Optimal feature selection for islanding detection in distributed generation. Issue 3 (17th September 2018)

• Record Type: Journal Article
• Authors: Chandak, Sheetal; Mishra, Manohar; Nayak, Subrat; Rout, Pravat Kumar
• Is Part Of: IET smart grid. Volume 1:Issue 3(2018)
• Page Start: 85
• Page End: 95
• Publication Date: 2018-09-17
• Subjects: pattern classification -- feature extraction -- distributed power generation -- power distribution faults -- learning (artificial intelligence) -- IEEE standards -- invertors -- evolutionary computation -- power engineering computing; multiobjective differential evolution algorithm -- kernel‐based extreme learning machine classifier -- optimum features -- standard objective functions -- designed IEEE 13 bus system -- IEEE 1547 standards -- selected optimal features -- islanded condition -- distributed generators -- optimal feature selection -- islanding detection -- distributed generation -- distributed power generation -- distribution system -- teething problems -- system protection -- anti‐islanding techniques -- feature evaluation -- particular detection feature -- wrapper feature selection approach; B0260 Optimisation techniques -- B8120J Distribution networks -- C1180 Optimisation techniques -- C6170K Knowledge engineering techniques -- C7410B Power engineering computing -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0021
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : The integration of the distributed power generation into a distribution system comes with several system problems. One of the teething problems related to system protection is islanding detection. Various anti‐islanding techniques based on feature evaluation were proposed in the recent past. However, they overlook the need for justifying the selection of a particular detection feature among all the possible measures. In this study, a wrapper feature selection approach is proposed where a modified multi‐objective differential evolution algorithm is coupled with a kernel‐based extreme learning machine classifier. To select the optimum features, five standard objective functions have been considered, such as dependability, security, accuracy, F‐measure, and the number of features. About 1864 cases have been generated from the designed IEEE 13 bus system to extract the sensitive features. IEEE 1547 standards have been considered while designing and testing the IEEE 13 bus system against islanding. The selected optimal features detect the islanded condition decisively for both synchronous and inverter‐based distributed generators. The features also validate their performance under noisy environment accurately with lesser computational time.