Power profile based selection and operation optimization of parallel-connected power converter combinations

Vogt, Thorsten; Peters, Andrea; Fröhleke, N.; Böcker, Joachim; Kempen, Stefan

doi:10.1109/ipec.2014.6870091

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…If not, assign the next state to the current state and set k = k + 1 end else Load the trained network weights and learned control strategy end If the SOC of a battery system exceeds the constraints given by (8) under the control of the EMS, this battery system stops working, and the EMS reallocates the ESS power. The flowchart of the optimal power allocation in each time period is presented in Fig.…”

Section: E Optimal Power Allocation Processmentioning

confidence: 99%

“…Several methods have been proposed for improving the overall operation efficiency by managing the operation configuration of parallel converters. Reference [8] proposed a method to improve the selection of an arrangement of converters based on efficiency considerations. A tertiary control method was proposed for improving the overall operation efficiency by adjusting the power allocation proportion for parallel DC-DC converters in [9].…”

Section: Introductionmentioning

confidence: 99%

“…In fluctuating operation situations, the number of operating battery systems and the battery charging/discharging status vary dynamically with time. The methods proposed in [8] - [11] are not This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). Y. Wu and L. Ge (corresponding author) are with the School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing, China (e-mail: y00450190414@njit.edu.cn; gele@njit.edu.cn).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station

Yuan

et al. 2022

Journal of Modern Power Systems and Clean Energy

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An energy storage station (ESS) usually includes multiple battery systems under parallel operation. In each battery system, a power conversion system (PCS) is used to connect the power system with the battery pack. When allocating the ESS power to multi-parallel PCSs in situations with fluctuating operation, the existing power control methods for parallel PCSs have difficulty in achieving the optimal efficiency during a long-term time period. In addition, existing Q-learning algorithms for adaptive power allocation suffer from the curse of dimensionality. To overcome these challenges, an adaptive power control method based on the double-layer Q-learning algorithm for n parallel PCSs of the ESS is proposed in this paper. First, a selection method for the power allocation coefficient is developed to avoid repeated actions. Then, the outer action space is divided into n + 1 power allocation modes according to the power allocation characteristics of the optimal operation efficiency. The inner layer uses an actor neural network to determine the optimal action strategy of power allocations in the non-steady state. Compared with existing power control methods, the proposed method achieves better performance for both static and dynamic operation efficiency optimization. The proposed method optimizes the overall operation efficiency of PCSs effectively under the fluctuating power outputs of the ESS.

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Section: E Optimal Power Allocation Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station

Yuan

et al. 2022

Journal of Modern Power Systems and Clean Energy

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“…It is worth noting that for the single-weight cRLS (13) only scalar quantities appear in (13a)-(13c) which leads to a minor computational effort to estimate ŝi . By continually identifying the transfer path vector, the disturbance d can be estimated using (10) and the transfer path estimate ŝ…”

Section: Transfer Path and Disturbance Identificationmentioning

confidence: 99%

“…As a result, an MMDS has an inherent fail operational capability and, thus, improved availability compared to an SMDS. Moreover, for drive applications with variable output power, frequently changing operating points and many partial load situations, a reduced average efficiency of an SMDS compared to its rated conditions must be accepted [3], [10]. Here, the efficiency at partial load can be increased by utilizing an MMDS, where only the minimum number of motors is involved to provide the currently demanded drive power and the motors that are temporarily not needed are mechanically and electrically decoupled [3].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Adaptive Torque Oscillation Compensation for Multi-Motor Drive Systems

Brosch

Rauhaus

Wallscheid

et al. 2022

IEEE Open J. Ind. Applicat.

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Multi-motor drive systems (MMDS) combine several drives that work together to fulfill one task. Compared to conventional single-motor drive systems modular product concepts can be realized with MMDS thanks to additional degrees of freedom. Because of their mechanical structure consisting of several shafts, clutches, and gear pairings, complex deflection shapes arise which lead to unintended torque oscillations.To compensate for these torque oscillations a data-driven adaptive multiple-input single-output scheme based on two cascaded recursive least squares estimators is proposed. Here, weights (manipulable amplitudes) for each order (multiple of a fundamental frequency) to be compensated are multiplied with unit amplitude harmonic signals of a reference oscillator and added as reference torques to the torque controllers of the MMDS's drives. These weights are continually adapted by an online identification of transfer paths and disturbances. Furthermore, the torque contribution of the individual drives concerning the compensation task can be changed at runtime utilizing a weighting matrix as tuning parameter by analytically solving a quadratic program with a linear equality constraint. Hence, the proposed algorithm is suitable for automatic self-commissioning requiring only marginal expert intervention. Experimental investigations prove the compensation capability of the approach whereby a reduction of the output torque's total harmonic distortion (THD) of up to 80% from 26.8% to 5.4% for a representative operation point is achieved.

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Adaptive Control Strategy for Improving the Efficiency and Reliability of Parallel Wind Power Converters by Optimizing Power Allocation

Chen

Cai

2018

IEEE Access

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Power profile based selection and operation optimization of parallel-connected power converter combinations

Cited by 6 publications

References 2 publications

Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station

Adaptive Power Control Based on Double-layer Q-learning Algorithm for Multi-parallel Power Conversion Systems in Energy Storage Station

Data-Driven Adaptive Torque Oscillation Compensation for Multi-Motor Drive Systems

Adaptive Control Strategy for Improving the Efficiency and Reliability of Parallel Wind Power Converters by Optimizing Power Allocation

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