Genetic algorithms applied to phasor estimation and frequency tracking in PMU development

Silva, Raphael Philipe Mendes da; Delbem, Alexandre C. B.; Coury, Denis Vinicius

doi:10.1016/j.ijepes.2012.07.070

Cited by 20 publications

(13 citation statements)

References 22 publications

(43 reference statements)

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“…As convex combination is formed using sparse and non-sparse adaptive filtering algorithms, x 1k and y 1k correspond to non-sparse adaptive filtering algorithm while x 2k and y 2k correspond to sparse adaptive filtering algorithm, respectively. Two state update equations for convex combination are given in (6) and (7).…”

Section: Filtering Algorithm Of Sparse H ∞mentioning

confidence: 99%

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Adaptive Three-phase Estimation of Sequence Components and Frequency Using H∞ Filter Based on Sparse Model

Subudhi¹,

Sahoo²,

Mishra³

2020

Journal of Modern Power Systems and Clean Energy

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The estimation of sequence or symmetrical components and frequency in three-phase unbalanced power system is of great importance for protection and relay. This paper proposes a new H ∞ filter based on sparse model to track the sequence components and the frequency of three-phase unbalanced power systems. The inclusion of sparsity improves the error convergence behavior of estimation model and hence short-duration non-stationary PQ events can easily be tracked in the time domain. The proposed model is developed using l 1 norm penalty in the cost function of H ∞ filter, which is quite suitable for estimation across all the three phases of an unbalanced system. This model uses real state space modeling across three phases to estimate amplitude and phase parameters of sequence components. However, frequency estimation uses complex state space modeling and Clarke transformation generates a complex measurement signal from the unbalanced three-phase voltages. The state vector used for frequency estimation consists of two state variables. The proposed sparse model is tested using distorted three-phase signals from IEEE-1159-PQE database and the data generated from experimental laboratory setup. The analysis of absolute and mean square error is presented to validate the performance of the proposed model.

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Section: Filtering Algorithm Of Sparse H ∞mentioning

confidence: 99%

“…Artificial intelligence and soft computing techniques such as neural network, fuzzy logic, and genetic algorithm are quite popular for tracking PQ disturbances [7], [8]. These methods can track the signal parameters accurately with longer tracking time and slower convergence.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Three-phase Estimation of Sequence Components and Frequency Using H∞ Filter Based on Sparse Model

Subudhi¹,

Sahoo²,

Mishra³

2020

Journal of Modern Power Systems and Clean Energy

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“…The discrete Fourier transform (DFT) is traditionally used in power systems, but recent propositions include modifications of DFT [12–17]. Other windowed methods propose the use of least‐squares (LS) solution [18–23], genetic algorithms [24], subspace‐based methods [25–30] and Taylor expansion with LS [31, 32]. In order to avoid time‐burden computation, recursive methods were also proposed, including recursive LS (RLS) [21, 33–35], Newton [36], Gauss–Newton methods [37, 38] and filter‐based approaches [39–43].…”

Section: Introductionmentioning

confidence: 99%

Signal processing techniques for synchrophasors considering short‐circuit signals: a comparative study

Lacerda

Barbalho

Monaro

et al. 2020

IET Generation, Transmission & Distribution

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“…Other possible use is in the monitoring and control of AC-AC or AC-DC-AC converters, which are used to transfer power between two systems with different frequency such as the wind turbines with variable speed interconnected with the power grid [19]. In this regard, the analysis of PMUs under signal frequency deviations has received special attention since they are directly related to the performance of the LP filters used in PMUs [20]. In other words, the output signal suffers magnitude and phase changes due to the LP filter frequency response, requiring a compensation stage if reliable and accurate results are needed, mainly considering that estimation errors may lead to undesirable results for PMUs applications.…”

Section: Introductionmentioning

confidence: 99%

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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Genetic algorithms applied to phasor estimation and frequency tracking in PMU development

Cited by 20 publications

References 22 publications

Adaptive Three-phase Estimation of Sequence Components and Frequency Using H∞ Filter Based on Sparse Model

Adaptive Three-phase Estimation of Sequence Components and Frequency Using H∞ Filter Based on Sparse Model

Signal processing techniques for synchrophasors considering short‐circuit signals: a comparative study

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

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