MACHINE LEARNING USING VERSION SPACES FOR A POWER DISTRIBUTION NETWORK FAULT DIAGNOSTICIAN

Abstract There has been much interest in the application of expert systems to a wide variety of power system problems. One important application is the diagnosis of electrical faults in power distribution systems. A common problem with expert systems is the ‘knowledge acquisition bottleneck’ which arises with the generation of rules for the expert system, and there is benefit in automating this procedure as much as possible. This paper presents a fault diagnostician which uses a version space to learn from data in a SCADA system. The end user specifies background knowledge for use by the version space algorithm, but other than this the procedure is automatic. A test system was implemented and evaluated with the aid of a distribution network simulator. The results of this evaluation are presented.