The study looked at Nigerian 330kv transmission network of a 48 bus system. The identified vulnerable buses in the system (Maiduguri bus, Jalingo bus, Yola bus, Damaturu bus and Gombe bus) were optimally compensated using static var compensator. In order to assess the voltage stability of the Nigerian 330KV transmission network after optimal compensation, artificial neural networks were introduced. The artificial neural network was introduced. Artificial neural network simulation showed that blue, green and red plot indicates the training, validation and test mode respectively. The performance regarding each iteration was calculated and the point where the three plots coincided was chosen to be the best performance as it became the best line of fit. The best validation performance during the training process is 10.4258 at epoch 4 which indicates how much minimized Mean-Square Error (MSE) occurred during the training. Also, the regression plot of the artificial neural network output against the targetsreveals the fitness of the training result. Regression = 1 indicates there is an exact linear relationship between outputs and targets and Regression=0 indicates no linear relationship between the output and the target. The regression plot shows the R value equals to 0.9992 for training, 0.99993 for validation, and 0.99855 for testing. This shows that the applied ANN model, training, testing and validation are significantly acceptable and a perfect regression existed between the output and the target.
Chukwuka L. Onita , Osazee E. Ogbeifun , Harmon E. Okilo , Bright Z. Ogoro "Voltage Stability Assessment of the Nigerian 330kv Transmission Network Using Artificial Neural Networks" Iconic Research And Engineering Journals Volume 6 Issue 1 2022 Page 295-306
Chukwuka L. Onita , Osazee E. Ogbeifun , Harmon E. Okilo , Bright Z. Ogoro "Voltage Stability Assessment of the Nigerian 330kv Transmission Network Using Artificial Neural Networks" Iconic Research And Engineering Journals, 6(1)