Active disturbance rejection controller for multi-area interconnected power system based on reinforcement learning

Zheng, Youquan; Chen, Zengqiang; Huang, Zhaoyang; Sun, Mingwei; Sun, Qinglin

doi:10.1016/j.neucom.2020.03.070

Cited by 32 publications

(20 citation statements)

References 21 publications

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“…where r is the instant reward value obtained by performing an action in RL, and the design of r in this article is shown in Equation (28).…”

Section: Working Process Of Double Dqnmentioning

confidence: 99%

“…For example, Chen et al 27 used LADRC to control the heading angle of the ship without considering dynamics and used Q learning to optimize the controller parameters. Zheng et al 28 applied the Q-learning-optimized LADRC to the power system and got a good control effect. However, in practice, in the optimization process of Q learning, the system state must be discretized, which may generate a large state space and is inconvenient for the storage and calculation of the Q table.…”

Section: Introductionmentioning

confidence: 99%

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An intelligent course keeping active disturbance rejection controller based on double deep Q‐network for towing system of unpowered cylindrical drilling platform

Zheng

Tao

Sun

et al. 2021

Intl J Robust & Nonlinear

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Towing is a widely used mode of transportation in offshore engineering, and towing of unpowered platforms is of particular significance. However, the addition of unpowered facilities has increased the difficulty of ship maneuvering. Moreover, the marine environment is complex and changeable, and sudden winds or waves can have unpredictable effects on the towing process. Therefore, it is of great significance to overcome the influence of the harsh marine environment while navigating the towing system following a planned course to a target sea area. To tackle the time-varying disturbances, a course control method for a towing system of unpowered cylindrical drilling platform is designed based on double deep Q-network (DQN) optimized linear active disturbance rejection control (LADRC). To be specific, to tackle the difficulty of LADRC tuning, double DQN is applied to select the best parameters of the LADRC at any time according to the states of the system, without relying on the specific information of the model and the controller. The course control performance of the towing system is evaluated in a simulation environment under various disturbances. Moreover, the Monte Carlo experiment is used to test the robustness of the controller when the ship's mass changes and the robustness of the proposed method is verified by testing with various heading angles. The results show that the LADRC with adaptive parameters optimized by double DQN performs well under external interference and inherent uncertainty, and compared with the traditional LADRC, the proposed method has better course control effects.

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“…where r is the instant reward value obtained by performing an action in RL, and the design of r in this article is shown in Equation (28).…”

Section: Working Process Of Double Dqnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An intelligent course keeping active disturbance rejection controller based on double deep Q‐network for towing system of unpowered cylindrical drilling platform

Zheng

Tao

Sun

et al. 2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Rahman et al [17] compared the control effects of ADRC and PID for an LFC system, and the simulation results showed that ADRC is a powerful substitute for PID and has significant performance advantages for LFC. Zheng et al [18] applied ADRC to a three-area interconnected power system both in regulated and deregulated environments. It is worth mentioning that most of the controllers in ADRC currently designed for LFC use linear ADRC (LADRC), which was proposed by Gao [19], where the internal structures of the ADRC method have been greatly simplified.…”

Section: Introductionmentioning

confidence: 99%

Power System Load Frequency Active Disturbance Rejection Control via Reinforcement Learning-Based Memetic Particle Swarm Optimization

et al. 2021

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Load frequency control (LFC) is necessary to guarantee the safe operation of power systems. Aiming at the frequency and power stability problems caused by load disturbances in interconnected power systems, active disturbance rejection control (ADRC) was designed. There are eight parameters that need to be adjusted for an ADRC, which are challenging to adjust manually, thus limiting the development of this approach in industrial applications. Regardless of the theory or application, there is still no unified and efficient parameter optimization method. The traditional particle swarm optimization (PSO) algorithm suffers from premature convergence and a high computational cost. Therefore, in this paper, we utilize an improved PSO algorithm, a reinforcement-learning-based memetic particle swarm optimization (RLMPSO), for the parameter tuning of ADRC to obtain better control performance for the controlled system. Finally, to highlight the advantages of the proposed RLMPSO-ADRC method and to prove its superiority, the results were compared with other control algorithms in both a traditional non-reheat two-area thermal power system and a non-linear power system with a governor dead band (GDB) and a generation rate constraint (GRC). Moreover, the robustness of the proposed method was tested by simulations with parameter perturbations and different working conditions. The simulation results showed that the proposed method can meet the demand for the frequency deviation to stabilize to 0 in LFC with higher performance, and it is worthy of popularization and application.

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“…Recently, research has been conducted on reinforcement learning to adjust controller parameters. For example, Chen [27] and Zheng [28] applied Q learning to parameter tuning of LADRC. Nevertheless, Q learning is more suitable for systems with limited states and limited actions.…”

Section: Introductionmentioning

confidence: 99%

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

et al. 2021

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To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.

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Active disturbance rejection controller for multi-area interconnected power system based on reinforcement learning

Cited by 32 publications

References 21 publications

An intelligent course keeping active disturbance rejection controller based on double deep Q‐network for towing system of unpowered cylindrical drilling platform

An intelligent course keeping active disturbance rejection controller based on double deep Q‐network for towing system of unpowered cylindrical drilling platform

Power System Load Frequency Active Disturbance Rejection Control via Reinforcement Learning-Based Memetic Particle Swarm Optimization

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

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