ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

Widi Aribowo
Journal article Sinergi • Februari 2020

Unduh teks lengkap
(English, 8 pages)

Abstrak

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility. In recent years, Neural networks have been very victorious in several signal processing and control applications. Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods.

Metrik

  • 254 kali dilihat
  • 120 kali diunduh

Jurnal

Sinergi

Jurnal Ilmiah Sinergi adalah peer-reviewed jurnal yang diterbitkan 3 (tiga) kali dalam setahun, y... tampilkan semua