Abstract: A method is designed and implemented to identify and quantify the different losses that are possible in a solar power plant. Data is acquired through RETINA's remote nodes from the SCADA systems which are connected to the electrical meters and sensors attached to inverters and combiner boxes in a solar power plant. The resulting data is cleansed, filtered, and archived into a data-warehouse to estimate the solar losses by devising and estimating against an “ideal” combiner box current trend. The quantified losses further form the input to a system which identifies an optimal cleaning schedule of the power plant with specifications about the labor and resources that are used.

Abstract: A method for predicting component failure in a drive train assembly of a wind turbine comprises acquiring data from a plurality of wind turbine sensors pertaining to one or more components of the drive train assembly. The data is fed into one or more RETINA remote nodes and is filtering and aggregating into time intervals. The data is archived in a centralized data-warehouse and is used to build a machine learning model configured to determine ideal temperatures of components in the drive train assembly. The ideal temperatures are compared to actual measured temperatures in order to determine one or more temperature deviations. The one or more temperature deviations are used to determine a severity index score. An alert is generated corresponding to a high severity index score, wherein the alert informs of a likely imminent component failure.