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Hassan Bevrani

Hassan Bevrani

Academic rank: Professor
Education: PhD.
ScopusId: 55913436700
Faculty: Faculty of Engineering
Address: Dept. Of Electrical and Computer Eng, University of Kurdistan, Allameh Hamdi Blvd, Sanandaj PO Box 416, P. C: 66177-15175, Kurdistan, Iran
Phone: +98-87-33624001


Load Shedding in the Presence of Renewable Energy Sources
load shedding, wind turbines, emergency control, neural network, P-V curves, restructuring
Researchers Masoud Rashidi Nejad(Advisor)، Hassan Bevrani(PrimaryAdvisor)، AbolGasem Tikdari(Student)


Re-evaluation of emergency control and protection schemes for distribution and transmission networks are one of the main problems posed by wind turbines in power systems. Change of operational conditions and dynamic characteristics influence the requirements to control and protection parameters. Introducing a significant wind power into power systems leads to new undesirable oscillations. The local and inter-modal oscillations during large disturbances can cause frequency and voltage relays to measure a quantity at a location that is different to the actual underlying system voltage and frequency gradient. From an operational point of view, this issue is important for those networks that use the protective voltage and frequency relays to re-evaluate their tuning strategies. In this dissertation first, an overview of the key issues in the use of high wind power penetration in power system emergency control is presented. The impact of wind power fluctuation on system frequency, voltage and frequency gradient is analyzed. The need for the revising of tuning strategies for frequency protective relays, automatic under-frequency load shedding (UFLS) and under-voltage load shedding (UVLS) relays are also emphasized. In the present dissertation, necessity of considering both system frequency and voltage indices to design an effective power system emergency control plan is shown. Then, an intelligent artificial neural network (ANN) based emergency control scheme considering the dynamic impacts of wind turbines is proposed. In the developed algorithm, following an event, the related contingency is determined by an appropriate ANN using the online measured tie-line powers. A comprehensive voltage stability analysis in the presence of the wind turbines is presented. Another intelligent ANN is used to examine the stability margin by estimating the system powervoltage (P-V) curves. The system frequency gradient and stability information are properly used by an effective load shed