Optimum frequency response with decentralized drivers in power system

Abstract

This document presents a novel methodology to optimize the power system frequency response based on the implementation of decentralized controllers. The proposed method focuses on the control of the internal rotor angle in the selected generator, which allows its governor to act as a decentralized controller that requires as input local measurements. Through a proposed optimization process, it is first possible to establish a set of optimal parameters for each controller and, second, to determine the required phasor measurement units (PMUs) and their locations that are necessary to be installed in a power system. This comprehensive proposal is based on the time domain evaluation of the frequency response when multiple scenarios of deviations between demand and generation occur. Finally, the new proposal is applied to the IEEE 39-bus test system to validate its advantages; for this, the test power system as well as the proposed controllers are implemented in DIgSILENT PowerFactory, along with Python to manage the optimization process.

ACO-Based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications

“…In the domains of science and engineering, several metaheuristic algorithms have garnered substantial popularity, including the genetic algorithm [14], cuckoo search (CS) [15], ant colony optimization (ACO), and particle swarm optimization (PSO) [16]. Notably, Mohiz et al [15] delved into a comprehensive exploration of diverse metaheuristic algorithms, discerning their effectiveness in optimizing task placement within network-on-chip cores.…”