Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory

Abstract

Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages.

Power Grid Monitoring Event Recognition Method Integrating Knowledge Graph and Deep Learning

“…With massive distributed resources connected to the power grid, the alarm information increases daily. The traditional signal-oriented monitoring method is easy to cause a large amount of alarm information to be accumulated in the alarm window, which brings enormous pressure to the monitoring personnel, resulting in missed judgments and misjudgments (Sun et al, 2019;Huang et al, 2021). In order to improve the processing efficiency of alarm information, the power grid dispatching department actively promotes the "eventization" of alarm information.…”

Data-Driven Anomaly Detection and Event Log Profiling of SCADA Alarms

“…Intelligent alarm processing in power systems is not new; the first works were mainly rule-based expert systems that filtered and prioritized alarms to provide information to human operators [6]. According to Sun et al, current alarmclassification methods are mainly based on rules and, thus, the authors propose a new classification method based on information theory (using four definitions for information entropy) and on an analytic hierarchy process to classify alarm messages (using alarm message entropy as indices) [7]. Moreover, as indicated in [8], the majority of said early works were essentially fault location systems.…”

“…• Data-driven methodology (not based in optimization problems like in [17]) to segment and categorize historical event log data, helping human operators to identify different groups of occurrences in HV lines automatically, and exclusively based on their event profiles (i.e., without using a pre-existing class in a supervised learning fashion, e.g., [5]). The present work can also leverage from the information-based approach proposed in [7] to evaluate the information value of alarm messages and alarm pre-processing. The remainder of this paper is organized as follows: Section II describes the data analytics framework, including the main aspects and challenges of the raw dataset and an overview of the data pre-processing pipeline.…”