Voltage regulator water level prediction method based on cost-sensitive LSTM cyclic neural network

A cyclic neural network, cost-sensitive technology, applied in the field of ship nuclear power regulator water level prediction, can solve the problem of large influence on model prediction ability, achieve strong learning ability and prediction ability, good accuracy, good robustness and stability sexual effect

Inactive Publication Date: 2018-10-12
ARMY MILITARY TRANSPORTATION UNIV OF PLA ZHENJIANG
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Problems solved by technology

Support vector regression has the advantages of fast learning speed and good generalization ability, but it also has the disadvantages of failing to make effective use of data information other than support vectors, and the selection of kernel functions and related parameters has a greater impact on the predictive ability of the model. It is assumed that the input data are independent and identically distributed, but the operating parameters of the nuclear power plant have strong time-series characteristics

Method used

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  • Voltage regulator water level prediction method based on cost-sensitive LSTM cyclic neural network
  • Voltage regulator water level prediction method based on cost-sensitive LSTM cyclic neural network
  • Voltage regulator water level prediction method based on cost-sensitive LSTM cyclic neural network

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Embodiment 1

[0040] A method for predicting the water level of a voltage regulator based on a cost-sensitive LSTM recurrent neural network, comprising the following steps:

[0041] S1, select p parameters with a high degree of coupling with the water level of the regulator as an input parameter.

[0042] S2. Construct the LSTM-based regulator water level prediction model and its framework. The specific step S2 includes two steps, S21, applying the RUN model to calculate the hidden layer sequence and the output sequence. The difference between the structure of RNN neuron and standard neuron is that it has a loop structure, which can transfer the information of the previous state to the current state, such as figure 1 As shown, when the input is a time series, it can be unrolled into a series of interconnected standard neurons. For a given input sequence of length n X =( x 1 ; x 2 ;…; x n ), applying a standard RNN model (such as figure 2 shown), a hidden layer sequence can be ...

Embodiment 2

[0059] The difference between this embodiment and Embodiment 1 is that in this embodiment, the LSTM model is modified, and the traditional LSTM regression model uses formula (9) as the loss function, which actually implies an assumption: the prediction error of the training sample have the same weight. In the experiment, we found that this is unreasonable. Therefore, the sample weight variable was introduced to improve the loss function of the original model. The improved model loss function is as follows:

[0060] (10).

[0061] in, is the sample weight at time t. The error of the LSTM model will increase significantly in the following two cases:

[0062] 1) when second derivative with respect to time When increasing, the usual error will also increase suddenly. image 3 middle curve at point The slope changes significantly, and the error The rate of increase over time also points increased significantly afterwards. Introduce parameters Represent time...

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Abstract

The present invention relates to the technical field of water level prediction of ship nuclear power voltage regulators, and particularly relates to a voltage regulator water level prediction method based on a cost-sensitive LSTM cyclic neural network. The voltage regulator water level prediction method based on the cost-sensitive LSTM cyclic neural network comprises the following steps: S1, selecting p parameters (as shown in the specification) with a relatively high degree of coupling with a voltage regulator water level as input parameters; S2, constructing a LSTM-based voltage regulator water level prediction model and a framework thereof; and S3, using a BPTT algorithm to train and optimize the water level prediction model constructed in step S2. According to the method provided by the present invention, the LSTM model can better approximate the true value of the water level than the SVR model and the BP neural network model, and has stronger learning ability and prediction ability, and the cost-sensitive LSTM model has better precision and faster convergence.

Description

technical field [0001] The invention relates to the technical field of forecasting the water level of a ship's nuclear power regulator, in particular to a method for predicting the water level of a regulator based on a cost-sensitive LSTM cycle neural network. Background technique [0002] The water level of the pressurizer is a very important state parameter of the marine pressurized water reactor, and it is an important basis for the operator to grasp the operating state of the reactor and judge the transient operation. Affected by load fluctuations of ship nuclear power plants and harsh working conditions of high temperature and high humidity, voltage regulators are prone to steam-water mixing and measurement failures, resulting in problems such as false water levels or abnormal displays. Under these circumstances, the operator will not be able to obtain the real situation of the water level of the pressurizer, the difficulty of operation will increase, and the probabilit...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/30G06F17/50G06N3/08
CPCG06N3/084G06Q10/04G06Q50/30G06F30/15G06F30/20
Inventor 张锦潘志松王晓龙赵诚沈军
Owner ARMY MILITARY TRANSPORTATION UNIV OF PLA ZHENJIANG
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