Low-frequency oscillation monitoring method for power system

Abstract

The invention discloses a low-frequency oscillation monitoring method for a power system, and the method applies the oscillation feature identification method to carry out the preliminary identification of whether sampling data contains oscillation information or not, introduces the general digital signal processing method-digital filter method to extract related oscillation components, applies the wave peak indirect identification method to calculate oscillation amplitude through phase angle and amplitude value which are corresponding to each sampling value in a period wave and applies multi-level alarm thresholds to highlight important alarm information. The method can effectively improve the pertinence and the accuracy of the low-frequency oscillation on-line monitoring.

Description

technical field [0001] The invention proposes a low-frequency oscillation monitoring method of a power system, which belongs to the theory and analysis field of dynamic power systems. Background technique [0002] 1. Introduction to Low Frequency Oscillation [0003] The system stability problem, that is, whether the power system maintains stable operation after being subjected to various disturbances, is the core problem that needs to be studied and solved in the dynamic power system. System stability problems include three categories: transient stability, dynamic stability and static stability. When the power system is subjected to small disturbances, the continuous low-frequency oscillation between generator rotors caused by insufficient damping belongs to the category of system dynamic stability analysis. [0004] The oscillation frequency of this oscillation phenomenon is generally 0.2 to 2.5 Hz. Since low-frequency oscillation occurs, the power value on the transmiss...

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Problems solved by technology

In the first method, due to the poor anti-noise performance of the Prony algorithm, it is generally necessary to add a filtering module to the algorithm

Continuous filtering and prony analysis of a monitoring quantity consumes a lot of CPU resources. When the number of monitored signals increases, the system resources occupied by the algorithm will reach an unacceptable level

In the second method, the period-amplitude detection method is simple in principle and fast in operation speed, but this method analyzes the original data, and it is difficult to avoid false alarms caused by meaningless spikes contained in the original sampling values

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