Photovoltaic Equipment Research Articles

Anomaly detection of photovoltaic power generation based on quantile regression recurrent neural network

Distributed photovoltaic (PV) power generation systems are widely spread. Moreover, due to the randomness of meteorological conditions and the complexity of installation environments, it is difficult to eliminate the interference of factors such as meteorological fluctuations in the monitoring of abnormal states of PV equipment. Based on this, this paper proposes a PV power generation anomaly detection method based on Quantile Regression Recurrent Neural Network (QRRNN). First, the characteristics of solar irradiance on clear days are analyzed, and the clear day masking method is used to eliminate the interference of cloudy and rainy weather. Then, the output correlation of different power stations is analyzed to obtain PV stations with high output correlation as the horizontal reference, which is used to exclude interferences such as permanent faults at the power stations. At the same time, vertical comparison of the output curves of the station under test on different clear days is conducted to eliminate interference factors such as weather and environmental conditions. Subsequently, the metered active power output data, which is free from interference, is input into the QRRNN model to obtain the normal active power output range of the PV. The power threshold of the normal output range is utilized to identify anomalies in PV power generation. Finally, simulation analysis of actual PV system data is conducted, and the results show that the method can effectively identify PV power generation anomalies and has high accuracy in PV fault detection.

Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

A novel data-driven state evaluation approach for photovoltaic arrays in uncertain shading scenarios

Accurately determining the operating state of photovoltaic (PV) power-generation equipment and providing accurate support for maintenance management requires evaluation of the states of PV arrays under partial shading conditions. Results of such evaluation enable the optimization of maintenance measures to reduce maintenance costs and extend the lifespan of PV modules, thereby increasing the overall revenue of the PV power station. Therefore, this study proposes a novel state-evaluation approach based on current-voltage (I-V) curves under partial shading conditions. Based on the size of the pixel values in this curve, the proposed novel state-evaluation was performed using the proportion of the values of the normal and shaded curves under the same environmental conditions. Specifically, the feature variables (open-circuit voltage, short-circuit current) and pixel values within the I-V curve were analyzed and calculated using a sensitivity algorithm and computer vision algorithms (Canny edge-detection algorithm and Green's theorem). Based on the mapping relationship between feature variables and pixels, an improved Gaussian-process regression method with a combined kernel function (dot product and Matérn function) was proposed to predict the pixel values under the corresponding normal curve. This combined kernel function effectively captured causal relationships and handled outliers in the test dataset to improve the robustness of the model. Finally, the ratios of the pixel values were calculated using the predicted pixels within the normal curve and the calculated pixels within the shading curve. Experiments demonstrated that the root mean square error (RMSE), mean absolute error (MAE), R squared (R2) and log likelihood of the improved Gaussian-process regression method reached 0.011, 0.0086, 0.9996, and 24.7096, respectively.

Long-term path planning with optimal deployment of a charging station for monitoring photovoltaic solar farms

Quadrotor technology has become increasingly important in the field of photovoltaic (PV) solar farm monitoring, but short battery life is one of the primary factors limiting its further application. To address above issue, this study proposes a linear temporal logic (LTL)-based path planning algorithm that considers the need for charging together with multiple visits to PV equipments, a single visit to communication equipment, and the avoidance of restricted regions, which enables the long-term monitoring of PV solar farms. Particularly, the location of a charging station can be determined optimally and efficiently by a heuristic algorithm based on the nonlinear programming and branch and bound (NLP-BB) algorithm. The simulation results indicate that the proposed method effectively solves the long-term monitoring path planning problem that is coupled with the optimal deployment of the charging station.

An Early Warning Method for Volleyball Players with Human Bioelectric Energy

Bioelectric energy is a weak electric current generated by the human body itself, and the change in its value can indicate human injury, which can be used as a competitive physical injury judgment information. However, in the past, bioelectric energy measurement was mainly based on the standard voltage of 220 V, and it was not possible to conduct follow-up tests, so the scope of application was limited. Photovoltaic equipment in new energy has the characteristics of being easy to carry and can meet the needs of bioelectric energy testing at the theoretical level. In order to improve the application depth of new energy equipment and improve the safety of volleyball competition. Therefore, from the perspective of new energy, this paper tests the human bioelectric energy, collects current, voltage and power flow signals, combines the different functions to reduce the data dimensionality, calculates the abnormal changes of bioelectricity, and forms an abnormal data set. Finally, according to the injury characteristics of the human body, the volleyball economy is given an early warning. The results show that photovoltaic cells can meet the measurement requirements of human bioenergy, and the detection accuracy rate of joint injury, negative emotion, muscle strain and respiratory system injury reaches 80~83%, the abnormal electrical energy recognition rate reaches 90~95%, and the athlete satisfaction rate reaches 98%, and the wearing time is 1~3 months. Therefore, photovoltaic equipment can meet the identification requirements of human bioelectricity and has practical value in assisting competitive training and injury early warning.

Photovoltaic systems, costs, and electrical and electronic waste in the Legal Amazon: An evaluation of the Luz para Todos Program

Despite its significance in regulating the global climate and maintaining biodiversity, the vastness of the Amazon geography imposes challenges for its residents, including limited access to electricity. The Luz para Todos Program proposes to universalize access to the public electricity service in remote regions of the Legal Amazon with renewable energy sources. From this perspective, the work assessed the ability of sector agents to meet the demand for universalization of the Luz para Todos Program, the economic aspects involved in the universalization process, and the waste management and reverse logistics of these systems. The findings indicate a demand for up to 15 million photovoltaic modules, batteries, and inverters with a maximum cost exceeding BRL 38 billion (USD 7.4 billion). Over 33 years, between 58 and 234 thousand tons of electronic waste would be generated. The program should change the panorama of installed photovoltaic capacity in the northern region of the country, thus placing considerable demand upon the solar industry to supply equipment for the installation of thousands of standalone systems across a vast territory, generating waste in places with inadequate infrastructure to manage them in an environmentally sensitive manner. The diversification of renewable energy sources could alleviate the burden on the service chain, curtail the waste generated by photovoltaic equipment, and stimulate the development of a service chain in regions gaining access to electricity services.

Impact of photovoltaic power generation on poverty alleviation in Jiangsu, China

The photovoltaic poverty alleviation project, part of the “Ten Major Precise Poverty Alleviation Projects” implemented by the Poverty Alleviation Office of the State Council, significantly contributes to eradicating poverty and rural revitalization. A difference-in-differences model was utilized in this study to assess this project's impact on rural households. This analysis used tracking data from households both with photovoltaic equipment installed and without in “S Town,” Jiangsu Province, from 2017 to 2021. The results indicate that photovoltaic installations lead to an increase in per capita disposable income, hence reducing poverty. However, further analysis suggests that better health and work capacity in disadvantaged households correlate with lesser benefits from the photovoltaic project in terms of income. The policy implications of these findings include a necessity for developing innovative models in the photovoltaic sector to enhance conditions for farmers, reducing their dependence on government subsidies.

SIIF: Semantic information interactive fusion network for photovoltaic defect segmentation

Any defects or damage on photovoltaic panels significantly reduce photoelectric conversion efficiency and service life. Therefore, the refined defect segmentation technology is the key to the quality inspection of photovoltaic equipment. However, the existing methods ignore the information interaction between multi-levels, causing semantic features to be easily lost and difficult to fuse. In this paper, we propose a segmentation network based on an interactive fusion of semantic information for solar cell defect identification, which can effectively interact with multi-level rich information and enhance the positioning capabilities of high-level semantics. Specifically, we propose a novel Multi-Level Pyramid Strategy (MLPS) to reduce the information gap between multi-level features. MLPS adopts a pooling distribution mechanism to enable multi-level features to interact effectively in the feature pyramid. In addition, to effectively integrate high-level positioning semantics into the neighbor level, we design a new Neighbor Attention Fusion module (NAF) to propagate high-level semantic information gradually. NAF learns spatial neighbor-level similarity combined with high-level attention to balance location information and contextual features. We perform comprehensive experiments to demonstrate the advancement of our approach. Our method achieves 84.3% MIOU on our solar cell dataset, outperforming existing state-of-the-art (SOTA) methods. To verify the generalization ability of our network, we train and evaluate the PSCDE dataset. The results show that our model achieves an excellent 98.35%MIOU.

THE IMPACT OF CHINA’S “DUAL-CARBON GOAL” ON THE STOCK MARKET: EVIDENCE FROM THE PHOTOVOLTAIC INDUSTRY

After the Chinese government’s announcement to achieve the goal of “carbon peaking and carbon neutrality”, the solar photovoltaic power generation industry in the country has experienced rapid development. This paper examines the impact of China’s “dual-carbon” goal on the country’s photovoltaic industry. Photovoltaic companies listed on the stock market are selected and the stock price data of these selected companies from June 1, 2020 to January 28, 2022 are tested for potential structural changes. The paper adopts the Quandt–Andrews test and the results show that the four selected companies have indeed undergone structural changes within one year after the announcement of the dual-carbon goal, and that the demand for photovoltaic power generation equipment has increased significantly. Finally, some suggestions are put forward for enhancing the photovoltaic industry after the COVID-19 pandemic.

Multi-Source Collaborative Control Technology of Photovoltaic Power Generation Based on Differential Evolution-Gray Wolf Optimization Algorithm

The existing photovoltaic power generation multi-source collaborative control technology cannot improve the control effect of microgrid, resulting in high peak load balancing cost of microgrid. Therefore, the photovoltaic power generation multi-source collaborative control technology based on differential evolution (DE)-gray wolf optimization (GWO) algorithm is adopted. The main components of the microgrid system, such as photovoltaic power generation equipment, hydroelectric power generation equipment, and battery pack, are modeled. With the goal of minimum peak cost and maximum transmission power, collaborative control of water, light and energy storage is carried out. The gray Wolf algorithm is used to solve the multi-objective problem of water and light storage in microgrid, and the gray Wolf algorithm is optimized by DE algorithm. Experiments show that the convergence speed of the optimized DEVOLU-Gray Wolf algorithm is obviously accelerated, and the problem of falling into the local optimal solution is completely avoided. After the application of this method, the photovoltaic, hydraulic, and battery of the microgrid run in order, ensure the smooth operation of the microgrid, and improve the economic benefit of the microgrid.

A comprehensive optimization mathematical model for wind solar energy storage complementary distribution network based on multi-regulatory devices under the background of renewable energy integration

In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues.

Experiment on the Uniformity of Internal Lighting in Zigzag Photovoltaic Greenhouses by Grating Plates

As one of the main projects of facility agriculture promotion, the PV (photovoltaic) greenhouse has the problems of PV power generation competing for light with crop production, strong indoor chiaroscuro, and uneven light distribution. The internal light uniformity is tested by a zigzag greenhouse model to compare the light transmission effects of different light-transmitting materials applied to PV greenhouses. Altogether, 20 line/inch 3 mm and 30 line/inch 3 mm, 40 line/inch 2 mm, 25 line/inch 4 mm grating plates and 2 mm and 3 mm thick ordinary glass were used as light-transmitting components, and the light intensity and light uniformity in the greenhouse were the measurement indicators. The results show that the use of grating plates as covering material can improve the light intensity at the intersection of light and dark, but the overall light transmittance is not as good as glass because it is plastic, which ages easily with low light transmittance. It can also improve the use of land under the shade of PV modules to provide a better growth environment for crops. The test results show that using grating plates can maximize the light intensity of the greenhouse and solve the problem of uneven distribution of light inside the greenhouse caused by obstruction of PV equipment and greenhouse framework. In sunny weather, the light intensity in three rows of the measurement points at the north side in the greenhouse is greater than 20,000 Lx, and the light environment in other areas is between 5000 Lx and 20,000 Lx, which is suitable for planting shade-loving crops.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the carbon peaking and carbon neutrality goals, the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network-transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

Design and Manufacture of Hand Held Photovoltaic Power Generation Equipment Cleaner

Design and Manufacture of Hand Held Photovoltaic Power Generation Equipment Cleaner

Monitoring solar heat intensity of dual axis solar tracker control system: New approach

Control of the amount of solar energy successfully absorbed by photovoltaic system equipment that can follow the movement of sunlight vertically and horizontally at any time is a very urgent need. The method used in this research activity is the utilization of the ESP8266 microcontroller as the primary control of photovoltaic movements that can follow the direction of sunlight based on information from the Light Dependent Resistor (LDR) sensor. The data variables measured and stored include current, voltage, and temperature, which are then processed and transmitted by the ESP 8266 microcontroller to the user through the Internet of Things (IoT) media to the BLINK application. The experimental results show that the proposed system using the ESP8266 microcontroller and LDR sensor increases efficiency, accuracy, speed, and convenience in the monitoring and control system of sunlight intensity by an average of 65%, compared with the manual monitoring system.

A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms

In order to reduce the negative impact of the large-scale grid connection of residential photovoltaic (PV) equipment on the distribution network, it is of great significance to realize the real-time accurate identification of the grid connection state and its switching of residential PV equipment from the distribution network side. This paper introduces a non-intrusive method for identifying residential PV systems using transient features, leveraging the temporal convolutional network (TCN) model with attention mechanisms. Firstly, the discrimination and redundancy of transient features for residential PV devices are measured using a feature selection method based on the semi-Fisher score and maximal information coefficient (MIC). This enables the construction of a subset of identification features that best characterize the PV devices. Subsequently, a sliding window two-sided cumulative sum (CUSUM) event detection algorithm, incorporating a time threshold, is proposed for the real-time capturing of PV state switching and grid connection behavioral events. This algorithm effectively filters out disturbances caused by the on/off cycles of low-power residential devices and captures the transient time windows of PV behaviors accurately. On this basis, a TCN model with attention mechanisms is proposed to match the discerned event features by assigning varying weights to different types of characteristics, thereby facilitating the precise recognition of a PV grid connection and state-switching events. Finally, the proposed method is validated on a custom-designed non-intrusive experimental platform, demonstrating its precision and real-time efficiency in practical applications.

Implementation of String Inverters for 50 MW Large Scale Solar Power Plant in Malaysia

The growth of large-scale solar photovoltaic (LSSPV) power plant site development in Malaysia has increased the demand for the reliability and potentiality of the system in achieving efficient monitoring, operation, and maintenance for the plants. A configuration of string inverters is applied in a 50 MW large-scale solar photovoltaic installation because of its flexibility and scalable technological advancements. This installation demonstrates that the plant can optimise the power output and monitor the performance of the system up to the string level with a cost-efficient implementation. The output from each photovoltaic (PV) string provides a guarantee of better maximum power point tracking (MPPT) capability. Instead of having shorter DC wires compared to the central inverter, this system offers the potentiality of monitoring the solar PV module at a string level, which also affords scalability for future expansion. The tests conducted verify that the string inverter configuration serves as a reliable outcome for grid code test (GCT) requirements. Furthermore, the string inverters provide a potential approach to achieving maximum power generation in different climate conditions, thus reducing the need for manpower in operation and maintenance activities. The string system offers a feasible implementation of effective configurations and decarbonisation for LSSPV in Malaysia without frequent shutdowns of plant generation due to defects or damage to PV equipment and modules.

An energy storage configuration planning strategy considering photovoltaic consumption

The extensive access to new energy resources will influence the grid’s economic operation and reliable power supply. This text considers the planning problem of the power company’s configuration in the energy-storage system. And the planning goal is to maximize the comprehensive benefits of the power company. The comprehensive benefit model of new energy resource costs and related revenue of power companies, as well as the operational characteristics of photovoltaic and energy-storage equipments, is analyzed, and a comprehensive operational strategy for energy storage systems is constructed. Optimizing energy storage configuration plans and operational strategies for power companies can improve the operations’ economic benefits and the utilization level of new energy generation.

Collaborative capacity planning method of wind-photovoltaic-storage equipment in microgrids considering different energy selling incomes

A microgrid is a promising small-scale power generation and distribution system. The selling prices of wind turbine equipment (WT), photovoltaic generation equipment (PV), and battery energy storage equipment (BES) have a significant impact on microgrid profits, which, in turn, affects the planning capacity of renewable energy. However, existing research has not yet conducted in-depth modeling and analysis for different kinds of energy generation electricity prices. This paper proposes an optimal capacity planning method for wind-photovoltaic-storage equipment, considering different energy selling incomes in microgrids. Stochastic characteristics of renewable energy (WT and PV), selling prices of different types of energy, and timing coupling characteristic are considered in the proposed model. In addition, the configuration capacities of WT, PV, and BES are modeled as discrete decision variables, according to the type of specific equipment. The comprehensive life cycle cost (LCC) is considered an objective function. It can be found that the proposed collaborative capacity planning model is a mathematical programming problem with complex nonlinear constraints and integer variables. To solve this problem, a cultural gray wolf optimization algorithm (CGWO) is applied in this paper. The proposed method’s efficiency, convergence, superiority, and effectiveness are verified through a case study. Moreover, the impact of different new energy sales prices on capacity planning results is also revealed in the article.

Technical, Economic and Environmental Feasibility for the Construction of an Interactive Solar Classroom in Lérida, Tolima; Colombia

Currently, the growth in energy demand in all sectors and the excessive use of fossil fuels has generated negative environmental impacts on the environment. Countries in Latin America are committed to increasing their school coverage, with projects that use renewable energy. In the municipality of Lerida, Tolima, Colombia; there is a need to expand school infrastructure in order to provide access to technology and reduce school dropout with the construction of solar classrooms.
 Aims: This research aims to evaluate the technical, economic and environmental feasibility for the construction of a classroom with photovoltaic energy system and technology equipment in the municipality of Lérida, Tolima, Colombia.
 Study Design: Case study, with quantitative, descriptive and prospective approach.
 Place and Duration of the Study: The case study was conducted in the municipality of Lérida, Department of Tolima, 73 kilometers from Ibagué in Colombia; between 2022 and 2023.
 Methodology: The methodology consisted in determining the size of a photovoltaic system based on the consumption of the selected equipment, the design and construction of the solar classroom with recyclable materials. Subsequently, the project was financially evaluated based on the social benefits that the project would provide. Finally, the environmental impact was evaluated to determine if the construction of the classroom is technically, financially and environmentally viable.
 Results: First, the costs and material for the civil works of the classroom were evaluated and it was determined that it was built with Lego-type plastic wood. Subsequently, the energy demand was determined with the technological equipment, identifying a demand of 2587 kW/hour, at 110V and with 8 hours of daily operation. With these data, the design of the photovoltaic system was carried out to cover the estimated demand, adjusted to the theoretical consumption of the equipment to be installed. With the energy demand, the selection of the number of panels was made, which resulted in 10 panels of 330 W with 5 effective hours of sun, a 3000 W inverter, a 4000 W regulator and 3 batteries of 100 Ah to maintain the energy autonomy of the classroom for 1 day. The photovoltaic system is projected with a useful life of 20 years, with the exception of the batteries, which is only 7 years. The next step was to carry out the cost-benefit analysis that determined the economic viability of the project, with a rate of return on investment of 3.6 years. Likewise, the benefits that the classroom would bring to the community and that are related to the objectives of developing quality education, with affordable energy and modern infrastructure were determined. The economic and social impacts were analyzed, which were positively high by making available technology available to the young people of Lérida that allows reducing the technological gap that generates inequalities and finally the environmental impact was evaluated, which turned out to be of low impact.
 Conclusion: Based on the results, it is concluded that the construction of a solar classroom in Lérida, Tolima in Colombia is technically, economically and environmentally feasible.