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Neural Computing and Applications
Published in: Neural Computing and Applications 5/2021

25-05-2020 | Original Article

Design of backtracking search heuristics for parameter estimation of power signals

Authors: Ammara Mehmood, Peng Shi, Muhammad Asif Zahoor Raja, Aneela Zameer, Naveed Ishtiaq Chaudhary

Published in: Neural Computing and Applications | Issue 5/2021

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Abstract

This study presents a novel implementation of evolutionary heuristics through backtracking search optimization algorithm (BSA) for accurate, efficient and robust parameter estimation of power signal models. The mathematical formulation of fitness function is accomplished by exploiting the approximation theory in mean squared errors between actual and estimated responses, as well as, true and approximated decision variables. Variants of BSA-based meta-heuristics are applied for parameter estimation problem of power signals for identification of amplitude, frequency and phase parameters for different scenarios of noise variation. Analysis of performance evaluation for BSAs is conducted through exhaustive statistical observations in terms of mean weight deviation, root mean square error and Thiel inequality coefficient-based assessment metrics, as well as, ANOVA tests for statistical significance.
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Literature
1.
Van Cutsem T, Vournas C (2007) Voltage stability of electric power systems. Springer, Berlin
2.
Dugan RC, McGranaghan MF, Beaty HW, Santoso S (1996) Electrical power systems quality, vol 2. Mcgraw-Hill, New York
3.
Phan AT, Wira P, Hermann G (2018) A dedicated state space for power system modeling and frequency and unbalance estimation. Evol Syst 9(1):57–69
4.
Acha E, Madrigal M (2001) Power systems harmonics. Wiley, New York
5.
Xu L, Ding F (2018) Iterative parameter estimation for signal models based on measured data. Circuits Syst Signal Process 37:1–24MathSciNetMATH
6.
Xu L, Xiong W, Alsaedi A, Hayat T (2018) Hierarchical parameter estimation for the frequency response based on the dynamical window data. Int J Control Autom Syst 16(4):1756–1764
7.
Li H, Zhou M, Wu R, Zhang Z, Zheng J (2018) Parameter estimation of air maneuvering target for multi-antenna system via reconstructing time samples and signal. Multidimens Syst Signal Process 29(2):621–641MathSciNetMATH
8.
Cao Y, Liu Z (2010) Signal frequency and parameter estimation for power systems using the hierarchical identification principle. Math Comput Model 52(5–6):854–861MathSciNetMATH
9.
Qian H, Zhao R, Chen T (2007) Interharmonics analysis based on interpolating windowed FFT algorithm. IEEE Trans Power Deliv 22(2):1064–1069
10.
Zhang Q, Liu H, Chen H, Li Q, Zhang Z (2008) A precise and adaptive algorithm for interharmonics measurement based on iterative DFT. IEEE Trans Power Deliv 23(4):1728–1735
11.
Zubair S, Chaudhary NI, Khan ZA, Wang W (2018) Momentum fractional LMS for power signal parameter estimation. Signal Process 142:441–449
12.
Chaudhary NI et al (2017) A new computing approach for power signal modeling using fractional adaptive algorithms. ISA Trans 68:189–202
13.
14.
Wan L, Ding F (2019) Decomposition-and gradient-based iterative identification algorithms for multivariable systems using the multi-innovation theory. Circuits Syst Signal Process 38:1–21
15.
Huang C, Liu L, Yuen C (2018) Asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions. IEEE Trans Veh Technol 67(10):10070–10075
16.
Xia H, Ji Y, Xu L, Hayat T (2019) Maximum likelihood-based recursive least-squares algorithm for multivariable systems with colored noises using the decomposition technique. Circuits Syst Signal Process 38:1–19
17.
Shuai Z, Zhang J, Tang L, Teng Z, Wen H (2018) Frequency shifting and filtering algorithm for power system harmonic estimation. IEEE Trans Ind Inform 15:1554–1565
18.
Liu S, Ding F, Xu L, Hayat T (2019) Hierarchical principle-based iterative parameter estimation algorithm for dual-frequency signals. Circuits Syst Signal Process 38:1–18
19.
Hu B, Gharavi H (2018) A fast recursive algorithm for spectrum tracking in power grid systems. IEEE Trans Smart Grid 10:2882–2891
20.
Hu Y, Zhou Q, Yu H, Zhou Z, Ding F (2018) Two-stage generalized projection identification algorithms for stochastic systems. Circuits Syst Signal Process 38:1–17
21.
El Maani R, Radi B, El Hami A (2019) Multiobjective backtracking search algorithm: application to FSI. Struct Multidiscip Optim 59:1–21MathSciNet
22.
Mallick S, Kar R, Mandal D, Ghoshal SP (2016) CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution. J Exp Theor Artif Intell 28(4):719–749
23.
Nazri NN, Malik NN, Idoumghar L, Latiff NM, Ali S (2018) Backtracking search optimization for collaborative beamforming in wireless sensor networks. Telkomnika 16(4):1801–1808
24.
Askarzadeh A, dos Santos Coelho L (2014) A backtracking search algorithm combined with Burger’s chaotic map for parameter estimation of PEMFC electrochemical model. Int J Hydrog Energy 39(21):11165–11174
25.
Yang H, Yu J, Qiu Y, Li Q, Chen W (2018) A coordinated optimization method considering time-delay effect of islanded photovoltaic microgrid based on modified backtracking search algorithm. J Renew Sustain Energy 10(2):023503
26.
Zhou J, Zhang C, Peng T, Xu Y (2018) Parameter identification of pump turbine governing system using an improved backtracking search algorithm. Energies 11(7):1668
27.
Yan S, Zhou J, Zheng Y, Li C (2018) An improved hybrid backtracking search algorithm based T–S fuzzy model and its implementation to hydroelectric generating units. Neurocomputing 275:2066–2079
28.
Modiri-Delshad M, Kaboli SHA, Taslimi-Renani E, Rahim NA (2016) Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options. Energy 116:637–649
29.
Hannan MA, Lipu MSH, Hussain A, Saad MH, Ayob A (2018) Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm. IEEE Access 6:10069–10079
30.
Khan WU et al (2018) Backtracking search integrated with sequential quadratic programming for nonlinear active noise control systems. Appl Soft Comput 73:666–683
31.
Agarwal SK, Shah S, Kumar R (2015) Classification of mental tasks from EEG data using backtracking search optimization based neural classifier. Neurocomputing 166:397–403
32.
bin Mohd Zain MZ, Kanesan J, Kendall G, Chuah JH (2018) Optimization of fed-batch fermentation processes using the backtracking search algorithm. Expert Syst Appl 91:286–297
33.
Lin J (2019) Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time. Eng Appl Artif Intell 77:186–196
34.
Bian J, Xing J, Liu J, Li Z, Li H (2016) An adaptive and computationally efficient algorithm for parameters estimation of superimposed exponential signals with observations missing randomly. Digit Signal Process 48:148–162MathSciNet
35.
36.
Mehmood A, Chaudhary NI, Zameer A et al (2020) Novel computing paradigms for parameter estimation in power signal models. Neural Comput Appl 32:6253–6282CrossRef
37.
Zhou L, Li X, Xu H, Zhu P (2016) Multi-innovation stochastic gradient method for harmonic modelling of power signals. IET Signal Process 10(7):737–742
38.
Pan J, Yang X, Cai H, Mu B (2016) Image noise smoothing using a modified Kalman filter. Neurocomputing 173:1625–1629
39.
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144MathSciNetMATH
40.
Chatzipavlis A, Tsekouras GE, Trygonis V, Velegrakis AF, Tsimikas J, Rigos A, Hasiotis T, Salmas C (2019) Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm. Neural Comput Appl 31(6):1747–1763
41.
Wang H, Hu Z, Sun Y, Su Q, Xia X (2019) A novel modified BSA inspired by species evolution rule and simulated annealing principle for constrained engineering optimization problems. Neural Comput Appl 31(8):4157–4184
42.
Mehmood A et al (2019) Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems. ISA Trans 91:99–113
43.
Bhattacharjee K (2018) Economic dispatch problems using backtracking search optimization. Int J Energy Optim Eng (IJEOE) 7(2):39–60
44.
45.
Raja MAZ, Akhtar R, Chaudhary NI, Khan WU, Zhiyu Z, Jamil A, Zaman F (2020) Design of backtracking search optimization paradigm for joint amplitude-angle measurement of sources lying in fraunhofer zone. Measurement 149:106977
46.
Yu K, Liang JJ, Qu BY, Cheng Z, Wang H (2018) Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Appl Energy 226:408–422
47.
Mehmood A, Zameer A, Chaudhary NI, Raja MAZ (2019) Backtracking search heuristics for identification of electrical muscle stimulation models using Hammerstein structure. Appl Soft Comput 24:105705
48.
Kayri I, Gencoglu MT (2019) Predicting power production from a photovoltaic panel through artificial neural networks using atmospheric indicators. Neural Comput Appl 31(8):3573–3586
49.
50.
51.
Mishra SP, Dash PK (2019) Short-term prediction of wind power using a hybrid pseudo-inverse Legendre neural network and adaptive firefly algorithm. Neural Comput Appl 31(7):2243–2268
52.
Wang T, He X, Deng T (2019) Neural networks for power management optimal strategy in hybrid microgrid. Neural Comput Appl 31(7):2635–2647
Metadata
Title
Design of backtracking search heuristics for parameter estimation of power signals
Authors
Ammara Mehmood
Peng Shi
Muhammad Asif Zahoor Raja
Aneela Zameer
Naveed Ishtiaq Chaudhary
Publication date
25-05-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05029-9

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