Centralized protection and control system (CPC) is a new concept which is considered to be the path forward for addressing the foregoing challenges at substation levels. A CPC system is comprised of a high-performance computing platform within a substation capable of providing secure protection, control, monitoring, communication, and asset management functions by collecting the data those functions require using high-speed, time-synchronized measurements. Existing technologies have gained enough maturity to support the deployment of CPC, as demonstrated by some pilot projects. These novel technologies can significantly improve substation control/protection functions (and consequently that of the power grid) at an affordable cost with enhanced capability and maintainability. This project is aimed at introducing a methodology for CPC by, first, scoping the extent, quality and volume of information needed from IEDs to be shared with a central computing unit. A framework will then be defined for CPC as to how to process the information gathered to extract the substation topology and perform dynamic state estimation (DSE) at the substation level (which is a new perspective). Substation-oriented DSE is employed to minimize the impact of measurement errors by leveraging the redundant information collected from the corresponding protection/control zones. This helps distinguish erroneous measurements from anomalies that must be instantaneously dealt with by proper remedial actions. The project progresses the substation automation field in terms of (a) Speed (operating on sample values) and detecting abnormalities within a few samples, (b) Detection accuracy, and (c) Removing the need for coordination between disjointed protection/control functions.
This PhD project will realise CPC based upon substation-oriented DSE, which has great potential to transform how we operate substations. This will be achieved by defining focus areas to integrate the protection/control of disjointed zones and apparatus into a centralized unit. The developed framework ensures the capability of self-diagnostics, detection of hidden failures, and sufficient resilience against high-impact events. Hypothesis testing will be based on residual-based indices developed in and aimed at formulating the allowable communication latencies and operation boundaries that minimize the risk of system collapse.
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