Stability Analysis of Slopes Using Finite Element Method and Genetic Algorithm

Bhandary, Radhika; Krishnamoorthy, Agrahara; Rao, Asha Udaya

doi:10.1007/s10706-018-0730-5

Cited by 12 publications

(1 citation statement)

References 34 publications

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“…The traditional analysis found it difficult in the uncertain complex power system to operate/control for the future development of microgrid. Conventional optimization methods like a genetic algorithm (GA), 11,12 ant colony optimization (ACO), 13,14 and particle swarm optimization (PSO) 15,16 were categories under metaheuristic method and its convergence to attain an optimal solution to specified problems are less in robustness and its lack in computation with machine learning. AI-based models are trained with big data collected from the wide-area monitoring system (WAMS) to attain deep learning.…”

Section: Need Of Algorithm In Microgridmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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