A Novel Deep Reinforcement Learning Controller Based Type-II Fuzzy System: Frequency Regulation in Microgrids
A Novel Deep Reinforcement Learning Controller Based Type-II Fuzzy System: Frequency Regulation in Microgrids
复制标题
基于新型深度强化学习控制器的 II 类模糊系统：微电网频率调节
DOI：
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发表时间：
2021
影响因子：
5.3
通讯作者：
M. Gheisarnejad
中科院分区：
文献类型：
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作者：
M. Khooban;M. Gheisarnejad
近年来，以微电网（MG）形式的分布式发电技术的高渗透率，由于其能量由具有不确定性的可再生能源（RES）提供，增加了频率不稳定的风险。在这种情况下，为MG模型提供有效的负载频率控制（LFC）对于恢复非结构化电力系统的稳定性具有根本性作用。在本研究中，应用潮汐发电装置（TPU）和车辆到电网（V2G）的混合动力系统被有效地规划为孤立的MG。建议将基于单输入区间2型模糊逻辑控制器（SIT2-FLC）的新型分数梯度下降（FGD）作为主要LFC控制器，其中SIT2-FLC的不确定性足迹（FOU）系数经过专门调整以增强LFC性能。此外，考虑使用行为者-批评家框架的深度确定性策略梯度（DDPG）来生成补充控制动作，这对于通过适应负载扰动和RES的随机性来稳定频率很有用。最后，进行了模型在环（MiL）仿真，从系统角度评估了所提出的设计方法的可行性和适用性。
The high-penetration of distributed generation technologies in the form of MicroGrids (MGs), in recent years, has increased the risk of frequency instability since their energy is supplied by renewable energy resources (RESs) with uncertain nature. Under such circumstances, providing an MG model with an efficient load frequency control (LFC) has a fundamental role in restoring the stability of the unstructured power system. In this study, a hybrid power system with the application of the Tidal Power Unit (TPU) and Vehicle-to-Grid (V2G) is effectively planed as an isolated MG. A new fractional gradient descent (FGD) based on a single-input interval type-2 fuzzy logic controller (SIT2-FLC) is suggested as the main LFC controller, where the footprint of uncertainty (FOU) coefficient of the SIT2-FLC is specifically adjusted to enhance the LFC performance. Additionally, a deep deterministic policy gradient (DDPG) with the actor-critic framework is considered to generate the supplementary control action, which is useful for the frequency stabilization by adapting to the randomness of load disturbances and RESs. Lastly, a model-in-the-loop (MiL) simulation is conducted to appraise the feasibility and applicability of the suggested design method from a systemic perspective.