Micro Grid stability enhancement using SVC with fuzzy model reference learning controller algorithm

Eldessouky, Ahmed

doi:10.1109/sege.2015.7324595

Cited by 4 publications

(2 citation statements)

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“…It performs with a power system stabilizer and interline power flow controller under a multi‐area combinational system. Paper 118 proposed a novel method to enhance stability in a microgrid. Fuzzy‐based reference learning algorithm using SVC improves stability in the power system.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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“…Control scheme for the Fuzzy logic aided VAR stabilizer is presented in Figure12 b). Some extensive research has been done with an adaptive fuzzy controller and PI controller in [96]. It would be more suitable to develop two parallel controllers as instantaneous reactive power compensator and fuzzy controller which is equipped with the adaptation algorithm.…”

Section: Ac Voltage Controllingmentioning

confidence: 99%

Review of Applications of Fuzzy Logic in Multi-Agent-Based Control System of AC-DC Hybrid Microgrid

2019

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High penetration of renewable energies to smart grid technologies has lead the traditional electrical transmission and distribution network to undergo a significant evolution. Here, advanced control strategies are required to ensure the load balance in order to keep the voltage and frequency variation within an acceptable range. With the integration of dc energy sources and storages, another control constraint would be added due to the limitations in a storage system. Among recent developments in control techniques, fuzzy logic and multi agent systems have emerged due to its strong control action in an uncertain environment. Researchers have made satisfactory efforts in optimizing hybrid renewable energy sources using these technologies. This paper strives to highlight the approaches of controlling the energy storage systems, dc voltage, frequency, and ac voltage with the aid of the fuzzy logic and multi agent systems related controllers.

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