Uncertain Information Combination for Decision Making in Smart Grid BDI Agent Systems

In a smart grid SCADA (supervisory control and data acquisition) system, sensor information (e.g. temperature, voltage, frequency, etc.) from heterogeneous sources can be used to reason about the true system state (e.g. faults, attacks, etc.). Before this is possible, it is necessary to combine information in a consistent way. However, information may be uncertain or incomplete while the sensors may be unreliable or conflicting. To address these issues, we apply Dempster-Shafer (DS) theory to model the information from each source as a mass function. Each mass function is then discounted to reflect the reliability of the source. Finally, relevant mass functions (after evidence propagation) are combined using a contextdependent combination rule to produce a single combined mass function used for reasoning. We model a smart grid SCADA system in the beliefdesire-intention (BDI) multi-agent framework to demonstrate how our approach can be used to handle the combined uncertain sensor information. In particular, the combined mass function is transformed into a probability distribution for decision-making. Based on this result, the agent can determine which state is most plausible and insert a corresponding AgentSpeak belief atom into its belief base. These beliefs about the environment affect the selection of predefined plans, which in turn determine how the agent will behave. We also identify conditions when a combination should occur to ensure the reactiveness of the agent.