Advanced Reinforcement Learning Solution for Clock Skew Engineering: Modified Q-Table Update Technique for Peak Current and IR Drop Minimization

Beheshti-Shirazi, Sayed Aresh; Nazari, Najmeh; Gubbi, Kevin Immanuel; Latibari, Banafsheh Saber; Rafatirad, Setareh; Homayoun, Houman; Sasan, Avesta; Manoj, P. D. Sai

doi:10.1109/access.2023.3304534

Cited by 1 publication

(1 citation statement)

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“…In recent years, many people have studied the Q-table. In [20], the clock arrival time of each register is updated using the Q-table to maximize the designed clock arrival distribution to reduce the peak current. In [21], a learning-based dual Q power management method is proposed to extend the operating frequency to improve the embedded battery life and provide a sustainable operating energy.…”

Section: Introductionmentioning

confidence: 99%

Research on Q-Table Design for Maximum Power Point Tracking-Based Reinforcement Learning in PV Systems

Chen,

Lin,

et al. 2023

Energies

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Photovoltaic (PV) power generation is considered to be a clean energy source. Solar modules suffer from nonlinear behavior, which makes the maximum power point tracking (MPPT) technique for efficient PV systems particularly important. Conventional MPPT techniques are easy to implement but require fine tuning of their fixed step size. Unlike conventional MPPT, the MPPT based on reinforcement learning (RL-MPPT) has the potential to self-learn to tune step size, which is more adaptable to changing environments. As one of the typical RL algorithms, the Q-learning algorithm can find the optimal control strategy through the learned experiences stored in a Q-table. Thus, as the cornerstone of this algorithm, the Q-table has a significant impact on control ability. In this paper, a novel Q-table of reinforcement learning is proposed to maximize tracking efficiency with improved Q-table update technology. The proposed method discards the traditional MPPT idea and makes full use of the inherent characteristics of the Q-learning algorithm such as its fast dynamic response and simple algorithm principle. By establishing six kinds of Q-tables based on the RL-MPPT method, the optimal discretized state of a photovoltaic system is found to make full use of the energy of the photovoltaic system and reduce power loss. Therefore, under the En50530 dynamic test standard, this work compares the simulation and experimental results and their tracking efficiency using six kinds of Q-table, individually.

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Section: Introductionmentioning

confidence: 99%