Frequency Regulation of Power Systems with Self-Triggered Control under the Consideration of Communication Costs

Zhu, Zhiqin; Sun, Jia Lin; Qi, Guanqiu; Chai, Yi; Chen, Yinong

doi:10.3390/app7070688

Cited by 20 publications

(16 citation statements)

References 47 publications

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“…As shown in Figure 1, each area equips a turbine generator and DES, such as wind power, solar power, battery, etc. [20].…”

Section: Power Grid Frequency Dynamic Modelmentioning

confidence: 99%

“…γ i is the regulation weight. The sparse regulated learning iteration is demonstrated in Equation (20), which can be also derived by Equation (17):…”

Section: Structure Design By Sparse Neural Networkmentioning

confidence: 99%

“…If j / ∈ N * c (i), the iteration of Equation (20) usually obtains a small vec(W ij ) instead of vec(W ij ) =0 because it is an numerical method. Therefore, a threshold method is used in our algorithm to obtain N * c | δ .…”

Section: Structure Optimization Under Dos Attacksmentioning

confidence: 99%

“…Most existing research focused on the characteristics of steady state [19]. They ignored the transient and dynamic issues in control systems [20]. Another critical challenge is that most existing algorithms used a precisely analytical model as a premise [21].…”

Section: Introductionmentioning

confidence: 99%

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A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid

Sun

Zhu

2019

Applied Sciences

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With the rapid growth of distributed energy sources, power grid has become a flexible and complex networked control system. However, it increases the chances of being a denial-of-service attack, which degrades the performance of the power grid, even causing cascading failures. To mitigate negative effects from denial-of-service attack and enhance the reliability of the power grid, we propose a networked control system structure based optimization scheme that is derived from a Stackelberg game model for the frequency regulation of a power grid with distributed energy sources. In the proposed game model, both denial-of-service attacker and control system designer as a defender are considered without using any analytical model. For defenders, we propose a sparse neural network based DES control and system structure design scheme. The neural network is used to approximate the desired control output and reinforce signals for the improvements of short- and long-term performance. It also introduces the sparse regulation of column grouping in the neural network learning process to explore the structure of control system that involves the placement of sensor, distributed energy sources actuator, and communication topology. For denial-of-service attackers, the related attack constraints and attack rewards are established. The solution of game equilibrium is considered as an optimal solution for both denial-of-service attack strategy and control structure. An offline optimization algorithm is proposed to solve the game equilibrium. The effectiveness of proposed scheme is verified by two cases, which illustrate the optimal solutions of both control structure and denial-of-service attack strategy.

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“…As shown in Figure 1, each area equips a turbine generator and DES, such as wind power, solar power, battery, etc. [20].…”

Section: Power Grid Frequency Dynamic Modelmentioning

confidence: 99%

“…γ i is the regulation weight. The sparse regulated learning iteration is demonstrated in Equation (20), which can be also derived by Equation (17):…”

Section: Structure Design By Sparse Neural Networkmentioning

confidence: 99%

Section: Structure Optimization Under Dos Attacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid

Sun

Zhu

2019

Applied Sciences

Self Cite

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“…Sun, and G. Qi for a frequency-controlled power grid not only to improve the frequency regulation of the power grid as well as the input-to-state stability, but also to minimize the communication cost within the study system [3]. The second paper, authored by J.-W. Choi and M.-K. Kim, studies the voltage stability of a renewable-based power system (mainly driven by wind turbines) using Monte Carlo simulations (MCS) and probabilistic security-constrained optimal power flow techniques [4].…”

Section: Energy Systems Design Control and Optimizationmentioning

confidence: 99%

Special Issue on Advances in Integrated Energy Systems Design, Control and Optimization

Anvari‐Moghaddam

Guerrero

2017

Applied Sciences

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Self‐triggered control for linear systems based on hierarchical reinforcement learning

Chen,

Wan,

Luan

et al. 2024

Intl J Robust & Nonlinear

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In this article, we propose a model‐free self‐triggered control approach for linear systems using a hierarchical policy framework. This framework splits the self‐triggered control approach into top and bottom‐level policies. The top‐level policy generates a triggering time interval based on an initial control strategy, while the bottom‐level policy creates a control inputs guided by the top‐level sub‐goal. This division ensures that both strategies have their own independent tasks and optimization goals, facilitating a model‐free iterative design process for self‐triggered control. The present structure integrates with a dual‐Actor Critic algorithm, utilizing two interconnected neural networks to approximate control and trigger policies. It reflects the framework of hierarchical reinforcement learning, wherein top‐level policies guide bottom‐level decision‐making. It fosters the model‐free design of self‐triggered controller, thereby enhancing the efficiency of the learning process. To validate the effectiveness of our proposed method, we conduct a series of numerical simulations.

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Frequency Regulation of Power Systems with Self-Triggered Control under the Consideration of Communication Costs

Cited by 20 publications

References 47 publications

A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid

A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid

Special Issue on Advances in Integrated Energy Systems Design, Control and Optimization

Self‐triggered control for linear systems based on hierarchical reinforcement learning

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