Self-organizing T-S fuzzy neural network control method of grid-connected inverter

A fuzzy neural network and control method technology, applied in the field of grid-connected inverter control, can solve the problems of taking zero or random numbers, PID control can not be satisfied, the hidden layer has no physical meaning, etc., achieves good dynamic performance, is conducive to Mathematical analysis, calculation of simple effects

Inactive Publication Date: 2022-06-24
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0003] For the grid-connected inverter control system characterized by nonlinearity, the traditional PID control can no longer meet the requirements of the current high-precision control development trend; and the hidden layer of

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  • Self-organizing T-S fuzzy neural network control method of grid-connected inverter
  • Self-organizing T-S fuzzy neural network control method of grid-connected inverter
  • Self-organizing T-S fuzzy neural network control method of grid-connected inverter

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Embodiment Construction

[0015] specific implementation

[0016] Specific embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0017] figure 1 is the structure diagram of the strength self-judgment T-S type fuzzy neural network. The network is divided into an antecedent network and a consequent network, among which:

[0018] Antecedent network:

[0019] The first layer is the input layer, the input is the d-axis current error e t1 , d-axis current error rate of change q-axis current error e t2 and q-axis current error rate of change Each neuron node in this layer does not do any calculation, and directly introduces the output into the antecedent network of the T-S fuzzy neural network. The output of this layer node is equal to the input, that is

[0020] The second layer is the membership layer, each node represents a language variable value, and the Gaussian function is used to obtain the membership function μ j ,Right now In th...

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Abstract

The invention discloses a self-organizing T-S fuzzy neural network PID (Proportion Integration Differentiation) controller for controlling a grid-connected inverter. A concept of intensity activation is added to a controller algorithm on the basis of a T-S fuzzy neural network. The essence of the method is that a fuzzy rule layer activation intensity method is adopted, a neuron structure is adaptively adjusted according to an actual environment, a proper control structure is constructed, and the performance and scale of the whole network can be better determined and improved due to the online learning capability and the powerful fault-tolerant capability of the method, so that the control precision is improved. And meanwhile, a gradient descent method is adopted to adjust each parameter of the controller in real time, so that the control system has better anti-interference capability and dynamic performance.

Description

technical field [0001] The invention relates to the field of grid-connected inverter control, the field of inverter intelligent control, a neural network, and a fuzzy control theory Background technique [0002] With the rapid growth of the world's population and the vigorous development of the economy, the demand for energy is increasing, and fossil fuel energy still accounts for the vast majority of the current energy consumption structure. And due to the non-renewable nature of fossil fuel energy, the fossil energy available on the earth is gradually depleted. As the most populous country in the world, my country's energy situation is more severe. At the same time, the burning of fossil fuels such as coal, oil, and natural gas has caused environmental pollution, which threatens the survival and development of human beings. [0003] For the grid-connected inverter control system characterized by nonlinearity, the traditional PID control can no longer meet the requirements...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 康尔良朱金荣
Owner HARBIN UNIV OF SCI & TECH
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