A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid

Karim, Miftah Al; Currie, Jonathan; Lie, Tek Tjing

doi:10.1109/isgt-asia.2016.7796454

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…The secondary layer (Figure 12) keeps uncontrolled variations within acceptable limits for load or generation changes [26]. Several techniques have been introduced to manage voltage and frequency deviation at this level, including the Multilayer Perceptron (MLP) model [238], the use of Artificial Neural Networks (ANN) and genetic algorithms (GA) [239], the reinforcement learning (RL) approach [240], the Interval Type 2 (IT2) fuzzy system based on deep reinforcement learning (DRL) [241], the distributed machine learning (ML) method [242], and the Extreme Learning Machine (ELM) technique [243]. These methods have pros and cons, depending on the microgrid scenario.…”

Section: Exploring Ai-based Research Methodologies For Microgrid Controlmentioning

confidence: 99%

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Pillai

Valsala

Raj

et al. 2023

Designs

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Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research.

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Section: Exploring Ai-based Research Methodologies For Microgrid Controlmentioning

confidence: 99%

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Pillai

Valsala

Raj

et al. 2023

Designs

View full text Add to dashboard Cite

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“…It has better compensation accuracy and also allows for plug-and-play operation. In [87], the authors established several neural networks to distribute the secondary compensation based on an unsupervised ML algorithm and according to different load conditions. In [88], a platform based on Redis NoSQL (non-structured query language) database was proposed to implement a DRL-based microgrid MAS system, which provided a new idea for the implementation of ML-based microgrid control.…”

Section: Application Of ML On Secondary Controlmentioning

confidence: 99%

Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Oshnoei

Blaabjerg

et al. 2023

Sustainability

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Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

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“…For example, Karim et al [125] bring up a distributed secondary control method for maintaining rated voltage in an independent microgrid. This method trains a distributed machine learning algorithm based on different voltage stability conditions.…”

Section: Voltage Controlmentioning

confidence: 99%

“…In addition, a fuzzy logic-based diesel generator speed control scheme is designed for the same research problem. This method is sufficiently effective for diesel PV power generation systems, but it fails to suit microgrids based on wind PV, which indicates the meaningfulness of [125].…”

Section: Voltage Controlmentioning

confidence: 99%

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

Wei

Qin

et al. 2021

IET cyber-phys. syst.

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Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI-based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems. | INTRODUCTION | Energy internetNowadays, the rapid development of human society has led to the massive consumption of fossil energy, forcing mankind to face many challenges such as energy crisis, environmental pollution and global warming. Therefore, people began to pay attention to the production and utilization of renewable energy [1,2]. According to statistics in [3], the annual growth of the world's total wind and solar power generation since 2000 is 22% and 40%, respectively. It is estimated that by 2050, renewable energy will account for 80% of the total power generation in the United States [4]. However, the traditional power grid cannot adapt to the large-scale access of renewable energy due to their disadvantages of intermittence and randomicity [5], which limit the use of clean energy. Taking wind power as an example, China's wind power curtailment in 2016 was as high as 4.97 � 10 10 kWÂůh, accounting for 17% of China's total wind power generation [6].This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid

Cited by 11 publications

References 22 publications

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis

Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence‐based methods

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