Asset Condition and Operations Efficiency

The identification and prediction of potential failures can be improved using advanced analytics to search proactively and reduce risk to improve efficiency in energy generation. Algorithms, such as machine learning, are now quite extended in renewable energy control systems. These kinds of facilities are characterized by the presence of a great number of sensors feeding the SCADA systems (supervisory control and data acquisition systems), usually very sophisticated systems including a control interface and a client interface (the plant’s owner, distribution electric network administrator, etc.). Power and energy production measures are two of the most important variables managed by the SCADA. As principal system performance outputs, they can be exploited through data mining techniques to control system failures, since most of the systems failures directly affect the output power and the energy production efficiency. To that end, a methodology to introduce the use of different techniques for energy forecasting and to support condition-based maintenance is used in this paper. These techniques compete for the best possible replica of the production behavior patterns of a renewable energy (PV installation). In another Section a set of data mining (DM) techniques is used together with ANN algorithms, to solve the problem of identifying when asset behavior abnormalities appear (ANN), for which operating conditions are appreciated, and to what extent is energy efficiency impacted. Also, a proposed process is presented to deploy the implementation of these techniques.