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1. Model and ventilator performance online monitoring Based on neural network and mechanism ventilator performance monitoring combined model, successfully overcome the lack of accurate measurement of fluid flow through large diameter. In addition, with the popularization of distributed control systems (DCS) in power plants, the comprehensive utilization of information in DCS has become a new research direction. Taking into account the existing information in the DCS, combined with the on-site DCS system, only the fan inlet and outlet differential pressure sensor needs to be installed to complete the comprehensive monitoring of the important performance parameters of the fan. The fan regulator opening, speed, and motor side parameters required for performance monitoring can be accomplished by neural network-based ventilator performance monitoring combined modeling DCS communication.
2. Model and Ventilation System Pipe Network Fault Diagnosis In the actual operation of the power plant ventilation system, due to the operational complexity of the flue gas system, clogging and leakage in the pipe network sometimes occur. It can be seen from the above analysis that the model obtained above is a model for characterizing the performance characteristics of the ventilator itself, and has high sensitivity to failures in the pipe network that cause changes in pipe network resistance. The comprehensive resistance coefficient U of the pipe network resistance characteristic curve obtained by the model monitoring and the pressure signal of the important equipment inlet and outlet of the existing flue gas system in the DCS (furnace pressure, superheater, economizer, air preheater, dust collector, fan outlet) Pressure, etc., can constitute the feature vector of pipeline network fault diagnosis, and establish an online learning model of recurrent neural network suitable for real-time modeling, which can complete the diagnosis of the ash blocking and air pre-leakage of different parts of the pipe network system.
in conclusion
(1) The artificial neural network is used to fit the surface of the through-modulation performance curve with the form transformation, and the fitting precision is high, which can fully meet the engineering application.
(2) Established a model of ventilator performance monitoring modeled by neural network combined with DCS. The model is suitable for the fan to operate within the similar law. For the model of the shifting situation, the fan similarity theory is used. Relationships can be exported.
(3) This method can solve the faults such as surge prediction and rotational separation diagnosis when studying the law of the small flow area of ​​the ventilator.
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