Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

被引：0
作者：
Swarnkar N.K. [1 ]
Mahela O.P. [2 ]
Khan B. [3 ]
Lalwani M. [1 ]
机构：
[1] Department of Electrical Engineering, Rajasthan Technical University, Kota
[2] Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Limited, Jaipur
[3] Department of Electrical and Computer Engineering, Hawassa University, Awassa
来源：
Energy Engineering: Journal of the Association of Energy Engineering | 2022年 / 119卷 / 02期
关键词：
Decision rules; Hilbert transform; Multiple PQ disturbance; Power quality; Stockwell transform;
D O I：
10.32604/ee.2022.017703
中图分类号：
学科分类号：
摘要：
A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superim-posed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study. © 2022, Tech Science Press. All rights reserved.
引用
收藏
页码：473 / 497
页数：24
相关论文
共 28 条
  • [1] Subudhi U., Dash S., Detection and classification of power quality disturbances using GWO ELM, Journal of Industrial Information Integration, 22, 5, (2021)
  • [2] Shah R. N., Mahela O. P., Lohia S., Recognition and mitigation of power quality disturbances in renewable energy interfaced hybrid power grid, 12th IEEE International Conference on Computational Intelligence and Communication Networks, (2020)
  • [3] Pandya V., Agarwal S., Mahela O. P., Choudhary S., Recognition of power quality disturbances using hybrid algorithm based on combined features of stockwell transform and hilbert transform, IEEE International Students’ Conference on Electrical, Electronics and Computer Science, (2020)
  • [4] Khetarpal P., Tripathi M. M., A critical and comprehensive review on power quality disturbance detection and classification, Sustainable Computing: Informatics and Systems, 28, 1–11, (2020)
  • [5] Mahela O. P., Shaik A. G., Gupta N., A critical review of detection and classification of power quality events, Renewable and Sustainable Energy Reviews, 41, pp. 495-505, (2015)
  • [6] Sindi H., Nour M., Rawa M., Ozturk S., Polat K., A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification, Expert Systems with Applications, 174, (2021)
  • [7] Pandya V., Choudhary R. R., Mahela O. P., Choudhary S., Detection and classification of complex power quality disturbances using hybrid algorithm based on combined features of stockwell transform and hilbert transform, IEEE International Students’ Conference on Electrical, Electronics and Computer Science, (2020)
  • [8] Sindi H., Nour M., Rawa M., Ozturk S., Polat K., An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events, Expert Systems with Applications, 178, (2021)
  • [9] Hooshmand R., Enshaee A., Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm, Electric Power Systems Research, 80, 12, pp. 1552-1561, (2010)
  • [10] Saini R., Mahela O. P., Sharma D., Detection and classification of complex power quality disturbances using hilbert transform and rule based decision tree, IEEE 8th Power India International Conference, (2018)
123