Operation Of Photovoltaic Power Plants Research Articles

Investigation of Failures during Commissioning and Operation in Photovoltaic Power Systems

Considering global warming and environmental problems, the importance of renewable energy sources is increasing day by day. In particular, the effects of wind and solar power, which are variable renewable power sources, on the power system necessitate their evaluation in terms of the reliability of the power system. Photovoltaic panels, which enable the conversion of solar power into electrical power with semiconductors, have started to take an important place in global energy investments today. Photovoltaic power plants increase the demand for this energy source with continuous energy conversion depending on sunshine duration and radiation intensity. Among the renewable energy sources, the most easily utilized energy source, regardless of geographical conditions, is the sun. To prevent the energy production of PV power plants from being interrupted, it is necessary to address and analyze all kinds of faults that will affect power production in order to increase the reliability of the system. Academic studies in this field are generally grouped under two topics: classification of faults or modeling of electrical faults. Based on this, in this study, the problems that occur during the installation and operation of photovoltaic systems are classified, and the relevant faults are modeled and simulated in MATLAB Simulink version 23.2 (R2023b). Thus, a scientific approach to the problems of photovoltaic power plant operating conditions has been gained, which will be the basis for academic studies.

Photovoltaic power prediction based on sky images and tokens-to-token vision transformer

Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this paper, a new deep neural network structure based on vision transformer is proposed to combine sky images and Tokens-To-Token（T2T） for photovoltaic power prediction. The method uses an incremental tokenization module to aggregate neighboring image patches into tokens, which capture the local structural information of the clouds. Then, an efficient T2T-ViT backbone network is used to extract the global attentional relationships of the tokens for power prediction. In order to evaluate the performance of the proposed model, the method was compared with several deep learning architectures such as ResNet and GoogleNet on a dataset collected by the National Renewable Energy Laboratory in Colorado, USA. The results of power prediction were analysed using training loss, prediction error, and linear regression, and they show that the proposed method achieves higher prediction accuracy and lower error compared to the existing methods, especially in short- and ultra-short-term prediction. The paper demonstrates the potential of applying Transformer models to computer vision tasks for renewable energy forecasting. The results show that the proposed method achieves higher prediction accuracy and lower error than several deep learning architectures, such as ResNet and GoogleNet, especially in short- and ultra-short-term prediction.

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.

Comparison of harmonic emission in LV side of a large grid connected PV power plant

The use of solar photovoltaic (PV) in distribution networks has increased considerably in recent years. Although they have many advantages, PV systems can also result in complex power quality issues in distribution networks, like harmonic distortion, that can interfere with loads and controllers. Harmonic emission from different inverters and their aggregation is thus of interest. In this work, harmonic emission of two PV string inverters and two substations operating in a large PV power plant is presented. The distribution of the full range of current harmonics is analyzed, as well as the correlation between the values of each harmonic and the power level produced, by the inverter or substation, and the correlation between voltage and current harmonics. Significant differences are observed in the harmonic emission of the inverters, despite being under the same operating loading and grid conditions. The results obtained for the substations are in line to those for the inverters. This study was performed in a grid connected 12 MW PV power plant operating in Europe.

Causes and Prevention of Photovoltaic Module Hot Spot Effect

The photovoltaic module is the basic link in the photovoltaic power generation system, which has an important impact on the economic operation of photovoltaic power plants. Hot spot effects account for a large proportion of photovoltaic module failures, so it is of engineering significance to study them and put forward suggestions for fault prevention. Firstly, this paper briefly introduces the composition of photovoltaic power generation system and the structure of photovoltaic modules then analyzes the working process and typical models of photovoltaic modules as well as the causes of the hot spot effects of photovoltaic modules. Finally, it puts forward some measures to prevent faults to improve the operational reliability of photovoltaic modules.

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.

Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset

Accurate faults diagnosis for photovoltaic (PV) array is one of the vital factors that guarantee the reliable operation of PV power plant. Artificial intelligence (AI) based fault detection and diagnosis (FDD) models are promising techniques. In order to automatically extract the faults features from the raw electrical data of PV array and create efficient FDD model with small dataset, a FDD scheme using Wasserstein generative adversarial network (WGAN) and convolutional neural network (CNN) is designed. The proposed FDD model is consisting of three modules, a discriminator, a generator and a classifier for fault diagnosis. By analyzing sequential PV data in a 2-Dimension way, the proposed discriminator and generator learn the distribution of PV data under various PV system operations. Then they are utilized to generate more labeled samples to improve the performance of the CNN based classifier. Thus, the proposed FDD model can be trained only requiring minor labeled samples. A laboratory grid-connected PV system is established to experimentally investigate the performance of the developed method. The results demonstrate that the designed FDD model can accurately diagnose line-line and open circuit faults.

Short-term Forecasting Approach Based on bidirectional long short-term memory and convolutional neural network for Regional Photovoltaic Power Plants

Accurate photovoltaic (PV) generation output prediction is one of the effective ways to ensure the safe operation of power grid, develop reasonable dispatching plan and improve the efficiency of clean energy. With the large-scale operation of PV power plants in recent years, forecasting regional PV output becomes more significant. We proposed a short-term forecasting approach based on bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) for regional PV power plants. First, the k-means algorithm is used to divide power plants with similar generation characteristics into the same output subregion. Second, a representative power plant in each subregion is selected based on three correlation coefficients. Then, we develop a regional prediction model based on BiLSTM-CNN method. This model takes historical operation and meteorological data of the representative power plant as input, and takes the total subregional power generation as output. Finally, this short-term forecasting approach is tested using real data from PV power plants in Chuxiong and Dali region, Yunnan province, China. The comparison of numerical results shows this proposed method can effectively improve the short-term prediction accuracy of regional PV generation output.

Insolation calculations of a photovoltaic power plant taking into account location-based and weather parameters

In this study, we set out to develop a methodology for calculating insolation of a photovoltaic power plant taking into account the maximum number of significant input parameters and its territorial adaptation. To this end, simulation modelling implemented in the MATLAB environment was used. Functional possibilities for the synthesis of models using existing elements with the integration of algorithms and modelling results between the blocks of the Simulink sub-system were used. In terms of significant input parameters, geographical coordinates, local time, tilt of the receiving solar panel, modelled day, atmospheric transparency coefficient, albedo and azimuthal angle were considered. A computer model of a photovoltaic power plant was developed for investigating the operation of photovoltaic cells depending on the coordinates of their installation, geometric parameters of solar panels, as well as the temperature and reflectivity of the environment. The performed modelling of the photovoltaic power plant operation visualised graphic dependences of insolation on the tilt of the solar panel, atmospheric transparency coefficient, geographical coordinates of the object and the current month or day. According to the analysis, 15 variations in the solar panel tilt modifies insolation by 10–15%, while variations in the atmospheric transparency coefficient result in 30–50% variations of insolation. As a result, the daily insolation values for the city of Angarsk throughout a year can be modified by 1000–6500 W/m2. The presented results of investigating a regionally adapted photovoltaic power plant demonstrated the need for accounting for location-based and weather parameters during the calculation of insolation for determining the applicability of a plant. The proposed mathematical model for calculating insolation of a photovoltaic power plant can be used for the design and optimization of power supply systems in combination with the specified photovoltaic solar power plants.

Sensors placement for the faults detection and isolation based on bridge linked configuration of photovoltaic array

Introduction. The photovoltaic market has been increased over the last decade at a remarkable pace even during difficult economic times. Photovoltaic energy production becomes widely used because of its advantages as a renewable and clean energy source. It is eco-friendly, inexhaustible, easy to install, and the manufacturing time is relatively short. Photovoltaic modules have a theoretical lifespan of approximately 20 years. In real-life and for several reasons, some photovoltaic modules start to fail after being used for a period of 8 to 10 years. Therefore, to ensure safe and reliable operation of photovoltaic power plants in a timely manner, a monitoring system must be established in order to detect, isolate and resolve faults. The novelty of the proposed work consists in the development of a new model of sensors placement for faults detection in a photovoltaic system. The fault can be detected accurately after the analysis of changes in measured quantities. Purpose. Analysis of the possibility of the number and the position of the sensors into the strings in function of different faults. Methods. This new method is adapted to the bridge linked configuration. It can detect and locate failure points quickly and accurately by comparing the measured values. Results. The feasibility of the chosen model is proven by the simulation results under MATLAB/Simulink environment for several types of faults such as short-circuit current, open circuit voltage in the photovoltaic modules, partially and completely shaded cell and module.

State and Parameter Estimation of Photovoltaic Modules using Unscented Kalman Filters

This paper presents a parameter estimation technique for the circuit of a photovoltaic module. The proposed method is based on unscented Kalman filter for the joint estimation of the state variables and the parameters involved in the model equations, using external measurements only. A case study is presented where the obtained estimation errors remain lower than 3% in all cases. Additionally, three formulations of the Kalman filter are compared in terms of convergence and accuracy. This technique may contribute to the optimal operation of photovoltaic power plants and the maximization of the investor revenue.

Comparison of Environmental Impact Assessment Methods in the Assembly and Operation of Photovoltaic Power Plants: A Systematic Review in the Castilla—La Mancha Region

Solar energy is in high demand due to its environmental benefits and economic potential; however, concerns remain about the total impact it holds. In 2020, for Spain, Castilla-La Mancha was the second autonomous community with the highest photovoltaic energy production. Thus, a systematic review on 15 large-scale PV solar energy projects was carried out to assess the industry impacts, through environmental impact assessment (EIA), within the Autonomous Community of Castilla—La Mancha. An estimation of these impacts from a pre-operational approach is presented, based on primary energy needs and emissions discarded during its life cycle due to the manufacture, operation, and recycling of the photovoltaic modules. Based on both the life cycle assessment (LCA) and EIA, the approaches were compared with the results obtained. The obtained results suggest that determining the actual impacts of power plants in this region could provide justified information for the public administration and technicians in the measures for the installation and operation of PV plants and the future benefits of renewable solar technologies. Furthermore, the results indicate the possibility to recognize the relationship between the size of the plant and a high generation capacity, with a shorter time to pay for emissions from the manufacture and recycling of panels, suggesting that it is around 1.66–2.08 years for the Castilla-La Mancha region.

Experimental assessment of long-term performance degradation for a PV power plant operating in a desert maritime climate

Photovoltaic (PV) installations are being intensively deployed and their capacity are approaching to terawatt around the world. In this context, the long-term performance and reliability of PV modules, component and systems are critical metrics for the economic viability of PV power plants. Furthermore, this is linked to the environmental conditions in which the PV plants operate. In this study, the performance degradation of a 302.4 kWp solar PV power plant is investigated over the first five years of operation in the desert maritime climate of Djibouti. The analysis is led on collected data extracted from the PV installation and that have been pre-processed and filtered. For this purpose, several criteria have been introduced to identify anomalies such as grid outages. Then, the power degradation rate was considered using Photovoltaics for Utility Scale Applications (PVUSA) regression analysis and Performance Ratio (PR) analysis based on continuous monitoring data at 1-min intervals. The degradation rates were found to be 1.01 ± 0.38%/year for PR and 0.085 ± 0.68%/year for PVUSA. According that the PVUSA did not detect any degradation, new test conditions that were set by experimental data have been introduced. Finally, the resulting measure allowed a degradation rate of 0.74 ± 0.29%/year to be observed.

Solar Power Forecasting Using Deep Learning Techniques

The recent rapid and sudden growth of solar photovoltaic (PV) technology presents a future challenge for the electricity sector agents responsible for the coordination and distribution of electricity given the direct dependence of this type of technology on climatic and meteorological conditions. Therefore, the development of models that allow reliable future prediction, in the short term, of solar PV generation will be of paramount importance, in order to maintain a balanced and comprehensive operation. This article discusses a method for predicting the generated power, in the short term, of photovoltaic power plants, by means of deep learning techniques. To fulfill the above, a deep learning technique based on the Long Short Term Memory (LSTM) algorithm is evaluated with respect to its ability to forecast solar power data. An evaluation of the performance of the LSTM network has been conducted and compared it with the Multi-layer Perceptron (MLP) network using: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The prediction result shows that the LSTM network gives the best results for each category of days. Thus, it provides reliable information that enables more efficient operation of photovoltaic power plants in the future. The binomial formed by the concepts of deep learning and energy efficiency seems to have a promising future, especially regarding promoting energy sustainability, decarburization, and the digitization of the electricity sector.

Selected aspects of wind and photovoltaic power plant operation and their cooperation

Selected aspects of wind and photovoltaic power plant operation and their cooperation

REDUCING CLIMATE CHANGE BY INSTALLING A NEW PHOTOVOLTAIC POWER PLANT IN BULGARIA

Three new roof-mounted 10 kWp grid-connected photovoltaic (PV) power plants have been constructed in the Technology Park at the Technical University of Gabrovo, Bulgaria, as part of a project "Competence Center – Intelligent Mechatronic, Eco, and Energy Saving Systems and Technologies". Three different PV modules types have been used: monocrystalline silicon (mono-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIGS). New three power plants, together with the existing amorphous silicon and poly-crystalline silicon photovoltaic power plants at the TU-Gabrovo enhanced simultaneous testing of five different photovoltaic materials. A small 500 Wp mono-Si photovoltaic thermal hybrid solar collectors (PVT) PV system has also been constructed. The power plants feature a monitoring system for the meteorological and electrical operating parameters, which measures, displays, and stores data on solar radiation, temperature, wind speed, currents, voltages, and electrical power of each power plant. PV plants components' technical characteristics are given in the paper. The schemes describing the basic wiring diagram, disposition of the three PV subsystems on the roof of the building at the technology center have also been presented. The initial comparative software data for monitoring of meteorological and electrical operating characteristics of the three different types of PV subsystems are shown as well. According to the specific ecological equivalent of energy resources and energy for the region of Bulgaria, the data are presented on the saved CO2 emissions from the avoided production and transmission of electricity owing to the operation of photovoltaic power plants.

SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting

Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this study, a novel convolutional neural network (CNN) model, namely SolarNet, is proposed for short-term PV output power forecasting under different weather conditions and seasons. The proposed CNN model is designed as a parallel pooling structure to increase the forecasting performance. This structure consists of max-pooling and average-pooling blocks. The input parameters are the measured historical solar radiation, temperature, humidity, and active power data. The power data is decomposed into sub-components with the variational mode decomposition method and a data preprocessing and reconstruction process is utilized to obtain deep input feature maps. After input parameters are converted to hue-saturation-value (HSV) color space, the subsets feed to the input of the network. The experimental studies are performed with a case study using a 23.40 kW PVPP dataset from the Desert Knowledge Australia Solar Centre. The design CNN model is also compared with benchmark deep learning methods. In the experiments, the average correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the proposed method for 1-h different weather conditions are achieved as 0.9871, 0.3090, and 0.1750, respectively. The experimental results show that the proposed deep forecasting method has higher accuracy and stability in short-term PV power forecasting and outperforms the other deep learning methods.

Assessing shading losses of photovoltaic power plants based on string data

As decentralized energy supply systems are becoming more and more important for the global energy supply, – apart from a heightened focus on energy rationalization and energy efficiency – increasing the role of variable renewable energy sources, such as solar and wind energy, in energy consumption is gaining more and more significance. Since energy projects tend to be meant for the long term and involve considerable financial investments, it is indispensable to be aware of the country-specific regulatory background when establishing photovoltaic (PV) systems. The last few years have witnessed a trend that new PV power plants are mostly built using traditional crystalline PV technologies, which are prone to irreversible PV module damage due to shading effects. During the operation of PV power plants, anomalies causing loss of income and even fire hazard in extreme cases may occur. Thus, the identification of the problematic parts of the system is of utmost importance. This paper presents the energy relationships of shading by the example of a Hungarian PV system. The goal of this study is to introduce a methodology that can be used internationally to categorize the operational characteristics of the strings of PV power plants on the basis of monitoring data, which allows the assessment of the annual energy loss. The innovative novelty of the model is that its use can provide practical help for the operators of PV systems around the world, since the solution is easy to adapt to real-time supervisory and management platforms and it makes the localization of problematic strings possible, thusly allowing a more focused inspection of PV power plants. The novel practical benefit of the model is that by its use it becomes possible to detect any energy loss resulting from the spacing distance of the strings of PV power plants or faulty operation (the negative shading effects of trees and other objects, faulty inverter operation) by using a simpler calculating mechanism. The early detection of problems is essential for the protection of the PV modules, the subsequent reconstruction of the strings or even solving issues under guarantee. In addition, by assessing the annual energy loss caused by shading, it becomes possible to detect any negative change in the economic indicators of the investment.

A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

Operational and control approach for PV power plants to provide inertial response and primary frequency control support to power system black-start

The renewable generation growth, which includes photovoltaic power plants, has posed challenges for the planning and operation of contemporary power systems. High penetration levels of generation based on fully rated converters makes the black-start of power systems more difficult due to the low equivalent inertia of the system, the performance deterioration of the primary frequency control, the reduced number of black-start units, and the energization of large loads. In the context of the planning and operational challenges imposed by renewable generation, this work proposes an operational and control approach for centralized photovoltaic generation to provide inertial response and primary frequency control support to the black-start of bulk power systems. The proposed control aims to provide inertial response and primary frequency control support only during the system restoration process. The work also proposes an analytical formulation to assess the impact of the operating point of photovoltaic modules on the dynamics of photovoltaic units. The obtained results have shown that photovoltaic power plants are capable of providing support to the black-start of power systems, improving the dynamic and steady-state performance of the system frequency response during the black-start. The performed dynamic analyses have shown that the operational region of the photovoltaic modules significantly affects the dynamics of the power balance in the photovoltaic unit and, consequently, the dynamics of the load pick-up of the photovoltaic modules. The proposed dynamic analysis is useful to support the formulation of control approaches for de-loaded operation of photovoltaic power plants.