Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method

Sulistiawati, Irrine Budi and Priyadi, Ardyono and Qudsi, Ony Asrarul and Soeprijanto, Adi and Yorino, Naoto (2016) Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method. International Journal of Electrical Power & Energy Systems, 77. pp. 345-352. ISSN 01420615

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Abstract

The Critical Clearing Time (CCT) is a key issue for Transient Stability Assessment (TSA) in electrical power system operation, security, and maintenance. However, there are some difficulties in obtaining the CCT, which include the accuracy, fast computation, and robustness for TSA online. Therefore, obtaining the CCT is still an interesting topic for investigation. This paper proposes a new technique for obtaining CCT based on numerical calculations and artificial intelligence techniques. First, the CCT is calculated by the critical trajectory method based on critical generation. Second, the CCT is learned by Extreme Learning Machine (ELM). This proposed method has the ability to obtain the CCT with load changes, different fault occurrences, accuracy, and fast computation, and considering the controller. This proposed method is tested by the IEEE 3-machine 9-bus system and Java-Bali 500 kV 54-machine 25-bus system. The proposed method can provide accurate CCTs with an average error of 0.33% for the Neural Network (NN) method and an average error of 0.06% for the ELM method. The simulation result also shows that this method is a robust algorithm that can address several load changes and different locations of faults occurring. There are 29 load changes used to obtain the CCT, with 20 load changes included for the training process and 9 load changes not included.

Item Type:	Article
Uncontrolled Keywords:	Critical Clearing Time (CCT) Transient Stability Assessment (TSA) ELM (Extreme Learning Machine) Critical trajectory Load changing
Subjects:	Engineering > Electrical Engineering
Divisions:	Fakultas Teknologi Industri > Teknik Elektro S1
Depositing User:	Mr Sayekti Aditya Endra
Date Deposited:	04 Nov 2019 02:12
Last Modified:	14 Feb 2020 01:13
URI:	http://eprints.itn.ac.id/id/eprint/4419

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