| Title |
Artificial Neural Network for the Control Mechanism of OLTC on the Generator-Transformer Connected to Unstable Grid |
| Authors |
에라스터스 몽겔라 뮤쇼카(Erastus Mwongela Musyoka) ; 해럴드 치사노 오얀도(Harold Chisano Oyando) ; 장중구(Choong-koo Chang) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.2.342 |
| Keywords |
Artificial neural network (ANN); grid voltage degradation; generator output voltage; nuclear power plant (NPP); on load tap changer (OLTC) |
| Abstract |
The safe and economic operation of the nuclear power plant (NPP) requires that the plant be connected to a stable and reliable electric grid where the voltage and frequency can be controlled within predefined limits. For a country introducing NPP for the first time, such stable and reliable grid may not exist and the grid may experience voltage and frequency fluctuations beyond the acceptable limits for extended periods. This paper therefore analyzes the necessity of installing an on-load tap changer (OLTC) on the main transformer (MT) of the NPP for automatic and fine voltage control in the event of severe voltage degradation on the Nigeria’s electric grid. IEEE Std. C57.116-2014 power transfer equation and load flow analysis using the electrical transient analyzer program (ETAP®) approach were used for this study. The paper also proposes implementation of artificial neural network (ANN) as a control mechanism for the effective and efficient operation of the MT OLTC tap settings. The research is based on the hypothetical generator output voltage of the proposed advanced power reactor (APR1400) unit connected to the Nigerian electric grid. Implementing the ANN model for MT OLTC automatic control enables stable operation even for the large synchronous generators connected to highly fluctuating voltage grids exceeding the +/-5% voltage range. |