Large-scale Photovoltaic Power Plants Research Articles

Classification of Defect Photovoltaic Panel Images Using Matrox Imaging Library for Machine Vision Application

The maintenance of large-scale photovoltaic (PV) power plants has long been a challenging task. Currently, monitoring is carried out using electrical performance measurements or image processing, which have limited ability to detect faults, are time-consuming and costly, and cannot pinpoint the defect's precise location quickly. To address these challenges, this research focused on using deep learning techniques to classify defect and non-defect PV panels. The application provided deep learning algorithms capable of image classification in various classifiers. The image dataset was carefully curated and split into training and development datasets during the training model to ensure the highest accuracy for the prediction of the presence or absence of defects on the PV panel. Statistical measures, which are the average accuracy for the training model and average prediction, were employed to evaluate the classification performance of the defect PV panel model. The results demonstrated a remarkable total accuracy of model 99.9% for each class, and prediction results showed that almost 70% of defect PV panels were detected from the testing dataset. Furthermore, a comparative analysis was conducted to benchmark the findings against other algorithms. The practical implications of this research are significant, showcasing the effectiveness of deep learning algorithms and their compatibility with machine vision applications for the classification of defect PV panel images. By leveraging these techniques, solar farm operators can significantly improve maintenance management, thereby enhancing the efficiency and reliability of solar power generation and potentially saving significant costs.

Energy Flexibility in Aluminium Smelting: A Long-Term Feasibility Study Based on the Prospects of Electricity Load and Photovoltaic Production

This paper investigates the economic feasibility of utilising energy flexibility in aluminium production as a viable solution to leverage electricity surpluses arising from the increasing number of photovoltaic (PV) system installations. Future trends suggest that the generation capacity of PV systems will soon surpass consumption, leading to significant electricity surpluses, particularly during the summer. This surplus electricity, which is anticipated to be available at low prices, offers a unique opportunity to evaluate different investment and utilisation scenarios for aluminium production while simultaneously decreasing its environmental impact. The results demonstrate that, despite their high initial investment cost, large-scale PV power plants can potentially deliver maximum economic gains over a ten-year period. Conversely, the direct utilisation of surpluses without substantial investment can yield savings of up to EUR 17 million within the same time frame for Slovenia’s case with an aluminium smelter, which has a maximum power usage of 60 MW. The findings of this study have significant implications in terms of shaping future energy strategies and policies, emphasizing the value of integrating renewable energy sources and industrial processes for enhanced economic and environmental outcomes.

Locating the suitable large-scale solar farms in China's deserts with environmental considerations

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4–84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47–110 PWh which is 1.7–3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8–62.6 gigatons annually, offsetting 73–170 % of global emissions. Covering just 4.8–11.5 % of China's desert area (8 × 104–19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

Electrothermal Modeling of Photovoltaic Modules for the Detection of Hot-Spots Caused by Soiling

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots.

State-of-the-Art of Floating PV Development in the Indonesian Waters

Abstract In recent years, there has been a growing emphasis on reducing Indonesia’s reliance on fossil fuels for electricity generation by promoting the development of renewable energy-based power plants. Among the renewable energy sources, solar energy holds great potential in Indonesia, given its abundant availability throughout the year. This presents favourable prospects for the establishment of solar photovoltaic (PV) power plants, both in ground-mounted and floating sub-structure configurations. However, the deployment of ground-mounted PV plants raises concerns regarding land usage, particularly for large-scale installations. Moreover, the environmental temperature in tropical regions like Indonesia can adversely affect the efficiency of PV panels in land installations. Water bodies offer an alternative location for PV installations to mitigate these challenges, as water can help alleviate the temperature impact on PV panels. Considering Indonesia’s archipelago geography, seas emerge as an attractive option for large-scale PV power plant development. However, numerous challenges arise when implementing floating PV (FPV) technology in open seas. This paper explores the ongoing advancements in marine FPV technology and addresses the challenges associated with installing FPV systems in Indonesian seas. These efforts aim to support the fundamental development of the country’s electricity infrastructure, encompassing aspects such as reliability, affordability, and social acceptability.

Daily optimization of maintenance routing and scheduling in a large-scale photovoltaic power plant with time-varying output power

Photovoltaic (PV) power generation is an environmentally friendly and clean energy source with extensive and widespread installations worldwide. Consequently, the maintenance planning for PV power plants has become exceedingly significant. Owing to the time-varying output power of PV power plants, the optimization of daily maintenance planning for PV power plants should encompass not only the optimization of maintenance routes and task assignments, but also the consideration of reducing downtime costs resulting from failures and maintenance activities. To find the optimal route that minimizes the total cost, including travel, technician, downtime, and penalty costs, a PV power plant maintenance routing problem and its solution approach were investigated in this study. First, considering the influence of daily variations in solar radiation and temperature on output power, this study proposed an optimization problem for maintenance routing in a PV power plant. Next, linearization techniques with controllable error precision were developed to construct a mixed-integer linear programming model. Then, to efficiently solve large-scale problems, a heuristic algorithm was customized based on an adaptive large neighborhood search approach. Finally, the proposed approach was implemented at the Talatan PV power plant in Qinghai Province, China. Compared to the traditional shortest route model, which neglects the time-varying output power, the proposed method significantly reduces the total cost.

Control strategy and research on energy storage unit participation in power system frequency regulation based on VSG technology

When large-scale photovoltaic power plants are integrated into the grid, it will cause the grid-connected capacity to gradually expand, causing greater harm to the safe and stable operation of the power system, which will affect the FM characteristics of the power system. This requires the PV power plant to actively participate in power system frequency control. Through the PV virtual synchronous generator frequency control technology, coupled with the virtual synchronous PV power plant modeling, the PV new energy units can have the same frequency control characteristics as synchronous generator sets. In the virtual synchronous technology, due to the existence of damping windings, electromagnetic damping will be generated, and it is also due to virtual damping that the virtual synchronous technology has the ability to damp power oscillations. It is also because of the excellent virtual inertia and damping characteristics of the virtual synchronous generator in the grid that this FM technology helps to regulate the frequency fluctuations of the new energy grid, meet the requirements of the power system to maintain stable operation, and then realize the large-scale PV grid connection.

Effective protection scheme for transmission lines connected to large scale photovoltaic power plants

The performance of a distance relay can be significantly affected by various uncontrolled factors such as fault resistance, fault type, fault location and noise. Moreover, when photovoltaic power plants are integrated into electrical grids, they exhibit distinct fault current characteristics compared to conventional power systems with synchronous generators. These factors introduce variations in the recorded apparent impedance, deviating from the predefined settings of the distance relay. This paper presents an effective scheme for fault detection using ITD methodology. The ITD method decomposes the target signal into proper rotation and monotonic signals, which are then used to calculate the Energy Index. The performance of this scheme has been extensively investigated under various scenarios, including fault resistance, fault location, fault type, CT saturation, GC compliance, noise, and sampling frequency. To validate the effectiveness of the proposed scheme, simulations are conducted using both the PSCAD/EMTDC and MATLAB software. The proposed scheme is assessed using both the 2-bus and IEEE 39-bus test systems. The results show the merit and effectiveness of the proposed scheme in the accurate diagnosis of fault detection with 400 Ω fault resistance, in the presence of 20 dB noise, with variable sampling frequency (0.5-16 kHz) and variable irradiance values (600-1000(W/m2)) in power systems.

Novel and comprehensive approach for power loss estimation of soiled photovoltaic modules

The output power of photovoltaic (PV) generators is negatively affected by shading caused by surface soiling. Thus, it is crucial to evaluate the power loss level and plan cleaning schedules efficiently and economically, particularly in large-scale PV power plants. This paper proposes a novel and comprehensive solution that combines circuit topology analysis and image processing to estimate power loss in soiled PV panels. The performance evaluation is based on a wide range of soiling conditions, including sand, dust, and talcum powder, and is compared with a benchmarking system from prior studies. Compared with the best-performing data-driven-only method, the proposed scheme improves the estimation accuracy from 11.02% to 39.52% by exploring the circuit information.

Fault Detection and Location in Power Systems with Large-Scale Photovoltaic Power Plant by Adaptive Distance Relays

Fault Detection and Location in Power Systems with Large-Scale Photovoltaic Power Plant by Adaptive Distance Relays

Harmonic assessment on two photovoltaic inverter modes and mathematical models on low voltage network power quality

Power quality is a crucial aspect of designing a large-scale photovoltaic power plant, particularly regarding harmonics caused by inverter switching. This research aimed to analyze harmonics in a system using electrical transient analyzer program (ETAP) Power Station 20.5.0 to uncover the effect of irradiance on the inverters’ power quality running at 85% and 100% power factors. We analyzed both voltage and current total harmonic distortion (THD<sub>i</sub> and THD<sub>v</sub>) from the simulation and compared them with the mathematical model. Moreover, we analyzed the effect of changes in irradiance level on harmonics and reactive power penetration, which influenced power losses in transformers and cables. Inverters at 85% power factor experienced an increase in THD<sub>i</sub>, whereas those at 100% power factor decreased. Inverters with 85% power factor experienced more frequent switching, causing more prominent distortion. The magnitude of THD<sub>v</sub> increased proportionally with the rise of irradiance level. Inverters at 85% had a higher THD<sub>v</sub> value because of the excessive reactive power compensation when irradiance rose. Irradiance level had an inverse relationship with system losses since high irradiance levels led to lower losses as less power was required through transmission lines and transformers. Moreover, losses at 85% power factor were higher since the high harmonics caused additional losses.

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.

Performance evaluation of large-scale photovoltaic power plant in Saharan climate of Algeria based on real data

In this study we evaluate a large-scale, grid-connected photovoltaic power plant (LS-PVPP) in a hot climate in Adrar, Algeria. The plant's performance was evaluated using both real-world data from 2018 and simulations conducted with PVsyst in terms of various parameters, such as reference yield, capacity factor, performance ratio, statistical indicators, and carbon emission savings. The results indicate a significant reduction in carbon emissions by the LS-PVPP, with a final yield of 4.98 kWh/kWp/day, a performance ratio of 71.67 %, a capacity factor of 20.72 %, and a global system efficiency of 10.66 % and save 19,388 metric tons of carbon in 2018. The industrial approach analysis revealed a target performance ratio (PR) of 71.17 %. The difference of 0.5 % led to extra energy generation of 252.67 MWh and additional revenue of 27,957.42USD. The simulated data predicted a final yield of 5.36 kWh/kWp/day, a performance ratio of 77 %, a capacity factor of 22.38 %, a global system efficiency of 11.55 %. The aim of this research is to offer data on the performance of a (LS-PVPP) in a hot climate. Researchers, photovoltaic project developers, and stakeholders can utilize the findings to assess project viability, optimize performance, and maximize the environmental and economic benefits.

Dynamic Risk Assessment of Landslide Hazard for Large-Scale Photovoltaic Power Plants under Extreme Rainfall Conditions

Large-scale photovoltaic power plants located in highland mountainous areas are vulnerable to landslides due to extreme rainfall, posing a significant threat to the normal operation of photovoltaic power plants. However, limited research has been conducted on landslide risk assessment specifically tailored to large photovoltaic power plants, with most studies focusing on static assessments that lack long-term sustainability in risk assessment and prediction. In this paper, a dynamic study on landslide risk at a large photovoltaic power plant project under extreme rainfall conditions is conducted. Firstly, the factors in landslide susceptibility assessment based on typical landslide characteristics in the study area are selected and an assessment index database using mapping units to extract the relevant factors is established. Subsequently, the ANP-FBN model is employed to evaluate the landslide susceptibility of large photovoltaic power plant sites. Furthermore, an assessment index system for landslide hazard vulnerability is developed by considering population, economic, and material vulnerabilities, and the AHP method is adapted to assess landslides vulnerability in the study area. Finally, the landslide rainfall threshold with the susceptibility and vulnerability assessment results are coupled to achieve a dynamic assessment of landslide hazard risk at large photovoltaic power plant sites under extreme rainfall conditions. The findings highlight that the central valley and the eastern steep slope of the study area are the primary “high” and “very high” risk areas. Moreover, with the increase in rainfall duration, the risk level of landslide hazards in the study area also rises.

A Novel Fault Diagnosis Scheme for PV Plants Based on Real-Time System State Identification

A large-scale photovoltaic (PV) power plant is composed of hundreds or thousands of solar panels, which makes fault diagnosis a challenging problem due to its complexity. It is known that the PV power output characteristics dramatically change with environmental conditions. Thus, it is difficult to distinguish a short-circuit line fault from mismatching conditions induced by partial shading or unbalanced generation. The article proposes a novel fault diagnosis scheme that is based on real-time system identification to determine normal or abnormal operations. It does not require any additional hardware or measurement support. The identification and analysis include both short- and long-term characteristics of the generation system to mitigate disturbances and improve the robustness. The effectiveness of the proposed scheme is validated by simulations and experiments under a wide range of test conditions.

A Novel Operating State Evaluation Method for Photovoltaic Strings Based on TOPSIS and Its Application

PV strings are essential for energy conversion in large-scale photovoltaic (PV) power plants. The operating state of PV strings directly affects the power generation efficiency and economic benefits of PV power plants. In the process of evaluating PV arrays, a reference array needs to be identified. By comparing PV arrays with the reference array, the operational status of the PV arrays can be evaluated. However, in the actual operation of PV power stations, it is difficult to directly determine the reference state of a PV array due to random fluctuations in the PV power output. In order to solve the problems mentioned above, this paper proposes a method to select the reference state and perform a grading evaluation of PV strings. Additionally, the proposed method is based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, which is used to rank the performance of PV arrays to determine their status. In order to solve the problem of random fluctuations in PV power generation, a probability distribution model of the PV string conversion efficiency was built by using the kernel density estimation method. Then, the characteristic indicator of the PV string’s operating state was described by the output power of the PV string and its probability distribution model. Then, based on the operating characteristic indicator, the reference state of the PV string was determined using the TOPSIS method, and the grading evaluation of the operating state of the PV string was realized. Finally, the effectiveness of the proposed method was verified using the actual data of a PV power station.

LVRT and Reactive Power/Voltage Support of Utility-Scale PV Power Plants during Disturbance Conditions

This paper proposes a control technique for a large-scale grid-connected photovoltaic (PV) plant that maintains the connection of an inverter to the grid voltage under different types of faults, while injecting a reactive power to accommodate the required grid connection. This control strategy is suggested to improve the low-voltage ride-through (LVRT) capability of grid-connected PV power generation plants. A 20 MW solar PV power plant is modeled and simulated using Matlab/Simulink. The power plant is composed of 10 parallel groups of arrays with a power rating of 2 MWp. The solar PV arrays are connected to a medium-voltage side-rated 22 KV to the utility grid. A dynamic analysis of the grid-connected large-scale solar PV power plant is introduced. This analysis is accomplished in order to determine the impact of three-phase short-circuits at the point of common-coupling (PCC), where the solar PV power station is connected to ensure a practical voltage level by injecting active and reactive power. The reactive power support allows for faster restoration of voltage values at the PCC. When subjected to transient disturbances, the stability of the studied system relies on both the type of the disturbance and the initial operating situation. The disturbance may be either small, resulting from electrical load changes, or large, such as from a transmission line short-circuit (fault) and significant generator loss.

Environmental impacts of photovoltaic power plants in northwest China

In the past decade, approximately 17 % of the world's photovoltaic capacity has been installed in China, especially in the northwestern desert areas. The impacts of the construction and operation of large-scale photovoltaic power plants (PPPs) on local ecological environments have become urgent scientific issues in regional environmental protection decision-making. To quantitatively evaluate the local environmental impacts of the construction and operation of PPPs in the desert oasis region, thermal infrared and multispectral sensors mounted on unmanned aerial vehicles (UAVs) as well as X-ray fluorescence spectrometers and soil sensors were used in this study to monitor a large PPP in Northwest China. We found that the construction and operation of PPPs can promote biological soil crust development and vegetation growth and can thus improve the soil texture and nutrition. However, the Ca, S and Cl concentrations were found to be 3, 5 and 1.7 times higher inside the PPP area than outside the PPP area, respectively. In addition, the soil salinization is also more severe inside the PPP area. In future studies, it is essential to further elucidate the impacts of PPP operations and agricultural on desert ecosystems.

Automatic fault detection of utility-scale photovoltaic solar generators applying aerial infrared thermography and orthomosaicking

As large-scale Photovoltaic (PV) power plants are being expanded in installation number and capacity, aerial infrared thermography (aIRT) has proven to be effective in detecting at different phases of their development, construction and commissioning to operation and maintenance. However, evaluating the aerial imagery over hundreds of hectares fields of PV arrays is very time-consuming and subject to human error. This paper proposes a complete framework for automatically detecting faults in large-scale PV power plants and their physical location inside the plant site. To this end, a Mask-RCNN algorithm is developed and fine-tuned for instance segmentation using a dataset of 93 samples collected in an aIRT flight campaign in Brazil. The results are combined with orthomosaic techniques to create an orthomap of the PV system with the highlighted faults. The proposed method has been tested to automatically detect the faults in two power plants. Several tests were performed to improve the algorithm’s accuracy, resulting in high-accuracy results for detecting and localizing hot spots in PV plants and disconnected substrings. The resulting maps could successfully show the location of these faults with high accuracy (10% of false positives).

Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.