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All Journal Sinergi
Widi Aribowo
Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION Widi Aribowo
SINERGI Vol 24, No 1 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (330.205 KB) | DOI: 10.22441/sinergi.2020.1.005

Abstract

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. 
TUNING FOR POWER SYSTEM STABILIZER USING DISTRIBUTED TIME-DELAY NEURAL NETWORK Widi Aribowo
SINERGI Vol 22, No 3 (2018)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (211.434 KB) | DOI: 10.22441/sinergi.2018.3.009

Abstract

In this paper, a Distributed Time-Delay Neural Network (DTDNN) algorithm is used to control the Power System Stabilizer (PSS) parameters to find the reliable conditions. The proposed DTDNN algorithm apply tapped delay line memory to set the PSS. In this study, DTDNN consists of a DTDNN-identifier and a DTDNN-controller. The performance of the system with DTDNN-PSS controller is compared with a Recurrent Neural Network PSS (RNN-PSS) and Conventional PSS (C-PSS). The results show the effectiveness of DTDNN-PSS design, and superior robust performance for enhancement power system stability compared to other with different cases.
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