Low Frequency Oscillation Detection in Smart Power System using Refined Prony Analysis for Optimal Allocation of Supplementary Modulation Controller

Shetty, Dinesh; Prabhu, Nagesh

doi:10.1109/icoei.2019.8862704

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…It can be clearly seen that the identification results of each generator have corresponding modes in the PSASP small disturbance stability analysis results. Most of the generators FIGURE 9 The active power curves of the ten generators have participated in the eighth type of interval oscillation mode, with the corresponding frequency range from 0.5 to 0.7 Hz. The generator G1 that belongs to this oscillation mode has the largest amplitude among all generators, so it can be recognized as the dominant oscillation frequency feature.…”

Section: Lfo Signals In 10-machine 39-bus New England Power Systemmentioning

confidence: 99%

“…However, FFT-based methods treat the sampled data as stationary ones, and selection of sampling window obviously affects accuracy of FFT recognition to a large extent [7], therefore, this approach is not fit for modern power systems. The Prony analysis [8] is a widely used method that employs a linear combination of a set of exponential terms to fit the equally-spaced sampling data, from which the signal amplitude, initial phase, damping factor, oscillation frequency and other information can be obtained through analysis [9,10]. This method, however, is not suitable for noise environment and also fails to deal with the non-stationary signal.…”

Section: Introductionmentioning

confidence: 99%

“…y h [2] t + b y (9) where h [1] t denotes the output of forward layer, h [2] t denotes the output of backward layer, and f is generally a sigmoid function. Since BiLSTM is more suitable for situations where the learning goal is related to the complete input sequence, it is finally applied to the LFO modal feature recognition in this paper.…”

Section: Lstm Introductionmentioning

confidence: 99%

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Long short‐term memory‐based robust and qualitative modal feature identification of non‐stationary low‐frequency oscillation signals in power systems

Zhang

Zhang³

et al. 2021

IET Renewable Power Gen

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Low-frequency oscillation (LFO) analysis has become increasingly important in large scale power systems. The current LFO analysis methods regard the measured signal as stationary signal. Some methods, such as Prony and HHT, are too slow for online application. In order to solve these problems, based on the long short-term memory neural network (LSTM), this paper proposes a method to identify LFO modal features rapidly. It is the first time in this field to formulate LFO modal feature classification problem rather than LFO modal identification problem. In order to realize it, first, LFO's frequency and attenuation factor are artificially divided into 12 and 4 segments, respectively. For each segment, more than 20,000 data are generated and tagged as training set and test set. Then two bi-directional LSTM networks are constructed separately, and each network is trained alone with the data of the training set. Finally, the proposed method is tested and verified with the data from the test set, power systems simulation, and phasor measurement unit (PMU) of real power systems. The test results prove effectiveness of the proposed method and its advantage is demonstrated through comparison with the Prony, FFT and EDSNN methods.

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Section: Lfo Signals In 10-machine 39-bus New England Power Systemmentioning

confidence: 99%