Distributed Secondary Control Strategy Based on &lt;italic&gt;Q&lt;/italic&gt;-learning and Pinning Control for Droop-controlled Microgrids

Liu, Wei; Shen, Jun; Zhang, Sicong; Li, Na; Zhu, Ze; Liang, Liang; Wen, Zhen

doi:10.35833/mpce.2020.000705

Cited by 13 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [85], a secondary compensating control for deep learning-based aggregation of thermostatic control loads (TCLs) is proposed to mitigate voltage imbalance. In [86], the authors proposed a new distributed secondary control scheme based on the combination of Q-learning and pining control, and built a compensation function through a greedy strategy to realize frequency and voltage compensation. It has better compensation accuracy and also allows for plug-and-play operation.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…4. In this paper, considering that the frequency deviation has been compensated in the first layer of control, according to [16], the obtained discrete frequency state quantity is set as: S={(-∞,-0.06), [-0.06,-0.04), [-0.04,-0.02), [-0.02,0.02], (-0.02,0.04], (0.04,0.06], (0.06,+∞)}. The action is set to A={-0.07, -0.035, -0.05, -0.03, -0.005, 0, 0.005, 0.03, 0.05, 0.035, 0.07}.…”

Section: Proposed Novel Secondary Controlmentioning

confidence: 99%

A Distributed Two-Layer Frequency Compensation for Islanded Microgrids Based on Q-learning and PI Controllers

Li,

Gao,

Blaabjerg

et al. 2023

2023 8th IEEE Workshop on the Electronic Grid (eGRID)

View full text Add to dashboard Cite

Frequency instability generates significant challenges to the stability of the system. To solve the frequency deviation problem, the traditional secondary control uses PID controller to achieve frequency compensation for the primary control, but simultaneously the traditional PID controller has disadvantages such as poor dynamic performance and the need for manual tuning of parameters. The problems mentioned above will lead to poor compensation accuracy. To address such issue, this paper proposes a new frequency compensation scheme that divides the traditional frequency secondary control into two layers, the first layer uses an improved PID controller that considers the average value of output frequency of all distributed generators, and the second layer is based on Qlearning technology to compensate again. The proposed scheme shortens the response time and improves the control accuracy, and effectiveness is verified in MATLAB/Simulink.

show abstract

“…It primarily utilizes distributed control, with a small portion utilizing decentralized control. Some methods also employ centralized control, but only for a small number of scattered distributed energy sources [14][15]. Therefore, based on the division of distributed generation grids, the application of distributed coordinated control is feasible.…”

Section: Introductionmentioning

confidence: 99%

Distributed coordination voltage control of multiple grids in active distribution network

Zhao,

Wang,

Chen

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

View full text Add to dashboard Cite

With the rapid advancement of smart grids and energy innovation strategies, the widespread promotion of active distribution network control technology will become the key driving force behind the intelligent development of distribution grids. However, the intermittency and uncertainty of distributed power sources, the need for more inverters to participate in regulation, the diversification of load development trends, and the continuous improvement of user power supply requirements have posed significant challenges to the operation and regulation of active distribution networks. This article proposes a distributed generation voltage coordination control strategy based on the existing division of distribution grids. This strategy enables each grid to respond to the system's power generation requirements under changing system conditions, allowing each grid to have autonomous and precise control. Additionally, the effectiveness of the proposed control scheme is validated through simulation using the MATLAB/Simulink platform, where scenarios with sudden load changes are set up and simulated.

show abstract

Distributed Secondary Control Strategy Based on <italic>Q</italic>-learning and Pinning Control for Droop-controlled Microgrids

Cited by 13 publications

References 23 publications

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

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

A Distributed Two-Layer Frequency Compensation for Islanded Microgrids Based on Q-learning and PI Controllers

Distributed coordination voltage control of multiple grids in active distribution network

Contact Info

Product

Resources

About