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BiLSTM and attention fused power generation equipment anomaly prediction method and system

A technology of power generation equipment and attention, which is applied in the field of abnormal prediction of power generation equipment that integrates BiLSTM and attention, can solve problems such as inability to accurately predict the abnormality of power generation equipment, and achieve the effects of reducing the cost of abnormal prediction, improving accuracy, and making abnormal prediction convenient

Pending Publication Date: 2022-04-12
HUANENG CLEAN ENERGY RES INST
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] This application provides a method and system for predicting abnormality of power generation equipment that integrates BiLSTM and attention, so as to at least solve the technical problem that the abnormality of power generation equipment cannot be accurately predicted in the related art

Method used

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  • BiLSTM and attention fused power generation equipment anomaly prediction method and system
  • BiLSTM and attention fused power generation equipment anomaly prediction method and system
  • BiLSTM and attention fused power generation equipment anomaly prediction method and system

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Embodiment 1

[0034] figure 1 A flow chart of a method for predicting abnormality of power generation equipment that integrates BiLSTM and attention provided by an embodiment of the present disclosure, as shown in figure 1 As shown, the method includes:

[0035] Step 1: Obtain the operating data of the power generation equipment at the current moment and the meteorological data corresponding to the current moment of the power generation equipment, and preprocess the acquired data;

[0036] In the embodiment of the present disclosure, the acquired operation data of the power generation equipment at the current moment and the operation data in the historical period are obtained based on smart meters, sensors (that is, SCADA systems), and manual parameter input.

[0037] In an embodiment of the present disclosure, the preprocessing of the acquired data includes:

[0038] Perform data cleaning, noise or sentence completion, data format unification, and normalized data processing on the operat...

Embodiment 2

[0072] image 3 A structural diagram of an abnormality prediction system for power generation equipment that integrates BiLSTM and attention provided by an embodiment of the present disclosure, as shown in image 3 As shown, the system includes:

[0073] An acquisition module, configured to acquire the operating data of the power generation equipment at the current moment and the meteorological data corresponding to the current moment of the power generation equipment, and preprocess the acquired data;

[0074] A conversion module, configured to convert the preprocessed data into word vector text corresponding to the data;

[0075] A scoring module, configured to input the word vector text corresponding to the data into the pre-trained power generation equipment abnormality prediction model to obtain the score of the power generation equipment abnormality prediction;

[0076] A prediction module, configured to predict whether the power generation equipment is abnormal based ...

Embodiment 3

[0095] In order to realize the above-mentioned embodiments, the present disclosure also proposes a computer device.

[0096]The computer device provided in this embodiment includes a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the method in Embodiment 1 is implemented.

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Abstract

The invention relates to a BiLSTM and attention fused power generation equipment anomaly prediction method and system, and the method comprises the steps: obtaining the operation data of power generation equipment at the current moment and the meteorological data corresponding to the power generation equipment at the current moment, and carrying out the preprocessing of the obtained data; converting the preprocessed data into a word vector text corresponding to the data; inputting the word vector text corresponding to the data into a pre-trained power generation equipment anomaly prediction model to obtain a score of power generation equipment anomaly prediction; and predicting whether the power generation equipment is abnormal or not based on the score of abnormal prediction of the power generation equipment, and performing information reaching on a prediction result. According to the technical scheme provided by the invention, the accuracy of the score of the abnormal prediction of the power generation equipment is improved, the abnormal prediction of the power generation equipment is more convenient, and the abnormal prediction cost of the power generation equipment is also reduced.

Description

technical field [0001] The invention relates to the fields of artificial intelligence, deep learning, neural network, natural language processing, new energy, carbon neutralization, carbon peaking, and abnormal prediction of power generation equipment, and specifically relates to a method and system for abnormal prediction of power generation equipment that integrates BiLSTM and attention . Background technique [0002] With the rapid development and application of natural language understanding and deep learning technology, there are more and more applications of deep learning in the combination of new energy and artificial intelligence, especially when some power generation equipment is exposed to sunlight, severe cold, wind and rain and other natural environments. At the same time, as the means of collecting operating data of power generation equipment become more and more abundant, how to use these data to detect and predict the abnormal condition of power generation eq...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q10/04G06Q50/06
Inventor 曾谁飞王振荣刘艳贵黄思皖王青天张燧刘旭亮李小翔冯帆王海明沈伟文郑建飞邸智韦玮童彤任鑫杜静宇赵鹏程武青祝金涛朱俊杰吴昊吕亮
Owner HUANENG CLEAN ENERGY RES INST
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