Artificial intelligence and magnetic field based fault detection system for microgrid

Kumar, Aman; Kumar, Prince; Gadekar, Paritosh; Jain, Akshita; Jaiswal, Supriya; Khadse, Chetan B.

doi:10.1109/mysurucon52639.2021.9641536

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quick acceptance of AI in the control theory research domain in the form of intelligent control techniques [ 12 ] is mainly the result of machine learning (ML) progress, through strategies such as supervised learning (SL) and reinforcement learning (RL). In the early stages of investigation, AI technology was primarily integrated into control applications as a fault and anomaly detector or as an intelligent scheduler for module coordination [ 13 , 14 , 15 , 16 ]. However, the integration of ML methodologies rapidly escalated due to their efficient operation and quick responsiveness.…”

Section: Introductionmentioning

confidence: 99%

Revisiting Classical Controller Design and Tuning with Genetic Programming

García,

Velasco,

Angulo

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP’s functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications.

show abstract