Performance assessment of photovoltaic modules based on daily energy generation estimation
Abstract Performance assessment can improve photovoltaic (PV) plant economics by identifying the need for timely corrective actions. Performance assessment of PV plants is usually based on the comparison between measured and modeled outputs of PV plants, i.e. an alarm occurs for abnormal operation when a significant difference is detected. For such methods, it is critical to estimate the potential electricity generation of PV plants in normal operation. However, unpredictable conditions affecting solar modules pose challenges to develop reliable PV models. In this paper, a practical approach to improve estimating daily energy generation of PV plants for performance assessment is proposed, which includes two main components: (i) a data preprocessing method; (ii) sub models in different weather conditions. The proposed data preprocessing method detects outliers by comparing normalized outputs of adjacent inverters instantaneously. It is robust against erroneous measurements in normal operation. Sub models in different weather conditions are developed using a Principal Component Analysis and Support Vector Machine method for better representation of PV plant outputs. Results show that the proposed method can detect deviations between the estimated and the measured daily energy generation of 10% Moreover, false alarms, i.e. an abnormal point identified while the system is operating normally, are significantly reduced.