A Power Quality Disturbance Analysis Method Based on Blockchain and Secure Computation

The nonlinear characteristics in power grid can extremely easy lead to power quality disturbance and destroy the stability of the power grid operation. Most of existing power quality disturbance analysis methods transmit the collected electrical signals to the central server to extract the statistical characteristic and then the models are built by machine learning. However, in real environment there are issues such as weak privacy protection, complicated facility environment and over-reliance on artificial experience in models. For this reason, a power quality disturbance analysis method based on blockchain and secure computation was proposed. Firstly, a private chain based on the smart contract and the federated learning was constructed to protect data privacy and by means of certificateless encryption the device identity creditability was ensured. Secondly, by use of the model parameters based on Paillier cryptosystem the gradient security during the deep learning process was protected by homomorphic encryption, and an anomaly analysis model of power quality disturbance(abbr. PQD) based on variational mode decomposition and long short-term memory network was established to cover the shortage of traditional statistical feature modeling in coverage rate and accuracy. The experimental results on a real microgrid show that using the proposed model, the privacy, usability, security and accuracy can be taken into account.