A data-driven approach for designing STATCOM additional damping controller for wind farms

Zhang, Guozhou; Hu, Weihao; Cao, Dengqing; Yi, Jianbo; Huang, Qi; Chen, Zhe; Blaabjerg, Frede

doi:10.1016/j.ijepes.2019.105620

Cited by 48 publications

(36 citation statements)

References 28 publications

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“…Then, the DDPG is introduced to solve this problem to offer flexibility and robust control to power systems. In [102], to guarantee the stability of the system with different wind speeds, a data-driven approach is proposed for the adaptive robust control of static synchronous compensator with additional damper controller (STATCOM-ADC) to address the uncertainty of the system. In [103], the A3C-based agent is proposed for the self-tuning of proportional resonance power system stabilizer (PR-PSS) to enhance the damping of the hydropower dominant system.…”

Section: Operational Controlmentioning

confidence: 99%

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Cao

Zhao

et al. 2020

Journal of Modern Power Systems and Clean Energy

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231

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With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

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Section: Operational Controlmentioning

confidence: 99%

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Cao

Zhao

et al. 2020

Journal of Modern Power Systems and Clean Energy

Self Cite

231

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“…The profit obtained by selling electricity and natural gas is also considered, which is expressed in (16). Each sub-model is connected with an external grid so that electricity and natural gas in the considered system could be sold at a real-time price for profit.…”

Section: Cost Function For the Systemmentioning

confidence: 99%

“…Secondly, considering that there are complex state variables and many control variables in the system, the traditional numerical solution methods are limited by the dimension disaster problem to some extent. Besides, the deep reinforcement learning (DRL) algorithm has a good effect on solving the complex dynamic optimisation problem by the strategy of interaction with the environment in the case of a complex or unknown environment model [16,17]. Thus, cycling decay learning rate deep deterministic policy gradient (CDLR-DDPG), based on a state-ofthe-art DRL algorithm, is proposed to solve the optimal or suboptimal operation strategy of the system [18].…”

Section: Introductionmentioning

confidence: 99%

Economical operation strategy of an integrated energy system with wind power and power to gas technology – a DRL‐based approach

Zhang

Cao

et al. 2020

IET Renewable Power Generation

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With the rapid development of artificial intelligence, adopting advanced deep reinforcement learning (DRL) methodologies to solve the optimisation problem in power systems has become more effective. This study proposes a novel energy control method associated with DRL to solve the economical optimisation problems in an integrated energy system with wind power and power-to-gas technology. To consider the randomness of wind power and the flexibility of upper-level energy prices, the economical optimisation problem is formulated as a finite Markov decision-making process. Cycling decay learning rate deep deterministic policy gradient (CDLR-DDPG) algorithm is proposed to obtain the optimal operation strategy. A comparison among different benchmark methods is provided to demonstrate the superiority of CDLR-DDPG algorithm in optimising an economical problem for the considered system. P P2G rate rated input power of P2G

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“…Different advanced control techniques for STATCOM compensators [18][19][20][21][22][23] have been proposed to control the VAR flow between wind power energy and STATCOM. Thus, the voltage at the point of common coupling (PCC) can be improved to ensure power continuity.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the voltage at the point of common coupling (PCC) can be improved to ensure power continuity. These different control techniques, such as STATCOM-based fuzzy PI control [18], STATCOM-based adaptive control using deep deterministic policy gradient algorithm along with neural network estimation [19], the input-output feedback linearization of nonlinear STATCOM [20], robust STATCOM-based active disturbance rejection [21] and balance control techniques for imbalanced DC capacitor voltages in multilevel STATCOM units [22], have successfully been implemented to improve the overall power system efficiency. Additionally, as noted in [23], heuristic dynamic programming was used to promote oscillation damping by coordinating a reactive power control strategy involving STATCOM and a wind farm during fault periods.…”

Section: Introductionmentioning

confidence: 99%

Online API Controller for Performance Improvements in Multilevel STATCOMs

Alhejji¹

2020

International Journal of Engineering Research and Technology

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This paper investigates improvements in the safety and operating range of transmission lines by incorporating a static synchronous compensator (STATCOM). A multilevel STATCOM-based voltage source converter (VSC) is used to ensure the safety and continuity of delivered power to a grid and is applied for emitting reactive power and reducing voltage instability resulting from abnormal operation. The key contribution of this framework is an online adaptive proportional integral (API) controller applied to a STATCOMbased VSC control circuit to obtain rapid performance and voltage stability improvements for the generation system output. The system architecture is simulated in MATLAB/Simulink, including a generation integrated with an infinite bus and STATCOM-based VSC. The simulation indicated the voltage instability at the point of common coupling (PCC) effectively declined by approximately 8% using the API controller and declined by 10% for PI controller that is tuned by the grey wolf optimization (GWO) under a sudden load variation. The results corroborate the findings obtained for the API controllers, which are technically compared with those for the GWO-based PI controllers.

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A data-driven approach for designing STATCOM additional damping controller for wind farms

Cited by 48 publications

References 28 publications

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Economical operation strategy of an integrated energy system with wind power and power to gas technology – a DRL‐based approach

Online API Controller for Performance Improvements in Multilevel STATCOMs

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