Transient stability enhancement of power grid by neural network controlled BFCL considering cyber-attacks

Performance improvement of DFIG‐based wind farms using NARMA‐L2 controlled bridge‐type flux coupling non‐superconducting fault current limiter

“…Also, the cooperative control schemes require an appropriate software control algorithm combined with the FCLs to boost the performance of the FCLs. Over the years, different types of controllers have been used with different FCLs [19,21,29,30,32,[37][38][39][40][41][42][43][44]. The controllers can be initially classified into two categories, i.e.…”

“…The conventional controllers work in a straight‐forward way and they do not consider the fault severity such as the non‐linear feedback controllers do. The literature agrees that the non‐linear feedback controllers always perform better than those conventional controllers [38–40]. The non‐linear feedback controllers can be divided into two categories, i.e.…”

“…The model‐free intelligent non‐linear controllers can read the fault severity and update itself according to the system requirements and they do not need the dynamic equations of the system. The non‐linear equation‐based controllers [36, 41], fuzzy logic controller [32, 38], adaptive network‐based fuzzy interference system [40, 45], artificial neural network [39], genetic algorithm‐based optimisation [44] etc. are the convenient model‐free non‐linear control and optimisation techniques that could suit for FRT augmentation of the DFIGs.…”