Huanyue Liao

The bus conversion system in the hybrid AC/DC grid is usually composed of a three-phase interlinking converter and a DC-side input filter. The impedance interaction between the converter and the input filter may cause instability of the DC-link voltage. The previously proposed stabilization methods either require additional hardware or significantly impact the dynamic performance. Therefore, this paper presents a parallel virtual impedance regulator (PVIR), which adds desired virtual impedance in parallel with the input impedance of the converter. The proposed PVIR can precisely modify the impedance within the specific frequency range to minimize the impact on the system dynamics. The concept and detailed design procedure with the redundant design of the PVIR are introduced. The effect of the PVIR injection position is discussed. Finally, the experiment validates the effectiveness of the proposed PVIR.

The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system’s historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.