Wind turbine generator system fault intelligent diagnosis and early warning method based on random forests
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A wind turbine, intelligent diagnosis technology, applied in the electric power field, can solve problems such as large deviation, waste of personnel, and long time to find faults
Active Publication Date: 2017-09-19
MERIT DATA CO LTD
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Problems solved by technology
[0006] The invention overcomes the problems of long time to find faults, large deviations and waste of personnel in the fault diagnosis and monitoring of the traditional manual method
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Embodiment 1
[0042] A method for intelligent diagnosis and early warning of wind turbine faults based on random forests, including:
[0043] Construct the fault index system of wind turbines, and extract the historical data of wind turbine status as sample data;
[0044] Perform data exploration and preprocessing on the extracted wind turbine status historical sample data;
[0045] Construct a random forest-based intelligent fault diagnosis and early warning model for wind turbines, analyze and evaluate the model according to the model results;
[0046] Real-time diagnosis is realized by using the constructed random forest-based intelligent diagnosis and early warning model of wind turbine faults;
[0047] Collect more historical fault and normal data of wind turbines, train the model regularly, and update the model in time.
[0048] Preferably, the construction of the wind turbine failure index system and the extraction of wind turbine status historical data as sample data refer to:
...
Embodiment 2
[0065] see figure 1 , which shows a flow chart of a method for intelligent diagnosis and early warning of wind turbine faults based on random forests provided by an embodiment of the present invention, which may include:
[0066] S101: Construct a fault index system for wind turbines, and extract status data of wind turbines as sample data.
[0067] Through the analysis of common faults of wind turbines, 13 attributes (gear_temp, gear_raterotor_vol, ..., motor_temp_sd, power_mean, is_running) are selected as input attributes, and whether the wind turbines are running normally is used as output attributes to build a wind turbine failure index system;
[0068] Selectively extract some historical data of wind turbine status from the wind turbine automation system and equipment background control system as sample data. It should be pointed out that some historical data of wind turbine status are selectively extracted in this step because some required index data in the equipment ...
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Abstract
The invention discloses a wind turbine generator system fault intelligent diagnosis and early warning method based on random forests. The wind turbine generator system fault intelligent diagnosis and early warning method based on random forests includes the steps: extracting the historical data of the wind turbine generator system state as the sample data; performing exploratory analysis and preprocessing on the sample data; constructing a wind turbine generator system fault intelligent diagnosis and early warning model based on random forests, and analyzing and evaluating the model according to the model result; utilizing the model after analysis and evaluation to perform real-time diagnosis on wind turbine generator system equipment; and if the diagnosis result is not normal, sending out an alarm information by the model. The wind turbine generator system fault intelligent diagnosis and early warning method based on random forests utilizes the random forest algorithm and considers the overall characteristics of the index, so that the wind turbine generator system fault intelligent diagnosis and early warning method based on random forests can solve the problem that single index decides the equipment state and can also comprehensively consider the concealed knowledge relevance among many indexes so as to make comprehensive judgment on the output result.
Description
technical field [0001] The invention relates to the field of electric power technology, more specifically, a method for intelligent diagnosis and early warning of wind turbine faults based on random forests. Background technique [0002] Wind power is recognized as one of the closest commercial renewable energy technologies in the world. Under the background of today's emphasis on environmental protection and sustainable development, wind power generation that does not consume fossil fuels and has no environmental pollution is considered to be the cleanest form of energy utilization. Over the past 10 years, wind power has become the fastest growing renewable energy source in the world, thanks to an average annual growth rate of nearly 28%. [0003] With the rapid development of wind energy and the putting into operation of large-scale wind turbines, and because most of the turbines are installed in remote areas and the load is unstable, many wind turbines in my country have...
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