Intelligent Power Quality Disturbance Detection in Smart Grid System
- Resource Type
- Conference
- Authors
- Thakur, Amit Kumar; Bagga, Manav; Shukla, Harshit; Nadar, Harsh; Singh, Shiv P.
- Source
- 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Electrical, Electronics and Computer Engineering (UPCON), 2021 IEEE 8th Uttar Pradesh Section International Conference on. :1-6 Nov, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Support vector machines
Shape
Power quality
Power system harmonics
Distortion
Boosting
Classification algorithms
Power Quality
Machine Learning
Disturbance
Sag
Swell
- Language
- ISSN
- 2687-7767
Classification of accurate Power quality (PQ) disturbance is essential for power grid operation and control. Increasing the use of electronics loads makes power system signals contaminated with disturbance and distortion and, thus, increases the complexity of detecting and classifying PQ disturbance signals. This paper proposes a machine learning classifier for detecting and classifying PQ disturbances to address this issue. A large computer-based simulation for generating PQ disturbances in PSCAD/EMTDC environment has been carried out to show the effectiveness of the proposed classification framework. The results show better performance than several state-of-art methods in classifying single and multiple PQ disturbances signals from fewer input features.