Privacy-Preserving Distributed Entropy Filtering for Microgrids With Innovation Decomposition

Article Privacy-Preserving Distributed Entropy Filtering for Microgrids With Innovation Decomposition Yan Liang 1, Yangkai Chen 2, Dengfeng Pan 3,*, and Haifang Song 2 1 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China 2 Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 3 School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia * Correspondence: [email protected] Received: 31 December 2024 Accepted: 11 March 2025 Published: 25 March 2025 Abstract: Data leakage and cyberattacks are usually inevitable due mainly to the vulnerability of deployed communication networks. The paper proposes a distributed privacy-preserving filter based on the maximum correlation criteria for microgrids. An improved distributed structure is first constructed via adding decomposed innovation from neighbors in update steps. Then, an improved version of the maximum correntropy criterion is defined to evaluate the local filtering performance as well as the consensus performance by adding an innovation-related term. In light of fixed-point iterations and the adopted filter structure, the desired filter gains are obtained recursively by optimizing the proposed index. Furthermore, the profound analysis is performed to disclose that the filtering covariance of external eavesdroppers is larger than target-side filters and hence the privacy of the microgrids can be protected. Finally, an example is exploited to verify its effectiveness.