A Hybrid Approach for Detecting Anomalies in Microgrid with Blockchain Integration

Abstract

The exponential growth in power consumption demands a robust method to address and identify irregularities in distribution systems. This paper presents a novel approach integrating advanced machine learning with blockchain technology to enhance microgrid energy systems' anomaly detection and response times. The Isolation Forest algorithm is employed to identify outliers in power consumption. Custom statistical methods, such as Sudden Change Detection and Z-score, detect abrupt changes in power consumption patterns and statistical anomalies. To ensure prompt and automatic responses to identified irregularities, smart contracts are deployed on the Ethereum platform, enabling the instantaneous implementation of corrective measures. The system's real-time capabilities are enabled by the Web3 library, which establishes a direct connection between anomaly detection algorithms and smart contract execution, making the solution viable for practical deployment. The proposed model is demonstrated using a microgrid power consumption dataset, highlighting how smart contracts enable real-time detection and notification of anomalies. Upon identifying irregular power consumption, the smart contract recommends corrective actions, such as initiating load shedding and ensuring timely and transparent intervention. This integration of blockchain technology enhances the accuracy and efficiency of anomaly detection and provides a decentralized and autonomous solution for alerting system operators, reinforcing the security and reliability of microgrid energy systems.