Internet of Things-Based Transformer Load Disturbance Early Detection System with Secure Storage Using Blockchain

Abstract

Distribution transformers are one of the primary elements in power system, Over currents voltage sags, and swells can destroy or derange equipment and lead to service interruptions. In this paper, a light-weight IoT-enabled solution is presented for early detection of the transformer load abnormality and secure storage of the transformer data using the blockchain and IPFS (InterPlanetary File System). Load conditions are categorized as normal, warning, and fault based on the thresholding classification method. Measured results are hashed using SHA-256 and stored in Base Sepolia Testnet with smart contracts to provide an immutable and tamper-proof guarantee, while the original IoT data are kept in IPFS for storage that requires high scalability and low cost. Simulation results demonstrate that our proposed approach can not only accurately detect anomalies that occurred, but also guarantee the integrity, transparency, and audibility of the monitoring data. Due to its efficiency and flexibility, the system has a great potential to be deployed in a smart grid environment, which can be further developed in the future with mechanisms of tokenization for the incentive of continuous data reporting.

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