Robust Estimation Method of Power Flow Jacobian Matrix in Smart Distribution Network Considering Sparsity

Abstract

A robust estimation method for the Jacobian matrix of smart distribution network power flow considering sparsity, including: 1) Obtaining and numbering the number of nodes in the distribution network; 2) Obtaining the measurement data of the synchronized phasor measurement device; 3) Constructing Sensing matrix, let the row number m=1 of the current Jacobian matrix; 4) find the least squares solution as the estimated solution; 5) judge if the iteration termination condition is satisfied, then enter step 8); otherwise, enter step 6); 6) Solve the minimum correlation entropy model as the estimated solution; 7) The iteration termination condition is established, then enter step 8); otherwise, update the sensing matrix column number index set, return to step 6); 8) judge whether to complete all rows of the Jacobian matrix Estimate, if yes, output the estimation result, otherwise m=m+1, return to step 4). While utilizing the sparsity of the Jacobian matrix of the power flow, the present invention effectively avoids the bad influence of bad data in the measurement on the estimation result, and can still ensure the accuracy of the estimation in the case of bad data in the measurement.

Description

technical field [0001] The invention relates to a distribution network power flow Jacobian matrix estimation method. In particular, it involves a robust estimation method of Jacobian matrix for smart distribution network power flow considering sparsity. Background technique [0002] When large-scale renewable energy is connected to the distribution network, the randomness and fluctuation 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 a smart distribution network. The power flow Jacobian matrix is ​​an important parameter for analyzing the operating state of the distribution network and optimizing the control of the distribution network. Accurately obtaining the power flow Jacobian matrix is ​​an important premise and basis for analyzing the operating situati...

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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