On-line detection method and device of gas turbine based on lstm

A technology of gas turbine and detection method, which is applied in gas turbine engine test, jet engine test, neural learning method, etc., can solve the problems of control influence and large prediction error, and achieves high accuracy, large amount of data, and small analysis error. Effect

Active Publication Date: 2022-02-08
杭州华电江东热电有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Its disadvantage is that the prediction error is large, which has a great impact on subsequent control

Method used

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  • On-line detection method and device of gas turbine based on lstm
  • On-line detection method and device of gas turbine based on lstm
  • On-line detection method and device of gas turbine based on lstm

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Experimental program
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Effect test

Embodiment 1

[0048] like Figure 1-3 As shown, the proposed LSTM-based gas turbine anomaly online detection method and device include the following steps:

[0049] Step 1: Data collection. A gas turbine is mainly composed of three major components: a compressor, a combustor and a turbine. Select the main measuring point data from the monitoring software of the gas turbine, and analyze it as the input of the LSTM neural network. Measuring point data include: GT IGV position (angle), GT IGV position, air humidity, gas turbine compressor intake temperature, gas turbine intake filter differential pressure measurement point (1) and measurement point (2), gas turbine intake filter Internal pressure, gas turbine inlet static pressure, GT IGV position-1 measuring point (1) and measuring point (2), gas turbine compressor inlet temperature measuring point (1), measuring point (2) and measuring point (3), Gas turbine compressor outlet air temperature, gas turbine combustor shell pressure measuring...

Embodiment 2

[0082] An LSTM-based gas turbine online detection device with the above method installed, including a memory, a processor, an I / O device and an alarm device electrically connected to store a program for realizing the above method, and the I / O device is connected to a computer with monitoring software installed on the gas turbine and / or network to access and obtain real-time measurement point data.

[0083] The processor is connected to the hand-held user terminal through wireless transmission. Remote monitoring and early warning through handheld devices.

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Abstract

The invention belongs to the field of power plant safety control systems, and in particular relates to an LSTM-based gas turbine online detection method and device. It is characterized in that it includes the following steps: data collection; normalization processing; feature extraction; training LSTM abnormality detection model; abnormality online detection, inputting the data to be predicted into the trained detection model, obtaining the model prediction value, and converting the prediction value The absolute value is obtained by making a difference with the actual measured value of the sensor. If the absolute value exceeds the given threshold, it is determined that an abnormality has occurred. The invention is suitable for processing and predicting important events with relatively long intervals and delays in time series, and is suitable for analysis and fitting of time series. Make full use of the idea and technology of deep learning to automatically select and extract hidden features in the data information detected by equipment sensors, and then realize online anomaly detection based on real-time measurement point data of gas turbines. The invention has large amount of collected data, small analysis error and high accuracy rate of early warning results.

Description

technical field [0001] The invention belongs to the field of power plant safety control systems, and in particular relates to an LSTM-based gas turbine online detection method and device. Background technique [0002] Gas turbine is one of the most important power machines at present, and has a wide range of applications in aviation, electric power, ships and other fields. Gas turbines have complex structures and harsh working environments, which are prone to various failures. If they cannot be found and repaired in time, their safety and reliability will be seriously affected. As the application of gas turbines increases, people pay more and more attention to their working conditions. Once a gas turbine fails and shuts down, it will affect the stability of the power system, cause huge economic losses, and even affect the stable development of the national economy. For gas turbines The study of faults has also been put on the agenda. [0003] In the prior art, methods for ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M15/14G06N3/08
CPCG01M15/14G06N3/08
Inventor 王新保方继辉李勇辉
Owner 杭州华电江东热电有限公司
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