Operation Of Photovoltaic Power Station Research Articles

Spray-on steady-state study of multi-rotor cleaning unmanned aerial vehicle in operation of photovoltaic power station

By 2024, the photovoltaic industry will have solidified its position as a strategic emerging industry in China. Photovoltaic power stations are deploying solar photovoltaic panels extensively, necessitating the utmost maintenance of their cleanliness and the implementation of dust removal measures in these areas. This is crucial in minimizing the loss of light energy conversion, thus enhancing the overall efficiency of the solar energy system. Through the experimental methods of dynamic and static spraying cleaning, as well as numerical simulations based on the internal and external flow fields of spraying, the impacts of the dynamic and static behaviors, flight height, mission speed, and spraying flow rate of the multi-rotor cleaning drone on cleaning effectiveness are studied. The results indicate that the jet model of the external flow field of the drone has an incoming flow velocity of 4 m/s and a spraying flow rate of 1.7 L/min. The spraying model with a maximum pressure of 82.717 Pa in the internal flow field demonstrates stable fluid behavior and achieves a good level of spraying stability. The validation of dynamic and static spraying experiments confirmed that the optimal recommended height for accurate spraying and cleaning using the drone system is 3.0 m. The dynamic tests demonstrated that the lower height of 3.0 m also provides the most suitable spraying path, reaching 2.2 m at a speed of 0.5 m/s.Operation behaviour was assessed under three different situations ，an area of approximately 2095.2 m2 of photovoltaic panels on the Gobi Desert, 375 m2 of photovoltaic panels modules on the water surface and 875 m2 of photovoltaic panels on the roof of the multifunctional plant.Compare the spray cleaning of above areas with other forms of cleaning, this technique looks very competitive, such as manual operations.

Enhancing photovoltaic energy forecasting: a progressive approach using wavelet packet decomposition

Abstract Accurate photovoltaic (PV) energy forecasting plays a crucial role in the efficient operation of PV power stations. This study presents a novel hybrid machine-learning (ML) model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead. The proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting models. Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods, including Gaussian process regression, extreme learning machines, artificial neural networks and support vector machines, across all seasons. The proposed model exhibits lower normalized root mean square error (nRMSE) (2.116%) and root mean square error (RMSE) (208.233 kW) values, along with a higher coefficient of determination (R2) of 99.881%. Furthermore, the exceptional performance of the model is maintained even when tested with various prediction horizons. However, as the forecast horizon extends from 1.5 to 5.5 hours, the prediction accuracy decreases, evident by the increase in the RMSE (710.839 kW) and nRMSE (7.276%), and a decrease in R2 (98.462%). Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results. This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model, suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy, thereby contributing to more efficient grid management.

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.

Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism

With the proposed goal of “Carbon Neutrality”, photovoltaic energy is gradually gaining the leading role in energy transformation. At present, crystalline silicon cells are still the mainstream technology in the photovoltaic industry, but due to the similarity of defect characteristics and the small scale of the defects, automatic defect detection of photovoltaic cells (PV) by electroluminescence (EL) imaging is a challenging task. In order to better meet the growing demand for high-quality photovoltaic cell products in intelligent manufacturing and use, and ensure the safe and efficient operation of photovoltaic power stations, this paper proposes an improved abnormal detection method based on Faster R-CNN for the surface defect EL imaging of photovoltaic cells, which integrates a lightweight channel and spatial convolution attention module. It can analyze the crack defects in complex scenes more efficiently. The clustering algorithm was used to obtain a more targeted anchor frame for photovoltaic cells, which made the model converge faster and enhanced the detection ability. The normalized distance between the prediction box and the target box is minimized by considering the DIoU loss function for the overlapping area of the boundary box and the distance between the center points. The experiment shows that the average accuracy of surface defect detection for EL images of photovoltaic cells is improved by 14.87% compared with the original algorithm, which significantly improves the accuracy of defect detection. The model can better detect small target defects, meet the requirements of surface defect detection of photovoltaic cells, and proves that it has good application prospects in the field of photovoltaic cell defect detection.

Solar photovoltaic program helps turn deserts green in China: Evidence from satellite monitoring

Solar energy is considered one of the key solutions to the growing demand for energy and to reducing greenhouse gas emissions. Thanks to the relatively low cost of land use for solar energy and high power generation potential, a large number of photovoltaic (PV) power stations have been established in desert areas around the world. Despite the contribution to easing the energy crisis and combating climate change, large-scale construction and operation of PV power stations can change the land cover and affect the environment. However, few studies have focused on these special land cover changes, especially vegetation cover changes, which hinders understanding the effects of the extensive development of solar energy. Here, we used Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) based on Landsat images to monitor changes in vegetation abundance before and after the PV power stations deployment. To reduce the interference of PV shading on vegetation abundance estimation, we improved the vegetation (VG) fraction from SMA and developed the Photovoltaics-Adjusted Vegetation (PAVG) fraction for vegetation abundance measurements in PV power stations. Results show that PV power stations in China's 12 biggest deserts expanded from 0 to 102.56 km2 from 2011 to 2018, mainly distributed in the central part of north China. The desert vegetation in the deployment area of PV power stations presented a significant greening trend. Compared to 2010, the greening area reached 30.80 km2, accounting for 30% of the total area of PV power stations. Overall, the large-scale deployment of PV power stations has promoted desert greening, primarily due to government-led Photovoltaic Desert Control Projects and favorable climatic change. This study shows the great benefits of PV power stations in combating desertification and improving people's welfare, which bring sustainable economic, ecological and social prosperity in sandy ecosystems.

Experimental research of the power quality at the photovoltaic power stations in the power system of Uzbekistan

This article discusses the issues of ensuring the power quality. An analysis of the higher harmonics arising from the operation of photovoltaic power stations is made. The results of experimental research are presented. Based on the measurement and calculation data, time diagrams of changes in the parameters of the electrical system mode for the studied periods of time were constructed.

Research on Comprehensive Benefit Post Evaluation of Photovoltaic Poverty Alleviation Projects Based on FCM and SVM

Photovoltaic (PV) Poverty Alleviation makes full use of the solar energy in poverty-stricken areas so as to achieve sTable incomes increase for the poor households for 25 years. It is an advanced mode integrating new energy development, emission reduction and accurate poverty alleviation. Post evaluation of PV poverty alleviation project is of great guiding significance for new energy development planning, poverty alleviation promoting and construction and operation of PV power stations. Under the guidance of the practical experience of PV poverty alleviation in Jiangxi province, China, this paper firstly builds a comprehensive evaluation index system with 7 second-level targets based on the post evaluation theory. Then, considering that projects with different sizes, construction and operation mode have different features in the evaluation, this paper uses pattern recognition method based on fuzzy C-means clustering algorithm and support vector machine to classify the projects. Then comparative analysis is carried out within each class to achieve comprehensive benefits evaluation. The method can reduce the information loss of multi-index weighted aggregation of traditional post evaluation methods. The features of PV poverty alleviation projects are highlighted to help to find the weak points of the projects. So the evaluation results are more scientific and reasonable.

Solution to Photovoltaic Power Station’s Islanding Operation

When disconnected from the power system grid, the steadily working photovoltaic power plants would get into the state of islanding operation. Photovoltaic inverter has the function of preventing islanding operation, and state of art inverter designing all contained such components. However, when a photovoltaic power station was at large scale with numerous photovoltaic inverters, inverters would not perform the desired effect. This the paper propposed a novel detection method, which could effectively prevent islanding operation. The detection method used the characteristics of islanding operation to test whether a photovoltaic power station encounters such state. Experiments showed that the proposed method being accurate and fast, capable of preventing islanding operation of photovoltaic power station.

Research on Fault Detection Method of Photovoltaic Module

With the rapid development of large-scale photovoltaic power plants, the fault detection of photovoltaic modules has become an urgent problem. This paper studies the principle of fault detection of photovoltaic modules and the detection method based on ZigBee wireless transmission technology is proposed. It uses the ADC module on CC2530 to measure the voltage of three reverse parallel diodes in the photovoltaic module junction box, and sends the data collected by the terminal to the coordinator through ZigBee wireless transmission technology, and then according to the change of the voltage to determine fault location. In addition, this paper also proposes a fuzzy control algorithm to determine the fault type of PV module and design the corresponding fuzzy controller in MATLAB. The fault detection method presented in this paper is simple, reliable and low cost, and can accurately detect the fault location of photovoltaic modules. Therefore, it has practical significance for the efficient operation of photovoltaic power station.

Design and analysis of distributed photo-voltaic power station

After the location of the power station is determined, the construction scale of the solar grid connected photo-voltaic power station is mainly with the terrain conditions and the conditions of land use.The selection of solar cell components and installation methods, access to the power grid and other factors are related.In this paper, according to the existing construction conditions in the sewage plant, the area suitable for the layout of the solar cell array is selected.Accordmg to the structural characteristics of existing structures and the prediction of the power load in the sewage plant, the installed capacity of the plan is 4.9218MWp. A 10kV installed switch-gear station is built, and the 1 10kV outlet is connected to the total distribution room power station. The project is completed at once.Solar energy is converted to DC power through photo-voltaic array composed of PV modules. After three phase inverter (DC-AC) is converted to three-phase alternating current, it is converted to AC power that meets the requirements of public power grid and directly connected to the public power grid through the step-up transformer.The construction and operation of photo-voltaic power stations will not cause pollution to the environment, and can also provide some clean energy, which can reduce the use of fossil fuels such as coal.