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High-speed permanent magnet motors (HSPMMs) have received attention in direct drive systems from the industry. Modular multilevel converters (MMCs) have become the most practical solution for HSPMMs in industrial applications due to the advantages of their highly modular structure, high-quality output and high reliability. In this article, the symmetrical discrete-time model of the MMC for the HSPMM drive is presented. Based on this symmetrical discrete-time model, the discrete-time current regulator has been designed. Moreover, the submodel (SM) capacitor energy balance models for MMC have been described, and the energy balance control scheme is introduced. Finally, the simulation and experimental results show that the presented discrete-time current regulator has a strong dynamic response at high speed (15 kr/min), and the SM capacitor voltage balance control is also achieved. Compared with the conventional PI current controller, the proposed discrete-time current regulator improves the current loop dynamic performance at high-speed, and the energy balance control scheme can ensure the SM capacitor voltage balance of the MMC. [ABSTRACT FROM AUTHOR]

Nowadays, high-speed permanent magnet synchronous machines (HSPMSMs) are widely used in high-speed direct-drive applications. Moreover, modular multilevel converters (MMCs) have attracted a lot of attention. In this paper, the mathematical model of the MMC for the HSPMSM was built on high- and low-speed regions, respectively, and the control of each model is completely decoupled. Moreover, energy balance control schemes based on the common mode current injection of the MMC with HSPMSM drive applications have been proposed for high- and low-speed cases, respectively. Based on this model, the controller is proposed for high- and low-speed cases, respectively. Simulation and experiment results indicate that the proposed energy balance control method for an MMC in the high-speed PMSM drive application exhibits significant performance, which can ensure the energy balance of the MMC and eliminate the circulating current of the MMC directly. [ABSTRACT FROM AUTHOR]

The increasing complexity of multi-energy coordinated microgrids presents a challenge for traditional demand response providers to adapt to end users' multi-energy interactions. The primary aim of demand response providers is to maximize their total profits via designing a pricing strategy for end users. The main challenge lies in the fact that DRPs have no access to the end users' private preferences. To address this challenge, we propose a deep reinforcement learning-based approach to devise a coordinated scheduling and pricing strategy without requiring any private information. First, we develop an integrated scheduling model that combines power and gas demand response by converting multiple energy sources with different types of residential end users. Then, we formulate the pricing strategy as a Markov Decision Process with an unknown transition. The novel soft actor-critic algorithm is utilized to efficiently train neural networks with the entropy function and to learn the pricing strategies to maximize demand response providers' profits under various sources of uncertainties. Case studies are conducted to demonstrate the effectiveness of the proposed approach in both deterministic and stochastic environment settings. Our proposed approach is also shown to be effective in handling different levels of uncertainties and achieving the near-optimal pricing strategy. [ABSTRACT FROM AUTHOR]

This article proposes a sliding-mode position estimation method for high-speed surface-mounted permanent magnet synchronous machines with LCL filter. The implementation of the LCL filter aims at smoothing the motor current and reducing the iron loss caused by the harmonic currents. First, the discrete-time model of the LCL-filtered motor drive system is developed. Based on the developed model, the sliding-mode observer is proposed with more robustness against the parameter variation to estimate the back EMF, which contains the information of the rotor speed and position. Because of the elimination of the capacitor voltage sensors, the augmented sliding surface is designed to achieve arbitrary pole placement with only output feedback. Besides, considering the analog-to-digital scaling error and pulsewidth modulation harmonics, a reaching law with enhanced chattering suppression ability is proposed. Compared with the conventional methods, the chattering problem is well alleviated and thus the speed estimation ripple is much reduced. Finally, the effectiveness of the proposed method, even with the mismatched parameters adopted is validated at 100 kr/min with the sampling frequency 20 kHz. [ABSTRACT FROM AUTHOR]

This article proposes a compensation method of position estimation error for high-speed surface-mounted permanent magnet synchronous motors based on robust inductance estimation. The proposed method relies on the variation of the estimated $\delta$ -axis back back-electromotive force when a small current is injected into the $\gamma$ axis. The inductance estimation error is limited within ${\bf \pm \!5\%}$ when the nominal resistance and inductance vary $\bf \pm 30\%$ of their real values. With the estimated inductance, the position estimation error can be well compensated. Compared with the conventional current-injection method, the proposed method has enhanced robustness against the system noises. Benefiting from this, it is effective to estimate the inductance with a small injected current ($ \bf 0.5\%$ of the rated current), where the conventional methods fail. Finally, the effectiveness of the proposed method is validated by simulation and experiment results on a 100 kr/min (1.67 kHz) high-speed permanent magnet synchronous machines accurately with 10-kHz sampling frequency. [ABSTRACT FROM AUTHOR]