Abstract
Arc fault is one of the important reasons of electrical fires. In virtue of cross talk, randomness and weakness of series arc faults in low-voltage circuits, very few of techniques have been well used to protect loads from series arc faults. Thus, a novel detection method based on support vector machine is developed in this paper. If series arc fault occurs, high frequency signal energy in circuit will increase a lot, and current cycle integrals are variable and erratic. However, high frequency signal energy will be influenced by cross talk in a nearby branch circuit. Besides, current cycle integrals will also vary while the working states of circuit changed. To better describe series arc faults, two characteristics include high frequency signal energy and current integral difference are extracted as support vectors. Based on these support vectors, least squares support vector machine is used to distinguish series arc faults from normal working states. The validity of the developed method is verified via an arc fault experimental platform set up. The results show that series arc faults are well detected based on the developed method.
Highlights
In the light of statistical data from fire services, arc faults, over currents, short circuits, and leakages are the main reasons of electrical fires, and over 90% of electrical fires are caused by them [1,2,3]
In the experiments of the testing samples, the results show that the error rate of least squares support vector machine (LSSVM) is 4.0%
The main conclusions are: 1) High frequency signal energy of arc will increase a lot when series arc fault occurs in circuit, but it will be influenced by cross talk in a nearby branch circuit
Summary
In the light of statistical data from fire services, arc faults, over currents, short circuits, and leakages are the main reasons of electrical fires, and over 90% of electrical fires are caused by them [1,2,3]. There are three types of arc faults which contain earth arc fault, parallel arc fault and series arc fault. The characteristics of the former two are respectively similar to ground fault and over current. They are easy to be diagnosed [4]. The features of high frequency signals and current integrals are extracted as support vectors to classify normal states and arc fault states. A novel series arc fault detection method
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