This paper proposes a novel wireless power transfer (WPT) system with switch-controlled capacitor (SCC) where variable capacitance and efficiency optimization are realized. The optimum asymmetrical voltage cancellation (OAVC) control is applied to realize wide-ranging soft-switching operation for the active bridge converters. The pulse width of the active bridge inverter regulates the output voltage, and the pulse width of the active bridge rectifier optimizes power delivery efficiency. The SCCs in the transmitter and the receiver side are regulated for the zero-voltage switching (ZVS) operation and the resonant condition, respectively. The online estimation of mutual inductance is carried out to adapt the coupling deviations of the WPT system in real time. A 650 W prototype is built to verify the feasibility of the proposed WPT system and the effectiveness of the OAVC control. The experiment results show quite good performance and efficiency improvement for the WPT system at various loads.

The on-line detection of incipient faults for permanent magnet synchronous machine (PMSM) has been a great challenge in recent decades, where the fault features are extremely trivial and the machine fault type is normally unknown in advance. An online fault detection and discrimination of incipient faults for PMSM, including stator inter-turn short-circuit and resistive unbalance fault, is proposed in this paper. Firstly, zero-sequence voltage signal is collected and the zero-sequence voltage component (ZSVC) is used to detect the abnormal status of the PMSM. Then, high frequency (HF) signals are injected to the PMSM to discriminate the fault type. It shows that the amplitude of the HF component in ZVSC is proportional to the frequency of the injected signal under inter-turn short-circuit while it is constant under the resistive unbalance fault, and due to this difference, the fault type may be discriminated. Finally, the effectiveness of the theoretical analysis is validated by the simulation and experimental results.

The use flexibility of pocket parks is one of the essential factors that determine their service compatibility, efficiency, and attraction for park users in densely populated cities. To examine the factors that affect the use flexibility of pocket parks, we collected data on the periodic use of 74 pocket parks through a field survey in Nanjing and adopted the Shannon Wiener diversity index to quantitatively measure this variable. After using a multiple regression model series, we examined the internal and surrounding factors that affect the use flexibility of pocket parks on weekdays and weekends, respectively, and compared them with those that affect the use intensity of pocket parks. The results showed that paved ground and the mixed use of surrounding land promoted both use flexibility and intensity of pocket parks. Boundary buildings and surrounding residents only promoted their use flexibility, while tree canopies and exercising facilities only promoted the use intensity of pocket parks. A significant correlation (p < 0.05) was also found between the use flexibility and intensity on weekdays. These findings can contribute to future decisions regarding pocket park planning and design in Nanjing and similar cities.

This article proposes a winding condition monitoring scheme for inverter-fed permanent magnet synchronous machine (PMSM) based on high frequency (HF) current injection. By setting specific current references for the HF positive and negative sequence current regulators, HF current can be injected into different phase winding pairs, and then the HF line voltage deviations are extracted for winding condition monitoring. The amplitude of the extracted HF line voltage deviation is defined as the condition indicator to evaluate the condition of phase winding. In the proposed method, the conditions of all phase windings can be separately monitored, where not only inter-turn short-circuit fault (ISF) in one phase but also ISFs in multiple phases can be accurately identified. Simulations and experiments have been carried out to validate the proposed method. It shows that the proposed method has high sensitivity to ISF and negligible sensitivity to the operation conditions of the PMSM.

In the hybrid modular multilevel converter (MMC), the voltage imbalance between the capacitors of half-bridge submodule (HBSM) and full-bridge submodule (FBSM) would occur when the over-modulation index exceeds certain range, which is caused by the different charging/discharging properties of the HBSM and FBSM. An improved hybrid MMC with self-balancing branch (SBB) is proposed in this article to avoid the voltage imbalance under over-modulation situations. The SBBs could offer additional energy transmitting channel for each bypassed HBSM at the interval of negative arm voltage. Thus, the HBSMs could possess the same energy charging/discharging properties as the FBSMs, and then the voltage imbalance between the HBSM and FBSM capacitors could be avoided under any over-modulation indexes. The operation mechanism of the proposed SBB is revealed in detail, according to which the component parameter in the SBB could be selected. The effectiveness of the proposed hybrid MMC with SBBs is verified by the simulation and experimental results. Also, it shows that the proposed hybrid MMC could realize the voltage balancing under over-modulation situations with lower energy losses according to the comparisons with other MMC topologies.

Photovoltaic (PV) power interval forecasting models are commonly trained on data dominated by non-ramp samples, which may weaken uncertainty characterization during rapid power changes. This study proposes a ramp-aware quantile regression Temporal Fusion Transformer (RQR-TFT) that jointly estimates PV power quantiles and the probability of a future ramp event. Ramp labels are constructed from the normalized power change between adjacent sampling instants. A shared Temporal Fusion Transformer (TFT) encoder extracts temporal representations from historical PV power and meteorological variables, and two output branches perform quantile forecasting and ramp-event identification. Ramp-sample-weighted quantile loss and positive-class-weighted classification loss are jointly optimized to increase the influence of minority ramp samples. The proposed method is evaluated for 4 h ahead forecasting using measurements collected from a 50 MW PV power station during 2019–2020. For the nominal 90% prediction interval, RQR-TFT achieves a ramp-sample prediction interval coverage probability (${\mathrm{PICP}}_R$) of 0.864, an overall prediction interval normalized average width (PINAW) of 0.209, and an overall normalized interval score (NIS) of 0.365. The area under the precision–recall curve for ramp-event identification is 0.906. The results demonstrate improved ramp-sample coverage and overall interval quality, although ramp-sample coverage remains below the nominal level.