Artificial Neural Network Approach for Solving Power Flow Problem: A Case Study of Ayede 132 KV Power System, Nigeria

Abstract:

The main objective of this research work was to use Artificial Neural Network (ANN) based method for solving Power Flow Problem for a power system in Nigeria. This was achieved using the Backpropagation (multilayered feed-forward) Neural Network model. Two Backpropagation neural networks were designed and trained; one for computing voltage magnitudes on all buses and the other for computing voltage phase angles on all PV and PQ buses for different load and generation conditions for a 7-bus 132 kV power system in South-West Nigeria (Ayede). Due to unavailability of historical field records, data representing different scenarios of loading and/or generation conditions had to be generated using Newton-Raphson non-linear iterative method. A total of 250 scenarios were generated out of which 50% were used to train the ANNs, 25% were used for validation and the remaining 25% were used as test data for the ANNs. The test data results showed very high accuracy for the ANN used for computing voltage magnitudes for all test data with a Mean Square Error (MSE) of less than 10^-6. Also, the ANN used for computing voltage phase angles showed very high accuracy in about 80% of the test data and acceptable results in about 97% of the test data. The MSE for all the test data results for the ANN computing voltage phase angles was less than 10^-2.

• Periodical: Advanced Materials Research (Volume 367)
• Pages: 133-141
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.367.133
• Online since: October 2011
• Authors: P.B. Osofisan, J.O. Ilevbare
• Keywords: Artificial Neural Network (ANN), Mean Square Error, Non-Linear Iterative Method, Power Flow

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