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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Lazy reinforcement learning for real-time generation control of parallel cyber–physical–social energy systems
ترجمه فارسی عنوان مقاله:
یادگیری تقویتی اهسته برای کنترل زمان واقعی تولید سیستم های انرژی موازی سایبری - فیزیکی - اجتماعی
منبع:
Sciencedirect - Elsevier - Engineering Applications of Artificial Intelligence, 88 (2020) 103380. doi:10.1016/j.engappai.2019.103380
نویسنده:
Linfei Yin ∗, Shengyuan Li, Hui Liu
چکیده انگلیسی:
To learn human intelligence, the social system/human system is added to a cyber–physical energy system in
this paper. To accelerate the configuration process of the parameters of the cyber–physical energy system,
parallel systems based on artificial societies-computational experiments-parallel execution are added to the
cyber–physical energy system, i.e., a parallel cyber–physical–social energy system is proposed in this paper.
This paper proposes a real-time generation control framework to replace the conventional generation control
framework with multiple time scales, which consist of long-term time scale, short-term time scale, and real-time
scale. Since a lazy operator employed into reinforcement learning, a lazy reinforcement learning is proposed
for the real-time generation control framework. To reduce the real simulation time, multiple virtual parallel
cyber–physical–social energy systems and a real parallel cyber–physical–social energy system are built for the
real-time generation control of large-scale multi-area interconnected power systems. Compared with a total of
146016 conventional generation control algorithms and a relaxed artificial neural network in the simulation
of IEEE 10-generator 39-bus New-England power system, the proposed lazy reinforcement learning based realtime
generation control controller can obtain the highest control performance. The active power between
two areas and the systemic frequency deviation can be reduced by the lazy reinforcement learning, and the
simulation results verify the effectiveness and feasibility of the proposed lazy reinforcement learning based
real-time generation control controller for the parallel cyber–physical–social energy systems.
Keywords: Lazy reinforcement learning | Real-time generation control | Parallel cyber–physical–social energy systems | Artificial societies-computational | experiments-parallel execution | Unified time scale
قیمت: رایگان
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