Highly accurate detection of power quality disturbance using segmented and modified S-transform
Author:
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Reference35 articles.
1. Novel method based on variational mode decomposition and a random discriminative projection extreme learning machine for multiple power quality disturbance recognition;Zhao;IEEE Trans. Ind. Inf.,2019
2. Power quality disturbances recognition using adaptive chirp mode pursuit and grasshopper optimized support vector machines;Motlagh;Measurement,2021
3. A Kaiser window-based S-transform for time-frequency analysis of power quality signals;Liang;IEEE Trans. Ind. Inf.,2022
4. The mystery curve: a signal processing based power quality disturbance detection;Narayanaswami;IEEE Trans. Ind. Electron.,2021
5. Delta-bar-delta neural-network-based control approach for power quality improvement of solar-PV-interfaced distribution system;Shukl;IEEE Trans. Ind. Inf.,2020
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1. Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm;Sensors;2024-04-12
2. Power quality monitoring in electric grid integrating offshore wind energy: A review;Renewable and Sustainable Energy Reviews;2024-03
3. Quantizing branches' responsibility for voltage sags/swells and flicker events;Electric Power Systems Research;2023-12
4. A Novel Approach to Automatically Detect Power Quality Disturbances Based on Dynamic Pocket Network;2023 IEEE 13th International Workshop on Applied Measurements for Power Systems (AMPS);2023-09-27
5. Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model;IEEE Access;2023
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