Exploring the effectiveness of adaptive randomized sine cosine algorithm in wind integrated scenario based power system optimization with FACTS devices.

In modern power systems, increasing transmission efficiency and responsiveness is necessary to accommodate rising demand and constrained infrastructure development. In this study, the Adaptive Randomized Sine Cosine Algorithm (ARSCA) is introduced to solve the problem of optimal placement and settings of Flexible AC Transmission System (FACTS) devices-Thyristor-Controlled Series Capacitors (TCSC), Thyristor-Controlled Phase Shifters (TCPS), and Static VAR Compensators (SVC)-in the IEEE 30-bus test system. With dynamic load scenarios, ARSCA was shown to perform better by minimizing active power losses to 1.

Robust fault detection and classification in power transmission lines via ensemble machine learning models.

Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms-including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks-are evaluated.