Power short-term load predicating method based on fast periodic component extraction

A technology of short-term load forecasting and periodic components, applied in the field of signal processing, can solve the problems of unrealistic power grid, large changes in network structure and parameters, static network characteristics, etc.

Active Publication Date: 2014-04-23
STATE GRID CORP OF CHINA +1
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Problems solved by technology

Most of the above methods are based on the good observability of the power grid. It is necessary to know the parameters of the power grid and the topology of the power grid, and the network structure and parameters cannot have large changes.
However, the power system is essentially a complex nonlinear time-varying system. In addition to the cost of measurement and the possible ownership of different property rights in the power grid, it is unrealistic to observe the power grid well, and it is also unrealistic to require static network characteristics.
Therefore, the current methods for estimating and forecasting power loads have limitations and are not suitable for situations where the parameters and structure of the power grid are unknown.

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  • Power short-term load predicating method based on fast periodic component extraction
  • Power short-term load predicating method based on fast periodic component extraction
  • Power short-term load predicating method based on fast periodic component extraction

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Embodiment Construction

[0038] The present invention will be further described in detail below in conjunction with examples, but the embodiments of the present invention are not limited thereto.

[0039] like figure 1 As shown, the training data selected in this embodiment is the load situation of 30 days in April, Meishan City, Sichuan Province in 2013, and its waveform curve is as follows figure 2 As shown, the training data is 15 minutes to detect the load situation, a total of 96 data a day, and use the first 21 days (a total of 2016 data points) to directly predict the load data of 96 points on the 22nd day at one time.

[0040] First put figure 2 The training data is demeaned, and then the fast Fourier transform is performed to obtain the frequency domain of the data, such as image 3 shown. Extract each frequency component through the maximum spectral peak search, and keep the low frequency part as the trend component of the overall load in the first 21 days, until the maximum spectral pe...

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Abstract

The invention discloses a power short-term load predicating method based on fast periodic component extraction. According to the method, training data signals are subjected to spectral analysis, periodic and aperiodic components of the signals are sequentially extracted, then, the periodic components are subjected to cyclic predication, the aperiodic components are subjected to difference autoregressive moving average model predication, and the load condition of one day (the predication day) is obtained. The invention provides a novel fast detecting method for the short-term load predication of a power system, and solves the problem of the load short-term predication of a nonlinear system with complicated network structure and unstable parameters in the existing power system.

Description

technical field [0001] The invention relates to a signal processing method, in particular to a signal processing method for short-term load forecasting of a power system based on fast periodic component extraction and non-periodic component autoregressive sliding model estimation. Background technique [0002] The power system has become the cornerstone of modern society. Now with the formation of the power market environment, power supply has become a commercial service behavior, and electric energy has become a special commodity with various quality parameters and indicators that are directly related to the economic benefits of power companies. . Power load classification estimation (separation) and short-term forecasting are the basis and foundation of power grid economic and security scheduling, which play an important role in the safe and economic operation of the power system, and are the optimal scheduling of the power system (such as economic scheduling, unit optimal...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
Inventor 李琪林贺含峰舒勤马哲谢正军
Owner STATE GRID CORP OF CHINA
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