Enhancing SCADA Alarm Management for Power Grids using Large Language Models

SCADA alarm management poses many challenges for power grid control room operators. Vast quantities of information must be managed and filtered by human operators to identify the specific alarm of interest so that system issues can be promptly addressed. The use of knowledge based systems and decision support systems to assist in industrial decision making is well established but comes with heavy cost burdens for development and maintenance. This work demonstrates a possible approach to addressing this cost issue by utilising large language models (LLMs) to parse SCADA alarms and extract information to construct a knowledge base in the form of a knowledge graph. This knowledge base can then be used to construct a decision support system suitable for control room operators to make plain language queries that assist in their problem solving. The decision support approach facilitates better and faster decision making assisting the effective management of electrical distribution networks by control room operators. The case study shown in this work demonstrates that LLMs show promising capacity for knowledge base constructions and have the potential to unlock easy access and insights from large volumes of SCADA alarm data.