A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Nanda, Sarita; Dash, P.K.; Chakravorti, Tatiana; Hasan, Shazia

doi:10.1016/j.measurement.2016.03.026

Cited by 17 publications

(6 citation statements)

References 44 publications

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“…and (ii) the proposal, located after the phasor estimator, is an additional stage within the PMU, which aims to compensate magnitude to reduce the TVE under wide frequency deviations. The polynomial orders in (11) are those that satisfy the aforementioned error upper bounds. Figure 4 shows the results of using a frequency range of Fs/2 for the seven Fs values, that is, 10, 12, 15, 20, 30, 60 and 120 fps.…”

Section: Model Resultsmentioning

confidence: 99%

“…The polynomial orders in (11) are those that satisfy the aforementioned error upper bounds. Figure 4 shows the results of using a frequency range of Fs/2 for the seven Fs values, that is, 10, 12, 15, 20, 30, 60 and 120 fps.…”

Section: Steady-state Testmentioning

confidence: 99%

“…Despite being powerful and robust devices, PMUs can degrade their accuracy when they are subjected to waveforms of voltage and current that contain-modulations, sudden and abrupt changes of magnitude and/or phase angle and large frequency deviations [8]. These unwanted waveforms are associated with the dynamic load behavior, embedded generation and renewable sources of energy, among others [9][10][11][12]. One of the reasons why frequency deviations occur in autonomous networks is due to one-site generators [13].…”

Section: Introductionmentioning

confidence: 99%

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Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

et al. 2020

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Phasor measurement units (PMUs) are important elements in power systems to monitor and know the real network condition. In order to regulate the performance of PMUs, the IEEE Std. C37.118.1 stablishes two classes—P and M, where the phasor estimation is carried out using a quadrature oscillator and a low-pass (LP) filter for modulation and demodulation, respectively. The LP filter plays the most important role since it determines the accuracy, response time and rejection capability of both harmonics and aliased signals. In this regard and by considering that the M-class filters are used for more accurate measurements, the IEEE Std. presents different M-class filters for different reporting rates (when a result is given). However, they can degrade their performance under frequency deviations if the LP frequency response is not properly considered. In this work, a unified model for magnitude compensation under frequency deviations for all the M-class filters is proposed, providing the necessary values of compensation to improve their performance. The model considers the magnitude response of the M-class filters for different reporting rates, a normalized frequency range based on frequency dilation and a fitted two-variable function. The effectiveness of the proposal is verified using both static and dynamic conditions for frequency deviations. Besides that, a real-time simulator to generate test signals is also used to validate the proposed methodology.

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Section: Model Resultsmentioning

confidence: 99%

Section: Steady-state Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

et al. 2020

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“…An adaptive linear network (ADALINE) [13][14][15] for harmonic and interharmonic estimation (Martin) allows the computing of root mean square voltage and total harmonic distortion indices. Classification and detection of sags, swells, outages, and harmonics-interharmonics have been done using the indices computed before.…”

Section: Literature Reviewmentioning

confidence: 99%

Kalman Filters for Parameter Estimation of Nonstationary Signals

Nanda¹

2018

Kalman Filters - Theory for Advanced Applications

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An adaptive Taylor-Kalman filter with PSO tuning for tracking nonstationary signal parameters in a noisy environment with primary focus on time-varying power signals has been presented in this piece of work. In order to deal with the dynamic envelope of the power signal, second-order Taylor expansion has been used such that the Taylor coefficients are updated with the PSO-tuned Taylor-Kalman Filter algorithm. In addition to this, for fast convergence, a self-adaptive particle swarm optimization technique has been used for obtaining the optimal values of model and measurement error covariances of the Kalman filter. The proposed algorithm is linear and therefore has less computational burden, which is easier to be implemented on a hardware platform like DSP processor or FPGA. The proposed PSO-tuned Taylor-Kalman filter exhibits robust tracking capabilities even under changing signal dynamics, immune to critical noise conditions, harmonic contaminations, and also reveals excellent convergence properties.

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“…Thus, low-cost low-end protective relays with limited computational abilities are envisioned to be used for such applications to make the protection systems economically viable [15]. As a result, the use of sophisticated frequency estimation techniques based on the wavelet transform [16,17], advanced digital filters [18,19,20,21], iterative algorithm [22,23], genetic algorithm [24], Bayesian learning [25], Newton-type algorithm [26], machine learning [27] are limited for the application in the HV systems as they demand for higher clock speed and large memory blocks to execute large amount of assembly level instructions. This demands for computationally efficient frequency estimation technique for implementation in the low-end relays which is the focus of this work.…”

Section: Introductionmentioning

confidence: 99%

Power systems frequency estimation using amplitude tracking square wave for low-end protective relays

2019

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A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Cited by 17 publications

References 44 publications

Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

Kalman Filters for Parameter Estimation of Nonstationary Signals

Power systems frequency estimation using amplitude tracking square wave for low-end protective relays

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