Phasor Estimation Algorithm Based on Complex Frequency Filters for Digital Relaying

Jafarpisheh, Babak; Madani, Seyed M.; Parvaresh, Farzad

doi:10.1109/tim.2017.2783099

Cited by 20 publications

(9 citation statements)

References 26 publications

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“…in which ∆t is the sampling interval and is equal to 1/ f s (where f s is the sampling frequency), n denotes the sample number, and E ∆ = e −∆t/τ , called the damping factor, depends on the DDC time constant [4]. It is difficult to predetermine the DDC parameters as they depend on fault characteristics, such as, fault inception time/angle, fault location, and fault resistance [4][5][6]31]. However, their presence (usually in the current signals) can cause up to 15% error in the estimated phasor [31].…”

Section: Proposed Filter For Ddc Rejectionmentioning

confidence: 99%

“…To ensure the relevance of the measurements obtained from these devices for monitoring, protection, and control applications, it is necessary that the estimation algorithms used in them are accurate, robust against stray components, computationally efficient, and have low response time [1,2]. Hence, digital signal processing techniques such as discrete Fourier transform (DFT) [3][4][5][6][7][8][9][10][11], least squares (LS) [12][13][14][15], maximum likelihood [16], space vector transform [17], artificial neural networks [18], Hilbert transform [19], Stockwell transform [20], matrix pencil method [21], Kalman filters [22,23], subspace-based methods [24,25], and filter-based methods [26,27] have been proposed recently to estimate phasor and/or frequency under different operating conditions. However, many of the techniques mentioned above suffer from long response time during switching transients [9,13,20], high computational complexity [7,21,24], susceptibility to grid disturbances [12,18,22] and noise [19], lengthy observation window [7,10,11,[25]…”

Section: Introductionmentioning

confidence: 99%

“…Hence, digital signal processing techniques such as discrete Fourier transform (DFT) [3][4][5][6][7][8][9][10][11], least squares (LS) [12][13][14][15], maximum likelihood [16], space vector transform [17], artificial neural networks [18], Hilbert transform [19], Stockwell transform [20], matrix pencil method [21], Kalman filters [22,23], subspace-based methods [24,25], and filter-based methods [26,27] have been proposed recently to estimate phasor and/or frequency under different operating conditions. However, many of the techniques mentioned above suffer from long response time during switching transients [9,13,20], high computational complexity [7,21,24], susceptibility to grid disturbances [12,18,22] and noise [19], lengthy observation window [7,10,11,[25][26][27], and performance degradation due to: (i) off-nominal frequency condition [4][5][6]…”

Section: Introductionmentioning

confidence: 99%

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A Robust Algorithm for Real-Time Phasor and Frequency Estimation under Diverse System Conditions

Jafarpisheh

Pal

2021

Energies

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This paper presents a comprehensive approach for performing phasor and frequency estimation from voltage and/or current signals of the modern power system. Undesirable components, such as decaying DC, if present in the input signal, are first attenuated using a complex-gain filter. The initial estimates of phasor and frequency are obtained next using the discrete Fourier transform and an improved estimation of signal parameters via rotational invariance technique, respectively. Finally, the accuracy of phasor and frequency estimates are increased based on the identified system condition. Simulations performed to evaluate the proposed approach confirm that it can do fast and accurate estimation of phasor and frequency under diverse operating conditions, making it ideal for wide-area monitoring, protection, and control applications in power systems.

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Section: Proposed Filter For Ddc Rejectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robust Algorithm for Real-Time Phasor and Frequency Estimation under Diverse System Conditions

Jafarpisheh

Pal

2021

Energies

Self Cite

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“…They can be divided into the frequency domain and time domain algorithms. DFT (Discrete Fourier transform)-based methods [1] [2] [3] are well-known techniques for spectrum analysis of grid signal in the frequency domain. However, these techniques often assume that the grid voltage waveform is periodic and repetitive, which may lead to spectrum leakage problem due to the unsynchronized sampling effect, giving rise to errors in frequency and phase angle detection [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Application of Taylor-Kalman Filters Under a Distorted Grid Condition

Huang

2020

IEEE Access

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The Taylor-Kalman filter (TKF) is a linear filter that can be modeled using either an accurately modeled TKF (known as the modified TKF) or an approximately modeled TKF. The higher-order TKF can not only adapt to the small frequency deviation condition but also prevents certain harmonics leakage. It has been increasingly widely applied in power grid synchronization and other power system fields. However, to the best of authors' knowledge, systematical analyses of the impact of different models on the estimation performance and the methods for extraction of the shifted grid frequency have not been reported to date. The aim of this paper is to examine the TKF-based phase estimation algorithms (PEA) with regard to aspects such as the steady state, dynamic state and computational cost. Comparisons were carried out to evaluate the effects of the model and the order of TKF on the dynamic response and steady-state error. A strong tracking algorithm was also introduced to enhance the dynamic response. Several approaches for reducing the computational burden are given. Finally, combined with the moving average filter (MAF), which is a typical low-pass filter, an application example of TKF-based PEA was developed, and its performance was verified by experiments. INDEX TERMS Grid synchronization, Signal decomposition, phase estimation, Kalman filter (KF), Taylor-Kalman filter (TKF), moving average filter (MAF)

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“…With regard to conventional methods, the most used is the differential protection (87T) based on phasors [4]. Generally, this protection has integrated an improvement to correctly differentiate internal faults from inrush currents based on the harmonic content of the differential current.…”

Section: Introductionmentioning

confidence: 99%

Extended second central moment approach to detect turn‐to‐turn faults in power transformers

Esponda¹,

Vázquez²,

Andrade³

et al. 2019

IET Electric Power Applications

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This study describes an extended approach to the differential protection of power transformers to identify turn-to-turn faults based on the second central moment (SCM). The proposal uses the differential currents as the input signals to the algorithm. Differential currents are filtered and normalised to allow the proposed algorithm to be independent of the transformer parameters (power, reactance, connection etc.). Then, the SCM magnitude of the differential currents is computed and compared with an established threshold to detect the internal faults. If the SCM magnitude exceeds the limit, an internal fault is detected. Otherwise, the event is determined as a transient event or steady state. The proposed algorithm was implemented in MATLAB and was tested on a three-phase system using a Real-Time Digital Simulator. For laboratory experiments, a real 55-kVA transformer setup was used to validate the effectiveness of the algorithm. The algorithm successfully identified turn-to-turn faults from steady state, as well as during transformer energisations, in over 1000 cases.

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Phasor Estimation Algorithm Based on Complex Frequency Filters for Digital Relaying

Cited by 20 publications

References 26 publications

A Robust Algorithm for Real-Time Phasor and Frequency Estimation under Diverse System Conditions

A Robust Algorithm for Real-Time Phasor and Frequency Estimation under Diverse System Conditions

Analysis and Application of Taylor-Kalman Filters Under a Distorted Grid Condition

Extended second central moment approach to detect turn‐to‐turn faults in power transformers

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