CEEMDAN algorithm-based power system low-frequency oscillation mode identification method

A low-frequency oscillation, power system technology, applied in the direction of reducing/preventing power oscillation, circuit devices, AC network circuits, etc., can solve the problems of power supply equipment threats, cascading failures, power outages, etc., and achieve the effect of improving safe and stable operation

Active Publication Date: 2019-04-16
STATE GRID LIAONING ELECTRIC POWER RES INST +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the continuous increase of the scale of the interconnection system, large-capacity, ultra-long-distance AC and DC power transmission continues to increase, and the operation mode is complex and changeable. The risk of power grid accidents

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  • CEEMDAN algorithm-based power system low-frequency oscillation mode identification method
  • CEEMDAN algorithm-based power system low-frequency oscillation mode identification method
  • CEEMDAN algorithm-based power system low-frequency oscillation mode identification method

Examples

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Embodiment 1

[0066] The present invention is a method for identifying low-frequency oscillation modes of power systems based on the CEEMDAN algorithm, such as Figure 9 as shown, Figure 9 It is a flow chart of the present invention, comprising the following steps:

[0067] An application based on CEEMDAN and Hilbert-Huang transformation algorithm in power system oscillation mode identification, the calculation method includes the following steps:

[0068] 101 Use the data acquisition function of the wide-area measurement system to obtain the measured data of the power system, and use the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm to decompose each group of original low-frequency oscillation signals into The sum of several intrinsic mode functions (Intrinsic Mode function, IMF), each IMF component represents an oscillation mode;

[0069] 102 Preprocess each IMF component, and use the Teager energy operator to calculate the energy size and energ...

Embodiment 2

[0073] The scheme in embodiment 1 is further introduced below in conjunction with specific calculation formulas and examples, see the following description for details:

[0074] 201: Obtain the state measurement information of the power system from the wide area measurement system, and standardize the state measurement information;

[0075] Among them, standardization is a process of calculating the standard score, which is the process of dividing the difference between a number and the mean by the standard deviation. The standardized data is simple, easy to compare, and can fully show the relationship between the standard deviation of the data, and retain the original information of the data.

[0076] 202: The standardized generator rotor angle signal is used as the input signal to be identified, and the oscillation signal is decomposed by using the complete set of empirical mode decomposition of adaptive noise, so that each oscillation signal is decomposed into a signal that...

Embodiment 3

[0170] Below in conjunction with specific experimental data, the scheme in embodiment 1 and 2 is carried out feasibility verification, see the following description for details:

[0171] This example takes the 16-machine 68-node system as an example for simulation analysis. The 16-machine 68-node test system is as follows: figure 1 shown.

[0172] In the time-domain simulation process, a three-phase permanent short-circuit fault is set on the branch 46-49, and the fault occurs at 0.1s, the near-end fault is cut off at 0.2s, and the far-end fault is cut off at 0.22s. Unit 1 is selected as the reference unit, and the rotor angle signals of each generator set relative to G1 are used as input signals. The 16 generators generate a total of 15 sets of rotor angle signals, and the sampling frequency is 0.01s. After the power system is disturbed, the generator rotor angle swings curve like figure 2 As shown, the CEENDAN decomposition is performed on the rotor angle information of e...

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Abstract

The invention relates to the field of electrical engineering and particularly relates to a CEEMDAN algorithm-based power system low-frequency oscillation mode identification method. According to the method, the actual measurement data, including rotor angle signals of a generator, of a power system are acquired by utilizing a wide-area measurement system. After that, the complete-set empirical mode of the adaptive noise of each group of original low-frequency oscillation signals is decomposed into Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. The CEEMDAN algorithm is decomposed into a sum of a plurality of intrinsic mode functions (Intrinsic Modes) and IMFs, and each IMF component represents an oscillation mode. The energy value and the energy weight of each IMF component are calculated. Finally, the Hilbert-Huang transform is used for identifying the oscillation frequency and the damping ratio of the dominant oscillation mode, and the calculation result is compared with a characteristic value method. As a result, the safe and stable operation of the power system is ensured.

Description

technical field [0001] The invention relates to the field of electrical engineering, in particular to a method for identifying a low-frequency oscillation mode of a power system based on a CEEMDAN algorithm. Background technique [0002] With the increasing scale of interconnected systems, large-capacity, ultra-long-distance AC and DC transmission is increasing, and the operation mode is complex and changeable, the risk of grid accidents brought by low-frequency oscillations and the difficulty of oscillation control are also increasing. Low-frequency oscillation poses a great threat to power supply equipment, and may even induce cascading failures, causing large-scale power outages. Therefore, it is of great practical significance and engineering practical value to study the low-frequency oscillation mode identification method of the power system under the background of the continuous expansion of the interconnected power grid. [0003] PMU is Phasor Measurement Unit, synch...

Claims

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Application Information

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IPC IPC(8): H02J3/24
CPCH02J3/24H02J2203/20
Inventor 葛维春苏安龙张艳军高凯刘爱民孔剑虹刘劲松李斌李正文韩子娇屈超姜涛王长江殷祥翔梁旭昱
Owner STATE GRID LIAONING ELECTRIC POWER RES INST
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