Reliability enhancement of hybrid microgrid protection against communication data loss and converter faults using cubic-spline interpolation, Savitzky Golay filtering and GRU network

Abstract

A primary challenge towards adoption of hybrid AC-DC microgrid pertains to the complexity of the protection scheme. Among other challenges, the high resemblance of voltage and current dynamics for converter faults and line faults, hinders use of threshold-based protection schemes. Further the high dependence of the protection mechanism on the communication network results in reduced reliability during data packet loss in the communication channel. In this regard, a protection scheme is proposed based on the combination of cubic spline interpolation, Savitzky-Golay (SG) filtering, maximum overlap discrete wavelet packet transform (MODWPT) and gated recurrent unit (GRU)-neural network. The impact of data packet loss (modelled using Markov chain model) in the communication channel has been minimized by reconstructing the original signal from lossy data using spline interpolation and SG filter. With the reconstructed signal, significant attributes are extracted using MODWPT, which are further utilized to perform fault detection, fault classification and faulty section identification using a set of GRU classifiers. Unlike the reported techniques whose fault detection and classification accuracy are severely compromised during packet loss, the signal reconstruction module in the proposed framework makes it immune to packet loss. The inclusion of a module for distinguishing between converter faults and line faults allows for avoiding relay maloperation during switching failures. The proposed scheme has been extensively validated for a wide range of operating and fault scenarios under converter faults, PV array faults, varying degrees of packet loss, burst length and dynamic loading conditions. For packet loss rate ranging from 5 % to 30 % and burst length from 0.0167 s to 0.1 s, the proposed scheme achieves dependability and security in excess of 99.85 % and 99.46 % respectively.