Open Access Open Access  Restricted Access Subscription or Fee Access

Hybrid PSO Based K Nearest Neighbor Classifier for Intelligent Fault Diagnosis

Xiaoxia Wang, Liangyu Ma, Tao Wang

Abstract


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.

Keywords


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

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.