Abstract
High-precision synchronized measurement data with short measurement latency are required for applications of phasor measurement units (PMUs). This paper proposes a synchrophasor measurement method based on cascaded infinite impulse response (IIR) and dual finite impulse response (FIR) filters, meeting the M-class and P-class requirements in the IEC/IEEE 60255-118-1 standard. A low-group-delay IIR filter is designed to remove out-of-band interference components. Two FIR filters with different center frequencies are designed to filter out the fundamental negative frequency component and obtain synchrophasor estimates. The ratio of the amplitudes of the synchrophasor is used to calculate the frequency according to the one-to-one correspondence between the ratio of the amplitude frequency response of the FIR filters and the frequency. To shorten the response time introduced by IIR filter, a step identification and processing method based on the rate of change of frequency (RoCoF) is proposed and analyzed. The synchrophasor is accurately compensated based on the frequency and the frequency response of the IIR and FIR filters, achieving high-precision synchrophasor and frequency estimates with short measurement latency. Simulation and experiment tests demonstrate that the proposed method is superior to existing methods and can provide synchronized measurement data for M-class PMU applications with short measurement latency.
PHASOR measurement units (PMUs) have been widely deployed in power transmission and distribution networks to monitor their steady and dynamic behaviors [
The IEC/IEEE 60255-118-1 standard classifies PMUs into measurement (M-class) and protection (P-class) types based on the application scenarios [
Existing synchrophasor measurement methods can be divided into the frequency-domain and time-domain methods. The former includes windowed discrete Fourier transform (DFT) methods [
Researchers have proposed the Ip-DFT and its improvements to suppress the spectral leakage of the DFT method [
The TWLS and its enhancements are the extension of the DFT method, which can directly obtain the frequency and rate of change of frequency (RoCoF) estimates through the Taylor series [
Time-domain methods include the nonlinear least squares method [
The infinite impulse response (IIR) filter is rarely used for synchrophasor measurements due to its nonlinear phase delay. A synchrophasor measurement method based on the Hilbert transform has been proposed, where the IIR filter is used to filter out all negative frequency components in the spectrum, and the frequency and synchrophasor are estimated using the Ip-DFT method [
To address the above problems, a synchrophasor measurement method based on cascaded IIR and dual FIR filters is proposed to provide synchronized measurement data for power system applications requiring short measurement latency and high measurement accuracy. The main contributions of this paper are as follows.
1) A bilateral low-pass IIR filter with low group delay is designed to filter out harmonics and OOBI components. The performance of different design methods for IIR filters is analyzed to achieve better dynamic measurement performance.
2) Dual FIR filters with different center frequencies are designed to filter out the negative frequency components and extract two synchrophasors with sampling values in one power cycle. Reconstruction and difference steps are used to suppress the long-term spectral leakage of negative frequency components.
3) Based on the extracted synchrophasors, real-time frequency is calculated using the ratio of the synchrophasors’ amplitudes. And the synchrophasor is accurately adjusted based on the estimated frequency and the frequency responses of the IIR and FIR filters.
4) A step identification method based on the RoCoF is proposed to reduce the response time of the IIR filtering process, and its sensitivity to different step sizes is analyzed.
The rest of this paper is organized as follows. Section II describes the measurement method and framework. Section III presents the filter design and step identification. The performance evaluation is presented in Section IV. Section V summarizes this paper.
The dynamic signal model of voltage and current considering harmonics and OOBI in the power system is expressed as:
(1) |
where i denotes the fundamental () and harmonic or inter-harmonic (), and m is the number of harmonic and inter-harmonic components; and are the amplitude and phase of the harmonic or out-of-band inter-harmonic components, respectively; is the constant part of the harmonic or out-of-band inter-harmonic frequency; and is the noise term.
If the noise term is ignored, (1) can be written in polar coordinates according to Euler’s formula:
(2) |
where and are the positive and negative frequency components of the harmonic or inter-harmonic, respectively. The fundamental synchrophasor and frequency are estimated by extracting . Therefore, it is necessary to suppress , , and .
Based on the relatively mature design methods of FIR filter, the frequency measurement method 2CBPF with short measurement latency oriented to P-class PMU is presented [
However, there are two main problems with IIR filters. First, their nonlinear phase delay can cause a distortion in the dynamic modulation components, making it difficult to eliminate the group delay using FIR filters that mark the timestamp in the middle of the window. It is necessary to compensate the synchrophasor accurately based on the frequency. Second, the IIR filter has a convergence process, resulting in a longer step response time than FIR methods.
In response to the nonlinear phase problem, this paper designs a low-group-delay IIR filter to enhance the dynamic performance and designs dual FIR filters with low orders to estimate the synchrophasor and frequency simultaneously, then compensates the amplitude and phase of the synchrophasor accurately based on the frequency. Due to flexible passband and stopband planning methods, the FIR filter can filter out the negative frequency components of the fundamental signal. In response to the convergence process after signal steps, this paper proposes a step identification and processing link based on RoCoF to ensure rapid convergence, as described in Section III.
The proposed method can suppress the OOBI and fundamental negative frequency components using a combination of IIR and FIR filters. The frequency domain diagram of the proposed method is shown in

Fig. 1 Frequency domain diagram of proposed method.
We use the dual FIR filters with different center frequencies to extract and suppress . For simplicity, the filtering process of one FIR filter is described, and the usage of the other filter is the same:
(3) |
where is the estimated synchrophasor at time tk, whose amplitude and phase are and , respectively; is the coefficient of the FIR filter, with an order of (N is a positive integer); is the signal sequence filtered by the IIR filter; is the sampling frequency; and are the frequency responses of the IIR and FIR filters, respectively; and and are the real amplitude and phase of the synchrophasor at time tk, respectively.
Due to the limitation of the filter order, the amplitude frequency response of the designed IIR and FIR filters is not completely flat within the measurement band, and the phase frequency response of the IIR filter is not linear. Therefore, the amplitude and phase of the initially calculated must be corrected based on the frequency. The signal frequency is estimated using the 2CBPF method [
The 2CBPF calculates the frequency according to the one-to-one correspondence between the ratio of the amplitude frequency response of the dual FIR filters and the frequency. The frequency calculation is as follows:
(4) |
(5) |
(6) |
where is the ratio of the frequency responses and of the dual FIR bandpass filters h1 and h2; and are the synchrophasors calculated by the dual FIR filters; is the estimated frequency; and is the ratio of their amplitudes.
Based on the frequency response of the IIR filter and the FIR filter, can be compensated to obtain the accurate synchrophasor :
(7) |
where , , , and are the amplitude frequency responses and phase frequency responses of the IIR and FIR filters at the measurement frequency , respectively.
The RoCoF is calculated from the backward difference of the frequency:
(8) |
where is the estimated RoCoF; is the calculation frequency; and M is the calculation interval.
Due to the difficulty in completely filtering out the negative frequency component of the fundamental wave when the order of the FIR filter is low, increasing the filter order can lead to a higher similarity between the dual FIR filters in the measurement band, resulting in a nonlinear relationship between their frequency response ratio and the frequency. Therefore, we reconstruct the negative frequency component of the fundamental wave and subtract it from the original signal to suppress the negative frequency component of the fundamental wave. The flowchart of the proposed method is shown in

Fig. 2 Flowchart of proposed method.
A low-pass IIR filter is used to suppress the OOBI. The IIR filter is designed using the Butterworth, Chebyshev type I and type II, ellipse, and the minimum P-norm methods with the same design requirements. The amplitude frequency response and phase frequency response of the IIR filters designed with different methods are shown in Figs.

Fig. 3 Amplitude frequency response and phase frequency response of IIR filters designed using different methods. (a) Amplitude frequency response. (b) Phase frequency response.

Fig. 4 Amplitude frequency response and phase frequency response of designed IIR filter. (a) Amplitude frequency response. (b) Phase frequency response.
Therefore, the minimum P-norm method is used in this paper to design the IIR filter using the filter design module in MATLAB 2018b. The parameters of the IIR filter are listed in Table I, and its frequency response is shown in
Parameter | Value |
---|---|
Sampling frequency | 1000 Hz |
Reporting frequency | 100 Hz |
Section | 2 |
Order | 8 |
Passband range | [0, 60]Hz |
Passband ripple | |
Out-of-band gain | |
Group delay (45-50 Hz) | 12.75 ms |
Group delay (50-55 Hz) | 13.88 ms |
Bandpass FIR filter design methods can be divided into direct and indirect methods. Direct methods include the least square-based class design method, iterative reweighted least square method, and other methods based on optimization criteria. Indirect methods design low-pass filter firstly and obtain a bandpass filter by the frequency shift. The low-pass filter design methods include window function method, frequency sampling method, and the equiripple method, etc. Since the low-pass filter design method is more mature and simple [
The Hanning, Blackman, Hamming, and other window functions have fast sidelobe attenuation and complete suppression of all harmonics. However, their passband attenuation is generally fast, resulting in low-dynamic performance, and the harmonic suppression performance is low when the fundamental frequency deviates from the rated value [
Therefore, we use the equiripple method to design the low-pass filters and obtain two complex bandpass filters by frequency shifting. The parameters of designed FIR filters are listed in Table II, and their amplitude frequency response and the ratio of dual FIR filters are shown in

Fig. 5 Amplitude frequency response and ratio of dual FIR filters. (a) Amplitude frequency response. (b) Ratio of dual FIR filter.
Parameter | Filter 1 | Filter 2 |
---|---|---|
Sampling frequency | 1000 Hz | 1000 Hz |
Reporting frequency | 100 Hz | 100 Hz |
Order | 21 | 21 |
Center frequency | 45 Hz | 55 Hz |
Passband range | [40, 50]Hz | [50, 60]Hz |
Passband ripple | dB | dB |
Gain at negative frequency component | dB | dB |
It should be noted that we do not use two FIR filters for negative frequency component suppression. Instead, each FIR filter independently suppresses the negative frequency components and extracts the fundamental synchrophasor. The main differences in the design of dual FIR filters are the center frequency and passband, which lead to different amplitude frequency responses within the measurement band.
However, the test has shown that the estimation accuracy of the method does not meet the requirements of the M-class PMU standard. The reason is that the fundamental negative frequency component is equivalent to an interference component with an amplitude of 100% of the fundamental positive frequency component, and its amplitude is much higher than the out-of-band components. Therefore, the FIR filter cannot completely filter out this component.
Therefore, we reconstruct the negative frequency component of the fundamental wave and determine the difference to suppress the negative frequency component of the fundamental wave. It is achieved using the calculation of phasor and frequency and subtracting the result from the original sampling values to obtain a value sequence, containing only the fundamental positive frequency component:
(9) |
(10) |
where and are the reconstructed values of the calculation of the fundamental negative and positive frequency components, respectively; , , and are the amplitude, phase, and frequency of the calculation of the fundamental synchrophasor, respectively; and is the reconstruction signal sequence.
The synchrophasor and frequency estimation values can be obtained by filtering and comparing the amplitudes. Since the FIR filter has already removed the negative frequency component of the fundamental wave, only one iteration of the reconstruction and subtraction is required to achieve good suppression of the negative frequency component at relatively low-computational complexity.
When a step occurs in the electrical quantity, IIR filtering requires time to converge, increasing the step response time of the proposed method. Therefore, based on the sensitivity of the 2CBPF method to the non-continuous signal, we propose a step identification method of electrical quantities based on the RoCoF. The flowchart of the proposed step identification and processing method is shown in

Fig. 6 Flowchart of step identification and processing method.
The tests related to the frequency variation in the IEC/IEEE 60255-118-1 standard include the frequency ramp test and the phase modulation (PM) test, with RoCoF of 1 Hz/s and 5 Hz/s, respectively. The maximum RoCoF in the severe fault event of the South Australia power grid in 2016 was 6 Hz/s. Therefore, we use a frequency difference threshold of 0.2 Hz, corresponding to RoCoF of 20 Hz/s with a reporting rate of 100 Hz to identify the electrical quantity steps. These settings are appropriate for most non-step dynamic scenarios.
Although the magnitude step specified in the IEC/IEEE 60255-118-1 standard is 10% times the amplitude value of fundamental wave and the phase step is 10°, the step in the actual power system may be lower than this value. Therefore, this paper analyzes the sensitivity of the proposed method to smaller range steps. The absolute values of RoCoF calculations under magnitude and phase steps are shown in

Fig. 7 Absolute value of RoCoF calculations under magnitude and phase steps. (a) Magnitude step. (b) Phase step.
We compare the proposed method (denoted as M1) with the method recommended by the IEC/IEEE 60255-118-1 standard [
The sampling frequency of the proposed method in the simulation and experimental tests is 1000 Hz, the reporting rate is 100 Hz, and the window length is 1 cycle (corresponding to a 20 ms time window length in a 50 Hz power system). The timestamp of the proposed method is 4 ms before the window to eliminate the group delay of the IIR and dual FIR filters. Therefore, the synchrophasor measurement latency is 24 ms. The data window lengths of M2, M3, and M4 are set to be 4, 2, and 4 cycles, respectively, with a sampling rate of 10000 Hz. Their timestamps are in the middle of the time window, the synchrophasor measurement latencies are 40 ms, 20 ms, and 40 ms, respectively, and the frequency measurement latencies are 50 ms, 20 ms, and 40 ms, respectively.
The static and dynamic test conditions for the M-class PMU in the IEC/IEEE 60255-118-1 standard are listed in Table III, where kx is the amplitude modulation (AM) depth, kp is the PM depth, Rf is the RoCoF in the frequency ramp test, ksx is the amplitude step size, and ksp is the phase step size. The fundamental component has a frequency offset of 0.5 Hz in the harmonic impact and dynamic modulation tests. In addition, 60 dB of Gaussian white noise is added to the simulation test signals to address the errors introduced by low-pass filtering, digital-to-analog conversion, and other processes during signal sampling and processing.
Test type | Parameter |
---|---|
Frequency offset | V, |
Harmonic distortion |
V, Hz, harmonic magnitude is, frequency is up to the |
OOBI | V, Hz, inter-harmonic magnitude is , frequency is 105 Hz to 145 Hz with an interval of 5 Hz |
Dynamic modulation | V, Hz, , AM: , , PM: , |
Frequency ramp | V, Hz-52 Hz, Hz/s |
Amplitude step Phase step |
, , |
The test signals are generated by MATLAB at a sampling frequency of 1 kHz. Because the signal models are known in advance, the true values of the frequency and RoCoF can be obtained. The proposed method and the comparison methods are used to estimate the synchrophasor, frequency, and RoCoF of the static and dynamic signals. The estimation errors are obtained by comparing the measured and true values.
Off-nominal tests are used to assess the measurement accuracy of the methods during the frequency offset.

Fig. 8 TVE and FE of different methods in off-nominal tests. (a) TVE. (b) FE.
Method | TVE (%) | FE (Hz) | RFE (Hz/s) |
---|---|---|---|
Std | 1.0000 | 0.0050 | 0.1000 |
M1 | 0.0100 | 0.0011 | 0.0520 |
M2 | 0.1300 | 0.0120 | 0.6900 |
M3 | 0.0170 | 0.0067 | 0.2600 |
M4 | 0.0055 | 0.0084 | 0.0028 |
Harmonic distortion tests are used to evaluate the ability of the method to suppress harmonic interference. The TVE and FE of different methods in the harmonic distortion tests are shown in

Fig. 9 TVE and FE of different methods in harmonic distortion tests. (a) TVE. (b) FE.
Method | TVE (%) | FE (Hz) | RFE (Hz/s) |
---|---|---|---|
Std | 1.0000 | 0.0250 | - |
M1 | 0.0067 | 0.0027 | 0.013 |
M2 | 0.0860 | 0.0010 | 0.013 |
M3 | 0.0670 | 0.0650 | 0.410 |
M4 | 0.0140 | 0.0021 | 0.014 |
OOBI tests are used to assess the ability of method to suppress OOBI. The results of the TVE and FE methods obtained from the OOBI tests are shown in

Fig. 10 TVE and FE of different methods in OOBI tests. (a) TVE. (b) FE.
Method | TVE (%) | FE (Hz) | RFE (Hz/s) |
---|---|---|---|
Std | 1.300 | 0.0100 | - |
M1 | 0.013 | 0.0028 | 0.140 |
M2 | 0.150 | 0.0160 | 0.310 |
M3 | 0.760 | 0.3900 | 7.300 |
M4 | 0.039 | 0.0140 | 0.170 |
Therefore, the accuracy of M2 is slightly lower than that of M1 and M4. M3 is suitable for P-class PMUs and does not consider OOBI. As the difference between the interference component frequency and harmonic frequency increases, the calculation error increases. When the out-of-band component amplitude is 0.1 times the fundamental wave amplitude, the frequency measurement error does not meet the standard requirements. The accuracy of M4 is significantly higher than that of M2, meeting the standard requirements. M1 uses an IIR filter to pre-filter the out-of-band components, resulting in good suppression of out-of-band components at all frequencies and the highest measurement accuracy.
The AM test is used to evaluate the dynamic performance of the methods. The TVE and FE of the different methods in AM tests are shown in

Fig. 11 TVE and FE of different methods in AM tests. (a) TVE. (b) FE.
Method | TVE (%) | FE (Hz) | RFE (Hz/s) |
---|---|---|---|
Std | 3.00 | 0.30000 | 14.0000 |
M1 | 0.22 | 0.02200 | 0.6800 |
M2 | 0.76 | 0.00670 | 0.2000 |
M3 | 0.28 | 0.01400 | 0.4000 |
M4 | 1.10 | 0.00013 | 0.0052 |
The TVE and FE of different methods in the PM test are shown in

Fig. 12 TVE and FE of different methods in PM tests. (a) TVE. (b) FE.
Method | TVE (%) | FE (Hz) | RFE (Hz/s) |
---|---|---|---|
Std | 3.00 | 0.300 | 14.00 |
M1 | 0.88 | 0.016 | 0.62 |
M2 | 0.68 | 0.040 | 1.50 |
M3 | 0.26 | 0.011 | 4.90 |
M4 | 0.99 | 0.030 | 1.20 |
Frequency ramp tests are used to simulate an out-of-step power system and evaluate the ability of the methods to suppress the negative frequency component of the fundamental component during the frequency offset and the flatness of the filter in the measurement frequency band. The maximum measurement errors of different methods in the frequency ramp tests are listed in Table IX. M1 has the smallest TVE and FE due to its smoother passband and additional suppression of negative frequency components.
Method | TVE (%) | FE (Hz) | RFE (Hz/s) |
---|---|---|---|
Std | 1.000 | 0.0100 | 0.2000 |
M1 | 0.021 | 0.0020 | 0.0260 |
M2 | 0.210 | 0.0100 | 0.4700 |
M3 | 0.300 | 0.0080 | 0.0370 |
M4 | 0.067 | 0.0068 | 0.0042 |
Due to the uneven passband of M2, the measurement error is relatively large. M3 has the shortest data window length and a relatively flat passband, but it does not perform processing on the negative frequency component during the frequency offset, resulting in low accuracy in phasor and frequency measurements. The iterative interpolation process of M4 increases the ability of DFT method to track the time-varying phase and frequency, resulting in significantly higher measurement accuracy than M2.
Step tests are used to simulate sudden changes in electrical quantities caused by switching operations and faults in the power system and test the response speed of the methods. The maximum measurement errors obtained from amplitude step tests and phase step tests are listed in
Method | Sampling points used | Multiplication | Addition |
---|---|---|---|
M1 | K | 18K | 18K |
M2 | 4K | 16K | 8K |
M3 | 2K | 8K | 8K |
M4 | 40K | 320K | 320K |
Method | TVE (ms) | FE (ms) | RFE (ms) |
---|---|---|---|
Std | 140 | 280 | 280 |
M1 | 29 | 42 | 46 |
M2 | 30 | 70 | 100 |
M3 | 19 | 36 | 42 |
M4 | 39 | 61 | 73 |
Method | TVE (ms) | FE (ms) | RFE (ms) |
---|---|---|---|
Std | 140 | 280 | 280 |
M1 | 31 | 42 | 46 |
M2 | 37 | 70 | 120 |
M3 | 24 | 39 | 42 |
M4 | 47 | 67 | 77 |
In the noise tests, Gaussian white noise is added to the static signal to test the anti-noise performance of the methods. The TVE and FE of different methods in noise tests are shown in

Fig. 13 TVE and FE of different methods in noise tests. (a) TVE. (b) FE.
Method | TVE (%) | FE (Hz) | RFE (Hz/s) |
---|---|---|---|
Std | 1.000 | 0.00500 | 0.100 |
M1 | 0.016 | 0.00210 | 0.180 |
M2 | 0.094 | 0.00250 | 0.170 |
M3 | 0.039 | 0.00680 | 0.560 |
M4 | 0.011 | 0.00087 | 0.064 |
Since the IEC/IEEE 60255-118-1 standard does not have noise test requirements, a static test is used to obtain Std. As shown in
The main computational complexity of the methods is shown in Table XIII. The number of sampling points in a cycle is K ( at the sampling rate of 1000 Hz). M1 is more computationally intensive than M3 due to the presence of IIR filtering process although it uses only one cycle sampling points. M2 is simple to compute and less computationally intensive. The high accuracy of M4 relies on the high sampling rate, which is most computationally intensive due to the high number of samples used and the presence of iterative computational process.
To study the simulation time consumption, the proposed method is tested using a laptop with a processor of AMD R7 4800 (base frequency of 2.9 GHz) and 16 GB of RAM, with the software environment of MATLAB R2020a. 600 simulations of M1 are performed, and the maximum simulation time of a single simulation is less than 3×1
In summary, M2 is more sensitive to the frequency offset and PM, and the frequency and RoCoF have the longest response time in the amplitude and phase angle steps. M3 has good performance for dynamic modulation and has the fastest response speed during the steps. However, its ability to suppress low-order harmonics, inter-harmonics, and noise is insufficient, resulting in lower frequency measurement accuracy than M1. M4 has the most accurate phasor estimation results when frequency offset occurs and the most accurate frequency estimation during the AM test. It is not sensitive to OOBI and noise, but its frequency estimation is susceptible to the frequency offset, and it has a relatively long response time. Although M1 has significant estimation errors under dynamic modulation, it is not sensitive to the frequency offset. In addition, it has strong resistance to OOBI and noise and a strong tracking ability when the frequency changes linearly, exhibiting more balanced static and dynamic performances.
A PMU prototype consisting of a synchronized timing module (NI 9467), voltage acquisition board (NI 9244), and real-time embedded controller (NI Crio-9039) is built, and M1 is implemented using LABVIEW language.
The hardware schematic diagram of the PMU prototype is shown in Fig. A1 of Appendix A. The synchrophasor and frequency estimation includes three parts: signal sampling, phasor and frequency estimation, and data uploading. The signal sampling is conducted by controlling NI 9467 (global position system (GPS) module) and NI 9244 (voltage acquisition module) using a field-programmable gate array (FPGA) (NI Crio-9039). The pulse per second is received from NI 9467, and a digital phase-locked loop (DPLL) is used to generate a sampling clock synchronized with GPS to NI 9244 for synchronized voltage signal sampling. Subsequently, the sample values from NI 9244 are encapsulated, and a data queue with timestamps is generated to match with the real-time module of NI Crio-9039.
The phasor estimation and data communication functions are included in the real-time module of NI Crio-9039, including receiving sampling data queues, estimating the phasor and frequency, outputting measurement results, generating data frames, and uploading them.
A PMU prototype test platform is created to evaluate the measurement accuracy of the PMU prototype, as shown in Fig. A2 of Appendix A.
The high-precision signal source OMICRON 256 plus is calibrated [
Test type | TVE (%) | FE (Hz) | REE (Hz/s) | |||
---|---|---|---|---|---|---|
Std | M1 | Std | M1 | Std | M1 | |
Off-nominal | 1.0 | 0.0150 | 0.005 | 0.0019 | 0.1 | 0.054 |
Harmonic | 1.0 | 0.0081 | 0.025 | 0.0029 | 0.012 | |
OOBI | 1.3 | 0.0210 | 0.010 | 0.0052 | 0.320 | |
AM | 3.0 | 0.2500 | 0.300 | 0.0270 | 14.0 | 0.780 |
PM | 3.0 | 0.8900 | 0.300 | 0.0160 | 14.0 | 0.680 |
Frequency ramp | 1.0 | 0.0260 | 0.010 | 0.0033 | 0.2 | 0.042 |
The accuracy of the phasor, frequency, and RoCoF is lower than that obtained from the simulations. For example, the TVE and FE of M1 are 0.010% and 0.0011 Hz in the simulation tests and 0.015% and 0.0019 Hz in the prototype tests, respectively, in the frequency offset tests. The reason is that the test system has uncertainty, and the PMU hardware has synchronization and sampling errors. The maximum estimation errors of the PMU prototype in all tests are smaller than the standard limit. Therefore, M1 can accurately estimate the fundamental synchrophasor and frequency.
This paper proposes a synchrophasor measurement method based on cascaded IIR and dual FIR filters. Synchronized measurement data provided for power system applications require short measurement latency and high accuracy measurement. The IIR filter can remove the harmonic and other OOBI, which is not considered in M3. The negative frequency component of the fundamental component is filtered by the dual FIR filters initially and subsequently eliminated by reconstruction and refinement. A step identification method based on RoCoF is proposed to avoiding convergence issues caused by IIR filter, and its sensitivity to different step sizes is analyzed. The latency of the proposed method is 24 ms, close to the requirements of the P-class PMU and shorter than most M-class PMUs. Simulations and experiments show that the synchrophasor accuracy is at least 3 times higher than the M-class standard requirements, and a minimum response time around 40 ms is achieved when magnitude or phase steps. Future studies will focus on the correction and repair of synchrophasor and frequency during transient step.
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