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The grid‐connected photovoltaic plants (GCPVPs) encounter various types of anomalies during fault occurrence in the main grid. During a fault occurrence on the grid side, the voltage of the point of common coupling (PCC) drops, causing the DC connection voltage to become too high for power balancing. This high DC voltage may damage the inverter and DC link equipment, which necessitates a reliable control scheme with low voltage passage requirements. These requirements, which obligate the GCPVP to remain connected to the main grid during abnormal conditions are generally known as low voltage ride‐through (LVRT). Herein, a developed LVRT method in the control approach that injects maximum rated current during a sag is proposed to rectify the shortcomings in single and multi‐inverter grid topologies. The proposed approach utilizes a current limiter combined with a DC braking chopper to guarantee the GCPVP's continuous participation in the power supply. Different scenarios including low and high power generations as well as two‐phase and three‐phase severe faults to the ground are considered. The results show that the proposed approach not only improves the performance in the single‐inverter system but also remedies the defects aggravated in the case of a GCPVP with several inverters. [ABSTRACT FROM AUTHOR]

Power supply system plays an important role in the operation of urban rail systems. It has to provide high‐quality energy for trains continuously. Train's energy consumption is varied directly with operation conditions such as train's headway, train configuration, line characteristic and many other factors. Therefore, using a powerful simulation tool for estimating energy consumption considering these factors seems inevitable. This paper tries to develop a hybrid model in which the train model is based on acquired data from real measurement and an adapted network electrical model. Train empirical model is based on neural network on the basis of online data measured in line one of Shiraz metro and used for training of model. Then benefiting from optimization algorithms, a novel model of energy supply system of trains, including Traction Substations (TSS) and Overhead Catenary System (OCS), is proposed. Finally, the behaviour of trains during operation in 15‐min headway was simulated and the results under same conditions were compared with practical measurements. Currently, this model helps to determine power system behaviour during different operation conditions, predicting energy consumption for different headways, Train's driver behaviour during acceleration, deceleration and braking and also operation risk analysis during traction substations outages. [ABSTRACT FROM AUTHOR]

This study proposes an approach for finding the optimal size of a high‐speed flywheel in an energy storage system based on battery‐flywheel cooperation, called battery‐flywheel energy storage system (BFESS), supplying an electric vehicle. Considering an energy‐based model for the flywheel and battery, including the losses, the optimal control theory is utilised as a two‐dimensional Pontryagin's minimum principle for solving the problem. The objective function of the flywheel sizing problem includes two terms, in which its maximum interaction with the system from the energy and filtering aspects are taken into consideration by a desirable weighting. While energy interaction is a common task, filtering the fast dynamics of the system such as the harsh conditions for the battery is the key role of the considered lightweight and high‐speed flywheel. A trade‐off between the energy contribution and filtering is made, targetted for the long‐term and short‐term dynamics in the drive cycles, respectively. The proposed methodology is presented in detail using some simple dynamics. Also, the sizing is carried out for the ECE‐15 urban drive cycle, the WLTC class‐3 and FTP‐75 drive cycles. The results confirm that by using the determined optimal size of the flywheel energy storage system (FESS), most of the dynamics could be handled from the filtering and energy interaction aspects. [ABSTRACT FROM AUTHOR]