CN-122018502-A - Mountain region photovoltaic power plant self-cleaning system based on intelligent regulation and control

Abstract

The invention belongs to the technical field of cleaning of photovoltaic power stations, and particularly relates to a mountain photovoltaic power station self-cleaning system based on intelligent regulation. The system comprises a sensor module, an intelligent cleaning strategy regulation and control module, an intelligent decision module and a cleaning equipment control module, wherein the sensor module is used for monitoring weather conditions and pollution conditions in real time and collecting relevant data, the intelligent cleaning strategy regulation and control module comprises an intelligent cleaning strategy regulation and control algorithm and dynamically adjusts the cleaning strategy according to the real-time weather conditions and the pollution conditions, the intelligent decision module predicts future cleaning requirements and automatically adjusts the cleaning strategy according to historical data and current environmental conditions through deep learning and data analysis, and the cleaning equipment control module regulates and controls the operation of the cleaning equipment according to the cleaning strategy. The system can reduce manual intervention, reduce operation cost, improve cleaning efficiency, and avoid the problems of excessive cleaning or insufficient cleaning, thereby improving the power generation efficiency of the photovoltaic panel.

Inventors

• SUN BOJIE
• Jia Jingdou
• PEI YANHUI
• ZHAO JINGYA
• Zhao Zhelun
• TAN HAIHONG
• SHI XIAOBO
• MA JIANGPING
• Bu Weicheng
• SU WEI
• WANG LINA
• LI XUDONG
• LI XIAOYUN
• WANG JIANQIAO

Assignees

• 复旦大学

Dates

Publication Date

20260512

Application Date

20260130

Claims (2)

1. 1. The mountain region photovoltaic power station self-cleaning system based on intelligent regulation and control is characterized by comprising a sensor module, an intelligent cleaning strategy regulation and control module, an intelligent decision module and a cleaning equipment control module, wherein: The sensor module is used for monitoring weather conditions and pollution conditions in real time and collecting related data; The sensor module comprises a plurality of sensors, namely a meteorological sensor, a pollution sensor and a photovoltaic panel pollution monitoring sensor, wherein the weather conditions comprise temperature, humidity, wind speed, precipitation and illumination intensity, and the pollution conditions comprise dust, sand, dirt and snow; The intelligent cleaning strategy regulation and control module designs an intelligent cleaning strategy regulation and control algorithm according to the data collected by the sensor module, namely dynamically regulating the cleaning strategy according to the real-time weather and pollution conditions, and the specific process is as follows: (1) The weather data analysis comprises real-time analysis of weather data and prediction of future weather changes, wherein the weather data analysis comprises the steps of increasing cleaning frequency under high-temperature arid weather, avoiding dust and sand accumulation, automatically delaying cleaning under rainy weather, and naturally cleaning a panel by utilizing rainfall; (2) The pollutant analysis comprises monitoring the pollution condition of the photovoltaic panel through a pollution sensor, automatically starting a cleaning program when the accumulation of pollutants including dust, sand, sewage and snow reaches a certain degree, and selecting the most suitable cleaning mode for cleaning; (3) The method comprises the steps of dynamically adjusting the cleaning frequency, namely dynamically adjusting the cleaning frequency and mode according to weather forecast and pollution monitoring data, wherein the method comprises the steps of increasing the cleaning frequency in an environment without precipitation for a long time, delaying cleaning in high humidity or precipitation weather, and reducing the energy consumption of cleaning; (4) The cleaning priority is established according to the pollution degree and weather change, and comprises the steps of preferentially cleaning a panel in the sand storm weather to avoid the influence of dust on the panel; The intelligent decision module predicts future cleaning demands according to historical data and current environmental conditions through deep learning and data analysis and automatically adjusts a cleaning strategy, and comprises the following steps of automatically increasing cleaning frequency if the power generation efficiency of a power station is low and weather is dry and rainless within a certain period of time so as to ensure that the efficiency of a panel is not reduced due to excessive accumulation of pollutants; the cleaning equipment control module regulates and controls the operation of the cleaning equipment according to a cleaning strategy, and comprises: (1) A spraying system for removing surface dirt by a spraying cleaning device in dry and high pollution weather; (2) The ultrasonic cleaning device activates ultrasonic cleaning equipment in sand and dust weather and removes sand and dust by blowing by wind power; (3) An automatic scraper system starts scraper equipment to clean under the condition of snow accumulation or harder dirt.
2. 2. The mountain photovoltaic power station self-cleaning system based on intelligent regulation and control according to claim 1 is characterized in that the mountain photovoltaic power station self-cleaning system based on intelligent regulation and control is specifically realized by adopting various models and algorithm combinations, including a decision tree model, a support vector machine model and a neural network model, wherein the models are combined with environmental data, photovoltaic panel state and cleaning equipment control information to realize intelligent regulation and control on a cleaning strategy, and the mountain photovoltaic power station self-cleaning system is specifically realized by using the following programming languages and technical stacks: (1) Machine learning framework: Scikit-learn, which is used for constructing and training the traditional machine learning model, including a support vector machine, a decision tree and a random forest model; TensorFlow/Keras a neural network model for the development of deep learning models, in particular for application in intelligent decision engines; XGBoost a decision process for implementing an efficient Gradient Boost Decision Tree (GBDT), optimizing cleaning strategies and dynamic adjustments; (2) Data processing and analysis: pandas for data processing, supporting cleaning, merging and analysis of raw data collected from the sensor modules; NumPy the steps of numerical calculation, optimized data processing and characteristic engineering; (3) Real-time data communication and control: The MQTT protocol is used for transmitting real-time data of a cleaning system in the photovoltaic power station and issuing control instructions to ensure that the system responds to environmental changes in real time; ROS: robotic means for real-time control of cleaning devices, in particular for performing cleaning tasks; (4) Front end and visualization platform: the JavaScript/HTML/CSS is used for developing a visual interface, realizing a remote monitoring platform, and checking a cleaning state and adjusting a strategy by a power supply station manager; And D3.Js, which is used for data visualization and realizes the graphic display of real-time monitoring data.

Description

Mountain region photovoltaic power plant self-cleaning system based on intelligent regulation and control Technical Field The invention belongs to the technical field of cleaning of photovoltaic power stations, and particularly relates to a self-cleaning system of a mountain photovoltaic power station. Background In the prior art, cleaning of photovoltaic power plants typically relies on manual or simple mechanical cleaning methods. The manual cleaning has the advantages of high labor intensity, low efficiency, high loss of the panel and increased cleaning cost and labor cost. Meanwhile, the traditional mechanical cleaning device often lacks intelligent regulation and control, can not timely adjust the cleaning strategy according to weather changes, pollution degrees and panel conditions, and easily causes too high or too low cleaning frequency to influence the long-term operation effect and energy conservation of the photovoltaic power station. In addition, with the increase of climate change and extreme weather events, the existing cleaning technology cannot flexibly cope with the cleaning requirements in special weather such as sand storm, snow cover and the like. Therefore, a more intelligent and adaptive cleaning solution is needed to improve self-cleaning efficiency of photovoltaic power stations, reduce energy waste and reduce operating costs. Disclosure of Invention The invention aims to provide an intelligent regulation-based mountain photovoltaic power station self-cleaning system so as to reduce manual intervention and improve the overall operation efficiency of a photovoltaic power station. The invention provides an intelligent regulation-based mountain photovoltaic power station self-cleaning system, which comprises a sensor module, an intelligent cleaning strategy regulation module, an intelligent decision module and a cleaning equipment control module, wherein: The sensor module is used for monitoring weather conditions and pollution conditions in real time and collecting related data; the sensor module comprises a plurality of sensors, such as a meteorological sensor, a pollution sensor, a photovoltaic panel pollution monitoring sensor and the like, wherein weather conditions comprise temperature, humidity, wind speed, precipitation, illumination intensity and the like, and pollution conditions comprise dust, sand dust, dirt, snow and the like; The intelligent cleaning strategy regulation and control module designs an intelligent cleaning strategy regulation and control algorithm according to the data collected by the sensor module, namely dynamically regulating the cleaning strategy according to the real-time weather and pollution conditions, and the specific process is as follows: (1) Weather data analysis the system analyzes weather data (wind speed, temperature, humidity, precipitation, etc.) in real time and predicts future weather changes. For example, in high temperature drought weather, the system can increase the cleaning frequency to avoid dust and sand accumulation, and in rainy weather, the system can automatically delay the cleaning to clean the panel naturally by rainfall. (2) Contaminant analysis the system monitors the contamination of the photovoltaic panel by means of a contamination sensor. When contaminants (e.g., dust, sand, sewage, snow, etc.) accumulate to some extent, the system automatically initiates the cleaning procedure and selects the most appropriate cleaning mode, such as spray cleaning or wind cleaning. (3) Dynamically adjusting the cleaning frequency, namely dynamically adjusting the cleaning frequency and mode of the system according to weather forecast and pollution monitoring data. In long-term, precipitation-free environments, the system may increase the frequency of cleaning, while in high humidity or precipitation weather, the system may delay cleaning, reducing clean energy consumption. (4) Cleaning priority, namely, an algorithm formulates the cleaning priority according to pollution degree and weather change. For example, in a sand storm weather, the system may prefer to clean the panel to avoid the effect of dust on the panel, and in a snow weather, the system may determine the cleaning operation based on the degree of snow accumulation. The intelligent decision module predicts future cleaning demands according to historical data and current environmental conditions through deep learning and data analysis, and automatically adjusts cleaning strategies. For example, if the power generation efficiency of the power station is low for a certain period of time and the weather is dry and rainless, the system automatically increases the cleaning frequency to ensure that the efficiency of the panel is not reduced due to excessive accumulation of pollutants. The cleaning equipment control module regulates and controls the operation of the cleaning equipment according to a cleaning strategy, and comprises: (1) Spray system in dry, highly contaminated weather