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Prediction method of transformer winding hot-spot temperature based on neural network

A technology of transformer winding and neural network is applied in the field of prediction of hot spot temperature of transformer winding, which can solve the problem of not being able to find the location of hot spot.

Inactive Publication Date: 2016-05-04
SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
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Problems solved by technology

This calculation method can only solve the temperature value of the hot spot, but cannot find the location of the hot spot. At the same time, there is still a certain gap between the results of these formulas and the measured data, so it is necessary to find a method that can get more accurate results.

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  • Prediction method of transformer winding hot-spot temperature based on neural network
  • Prediction method of transformer winding hot-spot temperature based on neural network
  • Prediction method of transformer winding hot-spot temperature based on neural network

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Embodiment Construction

[0043] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0044] Such as figure 1 As shown, a neural network-based prediction method for transformer winding hot spot temperature includes the following steps:

[0045] (1) Take the ambient temperature θ a , initial top oil temperature rise Δθ oi , top oil temperature θ o and the load factor K as the input data, the oil time constant τ 0 And the oil index x is the output data, and determine the number of nodes in the hidden layer, according to the formula Determine the number of nodes in the hidden layer, where n 1 is the number of hidden layer nodes, n is the number of input neurons, m is the number of output neurons, and a is a constant between 1 and 10. For the neural network formed by the selection of the number of hidden layer nodes, 10 Different training times, take the number of hidden layer nodes when the average training error is the sm...

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Abstract

The invention relates to a prediction method of transformer winding hot-spot temperature based on a neural network. After the neural network is optimized through the adoption of a genetic algorithm, the transformer winding hot-spot temperature value is obtained through the computation; the method comprises the following steps: (1) constructing the neural network; (2) normalizing input data and output data of the neural network; (3) optimizing a weight value and a threshold value of the neural network through the genetic algorithm; (4) training the neural network optimized through the genetic algorithm; (5) obtaining the output data through the utilization of real-time measured input data and trained neural network, and computing the hot-spot temperature value through computation. Compared with the prior art, the method disclosed by the invention has the advantage of being high in measurement precision.

Description

technical field [0001] The invention relates to a method for predicting hot spot temperature of transformer winding, in particular to a method for predicting hot spot temperature of transformer winding based on neural network. Background technique [0002] The difficulty in the study of transformer hot spot temperature lies in the complex mechanism of winding hot spot temperature and the uncertainty of hot spot temperature location. So far, there are two main methods to obtain the internal temperature of transformer. A neural network-based prediction method for transformer winding hot spot temperature is The direct measurement method, a neural network-based prediction method for the hot spot temperature of transformer windings is the indirect calculation method. [0003] The direct measurement method is to pre-embed a temperature measuring sensor near the wire or in the wire cake of the transformer to directly obtain the hot spot temperature of the winding. Embedded sensors...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/17
Inventor 魏本刚
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO
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