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NEURAL NETWORK IDENTIFICATION OF ELECTRIC POWER QUALITY INDICATORS OF COMPLEX POWER SYSTEMS |
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| รหัสดีโอไอ | |
| Creator | Tatyana Zhashkova *, Elena Meshcheryakova, Olga Yasarevskaya |
| Title | NEURAL NETWORK IDENTIFICATION OF ELECTRIC POWER QUALITY INDICATORS OF COMPLEX POWER SYSTEMS |
| Contributor | - |
| Publisher | TuEngr Group |
| Publication Year | 2562 |
| Journal Title | International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies |
| Journal Vol. | 10 |
| Journal No. | 9 |
| Page no. | 10A09C: 1-10 |
| Keyword | Neural network technology, Electric power quality indicators, Information system, power system, Engineering system, System for critical applications. |
| URL Website | http://tuengr.com/Vol10_9.html |
| Website title | ITJEMAST V10(8) 2019 @ TuEngr.com |
| ISSN | 2228-9860 |
| Abstract | The article reviews and analyzes the existing problems of electric power quality control in complex power systems, attracting attention to the requirements of reference documents on power quality changes. The procedures development of electric power quality indicators of complex power systems is under discussion. This work was carried out comparative modeling of calculations of basic electric power quality indicators by the direct method and neural network technology. An optimal configuration of a neural network for engineering systems for critical applications has been developed. Simulation system allows for a situation of frequency determination at a distorted signal as well as the presence of harmonics, interharmonics, and subharmonics in the signal, and voltage value deviation. The simulation finds that a frequency meter on the basis of a feedforward neural network has the least error. |
