{"title":"具有通信延迟的交流微电网智能频率控制:取决于稳定参数的在线调整方法","authors":"","doi":"10.1016/j.egyai.2024.100421","DOIUrl":null,"url":null,"abstract":"<div><p>Smart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. 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However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. 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引用次数: 0
摘要
人们已经采用智能控制技术来解决孤立微电网中的电力水平波动问题,从而降低频率不稳定的风险和潜在的供电质量下降。然而,面临的一个挑战是,采用这些计算复杂且无稳定性保护的方法可能不足以应对现实世界中因通信延迟而发生快速变化的高动态环境。本研究针对频率控制问题引入了一种灵活的实时方法,该方法使用的人工神经网络(ANN)受限于稳定区域。我们的解决方案集成了稳定 PID 控制器,该控制器通过小信号分析计算得出,并通过自动搜索最佳 ANN 权重和基于强化学习 (RL) 的选定约束进行调整。首先,我们应用稳定边界定位法和米哈伊洛夫准则设计稳定 PID 控制器,特别是解决通信延迟问题。接下来,我们通过自动流程在线完善控制器参数,确定最佳系数组合,利用受约束 ANN 在受限参数范围内管理频率偏差。我们的方法通过采用 RL 技术得到了进一步增强,该技术使用内插稳定性边界曲线来训练调整系统,以确保稳定性和性能。这种将 ANN、RL 和先进的 PID 调节方法结合在一起的独特方法,在我们如何处理隔离交流微电网中的频率控制问题方面迈出了一大步。实验表明,由于减少了参数搜索空间,我们的解决方案优于传统方法。特别是,与半约束和无约束方法相比,所提出的方法更能减少瞬态和稳态频率偏差。改进后的指标和稳定性分析表明,该方法提高了系统在变化条件下的性能和稳定性。
Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters
Smart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.