Securing the green grid: A data anomaly detection method for mitigating cyberattacks on smart meter measurements

Abstract

Smart meters, being a vital component in the advanced metering infrastructure (AMI), provide an opportunity to remotely monitor and control power usage and act like a bridge between customers and utilities. The installation of millions of smart meters in the power grid is a step forward towards a green transition. However, it also constitutes a massive cybersecurity vulnerability. Cyberattacks on AMI can result in inaccurate billing, energy theft, service disruptions, privacy breaches, network vulnerabilities, and malware distribution. Thus, utility companies should implement robust cyber-security measures to mitigate such risks. In order to assess the impact of cybersecurity breaches on AMI, this paper presents a cyber-attack scenario on grid measurements obtained via smart meters and assesses the stochastic grid estimations under attack. This paper also presents an efficient method for the detection and identification of anomalous data within the power grid by leveraging the distance between measurements and the confidence ellipse centered around the estimated value. To assess the proposed method, a comparative analysis is done against the chi-square test for detection and the largest normalized distribution test for the identification of bad data. Furthermore, by using a Danish low-voltage grid as a base case, this paper introduces two test cases to evaluate the performance of the proposed method under single and multiple-node cyber-attacks on the grid state estimation. Results show a notable improvement in accuracy when using the proposed method. Additionally, based on these numerical results, protective countermeasures are presented for the grid.