Three phase power quality disturbance detection and classification by SCGB Neural Network
| Autor: | Hiralal M. Suryawanshi, Madhuri A. Chaudhari, Chetan B. Khadse, Vijay B. Borghate |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science 020208 electrical & electronic engineering 02 engineering and technology USB law.invention Microcontroller Data acquisition law Harmonics 0202 electrical engineering electronic engineering information engineering MATLAB computer Simulation Test data Real-time testing computer.programming_language |
| Zdroj: | 2021 6th International Conference for Convergence in Technology (I2CT). |
| DOI: | 10.1109/i2ct51068.2021.9418004 |
| Popis: | An artificial neural network based three phase real time power quality analysis is proposed in this paper. The scaled conjugate gradient descent algorithm is used as a learning algorithm for neural network. The training and testing data required is generated from the experimental set up of three phase power quality disturbance generator. The exprimental setup is based on the changing voltage range which is controlled by solid state relays. The solid state relays are controlled by the microcontroller. The disturbances generated are sag, swell, interruption, harmonics with and without sag/swell. These disturbances are acquired with the help of NI USB 6361 data acquisition system. The initial training and testing is done in the MATLAB with the dataset collected from disturbance generator. The mathematical model of the trained network is developed and implemented in the LabVIEW for the real time testing. The real time results of the analysis are displayed in the LabVIEW graphic user interface console. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|