Conference,
Energy Management by Adaptive Neuro-Fuzzy For Under Frequency Load Shedding/Case Study
(2013)
Abstract
Energy management is the major concern for both
developing and developed countries. Energy sources are
scarce and expensive to develop and exploit, hence we should
confer a procedure to accumulate it by the use of load
shedding. The conventional method is to solve an optimal
power flow problem to find out the rescheduling for overload
alleviation. But this will not give the desired speed of
solution. Speed and accuracy of under frequency load
shedding (UFLS) has a vital role in its effectiveness for
preserving system stability and reducing energy loss. Initial
rate of change of frequency is a fast and potentially useful
signal to detect the overload when a disturbance accurse. This
paper presents a new method for solving UFLS problem by
using neural network and fuzzy logic controller. It also
presents fast and accurate load shedding technique based on
adaptive neuro-fuzzy controller for determining the amount
of load shed to avoid a cascading outage. The development of
new and accurate techniques for vulnerability control of
power systems can provide tools for improving the reliability,
continuity of power supply and reducing the energy loss. The
applicability of ANFIS is tested on a case study at Renigunta
220/132/33 KV sub- station.
