Electric power system noise self-adaptive robust state estimation method

Abstract

The invention provides an electric power system noise self-adaptive robust state estimation method. The method comprises the following steps that 1, L measuring fracture surfaces are obtained, wherein the L is an integer; 2, measuring error estimation is conducted on each measuring fracture surface to obtain the error vector; 3, the statistical learning method is used for carrying out estimation to obtain the parameters of a general Gaussian density model GGD on the basis of the error vector obtained through the estimation of the L measuring fracture surfaces to obtain the distribution type of measured noise, and the best corresponding robust state estimation model is selected according to the distribution type of the measured noise. According to the electric power system noise self-adaptive robust state estimation method, due to the fact that the distribution rules of the noise can be obtained through the statistical learning process, and the distribution rules of the noise are matched with the robust state estimation method in an on-line mode, so that the self-adaptive effect of various kinds of noise types is achieved, namely, the best estimation result which is closer to the state variable true value can be obtained in any noise distribution type.

Description

technical field [0001] The invention belongs to the field of electric power system scheduling automation, and in particular relates to a power system noise self-adaptive tolerance state estimation method. Background technique [0002] Power system state estimation is the foundation and core of the energy management system, and it is an indispensable guarantee link for the safe, reliable, high-quality and economical operation of the power system. The application of state estimation at home and abroad has a history of more than 40 years, and various state estimation methods have emerged during this period. [0003] Quantity measurement in state estimation generally includes three parts, namely, the true value of the quantity measurement, relative bad data and measurement noise. The optimal state estimate should not only be able to reliably identify bad data, but also be the best estimate under the corresponding type of measurement noise distribution. Traditional state estima...

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Problems solved by technology

However, there are still two disadvantages in this way: (1) WLS is not robust, so after WLS estimation, it is often necessary to use the maximum regularized residual test method or estimation identification method to identify bad data, but these methods are in In most cases, bad data with strong correlation cannot be effectively identified; (2) The number of measurements available for state estimation is limited after all. At this time, the law of large numbers no longer holds true, and the actual distribution of noise often does not conform to the Gaussian distribution (especially amplitude measurement), so there is no theoretical guarantee of the performance of the WLS estimation results

Like WLS, other traditional state estimation methods also have the same problem, and the minimum information loss method (Minimum Information Loss, MIL) proposed by domestic scholars has strong adaptability to noise, but it does not give the modeling dependence The specific method of obtaining the statistical law of noise

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