Real time power grid anomaly alarm based on optimized Apriori algorithm and cloud model

The power grid data is highly complex and diverse, and how to effectively mine and integrate this data to extract valuable information for risk assessment is currently a challenge. The optimized Apriori algorithm can mine valuable data, and cloud models can effectively model using power grid data to reflect the actual operation status of the power grid. Therefore, a real-time power grid anomaly alarm based on optimized Apriori algorithm and cloud model is proposed. Build a logical database based on the operation data of power grid equipment, combine the testing database of association rules and the optimized Apriori algorithm, mine valuable data from the logical database, and establish an evaluation index system. Using cloud models to construct a power grid risk assessment model for risk assessment of indicator data. A visual graph analysis model was built using integrated data of power grid operation risks to achieve real-time power grid alerts. The experimental results show that the recall rate of the optimized Apriori algorithm can reach up to 0.92, and the comprehensive evaluation index value can reach up to 0.98. This method can accurately mine valuable data on power grid operation and automatically filter interference, effectively evaluate power grid operation risks, and display alarm results on the alarm interface based on the time of risk occurrence.