Skip to main content
Top
Published in:

03-06-2022 | Original Paper

A new deep learning method for the classification of power quality disturbances in hybrid power system

Authors: Belkis Eristi, Huseyin Eristi

Published in: Electrical Engineering | Issue 6/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the advancement of technology, the demand for high quality and sustainable electrical energy has been increased due to the widespread use of electrical devices in our daily lives. The issue of power quality in the power system is of great importance for the smooth and long-lasting operation of the electrical devices. Besides, large penetration of the hybrid power system (HPS) into the existing power grid injects the inevitable issues related to the power quality. Therefore, it is very important to detect and eliminate the power quality disturbances (PQDs) in order to obtain quality power. This paper presents a new approach deep learning-based system that can detect PQDs in the HPS. A new feature extraction approach is used to obtain the optimum Stockwell Transform (ST) contour image by applying the ST to a PQD signal. The resulting image files are given to the convolutional neural network (CNN) algorithm. Besides, optimum hyperparameters of CNN are determined by using Bayesian optimization algorithm (BOA). Thus, a recognition approach that both effectively extracts the features of PQDs and has high classification performance is proposed in this paper. The proposed recognition system is named as ST and Bayesian optimization-based CNN (STBOACNN). In order to test the performance of the proposed STBOACNN approach, PQD data obtained from the HPS with converter-based distributed generations are used. The experimental results showed that the STBOACNN is a new and effective approach that can classify PQDs occurring in the HPS with high recognition performance.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Wang S, Chen H (2019) A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Appl Energy 235:1126–1140CrossRef Wang S, Chen H (2019) A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Appl Energy 235:1126–1140CrossRef
2.
go back to reference Khetarpal P, Tripathi MM (2020) A critical and comprehensive review on power quality disturbance detection and classification. Sustain Comput: Inf Syst 28:100417 Khetarpal P, Tripathi MM (2020) A critical and comprehensive review on power quality disturbance detection and classification. Sustain Comput: Inf Syst 28:100417
3.
go back to reference Ma Y, Xiao X, Wang Y (2020) Identifying the root cause of power system disturbances based on waveform templates. Electric Power Syst Res 180:106107CrossRef Ma Y, Xiao X, Wang Y (2020) Identifying the root cause of power system disturbances based on waveform templates. Electric Power Syst Res 180:106107CrossRef
4.
go back to reference Singh U, Singh SN (2019) A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework. Appl Soft Comput 74:216–225CrossRef Singh U, Singh SN (2019) A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework. Appl Soft Comput 74:216–225CrossRef
5.
go back to reference Ribeiro EG, Mendes TM, Dias GL, Faria ER, Viana FM, Barbosa BH, Ferreira DD (2018) Real-time system for automatic detection and classification of single and multiple power quality disturbances. Measurement 128:276–283CrossRef Ribeiro EG, Mendes TM, Dias GL, Faria ER, Viana FM, Barbosa BH, Ferreira DD (2018) Real-time system for automatic detection and classification of single and multiple power quality disturbances. Measurement 128:276–283CrossRef
6.
go back to reference Cortes-Robles O, Barocio E, Segundo J, Guillen D, Olivares-Galvan JC (2020) A qualitative-quantitative hybrid approach for power quality disturbance monitoring on microgrid systems. Measurement 154:107453CrossRef Cortes-Robles O, Barocio E, Segundo J, Guillen D, Olivares-Galvan JC (2020) A qualitative-quantitative hybrid approach for power quality disturbance monitoring on microgrid systems. Measurement 154:107453CrossRef
7.
go back to reference Jamali S, Farsa AR, Ghaffarzadeh N (2018) Identification of optimal features for fast and accurate classification of power quality disturbances. Measurement 116:565–574CrossRef Jamali S, Farsa AR, Ghaffarzadeh N (2018) Identification of optimal features for fast and accurate classification of power quality disturbances. Measurement 116:565–574CrossRef
8.
go back to reference Mahela OP, Shaik AG (2017) Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers. Appl Soft Comput 59:243–257CrossRef Mahela OP, Shaik AG (2017) Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers. Appl Soft Comput 59:243–257CrossRef
9.
go back to reference Karasu S, Saraç Z (2019) Investigation of power quality disturbances by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms. Swarm Evol Comput 44:1060–1072CrossRef Karasu S, Saraç Z (2019) Investigation of power quality disturbances by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms. Swarm Evol Comput 44:1060–1072CrossRef
10.
go back to reference Ray PK, Mohanty A, Panigrahi T (2019) Power quality analysis in solar PV integrated microgrid using independent component analysis and support vector machine. Optik 180:691–698CrossRef Ray PK, Mohanty A, Panigrahi T (2019) Power quality analysis in solar PV integrated microgrid using independent component analysis and support vector machine. Optik 180:691–698CrossRef
11.
go back to reference Kapoor R, Gupta R, Jha S, Kumar R (2018) Detection of power quality event using histogram of oriented gradients and support vector machine. Measurement 120:52–75CrossRef Kapoor R, Gupta R, Jha S, Kumar R (2018) Detection of power quality event using histogram of oriented gradients and support vector machine. Measurement 120:52–75CrossRef
12.
go back to reference Rajeshbabu S, Manikandan BV (2018) Detection and classification of power quality events by expert system using analytic hierarchy method. Cogn Syst Res 52:729–740CrossRef Rajeshbabu S, Manikandan BV (2018) Detection and classification of power quality events by expert system using analytic hierarchy method. Cogn Syst Res 52:729–740CrossRef
13.
go back to reference Radhakrishnan P, Ramaiyan K, Vinayagam A, Veerasamy V (2021) A stacking ensemble classification model for detection and classification of power quality disturbances in PV integrated power network. Measurement 175:109025CrossRef Radhakrishnan P, Ramaiyan K, Vinayagam A, Veerasamy V (2021) A stacking ensemble classification model for detection and classification of power quality disturbances in PV integrated power network. Measurement 175:109025CrossRef
14.
go back to reference Ma J, Zhang J, Xiao L, Chen K, Wu J (2017) Classification of power quality disturbances via deep learning. IETE Tech Rev 34(4):408–415CrossRef Ma J, Zhang J, Xiao L, Chen K, Wu J (2017) Classification of power quality disturbances via deep learning. IETE Tech Rev 34(4):408–415CrossRef
15.
go back to reference Liu H, Hussain F, Shen Y, Arif S, Nazir A, Abubakar M (2018) Complex power quality disturbances classification via curvelet transform and deep learning. Electric Power Syst Res 163:1–9CrossRef Liu H, Hussain F, Shen Y, Arif S, Nazir A, Abubakar M (2018) Complex power quality disturbances classification via curvelet transform and deep learning. Electric Power Syst Res 163:1–9CrossRef
16.
go back to reference Qiu W, Tang Q, Liu J, Teng Z, Yao W (2019) Power quality disturbances recognition using modified S transform and parallel stack sparse auto-encoder. Electr Power Syst Res 174:105876CrossRef Qiu W, Tang Q, Liu J, Teng Z, Yao W (2019) Power quality disturbances recognition using modified S transform and parallel stack sparse auto-encoder. Electr Power Syst Res 174:105876CrossRef
17.
go back to reference Mahela OP, Shaik AG, Khan B, Mahla R, Alhelou HH (2020) Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree. IEEE Access 8:173530–173547CrossRef Mahela OP, Shaik AG, Khan B, Mahla R, Alhelou HH (2020) Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree. IEEE Access 8:173530–173547CrossRef
18.
go back to reference Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44(4):998–1001CrossRef Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44(4):998–1001CrossRef
19.
go back to reference Mahela OP, Shaik AG (2017) Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering. Renewable Energy 106:37–51CrossRef Mahela OP, Shaik AG (2017) Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering. Renewable Energy 106:37–51CrossRef
20.
go back to reference Mishra S, Bhende CN, Panigrahi BK (2007) Detection and classification of power quality disturbances using S-transform and probabilistic neural network. IEEE Trans Power Deliv 23(1):280–287CrossRef Mishra S, Bhende CN, Panigrahi BK (2007) Detection and classification of power quality disturbances using S-transform and probabilistic neural network. IEEE Trans Power Deliv 23(1):280–287CrossRef
21.
go back to reference Erişti H, Yıldırım Ö, Erişti B, Demir Y (2014) Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine. Int J Electr Power Energy Syst 61:553–562CrossRef Erişti H, Yıldırım Ö, Erişti B, Demir Y (2014) Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine. Int J Electr Power Energy Syst 61:553–562CrossRef
22.
go back to reference Nielsen MA (2015) Neural networks and deep learning, vol 25. Determination press, San Francisco, CA Nielsen MA (2015) Neural networks and deep learning, vol 25. Determination press, San Francisco, CA
24.
go back to reference Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. CATENA 186:104249CrossRef Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. CATENA 186:104249CrossRef
25.
go back to reference LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551CrossRef LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551CrossRef
26.
go back to reference Ortega-Zamorano F, Jerez JM, Gómez I, Franco L (2017) Layer multiplexing FPGA implementation for deep back-propagation learning. Integr Comput-Aided Eng 24(2):171–185CrossRef Ortega-Zamorano F, Jerez JM, Gómez I, Franco L (2017) Layer multiplexing FPGA implementation for deep back-propagation learning. Integr Comput-Aided Eng 24(2):171–185CrossRef
27.
go back to reference Kolar D, Lisjak D, Pająk M, Gudlin M (2021) Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization. Sensors 21(7):2411CrossRef Kolar D, Lisjak D, Pająk M, Gudlin M (2021) Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization. Sensors 21(7):2411CrossRef
28.
go back to reference Kersting WH (1991) Radial distribution test feeders. IEEE Trans Power Syst 6(3):975–985CrossRef Kersting WH (1991) Radial distribution test feeders. IEEE Trans Power Syst 6(3):975–985CrossRef
29.
go back to reference Standard, I, IEEE Recommended Practice for Monitoring Electric Power Quality. IEEE Std 1159–2009. Standard, I, IEEE Recommended Practice for Monitoring Electric Power Quality. IEEE Std 1159–2009.
30.
go back to reference Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manage Process 5(2):1CrossRef Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manage Process 5(2):1CrossRef
31.
go back to reference Ekici S, Ucar F, Dandil B, Arghandeh R (2021) Power quality event classification using optimized Bayesian convolutional neural networks. Electr Eng 103(1):67–77CrossRef Ekici S, Ucar F, Dandil B, Arghandeh R (2021) Power quality event classification using optimized Bayesian convolutional neural networks. Electr Eng 103(1):67–77CrossRef
32.
go back to reference Piao M, Shon HS, Lee JY, Ryu KH (2014) Subspace projection method based clustering analysis in load profiling. IEEE Trans Power Syst 29(6):2628–2635CrossRef Piao M, Shon HS, Lee JY, Ryu KH (2014) Subspace projection method based clustering analysis in load profiling. IEEE Trans Power Syst 29(6):2628–2635CrossRef
33.
go back to reference Usman A, Choudhry MA (2022) A precision detection technique for power disturbance in electrical system. Electr Eng 104(2):781–796CrossRef Usman A, Choudhry MA (2022) A precision detection technique for power disturbance in electrical system. Electr Eng 104(2):781–796CrossRef
34.
go back to reference DawoodCK, B Z (2021) Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection. Int Trans Electr Energy Syst 31(5):e12868 DawoodCK, B Z (2021) Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection. Int Trans Electr Energy Syst 31(5):e12868
35.
go back to reference Shen Y, Abubakar M, Liu H, Hussain F (2019) Power quality disturbance monitoring and classification based on improved PCA and convolution neural network for wind-grid distribution systems. Energies 12(7):1280CrossRef Shen Y, Abubakar M, Liu H, Hussain F (2019) Power quality disturbance monitoring and classification based on improved PCA and convolution neural network for wind-grid distribution systems. Energies 12(7):1280CrossRef
36.
go back to reference Saini MK, Beniwal RK (2018) Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine. Int Trans Electr Energy Syst 28(4):e2519CrossRef Saini MK, Beniwal RK (2018) Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine. Int Trans Electr Energy Syst 28(4):e2519CrossRef
Metadata
Title
A new deep learning method for the classification of power quality disturbances in hybrid power system
Authors
Belkis Eristi
Huseyin Eristi
Publication date
03-06-2022
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 6/2022
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-022-01581-w