Gas compressor performance degradation prediction algorithm based on data driving
A predictive algorithm and data-driven technology, applied in the field of compressors, can solve problems such as large deviations, achieve the effects of increasing accuracy, improving gradient disappearance or explosion, and enhancing engineering practical value
Pending Publication Date: 2022-05-06
华能南京燃机发电有限公司
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[0004] The invention provides a data-driven compressor performance degradation prediction algorithm, which solves the problem that the existing compressor performance degradation prediction algorithm deviates greatly from the actual result
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[0051] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and examples.
[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is only some embodiments of the present invention, but not all embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordina...
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The invention provides a gas compressor performance degradation prediction algorithm based on data driving. The gas compressor performance degradation prediction algorithm comprises the steps that S1, a gas compressor performance change evaluation model is established; s11, collecting power plant data; s12, the efficiency and the flow capacity of the gas compressor serve as indexes for evaluating the performance change of the gas compressor; s13, establishing a thermodynamic model of the gas compressor; s14, calculating the efficiency and flow capacity of the key components of the gas compressor through the thermodynamic model to obtain theoretical efficiency and flow capacity; s21, establishing a deep neural network; and S22, introducing a recurrent neural network and a long short-term memory network to optimize the deep neural network to obtain a performance degradation prediction neural network. According to the method, a more accurate compressor performance analysis model is established by analyzing and processing a large amount of data generated by operation of the compressor and mining a hidden relationship between the big data and performance change, so that a simulation test is closer to actual operation of a real unit, and the model has higher engineering practical value.
Description
technical field [0001] The invention relates to the technical field of compressors, in particular to a data-driven compressor performance degradation prediction algorithm. Background technique [0002] The gas turbine is an internal combustion power machine that drives the impeller to rotate at high speed with the continuous flow of working fluid, and converts the energy of the fuel into output work. The main components are composed of a compressor, a combustion chamber, a turbine and various auxiliary systems. Among them, the press is responsible for compressing the air, compressing the gas at normal temperature and pressure into high-pressure gas and then inputting it into the combustion chamber for combustion. The high-temperature and high-pressure gas formed does work in the turbine. Therefore, the performance of the compressor directly affects the performance of the subsequent combustion chamber and turbine. Due to the variable operation process and harsh working enviro...
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Login to View More IPC IPC(8): G06F30/27G06F30/28G06N3/04G06N3/08G06F113/08G06F119/14
CPCG06F30/27G06F30/28G06N3/084G06F2113/08G06F2119/14G06N3/044G06N3/045
Inventor 唐寅艾容申周建黄庆王开柱陈志锋
Owner 华能南京燃机发电有限公司