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Yin Linfei,Cao Xinghui,Sun Zhixiang 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5
Multiple objectives of smart generation control (SGC) are considered. The paper proposes a distributed Pareto reinforcement learning (DPRL) based on game theory to address the multi-objective control problem (MOCP) of SGC of multi-area interconnected power systems (MAIPSs). The proposed DPRL consists of the framework of reinforcement learning and multiple Q matrices, which can provide multiple outputs for MOCPs. As a control algorithm based on the Markov decision process, the stability of the proposed approach can be guaranteed with the ability to provide dynamic control strategies online. With the Pareto concept of optimization algorithms introduced into a control algorithm, the proposed approach can obtain multiple comprehensive objectives. The case studies under the two-area and practical four-area power systems show that: compared with four reinforcement learning methods and a proportional-integral controller, the proposed DPRL can achieve the highest control performance with multiple objectives in MAIPSs. The case studies of SGC under two MAIPSs verify the feasibility and eff ectiveness of the DPRL for MOCPs.
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Linfei Yin,Xinghui Cao,Lichun Chen 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.12
A fractional-order proportional-integral-derivative (FO-PID) with two more fractional-order operations can achieve higher control performance than a proportional-integral-derivative (PID). Besides, an active disturbance rejection controller (ADRC) and a fractional-order active disturbance rejection controller (FO-ADRC) with more input information can obtain higher control performance than a PID, an FO-PID. To obtain higher control performance with more information and more fractional-order operations, this paper proposes a high-dimensional multiple fractional-order controller (HDMFOC). The HDMFOC cascades multiple high-dimensional controllers with fractional-order for a controller. The numerical simulations of an automatic generation control (AGC) system and an automatic voltage regulation (AVR) system compare the proposed approach with four other algorithms, i.e., the PID, FO-PID, ADRC, and FO-ADRC. Then, the HDMFOC achieves the highest control performances for AGC and AVR of power systems through multiple dimensional information and multiple fractional-order operations.
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