Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

Yin, Linfei; Zhao, Lulin; Yu, Tao; Zhang, Xiaoshun

doi:10.3390/app8112185

Cited by 21 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The suggested controller creates various control techniques depending upon the requirement of the operating scenarios by highly integrated artificial emotional functions including quadratics, exponential, and linear functions, and with the RL elements such as learning rate, reward function, and actions. Moreover, a deep forest RL algorithm is designed in [73] as a preventive strategy for the dual and triple areas AGC problems. The proposed method consists of two components of deep forest and multiple subsidiaries.…”

Section: Artificial Neural Network (Ann) Controlmentioning

confidence: 99%

Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview

et al. 2021

View full text Add to dashboard Cite

Automatic generation control (AGC) is primarily responsible for ensuring the smooth and efficient operation of an electric power system. The main goal of AGC is to keep the operating frequency under prescribed limits and maintain the interchange power at the intended level. Therefore, an AGC system must be supplemented with modern and intelligent control techniques to provide adequate power supply. This paper provides a comprehensive overview of various AGC models in diverse configurations of the power system. Initially, the history of power system AGC models is explored and the basic operation of AGC in a multi-area interconnected power system is presented. An in-depth analysis of various control methods used to mitigate the AGC issues is provided. Application of fast-acting energy storage devices, high voltage direct current (HVDC) interconnections, and flexible AC transmission systems (FACTS) devices in the AGC systems are investigated. Furthermore, AGC systems employed in different renewable energy generation systems are overviewed and are summarized in tabulated form. AGC techniques in different configurations of microgrid and smart grid are also presented in detail. A thorough overview of various AGC issues in a deregulated power system is provided by considering the different contract scenarios. Moreover, AGC systems with an additional objective of economic dispatch is investigated and an overview of worldwide AGC practices is provided. Finally, the paper concludes with an emphasis on the prospective study in the field of AGC.

show abstract

Section: Artificial Neural Network (Ann) Controlmentioning

confidence: 99%

Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview

et al. 2021

View full text Add to dashboard Cite

show abstract

“…105,106 The smart generation control is proposed using the DQL algorithm under a multiagent grid system to attain robustness. 107,108 The smart generation control proposed 109 to prevent large disturbance in the power system. To maintain thermal limit and energy-efficient in HVAC, an optimal model-based system is to implement.…”

Section: Planning and Stability Of Microgridmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

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

show abstract

“…The first two papers regard the application of ANNs to transmission systems [2] and weak connected or stand-alone distribution systems [3].…”

Section: Artificial Neural Network For Energy Systemsmentioning

confidence: 99%

“…Specifically paper [2] is about the reduction of occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances which are mainly caused by renewables. A preventive strategy for the automatic generation control (AGC) of power systems is proposed, but to mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, a deep forest reinforcement learning (DFRL) method is proposed as a preventive strategy for AGC.…”

Section: Artificial Neural Network For Energy Systemsmentioning

confidence: 99%