A Simple Algorithm for the Estimation of Phase Difference Between Two Sinusoidal Voltages

Vučijak, Nada M.; Saranovac, Lazar

doi:10.1109/tim.2010.2047155

Cited by 52 publications

(23 citation statements)

References 19 publications

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“…is recommended because the error of phase difference result that comes from the function sin(x) in (11) is minimal [14]. In this example, the number of samples in a signal period is divisible by 4, so that the introduced phase delay of approximately π/2 can be obtained with 50 sample delays.…”

Section: Bias Of Phase Difference Measurement Resultsmentioning

confidence: 99%

“…Two real-valued sinusoidal signals without a dc offset can be expressed as: 2 are the uncorrelated additive white Gaussian noise values [14]. The phase difference to be determined is 2 1 …”

Section: Msal Algorithmmentioning

confidence: 99%

“…The detailed derivation of estimating the phase difference between two sinusoidal signals, as well as margins of errors for the SAL and MSAL algorithms in different situations is presented in [14]. The main advantage of these algorithms is reducing the computational complexity while maintaining good accuracy.…”

Section: Msal Algorithmmentioning

confidence: 99%

“…Some values of the uncertainties in (14) are derived from the manufacturer specifications of SV, whereas others − from the calibration certificate of SV.…”

Section: Estimation Of Uncertainty For Samples Of Voltagesmentioning

confidence: 99%

“…Some of the algorithms are: the three-parameter sinus fitting algorithm (3PSF), the four-parameter sinus fitting algorithm (4PSF), the seven-parameter sinus fitting algorithm (7PSF) [10−13], the unbiased quadrature delay estimator (UQDE) [9], and the modified simple algorithm (MSAL) [14]. According to the MSAL algorithm, two new signals' phases shifted to the existing signals are introduced, and a complex formula is applied to a large number of signal samples to determine the phase difference between two sinusoidal signals.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Uncertainty of Phase Difference Determination in Presence of Bias

Saranovac¹,

Vučijak²

2016

Metrology and Measurement Systems

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Determination of the phase difference between two sinusoidal signals with noise components using samples of these signals is of interest in many measurement systems. The samples of signals are processed by one of many algorithms, such as 7PSF, UQDE and MSAL, to determine the phase difference. The phase difference result must be accompanied with estimation of the measurement uncertainty. The following issues are covered in this paper: the MSAL algorithm background, the ways of treating the bias influence on the phase difference result, comparison of results obtained by applying MSAL and the other mentioned algorithms to the same real signal samples, and evaluation of the uncertainty of the phase difference.

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Section: Bias Of Phase Difference Measurement Resultsmentioning

confidence: 99%

“…Two real-valued sinusoidal signals without a dc offset can be expressed as: 2 are the uncorrelated additive white Gaussian noise values [14]. The phase difference to be determined is 2 1 …”

Section: Msal Algorithmmentioning

confidence: 99%

Section: Msal Algorithmmentioning

confidence: 99%

“…Some values of the uncertainties in (14) are derived from the manufacturer specifications of SV, whereas others − from the calibration certificate of SV.…”

Section: Estimation Of Uncertainty For Samples Of Voltagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of Uncertainty of Phase Difference Determination in Presence of Bias

Saranovac¹,

Vučijak²

2016

Metrology and Measurement Systems

Self Cite

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Robust and nonlinear control literature survey (No. 23)

2011

Intl J Robust & Nonlinear

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of robust and nonlinear control six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Dr. Jiqiang Wang at jiqiang.wang@nuaa.edu.cn. THEORETICAL DEVELOPMENTS IN ROBUST CONTROLChang Y, Chen B. A fuzzy approach for robust reference tracking control design of nonlinear distributed parameter time delayed systems and its application. IEEE Transactions on Fuzzy Systems 2010; 18(6):1041-1057. Chen B, Wu C. Robust optimal reference-tracking design method for stochastic synthetic biology systems: T-S fuzzy approach. IEEE Transactions on Fuzzy Systems 2010; 18(6):1144-1159. Deng M, Bu N. Isomorphism-based robust right coprime factorization of nonlinear unstable plants with perturbations. IET Control Theory and Applications 2010; 4(11):2381-2390. Ding L, Wang H, Guan Z, Chen J. Tracking under additive white Gaussian noise effect. IET Control Theory and Applications 2010; 4(11):2471-2478. Dragan V. Stabilizing composite control for a class of linear systems modelled by singularly perturbed Ito differential equations. Automatica 2011; 47(1): 122-126. Du B, Lam J, Shu Z. Stabilization for state/input delay systems via static and integral output feedback. Automatica 2010; 46(12):2000-2007. Ebihara Y, Peaucelle D, Arzelier D. Periodically time-varying memory state-feedback controller synthesis for discrete time linear systems. Automatica 2011; 47(1):14-25. Engelken S, Patra S, Lanzon A, Petersen I. Stability analysis of negative imaginary systems with real parametric uncertainty-the single-input single-output case. IET Control Theory and Applications 2010; 4(11):2631-2638. Gassara H, El Hajjaji A, Chaabane M. Observerbased robust H ∞ reliable control for uncertain T-S fuzzy systems with state time delay. of robust optimal proportional-integral-derivative controller based on new interval polynomial stability criterion and Lyapunov theorem in the multiple parameters' perturbations circumstance. IET Control Theory and Applications 2010; 4(11):2427-2440. Mahmoud M, Xia Y. New stabilization schemes for discrete delay systems with uncertain nonlinear perturbations. IET Control Theory and Applications 2010; 4(12):2937-2946. Osinuga M, Patra S, Lanzon A. Smooth weight optimization in H ∞ loop-shaping design. Systems and Control Letters 2010; 59(11):663-670. Pfifer H, Hecker S. Generation of optimal linear parameter models for LFT-based robust stability analysis and control design. IEEE Transactions on Control Systems Technology 2011; 19(1): 118-131. Qiu J, Zhang T, Feng G, Liu H. Piecewise affine model-based H ∞ control of constrained nonlinear processes. IET Control Theory and Applications 2010; 4(11):231...

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Separable Multi-innovation Newton Iterative Modeling Algorithm for Multi-frequency Signals Based on the Sliding Measurement Window

2021

Circuits Syst Signal Process

130

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A Simple Algorithm for the Estimation of Phase Difference Between Two Sinusoidal Voltages

Cited by 52 publications

References 19 publications

Evaluation of Uncertainty of Phase Difference Determination in Presence of Bias

Evaluation of Uncertainty of Phase Difference Determination in Presence of Bias

Robust and nonlinear control literature survey (No. 23)

Separable Multi-innovation Newton Iterative Modeling Algorithm for Multi-frequency Signals Based on the Sliding Measurement Window

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