Abstract
Visual real-time monitoring is the premise of low frequency oscillation control in power grids. This paper showed a visual method for the control center of power grids to monitor low frequency oscillation. It processed the PMU real-time data with incomplete S-transform, and converted the waveforms to two-dimensional time-frequency figures which showed the initial time, frequency and amplitude of each low frequency oscillation mode directly. GPU was used to show figures and calculate FFT with the purpose of improving calculation efficiency. The results of practical cases show that the real-time characters of low frequency oscillation can be identified availably by this visualization real-time monitoring method which is helpful and suitable for practical application.
Highlights
With the expanding of large-scale interconnected power systems, structure and characteristics of the power system are more and more complex
Real-time monitoring is the basis of arranging operation modes legitimately, which can damp low frequency oscillation and improve the stability of power system [2]
Real-Time Monitoring of Low Frequency Oscillation Based on Incomplete S-Transform
Summary
With the expanding of large-scale interconnected power systems, structure and characteristics of the power system are more and more complex. Real-time monitoring is the basis of arranging operation modes legitimately, which can damp low frequency oscillation and improve the stability of power system [2]. For monitoring low frequency oscillation on-line, the WAMS uses PMU to collect operating data in different locations of power grids and sends them to the dispatch center. The real-time monitoring is implemented by analyzing the PMU real-time data artificially and directly. This method has two kinds of disadvan-. It can be applied to extract specific signal components of non-stationary signal [13] Results of those researches show that S-transform possesses good time-frequency characteristics and can separate frequency components. S-transform was applied to monitor low frequency oscillation on-line in this paper. This paper adopted parallel optimization algorithm on GPU [14] for improving the efficiency
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