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Static compensator (STATCOM) control method based on multi-model fuzzy neural network PI

A technology of fuzzy neural network and control method, applied in the field of STATCOM direct voltage control based on multi-model fuzzy neural network PI, can solve the problem that PI controller is difficult to meet the requirements of voltage control accuracy and so on

Inactive Publication Date: 2013-01-02
SHANGHAI JIAO TONG UNIV +2
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Problems solved by technology

However, STATCOM has nonlinear characteristics, and its equivalent parameters will change during operation, so it is difficult to meet the precision requirements of voltage control only by relying on PI controller

Method used

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  • Static compensator (STATCOM) control method based on multi-model fuzzy neural network PI
  • Static compensator (STATCOM) control method based on multi-model fuzzy neural network PI
  • Static compensator (STATCOM) control method based on multi-model fuzzy neural network PI

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Embodiment Construction

[0050] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0051] Such as Figure 6 , a STATCOM control method based on multi-model fuzzy neural network PI, comprising the following steps:

[0052] S1: According to the load side of the power distribution system, the voltage U of the access point is connected to different impact loads pcc The magnitude of the reduced magnitude, the distribution system is divided into three models M i (i=1,2,3). Such as figure 1 Circuit shown, including: power distribution system, STATCOM, load.

[0053] see figure 1 It can be seen that the main reason for the voltage change of the access point is due to the change of the load, figure 1 in, U S is the grid voltage (corresponding to the power distribution system), U c is the output voltage of STATCOM. u pcc is the access point voltage, Z s is the grid-side equivalent impedance, Z f is the equivalent impedance connect...

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Abstract

The invention relates to an STATCOM control method based on a multi-model fuzzy neural network PI. The method comprises the steps of S1, dividing the distribution system into three models, i,e., Mi (i=1, 2, 3) according to the reduced amplitude value of the voltage Upcc of an access point after a load side of a distribution system is connected with different impact loads; S2, designing a d-axis PI controller PIdi (i=1, 2, 3) and a q-axis PI cotroller PIqi (i=1, 2, 3) for each of models; and S3, enabling the fuzzy neural network model to comprises a fuzzy controller and a neural network. Control parameters kp and ki of the the d-axis PI controller PIdi (i=1, 2, 3) and the q-axis PI cotroller PIqi (i=1, 2, 3) in the Mi (i=1, 2, 3) are adjusted through the fuzzy neural network model. The method has the advantages that 10, the PI controller adopts a multi-model technology, and PI parameters are selected according to a model index in the technology, so that the PI controllers can adapt to the changes of the load of the access point; and 2), the control parameters kp and ki are adjusted by the fuzzy neural network, and complex workload caused by manual PI parameter adjustment is reduced greatly.

Description

technical field [0001] The invention relates to a static synchronous compensator in reactive power compensation of power system power quality, in particular to a STATCOM direct voltage control method based on a multi-model fuzzy neural network PI. Background technique [0002] One of the goals of the Static Synchronous Compensator (STATCOM) is to stabilize the voltage at the access point through reactive power compensation, so as to improve the power quality. Compared with the traditional Static Var Compensator (SVC), STATCOM has better Reactive power characteristics and voltage stability characteristics have gradually become a research hotspot in recent years. [0003] The main control method of STATCOM is the double-closed-loop PI control method, but this method requires too many PI controllers and is difficult to realize. In addition, there is a direct voltage control method, which eliminates the current inner loop in the double closed-loop control, reduces the PI contro...

Claims

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Application Information

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IPC IPC(8): H02J3/16G06N3/06
CPCY02E40/34Y02E40/16Y02E40/10Y02E40/30
Inventor 郑益慧王昕李立学周晨李军良王艳华谢宁王滨
Owner SHANGHAI JIAO TONG UNIV
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