Harmonic source identification method based on weighted regularization extreme learning machine

An extreme learning machine and identification method technology, which is applied in the field of harmonic source identification based on weighted regularization extreme learning machine, can solve the problems of difficult to accurately obtain power system network parameters and ensure the accuracy of positioning.

Pending Publication Date: 2019-11-12
ELECTRIC POWER RES INST STATE GRID SHANXI ELECTRIC POWER +1
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Problems solved by technology

The traditional harmonic source identification method is easily affected by factors such as changes in the characteristics of the load itself and the harmonic characteristics of adjacent buses, and it is difficult to accura

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  • Harmonic source identification method based on weighted regularization extreme learning machine
  • Harmonic source identification method based on weighted regularization extreme learning machine
  • Harmonic source identification method based on weighted regularization extreme learning machine

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[0029] Below in conjunction with accompanying drawing, example is described in detail, the harmonic source identification flow chart of WRELM estimator of the present invention is as follows figure 1 As shown, the specific steps are as follows:

[0030] Step S1: the optimal configuration method of the distribution network monitoring device. by figure 2 Taking IEEE14 node topology diagram as an example, the specific process is as follows: a) Objective function. In order to reduce the investment cost, the least measuring devices should be used to make the system globally observable, then the objective function J is: n i Indicates the situation of installing nodes in the system, when η i =1, it means that node i is equipped with a measuring device, when η i =0, it means that no measuring device is installed on node i; N is the total number of nodes in the network. b) Constraints. In order to ensure the overall observability of the system, at least one measuring device at...

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Abstract

The invention discloses a harmonic source identification method based on a weighted regularization extreme learning machine, and the method comprises the steps: carrying out the optimal configurationof measurement nodes of a power distribution network through employing a network topology method, and determining the number and installation positions of monitoring equipment; correspondingly forminga data pair according to the fundamental voltage amplitude, the harmonic voltage amplitude, the phase and the position vector of the bus where the harmonic source is located, which are obtained fromthe monitoring node, and forming a training data set and a test set of the harmonic source positioning estimator; and optimizing the activation function of the weighted regularization extreme learningmachine positioning estimator and the number of neurons in a hidden layer; and constructing a harmonic source positioning estimator by taking the amplitude and the phase vector of the obtained harmonic voltage as the input of a harmonic source positioning estimator model and taking the bus position vector of the corresponding harmonic source as the output, and identifying the harmonic source in the system.

Description

technical field [0001] The present invention relates to the technical field of harmonic source identification, in particular to a harmonic source identification method based on a Weighted and regularization extreme learning machine (WRELM). Background technique [0002] With the advancement of the Global Energy Internet, the harmonics generated by a large number of power electronic equipment are injected into the grid with the access of distributed power sources, and the proportion of nonlinear loads has also increased significantly. The resulting harmonics The pollution problem is getting worse. The harmonics generated by the harmonic source in the distribution network not only affect the normal use of electrical equipment, but also cause the reduction of the user's power quality, and even threaten the safe operation of the distribution network in severe cases. Therefore, effectively locating the harmonic source in the power system is not only beneficial to relevant person...

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Application Information

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IPC IPC(8): G06N3/06G06N3/08H02J3/00G01R23/16
CPCG06N3/08G06N3/061G01R23/16H02J3/00H02J3/01Y02E40/40
Inventor 雷达肖莹常潇李胜文杨超颖樊瑞刘翼肇王锬张敏李慧蓬赵军张世锋杨赟磊徐永海秦本双
Owner ELECTRIC POWER RES INST STATE GRID SHANXI ELECTRIC POWER
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