Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as "power quality (PQ) disturbances," highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time-frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.

Applied Science Research Center Applied Science Private University Amman 11937 Jordan

Department of Electrical and Computer Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

Department of Electrical and Electronics Engineering K S R M College of Engineering Kadapa 516005 India

Department of Electrical Engineering Graphic Era Dehradun 248002 India

ENET Centre VSB Technical University of Ostrava 708 00 Ostrava Czech Republic

Graphic Era Hill University Dehradun 248002 India

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

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Sankaran C. Power Quality. CRC Press; 2002.

Bajaj M, Singh AK. Grid integrated renewable DG systems: A review of power quality challenges and state‐of‐the‐art mitigation techniques. Int. J. Energy Res. 2020;44(1):26–69. doi: 10.1002/er.4847. DOI

Lieberman DG, Troncosco RJR, Rios RAO, Perez AG. Techniques and methodologies for power quality analysis and disturbances classification in power systems: A review. IET Gener. Trans. Distrib. 2010;5(4):519–529. doi: 10.1049/iet-gtd.2010.0466. DOI

Collins ER, Perry CH. Characterization of power quality events. IEEE/TDC. 2008 doi: 10.1109/TDC.2008.4517205. DOI

Wang ZQ, Zhu SZ. Comparative study on power quality disturbance magnitude characterization. Power Syst. Technol. 2002;1:106–111.

Santoso S, Powers EJ, Grady WM, Hofmann P. Power quality assessment via wavelet transform analysis. IEEE Trans. Power Deliv. 1996;11(2):924–930. doi: 10.1109/61.489353. DOI

Naidoo R, Pillay P. A new method of voltage sag and swell detection. IEEE Trans. Power Deliv. 2007;22(2):1056–1063. doi: 10.1109/TPWRD.2007.893185. DOI

Tanaboylu, N. S., Collins, E. R. & Chaney, P. R. Voltage disturbance evaluation using the missing voltage technique. in Proc. 8th Conf. Harmonics Quality of Power, pp. 577–582 (1998).

Sushama M, Tulasi Ram G, Jaya Laxmi A. Detection of power quality disturbances using wavelet transforms. Int. J. Comput., Internet Manag. 2010;18(1):61–66.

Yang HT, Liao CC. A de-noising scheme for enhancing wavelet-based power quality monitoring system. IEEE Trans. Power Deliv. 2001;16(3):353–360. doi: 10.1109/61.924810. DOI

Parsons AC, Grady WM, Powers EJ. A wavelet-based procedure for automatically determining the beginning and end of transmission system voltage sags. Proc. IEEE Power Energy Soc. 1999;2:1310–1315.

Priyadarshini, M. S., Krishna, D., Reddy, M. B., Bhatt, A., Bajaj, M. & Mahmoud, M. M. Continuous wavelet transform based visualization of transient and short duration voltage variations. in 4th IEEE Global Conference for Advancement in Technology (GCAT), Bangalore, India, 1–6,10.1109/GCAT59970.2023.10353457 (2023).

Costa FB, Souza BA, Brito NSD. A wavelet-based algorithm to analyze oscillographic data with single and multiple disturbances. IEEE/PES. 2008 doi: 10.1109/PES.2008.4596693. DOI

Poisson O, Rioual P, Meunier M. Detection and measurement of power quality disturbances using wavelet transform. IEEE Trans. Power Deliv. 2000;15(3):1039–1044. doi: 10.1109/61.871372. DOI

Singh, B., Shahani D. T. & Kumar, R. Recognition of power quality events using DT-DWT based complex wavelet transform. in IEEE Power India Conference (2012).

Costa, F. B., Souza B. A. & Brito, N. S. D. Real-time detection of voltage sags based on wavelet transform. in IEEE/PES Transmission and Distribution Conference and Exposition, Latin America, 537–542 (2010).

Costa FB, Driesen J. Assessment of voltage sag indices based on scaling and wavelet coefficient energy analysis. IEEE Trans. Power Deliv. 2013;28(1):336–346. doi: 10.1109/TPWRD.2012.2218626. DOI

Anis Ibrahim WR, Morcos MM. Artificial intelligence and advanced mathematical tools for power quality applications: A survey. IEEE Trans. Power Deliv. 2002;17(2):668–673. doi: 10.1109/61.997958. DOI

Samanta IS, Panda S, Rout PK, Bajaj M, Piecha M, Blazek V, Prokop L. A comprehensive review of deep-learning applications to power quality analysis. Energies. 2023;16:4406. doi: 10.3390/en16114406. DOI

Thamizh Thentral TM, Palanisamy R, Usha S, Bajaj M, Zawbaa HM, Kamel S. Analysis of power quality issues of different types of household applications. Energy Rep. 2022;8:5370–5386. doi: 10.1016/j.egyr.2022.04.010. DOI

Bajaj, M., Aggarwal, S. & Singh, A. K. Power quality concerns with integration of RESs into the smart power grid and associated mitigation techniques. in 2020 IEEE 9th Power India International Conference (PIICON), Sonepat, India, 1–6. 10.1109/PIICON49524.2020.9113008 (2020).

Mallat S. A Wavelet Tour of Signal Processing. Elsevier Publications; 2009.

Tan RHG, Ramachandra Murthy VK. Numerical model framework of power quality events. Eur. J. Sci. Res. 2010;43(1):30–47.

Bajaj M, Aymen F, Alowaidi M, Sharma NK, Mishra S, Sharma SK. A Lyapunov-function based controller for 3-phase shunt active power filter and performance assessment considering different system scenarios. IEEE Access. 2021;9:66079–66102. doi: 10.1109/ACCESS.2021.3075274. DOI

Bajaj M, Singh AK, Alowaidi M, Sharma NK, Sharma SK, Mishra S. Power quality assessment of distorted distribution networks incorporating renewable distributed generation systems based on the analytic hierarchy process. IEEE Access. 2020;8:145713–145737. doi: 10.1109/ACCESS.2020.3014288. DOI

Bajaj M, Singh AK. An analytic hierarchy process-based novel approach for benchmarking the power quality performance of grid-integrated renewable energy systems. Electr. Eng. 2020;102(3):1153–1173. doi: 10.1007/s00202-020-00938-3. DOI

Misiti M, Misiti Y, Oppenheim G, Poggi J-M. Wavelet Toolbox for Use with MATLAB: User’s Guide, Version 3. The Mathworks Inc; 2006.

Francisco Aguiar, L. & Joana Soares, M. The continuous wavelet transform: A Primer. NIPE Working Papers 23/2010, NIPE–University of Minho (2010).

Wang H, Wu X, Zheng X, Yuan X. Model predictive current control of nine-phase open-end winding PMSMs with an online virtual vector synthesis strategy. IEEE Trans. Ind. Electron. 2022 doi: 10.1109/TIE.2022.3174241. DOI

Liu S, Liu C. Virtual-vector-based robust predictive current control for dual three-phase PMSM. IEEE Trans. Ind. Electron. 2021;68(3):2048–2058. doi: 10.1109/TIE.2020.2973905. DOI

Liu S, Liu C. Direct harmonic current control scheme for dual three-phase PMSM drive system. IEEE Trans. Power Electron. 2021;36(10):11647–11657. doi: 10.1109/TPEL.2021.3069862. DOI

Song X, Wang H, Ma X, Yuan X, Wu X. Robust model predictive current control for a nine-phase open-end winding PMSM with high computational efficiency. IEEE Trans. Power Electron. 2023;38(11):13933–13943. doi: 10.1109/TPEL.2023.3309308. DOI

Yang C, Wu Z, Li X, Fars A. “Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response”, and electric vehicles. Energy. 2024;288:129680. doi: 10.1016/j.energy.2023.129680. DOI

Zhang X, Gong L, Zhao X, Li R, Yang L, Wang B. Voltage and frequency stabilization control strategy of virtual synchronous generator based on small signal model. Energy Rep. 2023;9:583–590. doi: 10.1016/j.egyr.2023.03.071. DOI

Liu Y, Liu X, Li X, Yuan H, Xue Y. Model predictive control-based dual-mode operation of an energy-stored quasi-Z-source photovoltaic power system. IEEE Trans. Ind. Electron. 2023;70(9):9169–9180. doi: 10.1109/TIE.2022.3215451. DOI

Li S, Zhao X, Liang W, Hossain MT, Zhang Z. A fast and accurate calculation method of line breaking power flow based on Taylor expansion. Front. Energy Res. 2022 doi: 10.3389/fenrg.2022.943946. DOI

Li M, Wang T, Chu F, Han Q, Qin Z, Zuo MJ. Scaling-basis chirplet transform. IEEE Trans. Ind. Electron. 2021;68(9):8777–8788. doi: 10.1109/TIE.2020.3013537. DOI

Chen C, Wu X, Yuan X, Zheng X. A new technique for the subdomain method in predicting electromagnetic performance of surface-mounted permanent magnet motors with shaped magnets and a quasi-regular polygon rotor core. IEEE Trans. Energy Convers. 2022 doi: 10.1109/TEC.2022.3217042. DOI

González R, Munoz AM, de la Rosa JG. Two applications for power quality analysis using the matlab wavelet toolbox. Renew. Energy Power Qual. J. 2005;1:35–39. doi: 10.24084/repqj03.215. DOI

Priyadarshini MS, Sushama M. Performance of Static VAR Compensator for Changes in Voltage due to Sag and Swell. In: Saini HS, Srinivas T, Vinod Kumar DM, Chandragupta Mauryan KS, editors. Innovations in Electrical and Electronics Engineering. Lecture Notes in Electrical Engineering. Springer; 2020. pp. 225–233.

Priyadarshini MS, Krishna D, Kumar KV, Amaresh K, Srikanth Goud B, Bajaj M, Altameem T, El-Shafai W, Fouda MM. Significance of harmonic filters by computation of short-time Fourier transform-based time-frequency representation of supply voltage. Energies. 2023;16(5):2194. doi: 10.3390/en16052194. DOI

Madur, P. S., Malaji, S. & Kuruva, R. Wavelet transform based statistical feature extraction of power quality disturbances. in AIP Conf. Proc. 11 September 2755(1), 020012. 10.1063/5.0148333 (2023).

Guo T, Zhang T, Lim E, López-Benítez M, Ma F, Yu L. A review of wavelet analysis and its applications: Challenges and opportunities. IEEE Access. 2022;10:58869–58903. doi: 10.1109/ACCESS.2022.3179517. DOI

Song J, Mingotti A, Zhang J, Peretto L, Wen H. Fast iterative-interpolated DFT phasor estimator considering out-of-band interference. IEEE Trans. Instrum. Meas. 2022 doi: 10.1109/TIM.2022.3203459. DOI

Li F, Wu B, Liu N, Hu Y, Wu H. Seismic time-frequency analysis via adaptive mode separation-based wavelet transform. IEEE Geosci. Remote Sens. Lett. 2020;17(4):696–700. doi: 10.1109/LGRS.2019.2930583. DOI

Song J, Mingotti A, Zhang J, Peretto L, Wen H. Accurate damping factor and frequency estimation for damped real-valued sinusoidal signals. IEEE Trans. Instrum. Meas. 2022 doi: 10.1109/TIM.2022.3220300. DOI

Gupta P, Mukhopadhyay S. Extraction of group velocity dispersion curves of surface waves using continuous wavelet transform. IEEE Geosci. Remote Sens. Lett. 2023 doi: 10.1109/LGRS.2023.3287470. DOI

Zhu L, Li Z, Hou K. Effect of radical scavenger on electrical tree in cross-linked polyethylene with large harmonic superimposed DC voltage. High Voltage. 2022 doi: 10.1049/hve2.12302. DOI

Nasr R, et al. Mean Scatterer spacing estimation using Cepstrum-based continuous wavelet transform. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 2020;67(6):1118–1126. doi: 10.1109/TUFFC.2020.2963955. PubMed DOI

Ding Z, Wu X, Chen C, Yuan X. Magnetic field analysis of surface-mounted permanent magnet motors based on an improved conformal mapping method. IEEE Trans. Ind. Appl. 2023;59(2):1689–1698. doi: 10.1109/TIA.2022.3228509. DOI

Gu, C. VFTO spectrum analysis method based on continuous wavelet transform. in 2018 10th International Conference on Communications, Circuits and Systems (ICCCAS), Chengdu, China, 369–372, 10.1109/ICCCAS.2018.8768965 (2018).

Zhang X, Lu Z, Yuan X, Wang Y, Shen X. L2-gain adaptive robust control for hybrid energy storage system in electric vehicles. IEEE Trans. Power Electron. 2021;36(6):7319–7332. doi: 10.1109/TPEL.2020.3041653. DOI

Yao, Z., Yang, J., Zheng, J., Li, Y., Xie, D. & Wu, J. Cable fault location and signal separation based on continuous wavelet transform. in 2023 IEEE 4th International Conference on Electrical Materials and Power Equipment (ICEMPE), Shanghai, China, 1–4. 10.1109/ICEMPE57831.2023.10139333 (2023).

Zhou G, Xu C, Zhang H, Zhou X, Zhao D, Wu G, Lin J, Liu Z, Yang J, Nong X, Zhang L. PMT gain self-adjustment system for high-accuracy echo signal detection. Int. J. Remote Sens. 2022;43(19–24):7213–7235. doi: 10.1080/01431161.2022.2155089. DOI

Mukherjee N, Chattopadhyaya A, Chattopadhyay S, Sengupta S. Discrete-wavelet-transform and Stockwell-transform-based statistical parameters estimation for fault analysis in grid-connected wind power system. IEEE Syst. J. 2020;14(3):4320–4328. doi: 10.1109/JSYST.2020.2984132. DOI

Wang H, Sun W, Jiang D, Qu R. A MTPA and flux-weakening curve identification method based on physics-informed network without calibration. IEEE Trans. Power Electron. 2023;38(10):12370–12375. doi: 10.1109/TPEL.2023.3295913. DOI

Irene Yu-Hua G, Styvaktakis E. Bridge the gap: Signal processing for power quality applications. Electr. Power Syst. Res. 2003;66(1):83–96. doi: 10.1016/S0378-7796(03)00074-9. DOI

Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989;11(7):674–692. doi: 10.1109/34.192463. DOI

Korkmaz, S. A. & Esmeray, F. Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method. in 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, Turkey, 1–6. 10.1109/ISDFS.2018.8355326 (2018)

Shen Y, Liu D, Liang W, Zhang X. Current reconstruction of three-phase voltage source inverters considering current ripple. IEEE Trans. Transp. Electrific. 2023;9(1):1416–1427. doi: 10.1109/TTE.2022.3199431. DOI

Abdelemam, A. M., Zeineldin, H. H., Al-Durra, A., Moursi, M. S. E. & El-Saadany, E. F. Inverter based islanded Microgrid protection using discrete wavelet transform. in 2023 IEEE Conference on Power Electronics and Renewable Energy (CPERE), Luxor, Egypt, 1–6, 10.1109/CPERE56564.2023.10119608 (2023).

Sun Q, Lyu G, Liu X, Niu F, Gan C. Virtual current compensation-based quasi-sinusoidal-wave excitation scheme for switched reluctance motor drives. IEEE Trans. Ind. Electron. 2023 doi: 10.1109/TIE.2023.3333056. DOI

Nusev G, Juricic D, Gaberscek M, Moskon J, Boskoski P. Fast impedance measurement of Li-ion battery using discrete random binary excitation and wavelet transform. IEEE Access. 2021;9:46152–46165. doi: 10.1109/ACCESS.2021.3058368. DOI

Wang Y, Xia F, Wang Y, Xiao X. Harmonic transfer function based single-input single-output impedance modeling of LCCHVDC systems. J. Mod. Power Syst. Clean Energy. 2023 doi: 10.35833/MPCE.2023.000093. DOI

Wang Y, Chen P, Yong J, Wilsun Xu, Shuangting Xu, Liu Ke. A comprehensive investigation on the selection of high-pass harmonic filters. IEEE Trans. Power Deliv. 2022;37(5):4212–4226. doi: 10.1109/TPWRD.2022.3147835. DOI

Kamble, S. & Dupare, I. Detection of power quality disturbances using wavelet transform and artificial neural network. in Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives, 1–5 (2014).

Gouda M, Salama MMA, Sultan MR, Chikhani AY. Power quality detection and classification using wavelet multi resolution signal decomposition. IEEE Trans. Power Deliv. 1999;14(4):1469–1476. doi: 10.1109/61.796242. DOI

Soman KP, Ramachandran KI, Resmi NG. Insight into Wavelets from Theory to Practice. PHI Learning Private Ltd; 2010.

Sakib, M. S., Islam, M. R., Tanim, S. M. S. H., Alam, M. S., Shafiullah, M. & Ali, A. Signal processing-based artificial intelligence approach for power quality disturbance identification. in 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh, 1–6, 10.1109/ICAEEE54957.2022.9836389 (2022).

Liang J, Feng J, Fang Z, Yanbo L, Yin G, Mao X, Jian W, Wang F. An energy-oriented torque-vector control framework for distributed drive electric vehicles. IEEE Trans. Transp. Electrific. 2023;9(3):4014–4031. doi: 10.1109/TTE.2022.3231933. DOI

Tuljapurkar, M. & Dharme, A. A. Wavelet based signal processing technique for classification of power quality disturbances.in 2014 Fifth International Conference on Signal and Image Processing, Bangalore, India, 337–342, 10.1109/ICSIP.2014.59 (2014).

Zhou S, Zhou G, Liu X, Zhao H. Dynamic freewheeling control for SIDO buck converter with fast transient performance, minimized cross-regulation, and high efficiency. IEEE Trans. Ind. Electron. 2023;70(2):1467–1477. doi: 10.1109/TIE.2022.3156169. DOI

Shariatinasab R, Akbari M, Rahmani B. Application of Wavelet Analysis in Power Systems. In: Baleanu D, editor. Advances in Wavelet Theory and their Applications in Engineering, Physics and Technology. InTech; 2012.

Ribeiro, M. V., Deckmann, S. M. & Romano, J. M. T. Adaptive filtering, wavelet and lapped transforms for power quality problem detection and identification. in 2003 IEEE International Symposium on Industrial Electronics (Cat. No.03TH8692), Rio de Janeiro, Brazil, 1, 301–306, 10.1109/ISIE.2003.1267263 (2003).

Yang M, Wang Y, Xiao X, Li Y. A robust damping control for virtual synchronous generators based on energy reshaping. IEEE Trans. Energy Convers. 2023;38(3):2146–2159. doi: 10.1109/TEC.2023.3260244. DOI

Dwivedi UD, Singh SN. Enhanced detection of power-quality events using intra and interscale dependencies of wavelet coefficients. IEEE Trans. Power Deliv. 2010;25(1):358–366. doi: 10.1109/TPWRD.2009.2027482. DOI

Priyadarshini, M. S. & Sushama, M. Classification of short-duration voltage variations using wavelet decomposition based entropy criteria. in International Conference on Wireless Communications, Signal Processing and Networking, 2192–2196 (2016).

Youssef, O. A. S. Online applications of wavelet transforms to power system relaying-Part II. in 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA, 1–7, 10.1109/PES.2007.385604 (2007).

Wang Z, Li J, Hu C, Li X, Zhu Y. Hybrid energy storage system and management strategy for motor drive with high torque overload. J. Energy Storage. 2024;75:109432. doi: 10.1016/j.est.2023.109432. DOI

Banerjee, S., Bhowmik, P. S. & Bohre, A. K. Detection and location of fault in microgrid using discrete wavelet transform based technique. in 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Durgapur, India, 417–421, 10.1109/CATCON56237.2022.10077697 (2022).

A hybrid approach for power quality event identification in power systems: Elasticnet Regression decomposition and optimized probabilistic neural networks

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