A continuous-time least mean-phase adaptive filter for power system frequency estimation

Rawat, Tarun Kumar; Parthasarathy, Harish

doi:10.1016/j.ijepes.2008.10.012

Cited by 26 publications

(17 citation statements)

References 11 publications

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“…The Clarke's transform based complex domain solutions for frequency tracking include phase locked loops (PLL) [16], least squares methods [17], Kalman filtering [18], and demodulation methods [19]. Recently, adaptive tracking algorithms based on the minimisation of mean square error have become a standard, as they are naturally suited to deal with noise, harmonics, and nonstationary environments [7,17,20]. However, as discussed in [21], unbalanced events make it difficult to calculate phase angle -the complex-valued signal obtained from an unbalanced three-phase voltage source is represented as an orthogonal sum of positive and negative sequences (SCT -see (2)).…”

Section: The Need For Frequency Estimation In Smart Gridmentioning

confidence: 99%

“…Observe from (6) that the instantaneous system frequency is obtained based on the phase information in e ȷω∆T , and thus the CLMS may be suboptimal, since it is primarily the phase rather than the magnitude that conveys useful information. The complex least-mean-phase (LMP) algorithm [33,20] is designed to minimise the phase error, based on the cost function…”

Section: Notice That For the Modelling Of The αβ Voltage In (5) We Omentioning

confidence: 99%

“…In (20), function sin −1 is a monotonic function, resulting in periodic oscillations in the estimated frequencyf (k). The cycle frequency due to undermodelling is 2f when using standard linear adaptive filters for frequency estimation of unbalanced power system, whereas for balanced power systems B(k) = 0, and the standard linear estimate in (20) is adequate.…”

Section: B(k)mentioning

confidence: 99%

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Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

Xia

Douglas

Mandic

2012

IEEE Signal Process. Mag.

161

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Signal processing is a crucial technology for the efficient use of limited and intermittent power resources in the smart grid of the future, and a number of challenges remain to be met. One major issue, as we move towards distributed energy production and use (microgrid) is real time estimation of power quality parameters (frequency, voltages, power factor). The accurate knowledge of frequency is a key parameter of a power system, and its optimal estimation becomes critical in the future smart grid, where the generation, loading and topology are all dynamically updated. In this work, we first consolidate the existing approaches to real-time frequency estimation in a three phase system, and then provide a unified framework for the estimation of the instantaneous frequency in both balanced and unbalanced conditions of a three phase power system. This is achieved by using recent developments in the statistics of complex variables (augmented statistics), by employing the associated widely linear models, and by rigorously accounting for the different degrees of noncircularity associated with various natures of frequency variations in real-world conditions. The usefulness of the proposed framework for frequency tracking in smart grids is illustrated in the context of two major issues in power quality control, namely the tracking of false frequency perturbations in the presence of unbalanced voltage sags (here both synthetic and real-world) and in adaptive frequency tracking in microgrids and islands where there is mismatch between production and consumption. THE NEED FOR FREQUENCY ESTIMATION IN SMART GRIDGovernments, utilities and consumers are all interested in making the ways we produce and use energy more efficient and sustainable. For the electrical power grid this involves fundamental paradigm shifts as we build a smart grid, adopt more renewable energy sources, and promote more energy efficient practices. A smart grid delivers electricity from suppliers to users using digital technology and has a number of properties, including incorporating all forms of energy generation and storage, using sensor information, enabling active participation by end users, being secure and reliable, and using optimization and control to make decisions [1]. This will require the interplay between sensor networks, generation systems, and the power grid, with key technologies from signal processing.It is estimated that the financial loss due to outages in the US economy approaches USD $45.7 billion annually, with power quality issues costing USD $6.7 billion annually [2,3]. Among them, voltage sags, that is, an increase in load current over up to few hundred cycles, are the most frequent problem [4] that severely affects medical centres, semiconductor plants, and broadcasting stations, among others [5]. Voltage sags are typically followed by frequency variations and occur due to switching between the main grid and microgrids, short circuits, motor starting, transformer inrush, fast reclosing of circuit breakers, unexpectedly large or ...

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Section: The Need For Frequency Estimation In Smart Gridmentioning

confidence: 99%

Section: Notice That For the Modelling Of The αβ Voltage In (5) We Omentioning

confidence: 99%

Section: B(k)mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

Xia

Douglas

Mandic

2012

IEEE Signal Process. Mag.

161

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“…In the past several decades, a variety of algorithms [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] for frequency estimation have been reported. Generally, the performance of a frequency estimation algorithm can be evaluated through static and dynamic tests.…”

Section: Introductionmentioning

confidence: 99%

“…The inter-harmonic component near the fundamental frequency, however, is still difficult to be filtered out. Other algorithms, such as the modified LMS algorithm [23], the least mean phase (LMP) algorithm [24] and the multi-harmonic least-squares fitting algorithm [25] are also reported. Reference [26] presents reviews of the existing several methods, outlining strengths and weaknesses of each one.…”

Section: Introductionmentioning

confidence: 99%

Power System Frequency Estimation Algorithm for Electric Energy Metering of Nonlinear Loads

Zhang¹,

Li²

2012

Metrology and Measurement Systems

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In this paper, a discrete wavelet transform (DWT) based approach is proposed for power system frequency estimation. Unlike the existing frequency estimators mainly used for power system monitoring and control, the proposed approach is developed for fundamental frequency estimation in the field of energy metering of nonlinear loads. The characteristics of a nonlinear load is that the power signal is heavily distorted, composed of harmonics, inter-harmonics and corrupted by noise. The main idea is to predetermine a series of frequency points, and the mean value of two frequency points nearest to the power system frequency is accepted as the approximate solution. Firstly the input signal is modulated with a series of modulating signals, whose frequencies are those frequency points. Then the modulated signals are decomposed into individual frequency bands using DWT, and differences between the maximum and minimum wavelet coefficients in the lowest frequency band are calculated. Similarities among power system frequency and those frequency points are judged by the differences. Simulation results have proven high immunity to noise, harmonic and inter-harmonic interferences. The proposed method is applicable for real-time power system frequency estimation for electric energy measurement of nonlinear loads.

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Frequency Estimation Methods for Smart Grid Systems

Mengüç

Acır

2018

Energy Systems in Electrical Engineering

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A continuous-time least mean-phase adaptive filter for power system frequency estimation

Cited by 26 publications

References 11 publications

Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

Power System Frequency Estimation Algorithm for Electric Energy Metering of Nonlinear Loads

Frequency Estimation Methods for Smart Grid Systems

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