Load flow Jacobian matrix robust estimation method with consideration to sparsity for intelligent power distribution network

Abstract

The invention discloses a load flow Jacobian matrix robust estimation method with the consideration to sparsity for an intelligent power distribution network, and the method comprises the following steps: 1) acquiring the number of nodes of the distribution network and numbering the nodes; 2) obtaining the measurement data of a synchronous phasor measurement device; 3) constructing a sensing matrix, and setting the line number of the load flow Jacobian matrix as m=1; 4) solving a least squares solution as an estimated solution; 5) switching to step 8) if an iteration termination condition is established, or else switching to step 6); 6) solving a minimized correlation entropy model as an estimated solution; 7) switching to step 8) if the iteration termination condition is established, or else updating the column number index set of the sensing matrix, and returning to step 6); 8) determining whether to complete the estimation of all rows of the Jacobian matrix or not: outputting an estimation result if the estimation of all rows of the Jacobian matrix is completed or else supposing m=m+1, and returning to step 4). The method effectively avoids the adverse impact on an estimation result from bad data in measurement while employing the sparsity of the load flow Jacobian matrix, and still can guarantee the estimation precision when the measurement data contains bad data.

Description

Technical field [0001] The invention relates to a method for estimating power flow Jacobian matrix of distribution network. In particular, it relates to a robust estimation method of the Jacobian matrix power flow of smart distribution network considering sparsity. Background technique [0002] Large-scale renewable energy is connected to the distribution network, and the randomness and volatility of its output put forward higher requirements for the operation monitoring and control of the distribution network. In order to improve the level of new energy consumption in the distribution network, the traditional distribution network has gradually developed into an intelligent distribution network. The power flow Jacobian matrix is ​​an important parameter for analyzing the operation status of the distribution network and optimizing the control of the distribution network. The accurate acquisition of the tidal current Jacobian matrix is ​​an important prerequisite and basis for the...

AI Technical Summary

Problems solved by technology

However, the least squares estimation is highly sensitive to bad data, if the measurement data contains bad data, it will cause the estimation results to seriously deviate from the true value

Although high-precision measurement data can be obtained through the synchrophasor measurement device, due to the influence of data acquisition, conversion and communication, the measurement data may still contain bad data, resulting in unusable estimation results. A more robust sparse recovery algorithm to estimate the power flow Jacobian matrix

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