Search

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.

Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincaré map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincaré map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process.