Abstract
Electric power quality is an issue for utilities, end users, manufacturers, and other customers. Poor power quality is the primary source of economic losses. The extensive use of sensitive electronic equipment devices has expanded as the globe has become more industrialized. Due to electronic system maintenance, power quality disturbances (PQD) may cause security difficulties and loss. To minimizing power quality events, the events must be identified and classified, so that appropriate preventive action must be taken. In this paper, preprocessing of power signals has been carried out using the dual-tree complex wavelet transform which increases classification accuracy by localizing disturbances based on phase information connected to time and frequency. “Phase space reconstruction using neural networks are used to reconstruct two-dimensional data in order to increase classification accuracy.” The best structure has been developed by testing and implementing the proposed technique in various network configurations with reduced complexity which has been authenticated by its accuracy in classifying the disturbances.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dubey DK (2015) Issues and challenges in the electricity sector in India. Bus Manag Rev 5(4):132
Navani JP, Sharma N, Sapra S (2014) Analysis of technical and non-technical losses in power system and its economic consequences in power sector. Int J Adv Electr Electron Eng 1(3):396–405
Mahela OP, Shaik AG, Gupta N (2015) A critical review of detection and classification of power quality events. Renew Sustain Energy Rev 41:495–505
Igual R, Medrano C, Schubert F (2019) Evaluation of automatic power quality classification in microgrids operating in islanded mode. In: 2019 IEEE Milan PowerTech, 2019. IEEE, pp 1–6
Deepthi K, Gottapu K, Bireddi E (2021) Assessment of power quality performance using change detection and DFT. Adv Aspects Eng Res 11:134–145
Liu H, Hu H, Chen H, Zhang L, Xing Y (2018) Fast and flexible selective harmonic extraction methods based on the generalized discrete Fourier transform. IEEE Trans Power Electron 33
Liu Z, Hu Q, Cui Y, Zhang Q (2014) A new detection approach of transient disturbances combining wavelet packet and Tsallis entropy. Neurocomputing 142:393–407
Angrisani L, Daponte P, Apuzzo MD, Testa A (1998) A measurement method based on the wavelet transform for power quality analysis. IEEE Trans Power Deliv 13:990–998
Basha CH, Rani C (2020) Different conventional and soft computing MPPT techniques for solar PV systems with high step-up boost converters: a comprehensive analysis. Energies 13(2):371
Hussaian Basha CH, Bansal V, Rani C, Brisilla RM, Odofin S (2020) Development of cuckoo search MPPT algorithm for partially shaded solar PV SEPIC converter. In: Soft computing for problem solving: SocProS 2018, vol 1. Springer, Singapore, pp 727–736
Hussaian Basha CH, Rani C (2020) Performance analysis of MPPT techniques for dynamic irradiation condition of solar PV. Int J Fuzzy Syst 22(8):2577–2598
Ramalingappa L, Manjunatha A (2022) Power quality event classification using complex wavelets phasor models and customized convolution neural network. IJECE 12(1):22–31
Hussaian Basha CH, Rani C, Odofin S (2018) Analysis and comparison of SEPIC, Landsman and Zeta converters for PV fed induction motor drive applications. In: 2018 international conference on computation of power, energy, information and communication (ICCPEIC). IEEE, pp 327–334
Basha CH, Murali M (2022) A new design of transformerless, non-isolated, high step-up DC-DC converter with hybrid fuzzy logic MPPT controller. Int J Circuit Theory Appl 50(1):272–297
De Yong D, Bhowmik S, Magnago F (2015) An effective power quality classifier using wavelet transform and support vector machines. Expert Syst Appl 42(15–16):6075–6081
Li J, Teng Z, Tang Q, Song J (2016) Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs. IEEE Trans Instrum Measur 65(10)
Hussaian Basha CH, Rani C, Brisilla RM, Odofin S (2020) Simulation of metaheuristic intelligence MPPT techniques for solar PV under partial shading condition. In: Soft computing for problem solving: SocProS 2018, vol 1. Springer, Singapore, pp 773–785
Ma J, Zhang J, Xiao L, Chen K, Wu J (2017) Classification of power quality disturbances via deep learning. IETE Tech Rev 34(4):408–415
Kiran SR, Basha CH, Singh VP, Dhanamjayulu C, Prusty BR, Khan B (2022) Reduced simulative performance analysis of variable step size ANN based MPPT techniques for partially shaded solar PV systems. IEEE Access 10:48875–48889
Kiran SR, Mariprasath T, Basha CH, Murali M, Reddy MB (2022) Thermal degrade analysis of solid insulating materials immersed in natural ester oil and mineral oil by DGA. Mater Today Proc 52:315–320
Kapoor R, Gupta R, Jha S, Kumar R (2018) Boosting performance of power quality event identification with KL divergence measure and standard deviation. Measurement 126:134–142
Shi X, Yang H, Xu Z, Zhang X, Farahani MR (2019) An independent component analysis classification for complex power quality disturbances with sparse auto encoder features. IEEE Access 7:20961–20966
Rodrigues WL Jr, Borges FAS, Rabelo RdAL, Rodrigues JJPC, Fernandes RAS, da Silva IN (2020) A methodology for detection and classification of power quality disturbances using a real-time operating system in the context of home energy management systems. Int J Energy Res 1–17
Abdelsalam AA, Hassanin AM, Hasanien HM (2021) Categorisation of power quality problems using long short-term memory networks. IET Gener Transm Distrib 15(10):1626–1639
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Likhitha, R., Aruna, M., Hussaian Basha, C.H., Prathibha, E. (2023). Classification of PQDs by Reconstruction of Complex Wavelet Phasor and a Feed-Forward Neural Network—Fully Connected Structure. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_28
Download citation
DOI: https://doi.org/10.1007/978-981-99-4577-1_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4576-4
Online ISBN: 978-981-99-4577-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)