A State-Observer-Based Protection Scheme for AC Microgrids with Recurrent Neural Network Assistance

Abstract

The microgrids operate in tie-up (TU) mode with the main grid normally, and operate in isolation (IN) mode without the main grid during faults. In a dynamic operational regime, protecting the microgrids is highly challenging. This article proposes a new microgrid protection scheme based on a state observer (SO) aided by a recurrent neural network (RNN). Initially, the particle filter (PF) serves as a SO to estimate the measured current/voltage signals from the corresponding bus. Then, a natural log of the difference between the estimated and measured current signal is taken to estimate the per-phase particle filter deviation (PFD). If the PFD of any single phase exceeds the preset threshold limit, the proposed scheme successfully detects and classifies the faults. Finally, the RNN is implemented on the SO-estimated voltage and current signals to retrieve the non-fundamental harmonic features, which are then utilized to compute RNN-based state observation energy (SOE). The directional attributes of the RNN-based SOE are employed for the localization of faults in a microgrid. The scheme is tested using Matlab® Simulink 2022b on an International Electrotechnical Commission (IEC) microgrid test bed. The results indicate the efficacy of the proposed method in the TU and IN operation regimes on radial, loop, and meshed networks. Furthermore, the scheme can detect both high-impedance (HI) and low-impedance (LI) faults with 99.6% of accuracy.

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

“…The proposed method detected faults accurately despite noise and communication delays. Some intelligent methods were also reported in previous work [19], [20]. Reference [21] introduced a fast-tripping protection scheme for DC microgrids, employing TWs and discrete wavelet transform (DWT) to detect high-frequency components of fault currents.…”

An Extensive Overview of Islanding Detection Strategies of Active Distributed Generations in Sustainable Microgrids

“…In addition, due to the halted utility operation caused by unintentional power system islands, ADG unit voltage and frequency levels may become severely impacted [18,19]. Moreover, in an islanding scenario, the ADG units cannot deliver enough fault current to trigger the traditional protective mechanisms [20][21][22]. Such islanding could harm the system's hardware, jeopardize the dependability of the power supply, and endanger the life of the maintenance personnel.…”

Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

“…A novel protection method based on deep learning was proposed in [27] to protect the microgrid from fault incidents. State observer with the recurrent neural network was suggested in ref [28] to identify, classify, and localize various faults. A hybrid classifier approach was utilized in [29] for fault location identification in AC microgrids.…”