光伏发电效率测试数据

采集与发电效率相关的多维度数据，并进行严格的关联与清洗：数据采集维度：
系统属性数据：组件型号、逆变器型号、安装倾角、方位角、电站容量。实时环境数据：太阳辐照度、环境温度、组件背板温度、风速、湿度。运行性能数据：直流侧电压/电流、交流侧功率输出、逆变器转换效率、设备运行状态。时间标识数据：精确到分钟级的时间戳，用于分析日、月、年及不同季节的性能变化。核心处理规则：效率计算：计算关键性能指标，如系统PR（性能比）、逆变器转换效率、单位容量发电量等，这是衡量发电效率的核心。数据关联与清洗：将环境数据与发电性能数据在时间点上精确对齐，并剔除因阴影遮挡、积雪、通信中断等导致的无效或异常数据。损失分解分析：将总效率损失分解到具体环节，如串联失配损失、逆变器clipping 损失、温升损失、遮挡损失、清洗损失等。

Multi-dimensional data related to power generation efficiency is collected, with strict association and cleaning conducted subsequently. The data collection dimensions are as follows:
1. System attribute data: component model, inverter model, installation tilt angle, azimuth angle, and power station capacity.
2. Real-time environmental data: solar irradiance, ambient temperature, component backsheet temperature, wind speed, and relative humidity.
3. Operational performance data: DC side voltage and current, AC side power output, inverter conversion efficiency, and equipment operating status.
4. Time stamp data: minute-level precise timestamps, which are used to analyze performance variations across days, months, years, and different seasons.

Core processing rules are specified as follows:
1. Efficiency calculation: Calculate key performance indicators including system PR (Performance Ratio), inverter conversion efficiency, and power generation per unit capacity, which serve as the core metrics for evaluating power generation efficiency.
2. Data association and cleaning: Precisely align environmental data and power generation performance data at the same time points, and remove invalid or abnormal data caused by shading, snow cover, communication interruption, and other abnormal conditions.
3. Loss decomposition analysis: Decompose the total efficiency loss into specific links, such as series mismatch loss, inverter clipping loss, temperature rise loss, shading loss, and cleaning loss.