Signal processing-based automated fault detection methods for smart grids

Abstract

Automated fault detection methods (AFDMs) are required to provide a more reliable and stable power system operation and protection. The faults in distributed generators (DGs) connected to the grid must be cleared on time according to the defined Grid Codes, so AFDMs are essential for DGs to improve a smart grid infrastructure. The application of the conventional fault detection methods is highly practical. Besides, difficulties in selecting threshold values, false detection conditions, noise effects in measurement, and uncertain fault conditions in several operating conditions make these methods less reliable. This chapter generally consists of five main sections. In the introduction, the current fault detection methods used in DG systems are examined. Then, intelligent power quality detection methods are classified, and AFDMs using signal processing techniques in literature are presented. In the second section, FT-based fault detection methods and, in the third section, WT-based fault detection methods are explained. In Section “Detection of Power Quality Events with Wavelet Transform”, different WT methods are also classified and examined. Section “Wavelet Transform-Based Power Quality Disturbance Detection” presents the experimental results obtained from a DG system in real time using the WT method. In this section, some power quality disturbances are investigated in real time by using WT method under different grid conditions. In the last section, the advantages of automatic fault detection methods based on signal processing are explained. As a result, this chapter will help the researchers about studying automated fault detection systems in smart grids consisting of distributed power generators. © Springer Nature Switzerland AG 2020.