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Sensors 2014, 14(5), 8794-8809; doi:10.3390/s140508794

Intelligent Prediction of Fan Rotation Stall in Power Plants Based on Pressure Sensor Data Measured In-Situ

1
School of Energy Power and Mechanical Engineering, North China Electric Power University, 619 Yonghua North Street, Baoding, 071003, China
2
Department of Mechanical Engineering, McGill University, 817 Sherbrooke Street West, Montreal, Quebec H3A 0C3, Canada
*
Author to whom correspondence should be addressed.
Received: 2 March 2014 / Revised: 6 May 2014 / Accepted: 8 May 2014 / Published: 19 May 2014
(This article belongs to the Section Physical Sensors)
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Abstract

Blower and exhaust fans consume over 30% of electricity in a thermal power plant, and faults of these fans due to rotation stalls are one of the most frequent reasons for power plant outage failures. To accurately predict the occurrence of fan rotation stalls, we propose a support vector regression machine (SVRM) model that predicts the fan internal pressures during operation, leaving ample time for rotation stall detection. We train the SVRM model using experimental data samples, and perform pressure data prediction using the trained SVRM model. To prove the feasibility of using the SVRM model for rotation stall prediction, we further process the predicted pressure data via wavelet-transform-based stall detection. By comparison of the detection results from the predicted and measured pressure data, we demonstrate that the SVRM model can accurately predict the fan pressure and guarantee reliable stall detection with a time advance of up to 0.0625 s. This superior pressure data prediction capability leaves significant time for effective control and prevention of fan rotation stall faults. This model has great potential for use in intelligent fan systems with stall prevention capability, which will ensure safe operation and improve the energy efficiency of power plants.
Keywords: rotation stall prediction; support vector machine; wavelet transform; energy efficiency rotation stall prediction; support vector machine; wavelet transform; energy efficiency
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Xu, X.; Wang, S.; Liu, J.; Liu, X. Intelligent Prediction of Fan Rotation Stall in Power Plants Based on Pressure Sensor Data Measured In-Situ. Sensors 2014, 14, 8794-8809.

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