Abstract:

: The unprecedented transformation of contemporary power systems, mainly evidenced by the high penetration of renewable energy generation and the shift from passive to active, bi-directional smart grids, has put an extraordinary burden on power system operation and control. The uncertainties created by the two aforementioned factors greatly propel the necessity of more accurate and robust system monitoring. Concurrently, an ever-increasing amount of electronics-based power generation makes frequency stability a major challenge to power system operation and control due to the vastly diminishing system inertia. As a crucial part of system control, frequency monitoring is utilized to identify potential stability issues and eventually prevent cascading power outages and blackouts. This paper proposes a dynamic state estimation (DSE) methodology that employs deep learning (DL) techniques based on feed-forward artificial neural networks (ANN) to accurately predict power system disturbances in terms of frequency disturbance events (FDE) caused by line outages, load/generation tripping events, and various types of faults. The proposed methodology was tested on a test grid model with high penetration of renewable generation, using an open-source data generator. It was concluded that DL-based DSE can be successfully utilized in the FDE detection process to improve frequency monitoring and control, and to maintain optimal performance, stability, and security of power systems.

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