Abstract
In this study, a new algorithm dealing with time-varying modes for determining and tracing multiple frequency oscillations in power systems is proposed. Multiple modes or resonance of forced and natural oscillations can have severe effects on a power system. Therefore, it is crucial to recognize the dominant natural and forced oscillations because the oscillation mode possesses time-varying features that depend on the system operating conditions or changes in the parameters. The salient features of the proposed algorithm include the use of a time-series-based approach to recognize undesired modes (including multiple oscillations over a wide frequency range), tracing time-varying modes as the power system operating condition changes, and effectively determining and applying the oscillation features before implementing the corresponding control measures in the power system. In this study, multiple frequency oscillation scenarios for the test system and practical measurement data for a recent incident that occurred in Korea Electric Power Corporation (KEPCO) system are discussed. Therefore, the proposed algorithm can be practically applied in wide-area monitoring systems, not only for a single forced oscillation or local mode detection but also for system-wide inter-area mode recognition.
Highlights
P OWER system oscillations are mainly produced by weak damping or periodic external input
Resonances between forced and natural oscillations can result in undesired instability or operational problems [1], [2]; they can have an adverse effect on the generators owing to the resonance between the forced oscillations and local modes, natural and forced responses can be distinguished from each other [3], [4]
The empirical technique of mode decomposition is widely studied for oscillation analysis associated with Hilbert-Huang Transformation (HHT) [9], [10]
Summary
P OWER system oscillations are mainly produced by weak damping or periodic external input. The proposed expanded GFE (EGFE) process is motivated by analytical methods for the nonlinear dynamics of distinct phenomena, such as oscillations [35], [36] These approaches are currently being revisited, considering the fact that certain phenomena can affect a weak grid or poorly damped system owing to inverter-based resources or cables [37]. The major advantages of the time-series-based algorithms for oscillation detection, such as the GFE process [26], are as follows: 1) Extraction of undesired multiple-mode frequencies and damping is possible using a time-series-based approach. This applies to post-mortem analysis and to the online PMU application. This study uses a positive decay rate (negative damping) to indicate an unstable response, and vice versa
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