A challenging problem in the protection of power transformers is the fault detection and
diagnosis (FDD). FDD has an essential role in the reliability and safety of modern power …

Compared with conventional methods of fault diagnosis for power transformers, which have
defects such as imperfect encoding and too absolute encoding boundaries, this paper …

Electric vehicle (EV) is a promising technology for reducing environmental impacts of road
transport. In this paper, a framework for optimal design of battery charging/swap stations in …

Industrial Internet-of-Things (IIoT) are highly vulnerable to cyber-attacks due to their open
deployment in unattended environments. Intrusion detection is an efficient solution to …

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due
to their nonlinear modeling capabilities. However, it is well known that in general, traditional …

In this paper, a computational framework for integrating wind power uncertainty and carbon
tax in economic dispatch (ED) model is developed. The probability of stochastic wind power …

A model-free based neural network control with time-delay estimation (TDE-MFNNC) for
lower extremity exoskeleton is presented in this paper. The lower limb exoskeleton which …

Accurate transformer fault diagnosis is crucial for maintaining the power system stability.
Due the complex operation condition of the transformer, its faults are with the characteristic …

The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis
(DGA) has been studied in the field of power transformer fault diagnosis. However, there are …

This book chapter proposes a new training algorithms for Radial Basis Function (RBF) using
a recently proposed optimization algorithm called Moth–Flame Optimizer (MFO). After …