Method for quickly identifying low-frequency oscillation modal characteristics of power system based on LSTM
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A low-frequency oscillation and power system technology, applied in character and pattern recognition, pattern recognition in signals, biological neural network models, etc., can solve problems such as noise-sensitive false modes, difficult to guarantee identification results, and insufficient identification accuracy
Pending Publication Date: 2021-01-05
STATE GRID SICHUAN ECONOMIC RES INST
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For example, the classic Prony algorithm uses a linear prediction model to identify parameters, which is sensitive to noise and is prone to false modes, making it difficult to obtain accurate identification results; HHT can realize adaptive decomposition of signals, and is also effective for complex oscillating signals, but the endpoint effect, Insufficient identification accuracy caused by mode mixing and spurious modes
Although many improvements have been made to address the deficiencies of these classical methods, it is still difficult to fundamentally solve them.
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Embodiment 1
[0132] In order to verify whether the algorithm can identify new oscillation modes superimposed in the oscillation process of the system, the ideal LFO test signal is constructed as follows:
[0133]
[0134] In formula (17), ε(t) represents a step function, and η(t) represents a noise signal.
[0135] Such as Figure 4As shown, the length of the LFO signal is 12s, and the signal-to-noise ratio SNR=10dB. Before 6s, the signal contains two modes, where the frequency f 1 = 1.32Hz, f 2 =0.87Hz, attenuation factor σ 1 =0.06, σ 2 =-0.28. When t=6s, a new oscillation mode is introduced, its frequency f 3 =2.07Hz, attenuation factor σ 3 =0.03. After 6s, due to the σ in the initial mode 2 =-0.28 belongs to the strong attenuation component, at this time the mode is no longer counted as the dominant mode, so the signal still contains two modes, where the frequency f 1 = 1.32Hz, f 3 =2.07Hz, attenuation factor σ 1 =0.06, σ 2 = 0.03.
[0136] Step 7: Obtain the measured da...
Embodiment 2
[0142] In order to verify the actual identification effect of the present invention, a piece of LFO measured data is obtained from the power system. Such as Figure 5 As shown in , this section of LFO signal is excited by two small disturbances, located at 4s and 7s respectively. In order to identify the LFO modal characteristics excited after the two disturbances, the data after the disturbance is intercepted as the LFO signal to be tested in this embodiment.
[0143] Step 7: Obtain the measured data of the LFO signal through the sliding time window. The sliding window length is 5s, the sliding interval is 1s, and the sampling frequency is 100Hz. In order to verify the applicability of the present invention in the case of multiple disturbances in the system, it is necessary to use sliding window sampling after two disturbances occur. In this embodiment, two segments of signals are selected as identification objects, which are 4.2-9.2s and 7.05-12.05s respectively. Among t...
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Abstract
The invention discloses a method for quickly identifying low-frequency oscillation modal characteristics of a power system based on an LSTM neural network, and provides a method for quickly identifying the low-frequency oscillation modal characteristics of the power system. The method is accurate in analysis and reasonable in design, and comprises the following steps of: generating LFO sample dataaccording to an EDSs mathematical model; respectively adopting a Hankel matrix and a sliding window FFT algorithm to carry out preprocessing operation on the LFO sample data; determining a classification criterion according to identification requirements, and dividing and marking LFO sample data according to the classification criterion for subsequent network training and testing; building an LSTM neural network model, inputting a training sample for network training, and determining whether network training is completed or not through testing the classification accuracy of the sample; inputting an LFO signal to be detected is input into the LSTM neural network through sliding window sampling, and completing identification of LFO frequency and attenuation factor modal characteristics through output analysis. The method has the advantages of rapid identification of the low-frequency oscillation modal characteristics of the power system, high reliability of the identification result andthe like.
Description
technical field [0001] The present invention relates to the technical field of power system stability and control, in particular to a method for quickly identifying modal characteristics of low frequency oscillation (LFO) in power systems based on long-short-term memory (LSTM) neural networks . Background technique [0002] With the increasing application of renewable energy and energy storage systems, the installed capacity of distributed power generation has grown rapidly. Power electronic inverters are widely used in distributed power generation. A large number of new energy grid-connected power generation equipment is connected to the grid through power electronic inverters, which brings great challenges to the planning and operation control of power systems. The power electronic inverter with the traditional control strategy focuses on the control of power generation and power quality, and has the characteristics of fast response and low inertia. A large number of trad...
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