Static voltage stability monitoring method based on deep neural network and impedance mode margin

Abstract

The invention discloses a static voltage stability monitoring method based on a deep neural network and an impedance mode margin. The method comprises the following steps: establishing a deep neural network model; setting a determined load level parameter, performing calculation of an impedance modulus margin algorithm under the parameter, and obtaining impedance modulus margin values of all system load nodes; sequencing the impedance modulus margins of all the load nodes, and finding out relatively weak nodes for observation; randomly setting the overall load level parameter of the system, calculating the impedance modulus margin value of a weak node, substituting multiple sets of input parameters and the impedance modulus margin value into the deep neural network to iteratively learn the characteristics of the deep neural network, and stopping iteration until an expected deep neural network model is obtained; and collecting input data in real time, predicting the impedance modulus margin value of the current weak node through the deep neural network, and judging the current system voltage stable state according to the impedance modulus margin value of the current weak node.

Description

technical field [0001] The invention relates to the technical field of power grid security, in particular to a static voltage stability monitoring method based on a deep neural network and impedance modulus margin. Background technique [0002] With the continuous increase of load demand, the scale of grid interconnection continues to increase, coupled with the large number of uncertain distributed energy sources in the power system and the continuous improvement of intelligence, voltage stability problems caused by reactive power that cannot be transmitted in a large range more prominent. Therefore, an efficient and accurate on-line voltage stability evaluation system is particularly important for preventing large-scale power outages. One of the key technologies is to propose a voltage stability online monitoring method that can be fast, accurate and adaptive to system state changes. [0003] The load margin index evaluation system based on continuous power flow, which is...

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Problems solved by technology

[0003] The load margin index evaluation system based on continuous power flow, which is widely used in the current online voltage stability evaluation system, requires a long period of offline analysis and calculation, and the average load growth method of the entire network does not conform to the actual disturbance method, and cannot give weak information. Defects such as link information and rapid adaptation to changes in the operating state of the system cannot meet the requirements of online monitoring

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