No abstract

Increasing integration of converter-interfaced renewable power generation challenges the stability of the frequency in the modern power systems due to the lack of inertia in this type of generators. In this regard, this paper establishes a control method for controlling multilevel converters as an interface between renewable power generation and the power grid, which enables them to operate as voltage-controlled sources, thus grid-forming capabilities like conventional synchronous generators (SGs). In fact, the proposed control technique mimics the behavior of synchronous generators in the control loop of the neutral point clamped (NPC) converter. Consequently, supportive functionalities for frequency stability i.e., inertia and frequency/power oscillation damping will be provided by the multilevel converter as a virtual synchronous generator (VSG). Simulation results in Matlab/Simulink verifies the well-designed inertia and damping features for the interfaced converter as well as appropriate performance of multilevel converter in reducing total harmonic distortion (THD).

Losses in light-emitting-diode (LED) driver cause increasing temperature and shorten their lifespan. Therefore, improving the efficiency of LED drivers not only saves energy but also is indispensable to increase their lifespan. In this study, a new LED driver topology is proposed to improve the performance of valley switching by decreasing the MOSFET switching losses. The proposed topology is designed in a way that the MOSFET works at the significantly lower switching and conduction losses in compared with conventional LED drivers. It elaborates how the proposed topology also improves the overall efficiency by decreasing power losses in other main elements of the driver including inductance, and diode. In addition, a new valley switching implementation is introduced for the new converter which decreases the cost and dimension of the LED drivers. The experimental results confirm the high efficient operation of the proposed LED driver by reaching the efficiency up to 97% at a wide range of operating voltage.

No abstract

Transient stability of grid-connected converters has become a critical threat to the power systems with high integration level of renewable power generations. Thus, this paper aims to study the transient stability of power synchronization control (PSC) and propose a developed control system by employing deep learning methods. In order to extract and predict the voltage trajectory of the grid-connected converter system, a long short-term memory (LSTM) network has been trained and then integrated to PSC for adapting the synchronization loop of the converter to the grid condition. In the proposed control system, active power reference and internal voltage of the converter are updated dynamically to both satisfy the low voltage ride through (LVRT) requirements of the grid and prevent the loss of synchronization of the converter. The developed control system is validated by time-domain simulations.