Dynamic frequency estimation measurement method after disturbance of power system

Abstract

The invention discloses a dynamic frequency estimation and measurement method after disturbance of a power system. The method mainly comprises the following steps: A, training: A1, carrying out dynamic time domain simulation to obtain 10 initial input vectors; a2, calculating 11 secondary input vectors; a3, splicing the vectors in the steps A1 and A2 into an input eigenvalue vector; A4, carrying out dynamic time domain simulation to obtain a dynamic frequency; a5, completing the training of the prediction model according to the data in the steps A3 and A4; b, testing: B1, monitoring 10 initialtest input vectors on line; b2, if the zero element exists in the electromagnetic power vector of the generator, judging that disturbance occurs, and turning to the step B3; b3, calculating 11 secondary test input vectors; b4, connecting the vectors in the steps B1 and B3 in series to form a test input characteristic value vector; and B5, inputting the test input eigenvalue vector into a prediction model, and outputting the disturbed estimated dynamic frequency by the model. The method is rapid in estimation and measurement, high in precision and small in error.

Description

technical field [0001] The invention relates to a dynamic frequency estimation and measurement method of a power system. Background technique [0002] Frequency is an important indicator to reflect the operation status of the power system. The frequency stability of the power system means that after the power system is severely disturbed (power loss, a generator in the system fails to stop power supply), there is a large imbalance between power generation and load demand, and the system frequency can still remain stable without frequency occurrence. The ability to crash. With the development of UHV DC transmission lines, when the DC transmission lines with continuously increasing transmission capacity are disconnected due to accidents and stop power supply, the threat to the frequency stability of the receiving end system also increases. At the same time, with the large-scale development and investment of new energy, the complexity of the power grid and the security risks ...

AI Technical Summary

Problems solved by technology

With the integration of new energy into the grid, the inertia of the system will decrease, and the frequency will change faster. There are higher requirements for the rapidity of online prediction of frequency stability. These methods are difficult to balance calculation accuracy and calculation efficiency.

In addition, due to the complexity, randomness, time-varying, geographical dispersion and other characteristics of the components and the system itself, it is difficult to accurately model, especially the model of the components such as the load of the system is usually difficult to accurately model, and the model parameters are usually based on Given empirical values ​​or standard values, it is difficult to truly reflect the nonlinear dynamic characteristics of the system

Therefore, the post-disturbance frequency response prediction method based on the system mathematical model is difficult to meet the accuracy and rapidity requirements of online applications

[0004] The machine method that has been applied to the estimation and measurement of power grid frequency is mainly the artificial neural network algorithm. Due to the limited feature extraction ability, the content of the predicted frequency is mostly the lowest value or steady-state value of the predicted frequency after the disturbance, which cannot completely and directly predict the dynamics of the frequency. There are usually problems of time-consuming prediction or inaccurate prediction results, which cannot meet the requirements of timely and reliable frequency stabilization control measures to prevent system frequency collapse and avoid power outage accidents

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