Method for diagnosing and analyzing failures of heliostat of tower-type solar power plant
A technology for tower solar power and fault diagnosis, which is applied in the testing of measuring devices, instruments, and mechanical components, etc., can solve problems such as fault diagnosis analysis and method of tower solar thermal power generation heliostats that have not been reported in public reports, and achieve diagnosis Accurate and reliable, the effect of improving reliability
Inactive Publication Date: 2011-11-16
HOHAI UNIV
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[0006] At present, there are more than 30 tower-type solar thermal power station projects that have been built or are under construction in the world, but there are no public reports on the fault diagnosis analysis and methods of tower-type solar thermal power generation heliostats
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[0021] In order to reduce the safety hazards of heliostat failure to the solar tower thermal power generation process, and improve the safety and reliability of heliostat operation. Invented a heliostat fault diagnosis method combining expert system and RBF artificial neural network to realize early detection, early prediction and early treatment of faults. For the faults of the transmission gearbox system of the heliostat, it is not easy to directly analyze the faults through the sensor, and the method of artificial neural network is used to diagnose the fault, and the method of expert knowledge is generally easy to analyze the cause of the fault except the gear box through the sensor. Implement a diagnosis.
[0022] (1) Expert system diagnosis
[0023] During the diagnosis process of the expert system, the system receives the on-site operation data in real time, after classification and preprocessing, matches with the pre-stored expert knowledge in the knowledge base, calls...
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The invention discloses a method for diagnosing and analyzing failures of a heliostat of a tower-type solar power plant. The method comprises steps as follows: regarding a measurement sensor which diagnoses azimuth angles, altitude angles, gearbox oil temperature and the like easily by utilizing sensor information, a data collection sensor is adopted to acquire a failure signal; the failure signal is classified and preprocessed, and matched with expert knowledge which is prestored in an expert diagnosis system knowledge base; a monitor index is called from a method base to search failure symptoms which have happened; a symptom-failure rule list is extracted, and all possible failure reasons are searched; other symptoms in all possible failure reasons are verified one by one through the failure-symptom rule list to check whether the symptoms are established or not; a failure diagnosis result is reasoned if the failures are established; regarding gear failure diagnosis, a radial basis function neutral network (RBFNN) method is adopted to diagnose the failures; and after the diagnosis technology is adopted on the heliostat, safety of a heat absorber and accessory equipment thereof can be guaranteed.
Description
technical field [0001] The present invention relates to a fault diagnosis technology of a key equipment heliostat in a solar thermal power station, specifically a fault diagnosis and analysis method for a solar tower thermal power station heliostat based on an expert system and artificial intelligence in parallel, It belongs to the field of energy power engineering and automatic control engineering. Background technique [0002] The tower-type solar thermal power station uses a heliostat field that independently tracks the sun to gather sunlight to a heat absorber fixed on the top of the tower to generate high temperature, heat the working fluid to generate superheated steam or high-temperature gas, and drive the steam turbine to generate electricity The unit or gas turbine generator set generates electricity, converting solar energy into electricity. The tower solar thermal power station mainly includes heliostat field system, solar heat absorber, heat transfer energy stor...
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IPC IPC(8): G01M11/00G01M13/00
Inventor 刘德有许昌郭铁铮万定生王成煜郑源郭苏
Owner HOHAI UNIV