The single current sensor operation (SCSO) has attracted widespread attention in the motor drive system because of its ability to reduce cost and volume. However, when the SCSO is used with the conventional space vector puleswidth modulation (SVPWM) technique, the current reconstruction unobservable area in the sector boundary and low modulation area will degrade the accuracy of sampling data. In this article, a mixed SVPWM (MSVPWM) control scheme is proposed to eliminate the unobservable area. With the proposed scheme, the zero vectors ( ${{{\bm V}}_{{\rm {0}}}}$ and ${{{\bm V}}_{{\rm {7}}}}$ ) are replaced by the complementary effective vectors to achieve the same effect in the sector boundary and low modulation area, and the SVPWM is applied to the remaining space vector plane. These inserted effective vectors make the current observation window time prolonged to ensure that the dc-link current data can be accurately acquired in the entire space vector plane. Hence, the high-quality reconstruction of the phase current can be realized with less impact on the output of current ripples and reconstruction errors. Moreover, the relationship between the rotation speed and the increase in switching losses under different switching frequencies is quantitatively analyzed. The effectiveness of the MSVPWM reconstruction algorithm was verified by experimental results obtained from a permanent-magnet synchronous servo motor.

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy.