光伏电站及其逆变器异常降额检测数据

逆变器的常见问题就是异常降额，而引起逆变器降额的原因又比较复杂，除了设备报出的降额原因外，其余原因一般不会告警，隐藏性较强，这增大了运维工作的难度和巨大的电量损失，有效的检测逆变器的异常降额产生的原因和电量损失，以便运维人员快速维护、及时处理、有效降低发电量损失。通过收集逆变器的发电数据和温度数据，通过阈值判断的方法准确识别出存在异常降额的低效逆变器，并给出逆变器异常降额产生的原因。具体步骤：1、数据来源：企业自身的生成数据，包括：逆变器的机内温度、散热器温度、有功功率、组串电压等；2、数据清洗，对于异常的生产数据，采用一些统计方法进行甄别和修复；3、数据应用，通过设备机内温度与有功功率之间的关联性进行联合处理；4、数据成果，根据算法规则识别出逆变器异常降额运行状态并计算损失电量，供业务参考。

A common issue with inverters is abnormal derating. The causes of inverter derating are complex, and most of them will not trigger alarms except those explicitly reported by the device itself, leading to strong concealment. This increases the difficulty of operation and maintenance (O&M) work and causes significant power generation losses. Therefore, effectively detecting the causes of abnormal derating in inverters and calculating the corresponding power losses can help O&M personnel conduct rapid maintenance and timely troubleshooting, thereby effectively reducing power generation losses. This dataset collects power generation data and temperature data of inverters, uses a threshold-based judgment method to accurately identify inefficient inverters with abnormal derating, and determines the causes of such derating. The specific workflow is as follows: 1. Data Source: Self-owned production data of the enterprise, including internal temperature of the inverter, radiator temperature, active power, string voltage, and other related parameters. 2. Data Cleaning: For abnormal production data, statistical methods are adopted to screen out erroneous records and perform repair work. 3. Data Application: Joint processing is conducted based on the correlation between the internal temperature of the inverter and its active power. 4. Data Outcomes: The abnormal derating operating state of inverters is identified according to algorithmic rules, and the lost power generation is calculated for business reference.

搜集汇总

数据集介绍

特点

该数据集包含光伏电站逆变器的运行数据，用于检测异常降额及其原因，每日更新，共888条记录。