A microgrid equivalent modeling method based on LSTM neural network

A neural network and equivalent modeling technology, applied in the direction of AC network circuits, electrical components, circuit devices, etc., can solve problems such as the interaction between micro-grids and large power grids

Inactive Publication Date: 2018-12-25
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0005] In order to improve simulation accuracy and simulation efficiency, the present invention provides a microgrid equivalent modeling method based on LSTM neural

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  • A microgrid equivalent modeling method based on LSTM neural network
  • A microgrid equivalent modeling method based on LSTM neural network
  • A microgrid equivalent modeling method based on LSTM neural network

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[0060] In order to enable those skilled in the art to better understand the technical solutions in the application, the technical solutions in the embodiments of the application are clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the application, and Not all examples. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0061] An equivalent modeling method of microgrid based on LSTM neural network, the specific steps are as follows:

[0062] Step 1: Collect the disturbance data of the public coupling point of the microgrid during the disturbance period;

[0063] Step 2: Determine the number of input and output nodes of the LSTM neural network according to the equivalent modeling requirements of the microgrid, and use the disturbance data collected in step 1 to ...

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Abstract

The invention discloses a microgrid equivalent modeling method based on LSTM neural network. The method comprises specific steps of 1, collecting The disturbance data of the common coupling point of the microgrid during the disturbance period; 2, according to the equivalent modeling requirement of the microgrid, determining the number of input and output nodes of the LSTM neural network, and offline training the LSTM neural network by utilize the disturbance data collected in the step 1; 3, according to the neural network trained offline in the step 2, obtaining a nonlinear equivalent model which can represent the running state of the microgrid. The invention utilizes artificial neural network to have good ability to deal with complex non-linear problems, and at the same time can well capture the dynamic characteristics of the electric power system, and the structure and parameters of the dynamic model are determined by the structure and parameters of the LSTM neural network. Only themeasured values of the common coupling points of the micro-grid are needed, and the specific parameters and topological structure of the micro-grid system are not required to be mastered. Moreover, adefinite model is not required to be established in advance when the micro-grid system is equivalent. Once the model is trained and tested, the dynamic equivalent model based on LSTM neural network can meet the needs of system simulation and analysis.

Description

technical field [0001] The invention relates to an equivalent modeling method of a microgrid based on an LSTM neural network, and belongs to the technical field of power system modeling and control. Background technique [0002] In order to achieve efficient, sustainable, economical and safe power supply, distributed power generation systems have been developed and utilized in large quantities due to their clean and renewable advantages. In order to improve the utilization efficiency of distributed power and give full play to the advantages of distributed power, a new access method - microgrid is proposed. A microgrid is a small-scale power system composed of distributed power sources, loads, and grid power distribution systems. The capacity is usually small, and it can realize self-control and self-management, with flexible operation modes and excellent dispatching performance. [0003] The simulation and fault prediction of microgrid is one of the key issues to ensure the...

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Application Information

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IPC IPC(8): H02J3/00
CPCH02J3/00H02J2203/20
Inventor 蔡昌春刘昊林倪建军张金波邓立华
Owner HOHAI UNIV CHANGZHOU
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