Abstract:

Aiming at the problem of decreased photovoltaic power generation efficiency caused by inaccurate start-up timing determination of photovoltaic cleaning robots， and based on a comprehensive consideration of power generation fluctuations induced by complex meteorological conditions and solar panel aging， an intelligent decision-making method for solar panel cleaning timing based on Multi-Sensor Fusion （MSF） was proposed to enhance the cleaning efficiency and the photovoltaic power generation efficiency of solar panels. Firstly， multi-source sensor data were collected in real-time by a microcontroller， such as solar panel output power and ambient temperature. Then， a predicted photovoltaic power generation value under the influence of multiple factors was calculated after the optimization of a Proportion Integration Differentiation （PID） algorithm. Finally， by comparing the predicted photovoltaic power generation value with the real-time power generation capacity of the solar panels， intelligent determination of the cleaning timing and automatic start-up control of the photovoltaic cleaning robot were implemented through the employment of data-level and decision-level fusion techniques. Experimental results indicate that the MSF-based method performs excellently across different test scenarios. The scenario of sunny weather with soiled solar panels， which best reflects the value of cleaning is taken as an example and a determination accuracy of 96% is achieved by the method. Compared with the decision-making method using the ResNet50-CA model， the MSF-based method achieves a relative accuracy improvement of 4.35% in the scenario of sunny weather with soiled panels. Furthermore， in the same scenario， compared to the decision-making methods based on the K-Nearest Neighbors （KNN） algorithm， Random Forest （RF） model， and Kalman Filter （KF）， the MSF-based method has the advantages more significant， with accuracy improvements of 35.21%， 45.45%， and 10.34%， respectively. It can be seen that the proposed method can enhance the timeliness and accuracy of cleaning operations effectively， providing a reliable technical solution for maintaining high efficiency of photovoltaic power generation systems under complex meteorological conditions and equipment aging states.

Key words: solar panel, power generation efficiency, Multi-Sensor Fusion (MSF), timing for cleaning, microcontroller

摘要：

针对光伏清洁机器人启动时机判定不准确而导致的光伏发电效率下降的问题，在综合考虑复杂气象条件及太阳能板老化引起的发电功率波动的基础上，提出一种基于多传感器融合（MSF）的太阳能板清洁时机智能决策方法，以提升太阳能板的清洁效率和光伏发电效率。首先，依托单片机实时采集太阳能板输出功率和环境温度等多源传感器数据；其次，计算经比例-积分-微分（PID）算法优化后的多因素作用下的光伏发电功率预测值；最后，通过对比光伏发电功率预测值与太阳能板实时发电功率，采用数据级与决策级融合技术实现对清洁时机的智能判定并自动控制光伏清洁机器人的启动。实验结果表明：基于MSF的方法在不同测试场景下均表现出色；以晴天但太阳能板受污染这一最能体现清洁价值的场景为例，该方法的判定准确率达到96%。与采用ResNet50-CA模型的决策方法相比，基于MSF的方法在晴天但太阳能板受污染的场景下实现了4.35%的相对准确率提升；此外，在相同的场景中，相较于以K近邻（KNN）算法、随机森林（RF）模型及卡尔曼滤波法（KF）为核心的决策方法，基于MSF的方法优势更显著，分别实现了35.21%、45.45%和10.34%的准确率提升。可见，所提方法能够有效提升清洁操作的时效性与精准度，为在复杂气象条件与设备老化状态下维持光伏系统的高效发电提供可靠的技术解决方案。

关键词: 太阳能板, 发电效率, 多传感器融合, 清洁时机, 单片机