Predicting Voltage Stability Indices of Nigerian 330kV 30 Bus Power Network Using an Auditory Machine Intelligence Technique
| Autor: | Emmanuel N. Osegi, Biobele A. Wokoma, Alex. O. Idachaba |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Polynomial
Artificial neural network Group method of data handling Computer science business.industry 020209 energy 020208 electrical & electronic engineering 02 engineering and technology Function (mathematics) Electric power system Quadratic equation Line (geometry) 0202 electrical engineering electronic engineering information engineering Point (geometry) Artificial intelligence business |
| Zdroj: | AFRICON |
| DOI: | 10.1109/africon46755.2019.9133915 |
| Popis: | In this paper, a novel approach for predicting voltage collapse point based on a quadratic line voltage stability index (q-LVSI) and an auditory machine intelligence technique called the AMI is presented. The technique is applied to some buses of the Nigerian 330kV-30bus power network. In order to validate the proposed technique, a comparison is made with the Group Method of Data Handling for time series (GMDH time-series ) which is a state-of-the-art polynomial function fitting neural network based on inductive learning and self-organization. The results of simulation studies show that the AMI technique is competitive with the GMDH time-series technique for a number of experimental simulation runs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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