Abstract
This paper proposes a new load shedding method based on the application of intelligent algorithms, the process of calculating and load shedding is carried out in two stages. Stage-1 uses a backpropagation neural network to classify faults in the system, thereby determining whether or not to shed the load in that particular case. Stage-2 uses an artificial neural network combined with an ant colony algorithm (ANN-ACO) to determine a load shedding strategy. The AHP algorithm is applied to propose load shedding strategies based on ranking the importance of loads in the system. The proposed method in the article helps to solve the integrated problem of load shedding, classifying the fault to determine whether or not to shedding the load and proposing a correct strategy for shedding the load. The IEEE 25-bus 8-generator power system is used to simulate and test the effectiveness of the proposed method, the results show that the frequency of recovery is good in the allowable range.
Highlights
Along with the development of economy and society, the load demand for electricity is increasing
Stage-2 uses an artificial neural network combined with an ant colony algorithm (ANN-Ant Colony Optimization (ACO)) to determine a load shedding strategy
The proposed method is tested on the IEEE 25-Bus Microgrid diagram (Vergara & Lopez, 2019)
Summary
Along with the development of economy and society, the load demand for electricity is increasing This requires the power system to develop in the direction of increasing scale and structure (Xu & Ma, 2021; Yin & Sun, 2021). There are many methods of shedding loads in the power system as in (Haes Alhelou & Hamedani Golshan, 2020; Sigrist & Echavarren, 2018) presents traditional shedding methods using under-frequency load shedding relays. These methods have the advantage of rapidly shedding the load which will help to restore the frequency to steady state. This sometimes leads to incorrect shedding situations, which results in losses for customers and power suppliers
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