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Adaptive Under Frequency Load Shedding Scheme Using Genetic Algorithm Based Artificial Neural Network

JEEE • 2020
العودة
معلومات البحث
المؤلفون ELZAWAWY Ahmed, ALI Mahmoud, BENDARY Fahmy, MANSOUR Wagdy
الكلمات المفتاحية islanding; blackout; artificial neural network; genetic algorithm; under frequency load shedding; system frequency response.
المجلة العلمية JEEE
الناشر Not Available
المجلد Not Available
العدد Not Available
الصفحات Not Available
publication.type International
رابط البحث Not Available
المواد المرفقة Not Available
الملخص
This paper presents two schemes of UFLS to
keep the system frequency within safe limits. GA-based
scheme is introduced as an offline method to get the
proper amount of shed load achieving the minimum
and steady state frequency within permissible limits.
Due to the probability of generation variation and
generating units outage during shedding process,
ANN-based scheme is presented as an online method
to adjust the proper amount of load shedding at any
amount of power deficit. Multi scenarios of
contingences are carried out on offline mode using GA
optimization technique to collect the training patterns
for ANN. The ANN-based scheme can consider the
generation variations during the load shedding
process. Although using this scheme may shed more
loads, it maintains the frequency to be within
permissible limits at various disturbance scenarios
particularly at the absence of secondary control. An
analytic system frequency response (SFR) model with
no secondary control incorporating UFLS scheme is
presented. The proposed method is compared with the
classical adaptive method to prove its effectiveness.
Results are presented in the form of time domain
simulations via MATLAB/SIMULINK.