Solar Photovoltaic Plant Research Articles

Forecasting and Prediction of Solar Energy in Solar Photovoltaic Plants

An accurate solar energy projection is essential for a better the degree to which renewable energy is integrated into the functioning of the present power system. Due to the availability of data at previously unheard-of granularities, data-driven algorithms may be utilised to improve solar energy forecasts. In this study, two deep learning algorithms—the k Nearest Neighbor and Random Forest—are presented as the foundational models for the improved, globally applicable stackable ensemble technique. The results the core models are merged with a significant gradient boost technique, improving the precision of solar PV production predictions. Tests were conducted on the suggested model on four separate datasets of solar power in order to provide a full evaluation. To give more information on how the system learns, this study also used the shapely additive explanation framework. Comparing the predicted outcomes allowed for an evaluation of the suggested model's efficacy. of the model to those of individual KNN, RF, and Bagging. The recommended ensemble method offers the most consistency and stability across several case studies and surpasses existing models by 10% to 12% in terms of performance despite weather variations of R2.

Photovoltaic energy production systems and the carbon market in Maranhao: a case study

The dissertation deals with a research carried out with data from a 168.4kWcc/120kWca photovoltaic solar plant installed in Itapecuru Mirim, in the state of Maranhão, with the objective of modeling a system for parameterization and possible calculation of the effect of the solar photovoltaic system on the market of carbon credit. Throughout the introduction and the theoretical framework, advances in photovoltaic solar technology in the world and in Brazil are presented. The project aims to carry out data collection such as power, voltage, current, yield and performance in a solar farm already obtained in the system and analyze the impact of the efficient use of solar energy. The system used as a base has the following characteristics: plant for distributed generation, in remote self-consumption mode, use of Canadian modules, kUmax line, CS3U-380|385|390|395|400MS, 1000V/1500V, number of boards: 440 units, 22 units of strings of 20 modules. and 2 units of Sungrow SG 60KTL inverters, total area: 0.31h, rated power: 1.36kWp, inverter power: 120Kw. The methodology consists of the following steps: First, discussing the role of the solar photovoltaic system in carbon crediting models, starting with a discussion of this type of system in the global scenario, the role in the global and regional scenario. Then, the impact of solar photovoltaic projects on the avoidance of atmospheric carbon emissions will be evaluated, and based on data obtained from the photovoltaic solar plant in Itapecuru Mirim - MA, a model and visualization of the impact result for the state will be carried out.

Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant

Malaysia's most plentiful renewable energy (RE) source, solar, only generate power during the day. High penetration of intermittent PV generation, especially from Large-Scale Solar Photovoltaic (LSSPV) plants, may complicate power system planning and operation. Hence, the need to accurately forecast solar generation for sustaining grid stability. This study identified the meteorological variables that significantly impact the power generation of an LSSPV plant using the Pearson Correlation Coefficient (PCC). The LSSPV plant, located in the central area of Malaysia, is used as a case study. To this end, a regression analysis model was developed using Artificial Neural Network (ANN) to predict solar generation with changing meteorological conditions. From August 1, 2020, to July 1, 202,1 and September 1, 2022, to November 1, 2022, a 5-minute dataset is collected. The meteorological variables with the strongest correlation on solar generation were the total global horizontal irradiance and global irradiance on the module plane selected by PCC, followed by PV module temperature, ambient temperature, wind speed, total slope and total horizontal irradiation. The final input to the forecasting model consists of four primary features that indicate a significant correlation with solar PV generation, which are total global horizontal irradiance (W/m²), PV module temperature (°C), ambient temperature (°C) and wind speed (m/s). The simulation findings reveal that the accuracy evaluation metrics are higher, with R² metrics improved by 45% when employing feature selection for prediction. While ANN has been commonly used for solar generation prediction, this study uniquely combines ANN with PCC as a feature selection technique. This method has not been widely explored in the literature and has resulted in significant improvements in the accuracy and robustness of the prediction model. Thus, our study provides a valuable contribution to solar energy prediction and can potentially enhance solar energy utilisation efficiency in various applications.

Environmental Sustainability in Vineyards under a Protected Designation of Origin in View of the Implementation of Photovoltaic Solar Energy Plants

Environmental sustainability is a fundamental asset in the development of all agricultural policies within the European Union. However, its practical application is finding important incompatibilities between several of its objectives, such as the preservation of the landscape and action against climate change, through the exploitation of renewable energies systems. Indeed, Spain and its viticulture under PDOs are facing an exponential dilemma: the change in use of agricultural wine grape producing land to install solar panels for renewable energy generation. Therefore, this study aims to provide an assessment of the community where the Alicante PDO is based, on the environmental sustainability of its vineyards in view of the implementation of photovoltaic solar energy plants, and to identify an optimal and socially sustainable design. Online research techniques ensuring anonymity were used, achieving a valuation of the quality of the wine-growing landscape and its associated cultural heritage in the territory of the of Alicante PDO. However, as the importance of solar energy generation is also valued, the preferred alternative would be a limited combination of use and design to maintain the wine-growing landscape. These terms should be reflected by competent administrations when authorizing future installations in the Alicante PDO.

Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant

Detecting and monitoring faults in solar photovoltaic (PV) systems is crucial to ensure optimal efficiency and prevent safety and fire hazards. However, the conventional operation and maintenance (O&M) of solar PV systems do not utilize machine learning for fault detection and classification. This poses challenges for plant operators, especially those managing large-scale solar (LSS) PV plants, who typically rely on manual approaches to screen large amounts of electrical data and inspect numerous string panels. Consequently, the cost of O&M is high. This study aims to use advanced machine-learning techniques to detect anomalies in a Large-Scale Photovoltaic (LSSPV) plant. The study collects data from the plant which located in the central of Peninsular Malaysia and employs K-Means for clustering and Long-Short Term Memory (LSTM) for anomaly detection in the predicted electrical current of string modules. The model is developed using Jupyter Notebook from the Python Package Index. To validate the accuracy of the proposed model, the study compares LSTM with Artificial Neural Network (ANN) using relative error as the evaluation metric. The results indicate that the LSTM method identifies anomalies in the predicted output current of the string modules more accurately and with lower relative error than the conventional ANN technique. The proposed model could help plant operators perform predictive maintenance of LSSPV plants at a minimal cost and time.

Performances Analysis of Three Grid-Tied Large-Scale Solar PV Plants in Varied Climatic Conditions: A Case Study in Algeria

Currently, for the determination of the suitable and optimal PV power plant according to the climate conditions of the concerned region, researchers focus on the estimation of certain performance factors, which are reported to be the key parameters for the analysis of the performances of grid-connected photovoltaic (PV) power systems. In this context, this paper focuses on on-site real-time analysis of the performance of three solar photovoltaic plants: Sidi-bel-Abbés (12 MWp), Laghouat (60 MWp), and Ghardaïa (1.1 MWp). These plants are located in different regions experiencing diverse climatic conditions in Algeria. The analysis was carried out by the standardized norms of IEC 61724, using monitoring data collected over one year. The photovoltaic power plants were evaluated in terms of performance factors, such as the reference yield (Yr), final yield (Yf), performance ratio (PR), and capacity factor (CF). On the other side, based on real data collected at the concerned sites, two linear functions depending on solar irradiance and the PV module temperature for each site are proposed for the evaluation of the generated alternative power output (PAC) for the three PV plants. The obtained results based on the study presented in this paper can help designers of PV power plants of different technologies and different climate conditions to precisely decide the convenient technology that allows the best production of the electrical energy for grid-tied PV systems. Furthermore, this study can contribute in giving a clear vision of the implementation of upcoming large-scale solar PV power plants in Algeria within the studied area and other areas.

Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression

This study investigates the surface parameters and environmental factors affecting the energy production of a 500 kWp photovoltaic (PV) solar power plant in Igdir province. Using both the PV panel characteristics and the weather conditions specific to the power plant location, a total of 7 detailed features were included. The estimation of the power plant efficiency, a novel contribution not found in previous studies, is also a major focus. The performance evaluation of different models, including feed-forward neural networks and multiple linear regression, demonstrates the effectiveness of artificial neural networks in capturing the complex relationships between features and efficiency despite limited data availability. Principal Component Analysis (PCA) was used to reduce feature dimensions, showing that even with a reduced feature set, accurate efficiency prediction is still achievable. Prediction using PCA is one of the novelties of the paper. The effects of solar irradiation, module power, and module temperature on power plant efficiency are revealed. The results provide valuable insights for optimizing energy investments in the Igdir region and highlight the potential of artificial neural networks in energy forecasting, demonstrating their suitability for capturing complex patterns in solar power plant efficiency.

Putting into practice a decision-making framework for a thorough performance and location evaluation of solar photovoltaic plants in India from distinctive climate zones

Solar energy utilization has evolved tremendously in many countries to compete with conventional energy sources due to abundant availability, ongoing module price declines, and efficiency gains. Besides these advantages, most solar power plants continue to underperform due to various technical and environmental factors. Some solar projects in India even failed to meet the generation targets. Hence, to achieve sustainable development goals and be future-ready, solar power plants must be sustainably developed for power generation. One should focus on every sustainable aspect before constructing a powerplant, i.e., proper location selection. This study focuses on this problem and considers seven solar photovoltaic plants from different climatic zones in India to compare their performance and location criteria using fuzzy multi-criteria decision-making methods. Fuzzy Stepwise Weight Assessment Ratio Analysis is used to determine the criteria significance, while Fuzzy COmplex PRoportional ASsessment and Fuzzy ELimination Et Choix Traduisant la REalité (ELimination Et Choice Translating REality) are used to rank the seven solar photovoltaic plants. Given solar photovoltaic plants' high initial investment cost, economic criteria precede all other evaluation criteria in assessment, followed by technical, environmental, and connectivity criteria. The social criteria are neglected here because of the minor societal concerns about deploying solar photovoltaic plants. According to the results, the Unchahar solar photovoltaic plant (from the composite climatic zone) outperforms other selected photovoltaic plants and is determined to be the most appropriate plant. This is due to the lowest levelized cost of energy and lowest total cost for the Unchahar solar photovoltaic plant, as these criteria are the most significant per the study. This study's findings will be useful to energy decision-makers and may serve as a reference for constructing and developing future solar plants. Further, this study will make recommendations for future solar photovoltaic plant development. The study also includes sensitivity analysis to determine the data's robustness. This study is very robust, as the normalized alternative is constant for each criterion, and the ranking remains the same.

Hybrid power generation plants: paths for expansion and feasibility in the use of renewable resources

In the last years, the participation of renewable sources in the global electro-energetic matrix has been around 25%, but there is still possibilities for expansion, both due to the availability of resources and the appeal of popular opinion in the face of the imminent scenario of climate change. Many studies have been developed in this direction and perhaps the biggest problems to be dealt with are: frequent intermittency, seasonal scarcity, configuring stochastic scenarios. The implementation of hybrid generation plants is a good solution to mitigate the aforementioned problems. The cooperative hybridization of solar photovoltaic, biomass thermal, wind and hydroelectric plants has been presented as an interesting option to mitigate the problems arising from uncertainties and thus enable the expansion of renewable natural resources in the electro-energetic matrix with operational safety.

New concept on 100.74 kWp floating solar photovoltaic plant and a real mechanical failures assessment: Case of study at Santa Clara hydroelectric power plant reservoir in southern Brazil

Structural failures and partial sinking were assessed in a 100.74 kWp floating solar photovoltaic power plant installed in southern Brazil, at the Santa Clara HPP reservoir. Their occurred during a torrential rain of 159.6 mm, accompanied by wind gusts of 72 km/h, resulting in the platform losing its linearity and causing permanent deformations in the metallic structures. Computer simulation tests calculated the instantaneous forces to be greater than 22 kN. Repairs were carried out replacing the mobile longitudinal fastening elements between modules with rigid and semi-rigid sets. To prevent direct contact between the mooring modules and the PV platform, transverse footbridge lines and a new concept of anchorage modules were introduced, incorporating semi-rigid elements to absorb energy from impacts. Since then, the anchoring systems have been functioning, even under adverse subtropical climate conditions, as occurred in July 2023, with an extratropical cyclone with wind gusts of around 150 km/h.

LM2L rule based adaptive filter controlled multi-functional SPV plant to enhance DG set utilization

In the existing solar photovoltaic (SPV) and diesel generator (DG) based standalone microgrid, the power factor of the DG set reaches around 0.5-0.6. Further, it encounters poor power quality performance due to harmonics currents of the local loads, coupled at the microgrid's point of interconnection (POI). All these issues significantly affect the DG set performance, causing additional losses and reducing its fuel efficiency. In addition, the SPV plant does not provide any ancillary services in the existing system and remains ideal most of the time, causing the under-utilization of microgrid's power converters. In this context, this work presents a least mean 2Lth (LM2L) rule-based adaptive filter-controlled SPV system, where its power converters are regulated to deliver multifunctional capabilities such as enhancing the DG set's power factor and power quality performance. As a result, the DG set power factor improves to unity, and the %THD value of its voltage and current reduce to below 5%. Hence, the fuel efficiency of the DG set is improved, line losses are decreased, and the additional load can be committed to the DG set capacity, which is kept reserved to supply reactive power to the load. Moreover, the modifications, which are earlier required in the DG set design to accommodate the distortion in its voltages and currents due to the nonlinear load, can be avoided. The LM2L algorithm is used as a prefilter for the measured load currents and determines their fundamental component. It has two operating rules, which decide the fundamental component's estimation depending on the system conditions. As a result, it demonstrates an improved steady state and dynamic performances compared to the existing adaptive filter algorithms. Test results verify the control and design of the microgrid on a laboratory prototype.

Modelling and simulation of an on grid 100-kW photovoltaic system

<span lang="EN-US">This work proposes a design of a solar radiation generator system to extract a maximum power of 100 kilowatts for the uses of 400 volts, 50 Hertz electrical network, under standard conditions (1000 Watts/m<sup>2</sup> and 25 °C). The main goal is to inject and control active and reactive power to the grid by a three-phase, one-stage solar grid-connected 100-kW photovoltaic (PV) plant, to keep the current's total harmonic distortion (THD) within the international requirements, and maintain a constant voltage regardless of solar radiation changes. This design consists of three control loops those are: the maximum power point tracking (MPPT), the DC-link voltage, and the current controller. The proposed system was simulated in MATLAB/Simulink environment and was applied on part of the Baghdad electrical network. The obtained results showed the efficiency of the proposed design and modelling.</span>

Data analytics for prediction of solar PV power generation and system performance: A real case of Bui Solar Generating Station, Ghana

The grid-connected solar power generated by the Bui Power Authority is sold to Ghana Grid Company Limited (GRIDCo) and other customers through bilateral contracts. However, there have been challenges in meeting the supply commitments due to the stochastic nature of solar energy. Fluctuating weather and climatic conditions make it difficult for operators to predict the output of the solar photovoltaic (PV) plant in advance. This has the tendency of leading to anticipated power loss or excess power not being managed effectively. In this study, the random forest and gradient boosting regressor algorithms were used to produce deterministic and probabilistic predictions of solar power generation by using data collected over an eleven-month span. Some of the findings show that the random forest model that produced probabilistic predictions performed better than the other compared models in terms of its accuracy. It produces reliable predictions with a normalized mean absolute error of 1.18%. This is deemed acceptable for operational purposes in the sphere of renewable energy prediction. As a result, these predictions are efficient enough to be used by utility companies as input to numerous decision-making problems during operations which consequently leads to the grid being managed effectively. It may also help them to choose the appropriate modelling approach for predicting solar PV power. Overall, this study supports the generation of clean and reliable energy through improved solar power prediction which directly contributes to African Union's agenda 2063 goals especially, in creating environmentally sustainable and climate resilient economies and communities. This is directly linked to the 7th and 13th UN sustainable developmental goals in ensuring access to affordable, reliable, sustainable, and modern energy for all and, combating climate change and its impacts.

Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show that both the methods can effectively estimate the PV Plant output, with the ANN model having a slightly higher accuracy than the polynomial regression model as ANNs can learn the nonlinear relationship between input stochastic parameters and output in a better manner. The study highlights the potential of machine learning techniques for optimizing the performance of solar PV plants and improving their efficiency.

Multicounty fuel constraint‐based dynamic economic dispatch problem using horse herd optimization

AbstractDue to the diminution of fossil fuels, the profitable usage of fuel for the generation of power is reducing. It is an extremely vital concern for power generation companies. The fossil fuel‐based power plant must operate within its fuel limits and contractual restrictions. Therefore, economic optimization is required to optimize the fuel cost for generation under fuel constraints. To address this issue, in this work, a horse herd optimization algorithm (HOA) is developed to solve multicounty fuel‐constrained dynamic economic dispatch with demand‐side management incorporating wind turbine generators, solar photovoltaic plants, and pumped hydro storage plants. The efficiency of the suggested HOA algorithm has been revealed on a selected test system. The HOA technique shows better convergence as compared with the other methods. Moreover, the computation time requirement of the HOA technique is also lower than that of other compared methods. From numerical results, it is observed that the consumption of fuel is sufficiently high if constraints are incorporated into the problem. Simulation results are matched up to those obtained from hierarchical particle swarm optimization with time‐varying acceleration coefficients, fast convergence evolutionary programming, and differential evolution. After comparison, it is observed that the recommended HOA offers a better‐quality solution.

Investigation of Transient Stability in Power System with Improved FRT Capable Solar PV Inverters

Most regions of the world have seen tremendous growth in distributed energy resources, especially photovoltaic (PV) generation. The penetration of PV generation into the power grid is high and hence it has a major impact in power system. The integration of solar PV plant into the power system has both negative as well as positive impacts on transient stability of the power system during faults. In this work, the proposed current amplitude limiting based Fault Ride Through (FRT) control algorithm is implemented in large scale power systems and enhancement of transient stability is also studied in terms of synchronous machine side and grid side parameters namely rotor speed deviation, load angle, relative rotor angle, active and reactive power. The transient stability of large-scale Grid Connected solar PV (GCPV) system is discussed in this article, when the system is incorporated with the proposed FRT control strategy. The large scale GCPV system is said to be stable, if the angular displacement between the machines in the system stays within predetermined limits. The parameters considered for transient analysis on the synchronous machine side are the rotor angle, rotor speed, and Critical Clearing Time. The proposed model is simulated in MATLAB Simulink and validated in 1 kW hardware setup of grid connected PV system which performs satisfactorily in terms of system parameters and transient stability improvement.

Power output forecasting of solar photovoltaic plant using LSTM

Renewable energy sources are gaining popularity, where solar photovolaics (PV) being the most preferred option due to its cleanliness, affordability, and abundance. The energy output of solar PV is primarily based on temperature & irradiance. Therefore, a weather-based intelligent model is needed for estimating solar energy output to fulfil energy demand and decision making. Predicting PV power output is essential for energy management, security, and operation. In addition to enhancing the output efficiency of PV power plants, the power grid's stability can be enhanced by enhancing the efficacy of PV power plants' electricity generation. This work focuses on LSTM and BPNN for forecasting solar plant power output and it is observed that their findings are virtually compatible with realistic power production in terms of MAE, MAPE, RMSPE, and R2 score. LSTM model comparisons with different layers for each weather season are also analysed. Comparing the extent of errors in the LSTM and BPNN models reveals that LSTM provides more accurate predictions.

Identifying and Analyzing the Technical Challenges of Grid Tide Inverters for PV System: A Case Study of Ethiopia and Zambia

Zambia and Ethiopia are two countries from southern and eastern of Africa due to their geographical location and other factor the environment condition is different. The research aims at identifying and analszing the technical performance issues of Grid tide Inverter (GTI) for PV system due to operating climate and others factors. In Zambia the study focused on the two main solar plants which is under ZESCO national grid at malty facility zone (MFZ), the first one is the 54 megawatts Bangweulu solar power plant by Neon Investment of France, second one is 34MW Ngonye solar photovoltaic (PV) plant. In Ethiopia eight min off-grid site under Ethiopian Electric Utility(EEU) is included. Beltu, Behima, Mino, Ungoge, Korhele,Tum, Omorate and Kofetu. A survey is conducted focusing on identifying and analyzing the technical performance issues of GTI in the above-mentioned area.
 Using Microsoft excel the environmental \climate condition, Solar radiation, Air temperature, Rain falls and Wind of the two country Zambia and Ethiopia is investigated and analysedfor each selected site. A questionnaire and a Sime structure interview is conducted. The basic inverter challenges are investigated from the collected data and the problem is analyzed based on the specific inverter data sheet. The data sheet of the inverter for each site under this study.
 Finally, the real-time performance of the inverter of the off-grid PV mini-grid system installed in a small remote town in Ethiopia and on-grid PV system from Zambia at multi facility zone is analysed using measured meteorological data. From on- grid and off-grid inverter performance challenges, which the researcher collected from different plant the main ones are climate or environmental effect and also over load. Overload is one of the frequent challenges in koftu, behama and amorita this can be due to high population growth and unexpected power demand from the community. 
 Environmental condition affects almost all solar plant site which is under this study. If we compere the climate of east which is Addis Ababa Ethiopia and southern of Africa Zambia Lusaka. The temperature of Lusaka is higher than Addis Ababa by 5.63°C and even in the plant under this study, temperature rise is one of the challenges on the inverter performance, sensitive electronic component bent due to excessive temperature.
 On the other side in Ethiopia Addis Ababa excessive rain affect the inverter. Finally, Addis Ababa has about 1.32 times more rainfall than Lusaka, (Addis Ababa’s 79.54mm vs Lusaka’s average of 60.34mm). For that reason, most of the inverter in Ethiopia on the mini off-grid site are affected by heavy rain condition.

Environmental studies of green hydrogen production by electrolytic process: A comparison of the use of electricity from solar PV, wind energy, and hydroelectric plants

In this paper is developed an environmental comparison analysis associated with electrolytic process of hydrogen production using renewable electricity generated by solar photovoltaic (PV), wind energy, and hydroelectric systems. The electrolytic process and the electricity generation are particularly explained, and the global warming potential (GWP) and greenhouse gases (GHG) emissions are analysed, highlighting advantages and disadvantages associated with the production of green hydrogen. The hydrogen production processes are compared considering those different scenarios, enabling the determination of pollutant emissions factors, as the equivalent dioxide carbon and ecological efficiency. As a conclusion, the sustainability study using environmental analysis showed that electricity from renewable sources (wind, solar and water) have ecological impacts on hydrogen production. Comparatively, the best level of ecological efficiency obtained was in the production of green hydrogen from electricity from hydroelectric plants, followed by wind and photovoltaic (PV) plants.

Insights into material recovery, revenue, and global warming potential of India’s end-of-life photovoltaic installations reveals the urgent need for blockchain-based solar passports

Growing photovoltaic (PV) panel waste is creating numerous environmental problems and an unfamiliar opportunity to add value and explore new business options in line with circular economy practices. As an immediate action, countries struggling with massive amounts of PV waste started exploring end-of-life management options, inventory analysis for PV waste, regulatory measures to handle, and asset monetization solutions for commissioning/decommissioning solar PV systems. Nevertheless, these actions have not been decisively implemented in many countries and have thus remained unfulfilled policy aspirations. In the context of India, it is worth noting that the country has established a set of industrial solid waste rules that are fairly comprehensive; however, they do not encompass the issue of PV waste. Hence, this study aims to understand India’s current stand on PV waste and provide a holistic solution for effective PV waste management in the long run. For this, a comprehensive look into India’s grid-connected PV plants was done to provide insights on the PV waste inventory potential, material recovery potential under various recycling approaches, possible revenues earned by selling recovered resources in the marketplace, and global warming potential for treated PV waste. Later, a survey-based discussion was done highlighting the reasons for uncertainties and potential pitfalls with installed solar inventory data considering ground reality situations. Lastly, based on the observed results and associated uncertainties, this study proposes the blockchain-based solar passports framework with a case illustrating the implementation as an urgent need in India to effectively monetize solar PV plant assets.