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Hybrid PSO Based K Nearest Neighbor Classifier for Intelligent Fault Diagnosis

Xiaoxia Wang, Liangyu Ma, Tao Wang


The early fault diagnosis of a thermal system is of premier importance for the safe and reliable operation of the whole power generation unit. It is a difficult task due to the structural complexity of the thermal system and the variable operating points of the system. A compact K nearest neighbor (KNN) classifier is proposed in this paper for identifying faults in a power plant thermal system operating at different load level. Particle swarm optimization algorithm is employed to generate a minimal set of prototypes to correctly represent a training set in order to improve the classification performance. After constructing an optimal set of prototypes for each class, K nearest neighbor classifier is utilized to diagnose faults by using the set of prototypes as reference. Typical faults of the high-pressure feedwater heater system are simulated under several different operating points on a full-scope simulator of a 600-MW coal-fired power unit and the results demonstrate the validity of the proposed approach.


K nearest neighbor classification, particle swarm optimization, power plant, fault diagnosis.

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