A Comprehensive Overview of Digital Signal Processing Methods for Voltage Disturbance Detection and Analysis in Modern Distribution Grids with Distributed Generation

Abstract

The rapid trends towards smart grids and implementation of distributed generation (DG) and renewable energy sources bring new challenges in power quality domain. Modern distribution grids have a higher amount of voltage disturbances due to DGs power converters, nonlinear loads and system faults. The ongoing research on development of new, faster and more reliable techniques for detection and analysis of voltage variations in order to prevent malfunction of equipment or to support gird and enhanced its operation, is at present very important topic. The paper presents a comprehensive overview of voltage disturbances detection and analysis methods, which use different digital signal processing techniques for use in modern distribution grids. Comprehensive, critical literature review encompassed wide range of methods, from standard, well-known ones over digital signal processing (DSP) ones to the advanced, hybrid methods. Simulation and laboratory evaluation of methods applied as part of gridtie inverter control is presented. Advantages and disadvantages are underlined and critical evaluation of selected methods is presented. The main criteria for evaluation of methods are the speed of detection, a reliability of methods, analysis capability and computational complexity (i.e. cost of application).

Redundant Photo-Voltaic Power Cell in a Highly Reliable System

“…change occurs, where R DS(on) 25 is the open drain-source channel resistor at 25 • C. If the temperature of the semiconductor layer increases by 100 • C, the resistance R DS(on)25 increases by 1.5 times. For other types of MOSFETs, this can be doubled.…”

“…Model-based [16][17][18][19][20][21]: the outputs of the system-model and the outputs of the real system are compared to each other. • Signal-based [22][23][24][25][26][27][28]: a diagnostic decision is made based on the measured signal. • Knowledge-based [29][30][31][32][33]: evaluation is based on a large volume of historic data.…”

A Highly Reliable, Modular, Redundant and Self-Monitoring PSU Architecture

“…In terms of robustness of such systems successful results have been achieved in the field of fault diagnosis and fault tolerant techniques [9] - [11]. Basically, we can define the following categories for fault diagnosis methods: [12]- [14] The outputs of the system-model and the outputs of the real system is compared  Signal-based [15]- [18] A diagnostic decision is made based on the measured signal  Knowledge-based [19]- [21] A large volume of historic data is needed  Hybrid and active [22]- [24] Combination of the previous methods based on their advantages…”