In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …

Regarding the continuing increase of anthropogenic carbon emissions in the power system
with growing energy consumption, researchers have focused on managing the demand side …

With the massive deployment of microgrids (MGs) and energy communities, various
stakeholders have been involved in distribution networks. Due to the underdeveloped …

This paper proposes a data-driven approach for optimal power flow (OPF) based on the
stacked extreme learning machine (SELM) framework. SELM has a fast training speed and …

This paper studies the problem of locating harmonic sources and estimating the distribution
of harmonic voltages in unbalanced three-phase power distribution systems. We develop an …

Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …

Probabilistic optimal power flow (POPF) plays a crucial role in ensuring the economic and
secure operation of power systems with multiple fluctuating loads and renewable energy …

This paper proposes a hybrid data-driven method for fast solutions of preventive security-
constrained optimal power flow (SCOPF) of power systems. The proposed method …

This paper provides an overview of the challenges and pathways involved in achieving a
low-carbon-oriented energy transition roadmap and power system planning strategy. The …

This article proposes a scheme aiming at solving the reconfiguration problem of distribution
power network (DPN) with high wind power penetrations. The virtue of the presented …