Improved Recursive Electromechanical Oscillations Monitoring Scheme: A Novel Distributed Approach


This paper improves the existing Kalman-primarily based technique for detecting electromechanical oscillations using Synchrophasor measurements. The novelty is the utilization of a distributed architecture to extract maximum a-posteriori (MAP) estimations of oscillatory parameters. This was achieved by an expectation maximization (EM) algorithm. To improve initial condition estimation, initial correlation information through a forward backward (FB) Kalman-like particle filter (KLPF) was integrated into the proposed scheme. Performance evaluation was conducted using IEEE New England thirty-nine-Bus system and Synchrophasor measurements collected from New Zealand Grid. The proposed method accurately extracted oscillatory parameters when the measurements were contaminated by continuous random tiny load fluctuations. The method also improved the potential of detecting multiple oscillations with similar frequencies.

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