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12-09-2018 | S.I.: Emerging Intelligent Algorithms for Edge-of-Things Computing | Issue 5/2019

Neural Computing and Applications 5/2019

Fault classification and detection in wind turbine using Cuckoo-optimized support vector machine

Journal:
Neural Computing and Applications > Issue 5/2019
Authors:
A. Agasthian, Rajendra Pamula, L. A. Kumaraswamidhas

Abstract

Fault detection in wind turbine which is identified with complete system monitoring under multi-fault scenario is proposed. When a fault is detected, its types and location are recognized for easy maintenance. Fault in wind turbines is caused due to the high speed of gearbox, generator bearing and the failures occurred in various parts. In wind farm, wind turbine condition monitoring is used to reduce the maintenance cost and also improves the accuracy. Generally, in wind turbine gearbox condition monitoring using sensor is a gainful method to monitor wind turbine performance and fault. This paper nominates a method to decide the parameters for support vector machine (SVM) in wind turbine called Cuckoo search optimization (CSO). The combination of optimization technique with classification technique is evaluated. MATLAB platform was used to evaluate the various faults under fixed value and gain factor conditions. Comparing the accuracy with SVM, particle swarm optimized SVM and k-nearest neighbor, the proposed fault detection and fault isolation technique (CSO-SVM) is improved by 2.5%, 3.5% and 6.5%, respectively. The result shows the CSO model based on SVM algorithm accomplishes the most accurate fault detection than the past models.

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