Neural network tuned fuzzy logic power system stabilizer design for SMIB
| Autor: | S. Vivekanandan, V. Kumar Chinnaiyan, P. K. Arun Kumar, C. Krishna Kumar |
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| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Rotor (electric) Computer science 020209 energy Stability (learning theory) 02 engineering and technology Permanent magnet synchronous generator Stabilizer (aeronautics) Signal Fuzzy logic law.invention Electric power system Control theory law 0202 electrical engineering electronic engineering information engineering |
| Zdroj: | 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). |
| DOI: | 10.1109/ic3i.2016.7918006 |
| Popis: | Steadiness of power system is a significant issue in power system operation. In this article design of Neural Network tuned Fuzzy logic power system stabilizer (NNTFLPSS) for single machine infinite bus (SMIB) system is proposed to settle down low frequency swinging that improves small signal stability in power system. The speed deviance and variation in speed deviance of the rotor of synchronous generator from the trained neural network were considered as the feedback to the fuzzy logic power system stabilizer (FLPSS) to recover the power system from small signal stability problem by refining damping oscillations. The comparative reading was noted for rotor speed deviances and rotor angle deviances using conventional PSS (CPSS), Fuzzy logic based power system stabilizer (FLPSS) and NNTFLPSS. The MATLAB simulation results obtained indicates the improved performance of NNTFLPSS over the CPSS and FLPSS. |
| Databáze: | OpenAIRE |
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