Transient Stability Assessment using Computational Intelligence
In a complex network integrated with Hybrid Distributed Generation
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Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, etc., the present power system network is now more complex than in the past. In recent time, Hybrid Distributed Generators (HDGs) have received increasing attention. This is because HDGs have the capability to meet peak demand. The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy s...
Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, etc., the present power system network is now more complex than in the past. In recent time, Hybrid Distributed Generators (HDGs) have received increasing attention. This is because HDGs have the capability to meet peak demand. The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. In order to carry out real time transient stability assessment a Computational Intelligence (CI) based algorithm coupled with Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time.
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