Bandwidth-aware and anti-outlier fusion-event-driven fault detection of networked photovoltaic microgrids with measurement outliers

Event-driven fault detection of networked photo-voltaic microgrids usually requires accurate measured data. In real engineering environment, stochastic fluctuating measurements caused by system disturbances, measurement noises and outliers are inevitable. However, existing event-driven mechanisms using single measured data are sensitive to such stochastic fluctuations, which could lead to redundant events induced by disturbances and noises, and false events and fault alarms resulted from measurement outliers, respectively. In order to solve this problem, a novel bandwidth-aware and anti-outlier fusion-event-driven mechanism is proposed for the first time. First, to cut down the redundant events, the fusion of historical measurements is utilized to reduce the stochastic fluctuations and smooth the measurements. Second, an extra upper threshold constraint is introduced to exclude the false events and fault alarms. In addition, a dynamic triggering threshold relying on the bandwidth status and the fused measurements is constructed to improve the mechanism’s flexibility to complex and time-varying network situation. Then, the closed-loop event-triggered H_∞ fault detection system with transmission delays is modeled as a time-varying delay system. With the help of Simpson rule, some sufficient conditions formulated by linear matrix inequalities are derived for co-designing the residual filter gain of fault detector and the triggering matrix. Finally, some simulation outcomes are presented for demonstration of the theoretical claims.