Photovoltaic (PV) and wind systems, coupled with grid connectivity to ensure uninterrupted power supply. The study's primary objective is to design an efficient HRES framework that optimally harnesses solar and wind energy for EV battery charging while maintaining grid compatibility. The research introduces a novel approach by incorporating a High Gain Zeta-SEPIC (HGZS) converter to enhance power output of PV system. A key innovation lies in the development of a Type 2 Fuzzy Maximum Power Point Tracking (MPPT) controller that ensures precise tracking of maximum power point (MPP), thus optimizing the operation of HGZS converter and maximizing the energy extraction from PV system. Additionally, a Doubly Fed Induction Generator (DFIG) based wind system, controlled by Proportional Integral (PI) controller is implemented, for maintaining controlled voltage levels, ensuring system stability, and enhancing overall performance under diverse operating conditions. Furthermore, the research focuses on grid interaction, allowing surplus energy generated by HRES to be efficiently supplied to grid, making it available for consumption during peak demand periods. A PI controller is implemented on the grid side to ensure effective voltage control and synchronization with the grid. The proposed HRES architecture is rigorously validated through extensive MATLAB simulations. The results demonstrate exceptional efficiency, with proposed converter achieving an efficiency rating of 99.6 % and significantly enhanced voltage gain.

A Comparative Analysis of series and parallel topologies of Perturb and Observe (P&O) and Incremental Conductance (InC) MPPT Algorithms for Photovoltaic System

Photovoltaic (PV) is considered one of the most important sources of energy in the world, owing to its inherent advantages of being cheap, environmental friendliness, and energy-efficient. To increase the output power of the solar PV system, it is most important to enforce it to operate at the highest possible maximum power point tracking (MPPT) algorithm lead an important role in optimization the performance of a photovoltaic (PV) generation systems. This paper compares two widely used MPPT algorithms, namely the Perturb and Observe (P&O) method and the Incremental Conductance (IC) method, known for their low cost and eases of realization. The algorithms were evaluated based on important parameters such as voltage, current, and power output for different combinations, with and without shading. The performance of the photovoltaic array was evaluated using the MATLAB Simulink toolbox. The simulation results demonstrate that the IC method can rapidly detect the maximum power point (MPP) when irradiance changes suddenly, with faster convergence and higher accuracy than the P&O algorithm. Simulations are performed to test the controller's capability oftracking the MPP when sudden variations in weather condition

Study of the effects of reference conditions on electrical properties estimation for PV modules under varying operating conditions

In traditional methods, electrical property estimation of photovoltaic (PV) modules is achieved through two steps. First, a certain condition (usually standard testing condition, STC) is selected as the reference condition, and the physical parameters under the reference condition are extracted from current–voltage data points. Second, physical parameters under varying operating conditions are obtained by transforming equations, and the electrical properties of the PV modules are estimated. In this paper, the influence of different reference conditions on the accuracy of performance estimation is studied. The estimation results using different reference conditions are compared to obtain the error distribution pattern, which has essential reference significance for the selection of reference conditions in practical applications. In addition, a method of the selecting reference condition is proposed. A new objective function is proposed by considering three key operating points for each I–V curve under different operating conditions, which balances accuracy and computational complexity. A large amount of experimental data for different types of PV modules are used to validate the effectiveness and accuracy of the proposed method. In comparison with the traditional methods using STC as the reference condition and existing method in Matlab/Simulink, the results obtained by the proposed method exhibit better accuracy. It can be further used to estimate the output power of PV system under varying operating conditions.

Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting

Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively.

A critical evaluation of maximum power point tracking techniques for PV systems working under partial shading conditions

Photovoltaic (PV) energy is a promising source of renewable energy which is sturdy and environmentally friendly. PV generation systems, once installed, produce electricity from solar irradiance without emitting greenhouse gases. To maximize the output power of PV systems, the maximum power point tracking system has been employed (MPPT). The MPPT constitutes a fundamental part of PV systems. In recent years, a large number of MPPT techniques have been proposed. This paper is set up to critically review some of the proposed maximum power point tracking (MPPT) techniques to handle the emergence of multiple MPPs in PV panel characteristics due to the partial shading conditions (PSCs). To define the working principle and the pros and cons of the different proposed techniques clearly and sequentially, they are divided into three groups as follows: conventional MPPT techniques, improved MPPT techniques and artificial intelligence- based MPPT techniques to deal with PSCs. The paper also critically summarizes the findings in terms of their performance in capturing the global maximum power point (GMPP) for PV systems operating under PSCs.

Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV) systems. In light of this requirement, this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment. To achieve this, different types of faults in grid-connected PV systems (GCPVs) and their impact on the energy loss associated with the electrical network are analyzed. A data-driven approach using neural networks (NNs) is proposed to achieve root cause analysis and localize the fault to the component level in the system. The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units (ANFIUs) to develop a power mismatch-based control unit (PMCU) for improving the power output of the GCPV. To develop the proposed framework, a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals. Further, the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV. The results identified 98.2% training accuracy and 43000 observations/sec prediction speed for the trained classifier, and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years. Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity. Solar-powered buses, metros, and cars use PV technology. Such technologies are always evolving. Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others. PVPG is intermittent and weather-dependent. Accurate forecasting and modelling of PV system output power are key to managing storage, delivery, and smart grids. With unparalleled data granularity, a data-driven system could better anticipate solar generation. Deep learning (DL) models have gained popularity due to their capacity to handle complex datasets and increase computing power. This article introduces the Galactic Swarm Optimization with Deep Belief Network (GSODBN-PPGF) model. The GSODBN-PPGF model predicts PV power production. The GSODBN-PPGF model normalises data using data scaling. DBN is used to forecast PV power output. The GSO algorithm boosts the DBN model’s predicted output. GSODBN-PPGF projected 0.002 after40 h but observed 0.063. The GSODBN-PPGF model validation is compared to existing approaches. Simulations showed that the GSODBN-PPGF model outperformed recent techniques. It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.

Location-aware green energy availability forecasting for multiple time frames in smart buildings: The case of Estonia

In recent years, renewable energies (RE) have gained more attention as they provide clean and sustainable energy. Affordable and clean energy is one of the sustainable development goals (SDG-7) of the UN. Solar energy is considered to be one of the world's most abundant renewable natural resources and can certainly contribute to meeting the SDGs. The photovoltaic (PV) panel converts solar energy into electrical energy without emitting greenhouse gases. PV panels generate power based on solar radiation received at a specific location over time. As a result, PV output power is difficult to forecast. Many public/private institutes generate green energy, and balancing demand and supply is vital. This research aims to forecast PV system output power based on weather and derived features using different machine learning (ML) models. A best-fitting model is developed based on the data to accurately predict output power. Moreover, different performance metrics are used to compare and evaluate the accuracy under different ML models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP) and Support Vector Regression (SVR). Compared to the recent research, our results demonstrate that the obtained prediction accuracy is superior, with performance gains reaching up to 14% (R-squared) in the case of the RF model and 25% (MAPE) in the case of the SVR model. The comparison result inferred a similar performance when RF and XGBoost were applied. On the other hand, the ML models MLP, SVR, and KNN give a similar prediction pattern.

A hybrid machine learning method with explicit time encoding for improved Malaysian photovoltaic power prediction

Nowadays, with the growing interest in green energy, further improvements in photovoltaic (PV) power systems are needed. In this regard, the main aim is to find an optimal method to predict the output power of PV systems to maintain a sustainable operation. Hence, this work proposes a hybrid Machine Learning (ML) method LASSO-RFR for an hourly PV power output prediction. The model consists of Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest Regressor (RFR), where the former model makes a prediction and the latter fine tune it by the addition or subtraction of a relatively small value. The proposed model outperformed other models when tested on real data recorded from 2016 to 2019 for three Malaysian PV systems, namely Thin-Film (TF), Monocrystalline (MC), and Polycrystalline (PC). LASSO-RFR attained the lowest root mean square error (RMSE) of 23.7, 18.2, and 20.8 Wh/m2 for the TF, MC, and PC, respectively. This work also highlights the importance of explicit time encoding in improving PV power prediction. Although it is used to be ignored in the literature when developing ML models, the time feature is the second most influencing factor of PV power prediction after solar irradiance, as shown by the SHAP analysis (shapely additive explanation). For the study implications, the developed prediction model can assist the industry in predicting 1 h ahead of PV power output, demand-side management, and building operations and maintenance.

Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review

In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages and disadvantages. Hence, a proper review of these methods is essential. In this paper, a state-of-the-art review on various MPPT techniques based on their classifications, such as offline, online, and hybrid techniques under uniform and nonuniform irradiances, is presented. In comparison to offline and online MPPT methods, intelligent MPPT techniques have better tracking accuracy and tracking efficiency with less steady state oscillations. Unlike online and offline techniques, intelligent methods track the global MPP under partial shade conditions. This review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for additional research and development in the MPPT field.

Effect of Increasing Solar Radiation Reflected with Mirrors Perpendicularly on the Power Output of Photovoltaic System with Cooling

Solar energy is one of the most popular renewable energies in the world as solar energy can be easily accessed throughout most places of the world. However, converting solar energy into other sources of energy is not very cheap and numerous efficiency improvements methods are under investigation by researchers around the world. The current study proposes a low concentration photovoltaic system (LCPVS) with 4 mirrors and cooling. The aim of this project was to examine how using mirrors to concentrate solar radiation affects the amount of power generated by solar panels. The mirrors (ranging from 1 to 4) radiated the sun reflection onto the panel with a cooling system. The results obtained from the proposed project were compared to the results collected from the normal panel without concentration to measure the differences which revealed that the proposed system output power was almost three times more than conventional photovoltaic (PV) panel without concentration.

An efficient hybrid photovoltaic battery power system based grid-connected applications

Power management systems for grid-tied photovoltaic-battery power systems are the focus of this research. Solar photovoltaic (PV) panels, lithium-ion batteries, and a voltage source inverter (VSC) are all part of the system. By employing the fuzzy logic (FL) technique, a PV system's power output can be maximized in a variety of weather circumstances. In addition, the state-of-charge-based power management system (PMS) was investigated to manage power sharing between sources and the grid and then manages the battery module's charge/discharge process. Active-reactive (PQ) control was used on the VSC converter while it was synced with the grid and regulated. In order to model and simulate the suggested system under various solar irradiances, Matlab/Simulink was employed. In contrast to the standard grid-connected inverter, which operates without batteries, the simulation results showed that adding the battery energy storage system BESS increased the system's performance. A grid-connected inverter that makes use of BESS can prevent the absence of PV energy or shading of the arrays. To explain why PMS is so effective, the simulations show that the injected grid current is more stable and has less total harmonic distortion (THD).

Maximum Power Point Tracking using modified Particle Swarm Optimization Technique

Abstract: Maximum power point tracking is an essential and vital technique generally applied in SPV technology under uniform and partial shading conditions. The existence of partially shaded conditions leads to the presence of several peaks on PV curves, which decrease the efficiency of conventional techniques. Hence, the proposed algorithm, which is based on the modified particle-swarm optimization (MPSO) technique, increases the output power of PV systems under such abnormal conditions and has a better performance compared to other methods. The proposed method is examined under several scenarios for partial shading condition and non-uniform irradiation levels using Matlab and to investigate its effectiveness adequately. The experimental results show that the proposed method can decrease the interference of the local maximum power-point to cause the PV system to operate at a global maximum power-point. The efficiency of the MPSO is achieved with the least number of steady-state oscillations under partial shading conditions as compared with the other methods. Keywords: Photovoltaic(PV), Particle Swarm Optimization(PSO), Modified Particle Swarm Optimization(MPSO), Partial shading condition(PSC), Velocity step function.

Retraction Note to: IOT based statistical performance improvement technique on the power output of photovoltaic system

Retraction Note to: IOT based statistical performance improvement technique on the power output of photovoltaic system

Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.