Adaptive middleware for real-time prescriptive analytics in large scale power systems

The increased digitalization of power systems poses both opportunities and challenges for system operators. GPS time-synchronized high-resolution data streams emanating from measurement devices distributed over a wide area enable the detection of disturbances and the real-time monitoring of consequences as they are evolving, such as undamped oscillations. Processing these data streams is not possible with state-of-the-art SCADA systems that poll data asynchronously at much lower time intervals. Moreover, real-time analysis on fresh streaming data at the enterprise level is an unresolved challenge. In this paper we propose an adaptive middleware concept that can make better use of available data processing resources by enabling distributed computation both on the enterprise and on the field level. We apply the concept of linked data to provide a map for moving the computation to the data it requires for analysis. If based on the IEC 61850 standard semantic data model, the linked data concept additionally yields location and domain awareness that can be leveraged for real-time prescriptive analytics in the field. Another advantage of the proposed adaptive middleware is the abstraction of computational resources: Analytical programs can be written once and then be used to process historical data residing on servers on the enterprise level as well on the distributed devices that originated the data to enable fast analysis of events as they are unfolding.