Variable Constraint based Least Mean Square algorithm for power system harmonic parameter estimation

Singh, Santosh Kumar; Sinha, Nilotpal; Goswami, Arup Kumar; Sinha, Nidul

doi:10.1016/j.ijepes.2015.04.018

Cited by 16 publications

(3 citation statements)

References 26 publications

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“…Construct a matric , and write it as: (8) Then Eq. 6 can be shorted as: (9) In order to get , least square method [10,11,12] can be used. Approximate error can be denoted as :…”

Section: Ifmentioning

confidence: 99%

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A Novel Data Collection Strategy Based on Fourier Transform in Power System WSN

Wu¹,

Xiao²,

Wu³

et al. 2015

Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering

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Temperature detection is now used for risk early warning in power system. Adopting wireless sensor network to collect temperature information is one of the most crucial applications. In order to accomplish temperature information gathering, hundreds of sensor nodes are placed in power system equipment, such as transformer, relay protection device, mutual inductor, to name a few. Studies have shown that, for nodes, message transmission consuming more energy than local data computing. It's unrealistic to configure batteries for sensor nodes or replace them on a large scale in power system. For the purpose of lengthening the lifetime of wireless sensor network and reducing energy consumption, the amount of message transmission should be minimized. In this paper, a new data collection strategy in power system based on Fourier transform is proposed. Because of periodic character of temperature detection data, Fourier transform is adopted to linearly fit these data in this paper, and least square strategy is used to optimize parameters. The sink nodes transmit model parameters to the base station instead of thousands of data, when the error is in the range of confidence level. Experiments show that the proposed strategy obtains greater fitness and less error between the measured data and predicted data.

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“…Construct a matric , and write it as: (8) Then Eq. 6 can be shorted as: (9) In order to get , least square method [10,11,12] can be used. Approximate error can be denoted as :…”

Section: Ifmentioning

confidence: 99%

“…10 into Eq. 11, then a new formula is obtained: (12) Now in order to get the minimal , we can derivate by each , thus we can minimize the value of 2-norm. Derivative equation is:…”

Section: Ifmentioning

confidence: 99%

A Novel Data Collection Strategy Based on Fourier Transform in Power System WSN

Wu¹,

Xiao²,

Wu³

et al. 2015

Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The research community proposed various schemes for powers signals estimation [7][8][9]] and a number of papers have been presented in the literature that pivots the estimation of harmonics contamination [10]. These techniques include fast harmonics estimation using EA-AWNN by Sachin K. Jain et al [11], exponential modulation integral observer by Hin Say Lam et al [12], compressive sensing by Palczynska [13], Bayesian approach used by Gabriele D Antona et al [14], ESPIRIT associated filter bank scheme by Santos [15], Kalman filter [16,17], least mean square based algorithms [18,19], and recursive least square strategy [20].…”

Section: Introductionmentioning

confidence: 99%

Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics

2023

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The sustainable power development requires the study of power quality while taking into account of electrical equipment is an important aspect because it highly compromises the overall efficiency including quality, reliability and continuity of power flow. The aim for smooth power flow is only accomplished if compatibility is met between all the instruments connected to the system. The odd harmonics both on amplitude and phase domain must be known in order to exactly cop up with their adverse effects on overall working of the system. In this regard, parameter estimation is performed in detail for diverse generation size (gs) and particle size (ps), besides for altered signal to noise ratio. Firefly optimization technique under different scenarios for both phase and amplitude parameters accurately estimated the power signal harmonics and proved its robustness under different noise levels. The MSE values achieved by FFO are 6.54 × 10−3, 1.04 × 10−5 and 1.35 × 10−6 for 20 dB, 50 dB and 80 dB respectively for gs = 200 in case study 1. While the respective results in case study 2 are 7.33 × 10−3, 6.67 × 10−6 and 6.59 × 10−9 for gs = 1000. Whereas no significant effect in performance is seen with the change in ps values.

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