Abstract: Employing artificial intelligence to detect anomalies associated with plane of array irradiance (POAI) sensors at photovoltaic (PV) power systems is discussed. One example is a method that includes determining, via analyzing plane of array irradiance (POAI) sensor data from a POAI sensor of a PV system with a trained classifier, that the POAI sensor data corresponds to a scenario of a set of scenarios. The set of scenarios comprises a sensor anomaly scenario and one or more of a clear sky scenario, an overcast scenario, or a mix of sun and cloud scenario, and the sensor anomaly scenario characterizes a decreased effectiveness of the POAI sensor. The method also includes, in response to determining that the POAI sensor data corresponds to the sensor anomaly scenario: generating an alert that indicates a sensor anomaly at the POAI sensor; and replacing the POAI sensor data with substitute data.

Abstract: Systems and methods for monitoring an operational system. A data set with output power values and associated environmental data values for an electrical generation system are accumulated. Statistical relationships are determined for output power values and environmental data values. Outlying data is determined based on the statistical relationships and are removed from the data set to create selected data. A regression model is developed from the selected data to map predicted output power values to values of environmental data. Data with present output power values and present associated environmental data for the electrical generation system are later received. Predicted output power values are predicted by the regression model for the present associated environmental data. An output power discrepancy is identified by comparing the predicted output power to the present output power. A notification of an anomaly is provided based on identification of the output power discrepancy.

Abstract: Systems and methods for monitoring an operational system. A data set with output power values and associated environmental data values for an electrical generation system are accumulated. Statistical relationships are determined for output power values and environmental data values. Outlying data is determined based on the statistical relationships and are removed from the data set to create selected data. A regression model is developed from the selected data to map predicted output power values to values of environmental data. Data with present output power values and present associated environmental data for the electrical generation system are later received. Predicted output power values are predicted by the regression model for the present associated environmental data. An output power discrepancy is identified by comparing the predicted output power to the present output power. A notification of an anomaly is provided based on identification of the output power discrepancy.