Comprehensive review of IDMs in DG systems. Issue 1 (24th January 2019)

• Record Type: Journal Article
• Authors: Manikonda, Santhosh K.G.; Gaonkar, Dattatraya Narayan
• Is Part Of: IET smart grid. Volume 2:Issue 1(2019)
• Page Start: 11
• Page End: 24
• Publication Date: 2019-01-24
• Subjects: distributed power generation -- signal classification -- signal detection -- power system interconnection; computational intelligence methods -- IDMs -- detection time -- detection accuracy -- DG systems -- distributed generation -- renewable energy sources -- islanding detection methods -- classical methods -- signal processing‐based methods -- SP‐based methods -- SP; B6140M Signal detection -- B8110B Power system management, operation and economics -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0096
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Distributed generation (DG) offers solution to the ever increasing energy needs by generating energy at the consumer end, in most cases, by means of renewable energy sources. Islanding detection is an important aspect of interconnecting a DG to the utility. This study presents comprehensive review of various islanding detection techniques along with their relative advantages and disadvantages. A broad classification of islanding detection methods (IDMs) is laid out as classical methods, signal processing (SP)‐based methods, and computational intelligence‐based methods with a focus on SP‐based methods and computational intelligence‐based methods. The evolution of SP techniques used for islanding detection is presented along with the merits and shortcomings of each technique. Furthermore, the advent of computational intelligence methods based IDMs are discussed along with their merits and demerits. An insight into various islanding methods based on quantitative measures of performance indices such as detection time, detection accuracy, and efficiency are tabulated and presented. Finally, the prospective direction of research for IDMs is also presented.