Battery energy storage systems (BESSs) play a key role in the renewable energy transition. Meanwhile, BESSs along with other electric grid components are leveraging the Internet-of-things paradigm. As a downside, they become vulnerable to cyberattacks. The detection of cyberattacks against BESSs is becoming crucial for system redundancy. We identified a gap in the existing BESS defense research and formulated new types of attacks against a BESS and their detection methods. The attack detection is divided into a forecast-based approach and long-term pattern analysis. We perform a main factor analysis of machine-learning-based methods to forecast the behavior of a BESS. In addition, we observe approaches that can be adapted for the BESS cyber secure design. To provide a thorough investigation, the attacks are classified based on a targeted battery service along with data features that the attack targets. 

Cyberattack detection methods for battery energy storage systems

In this paper, a novel approach for predicting voltage collapse point based on a quadratic line voltage stability index (q-LVSI) and an auditory machine intelligence technique called the AMI is presented. The technique is applied to some buses of the Nigerian 330kV-30bus power network. In order to validate the proposed technique, a comparison is made with the Group Method of Data Handling for time series (GMDH time-series ) which is a state-of-the-art polynomial function fitting neural network based on inductive learning and self-organization. The results of simulation studies show that the AMI technique is competitive with the GMDH time-series technique for a number of experimental simulation runs.

Alex. O. Idachaba

Papers

Predicting Voltage Stability Indices of Nigerian 330kV 30 Bus Power Network Using an Auditory Machine Intelligence Technique