Power Quality Monitors Displacement Based on Voltage Sags Propagation Mechanism and Grid Reliability Indexes

Abstract

Nowadays, voltage sag continues to remain a critical PQ issue in the industry. Since it is not possible to install a voltage analyzer on every node, optimal monitoring locations must be determined. However, during the PQ monitoring campaign of the Lithuanian DSO grid, the execution of this task was inhibited by the lack of knowledge and literature about the fundamentals of voltage sags propagation. Therefore, the first part of this paper investigates the propagation paths of voltage sags by using a voltage sag matrix method on the created test grid, which for the first time is BRELL-based. This paper is the first to not only investigate voltage sags propagation paths (both downward and upward) in detail, but also to investigate them complexly, encompassing all four types of short-circuits, both voltages (phase-to-phase and phase-to-ground), and interconnections with other PQ events (interruption, transient, unbalance). The result has established a generalization of voltage sags propagation mechanisms by inductive reasoning and laid the foundation for the further development of PQ theory. The second part of this paper focuses on the Lithuanian DSO grid and is one of the first to investigate voltage sags/interruptions through the prism of both relationship with reliability indexes (SAIFI and SAIDI) and primary causes. For this purpose, we created our own scientific methodology, applying known probabilistic and statistical methods along with newly proposed approaches, in particular for ACR success evaluation and estimation of functions similarity.

IIR Shelving Filter, Support Vector Machine and k-Nearest Neighbors Algorithm Application for Voltage Transients and Short-Duration RMS Variations Analysis

“…In this paper, the small paragraph of scientific achievements of earlier times is given not without reason-all ideas (approaches) are potentially suitable (beneficial) for joint PQ and AI research. For example, the usage of logical reasoning in the field of PQ to discuss the results obtained can be found in [9]. Soft skills and philosophical insights during results interpretation are just as important as software code writing; thus, it is inseparable from the knowledge in philosophy, development of scientific ideas and their spread, etc.…”

“…These instances are related with the following tasks: cybersecurity, localization of PQ emission source (fault), impact assessment (e.g., damage to end-user equipment), and features extraction (also including characterization and detection). For example, the localization can be determined either qualitatively or quantitatively (Figure 1a): the identification of voltage sag source side (upstream or downstream) is a qualitative approach (see [9] for more information), while the estimation of a distance to the fault node in units of length is a quantitative approach (see [2] for more information about the case of PQ monitors displacement algorithms). Please note that the classification itself often includes a wider range of tasks such as an event detection and features extraction.…”

“…For example, in this work, transients are classified by their primary cause (commutation or atmospheric), and short circuits are classified by their type (three-phase, two-phase-to-ground, two-phase, and single-phase). However, currently, there is a lack of research (data) about primary causes of PQ events [9].…”

“…8 Deep features are complex patterns extracted from the intermediate layers of CNN. 9 The distance (location) is expressed in numbers. 10 All four types of short circuit are encompassed.…”

The influence of the operation of voltage transformers of 35/10 kV substations on the errors of the electricity consumption accounting system

“…Такі системи використовується для моніторингу, контролю, а також для збору інформації щодо споживання електроенергії в реальному часі [1][2][3]. У [4] наведені методи вимірювання та інтерпретаціі результатів моніторингу параметрів АСКОЕ в електроенергетичних системах. В своєму досліджені автори [5] пропонують застосування штучного інтелекту для стохастичного оцінювання похибки вимірювань в системах електроспоживання.…”