A Two-stage Consensus-empowered Measurement Data Sharing Approach for Distribution Networks Based on Lightweight Fuzzy Petri Nets

With the widespread deployment of distribution network equipment, measurement data is growing exponentially. The secure sharing and interactive traceability of massive distribution measurement data present considerable challenges. Therefore, a two-stage consensus-empowered measurement data sharing approach for distribution networks is proposed based on lightweight fuzzy Petri nets (FPNs). First, a two-stage consensus-sharing architecture for distribution measurement data interaction is constructed based on lightweight FPN. Subsequently, a two-stage consensus-empowerment measurement data approach for distribution networks is proposed based on lightweight FPN nodes credibility evaluation. Specifically, the cluster head nodes are selected for adaptive multi-dimensional node trust indicators. These multi-clustering based on dimensional node trust indicators are subsequently trained by a generative adversarial network (GAN) to generate the initial state vector, and an iteration threshold is set to reduce the number of lightweight FPN iterations. Subsequently, the Top-J ranking method is utilized to select consensus nodes with higher trust values and to complete the two-stage consensus within and between clusters. Finally, the superiority of the proposed algorithm is validated through simulations in terms of nodes trustworthiness, consensus delay, and CPU occupancy. Moreover, the ability of secure sharing and interactive traceability of massive distribution measurement data is enhanced.