Statistical characteristic-based electric energy meter and power grid parameter estimation method of power utilization and acquisition terminal

Abstract

The invention discloses an electric energy meter based on statistical characteristics and a power grid parameter estimation method of a using and collecting terminal. The method comprises the following steps: establishing and writing a wave recording cache; judging whether the three-phase signal has phase loss or not according to the statistical characteristics of the recording cache; judging whether the current working condition is a reverse phase sequence or not by using a Pearson correlation coefficient; the zero crossing points recorded by the wave recording cache are grouped according to phases to which the zero crossing points belong, and power grid frequency estimation value calculation is carried out respectively; and establishing a three-phase zero crossing point array according to the effective phase number and the recording cache, and estimating the inter-phase included angle based on the three-phase zero crossing point array. According to the method, the judgment logic and the calculation logic of related steps are simplified, the selection problem of the reference phase is weakened, estimation calculation of different electrical parameters is combined into one process, the calculation efficiency is improved, and resource consumption is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of measuring electrical variables, and particularly relates to a method for estimating power grid parameters. Background Art

[0002] In modern electric energy meter metering technology, accurate estimation of power grid parameters is the key to ensuring the efficient and stable operation of the power system. Power grid parameters mainly include power grid frequency, phase angle, phase sequence, etc. The accurate measurement and real-time monitoring of these parameters not only play an important role in the accurate metering of electric energy meters, but also provide important basis for fault diagnosis, load forecasting and optimal dispatching of the power system. Therefore, rapid and accurate estimation of power grid parameters has important engineering significance.

[0003] In engineering implementation, the zero-crossing estimation method is widely used for estimating parameters such as power grid frequency, phase angle, and phase. This method is based on the sine characteristics of voltage or current signals, and theoretically there will be only one positive zero-crossing within one cycle. The specific implementation steps are as follows:

[0004] 1. Zero-crossing detection: When the value of a certain sampling point is less than 0 and the value of the next sampling point is greater than or equal to 0, this sampling point is recorded as a zero-crossing.

[0005] 2. Phase loss state judgment: Judgment is made based on the magnitudes of the effective values of voltage and current. For example, calculate the effective value of the 4-cycle sampling data. If the effective value of a certain phase is less than 20% of the rated value, it is considered that this phase is missing.

[0006] 3. Power grid frequency estimation: By statistically calculating the time difference between several zero-crossings, the estimated value of the power grid frequency is calculated using the time difference of the in-phase zero-crossings. The power grid frequency usually uses the voltage of phase A as a reference. If phase A is missing, phase B is used as a reference, otherwise phase C is used as a reference. Starting from the zero-crossing of the first reference phase, after several cycles, record the time interval between the end zero-crossing and the starting point, and the average period of the signal can be obtained. Take the reciprocal to get the power grid frequency value.

[0007] 4. Phase angle estimation: The phase angle is calculated using the time difference of the zero-crossings between phases. First, select the reference phase according to the phase loss state. If there is no phase loss, phase A is used as a reference, and the angles between AB, AC, and BC can be calculated; if phase A is missing, phase B is used as a reference, and only the angle between BC can be calculated; if phase B is missing, phase A is used as a reference, and only the angle between AC can be calculated; if phase C is missing, phase A is used as a reference, and only the angle between AB can be calculated; if the number of missing phases is greater than one, the angle between phases cannot be calculated. Starting from the first zero-crossing of the reference phase, record the interval time between the zero-crossings of the remaining phases and the starting point, and the angle between phases can be calculated according to the power grid frequency.

[0008] 5. Phase sequence judgment: The phase sequence is judged according to the order of zero-crossings.

[0009] Although the zero-crossing estimation method has been widely used in power grid parameter estimation, there are still problems of low calculation efficiency and large resource consumption. The reasons are as follows:

[0010] 1. There is a dependence between different estimation items. For example, both frequency estimation and phase angle estimation depend on the open-phase state judgment result, resulting in a lag in some calculation steps.

[0011] 2. The calculation amount of key steps is large and the logic is complex, which further reduces the calculation efficiency. For example, the open-phase is judged based on the effective value, which requires a large amount of calculation; when judging the phase sequence based on the zero-crossing order, various possible situations need to be considered to obtain a more accurate result, involving very complex judgment logic; the open-phase state will also affect the specific logic adopted by frequency estimation and phase angle estimation, with many logic branches.

[0012] 3. The calculation and judgment of each estimation item are independent of each other. It is necessary to record zero-crossings and perform related calculations separately, which not only occupies more computing resources but also has duplication, further increasing the estimation time. Summary of the Invention

[0013] The present invention proposes a method for estimating power grid parameters of an electric energy meter and a power consumption collection terminal based on statistical characteristics, and its purpose is to solve the problems of low calculation efficiency and large resource consumption existing in the existing methods.

[0014] The technical solution of the present invention is as follows:

[0015] A method for estimating power grid parameters of an electric energy meter and a power consumption collection terminal based on statistical characteristics, the steps include:

[0016] Step 1: Establish a recording buffer;

[0017] Step 2: Sample the three-phase signals, and sequentially judge whether each sampling point is a zero-crossing point according to the time sequence. Package the obtained zero-crossing time and the phase to which the zero-crossing point belongs, and store them as new elements in the recording buffer until the recording buffer is full;

[0018] Step 3: Judge whether there is an open-phase in the three-phase signals according to the statistical characteristics of the recording buffer;

[0019] Step 4: Use the Pearson correlation coefficient to judge whether the current working condition is reverse phase sequence;

[0020] Step 5: Group the zero-crossings recorded in the recording buffer according to the belonging phase, and calculate the estimated value of the power grid frequency respectively;

[0021] Step 6: Establish a three-phase zero-crossing array according to the number of effective phases and the recording buffer, and estimate the phase angle between phases based on the three-phase zero-crossing array.

[0022] As a further improvement of the above-mentioned method for estimating power grid parameters of electric energy meters and power consumption and acquisition terminals based on statistical features: the recording buffer is an array with a length of N, where N is an integer multiple of 3, and the i-th element in the recording buffer is denoted as p i ;

[0023] All elements in the recording buffer contain two attributes: the zero-crossing time time and the phase label phase; the phase label phase takes values of 0, 1, or 2, with phase A denoted as 0, phase B denoted as 1, and phase C denoted as 2.

[0024] As a further improvement of the above-mentioned method for estimating power grid parameters of electric energy meters and power consumption and acquisition terminals based on statistical features, in step 2, the basis for judging zero-crossing points is: if the value of a sampling point at a certain moment in a certain phase is greater than or equal to 0, and the value of the previous sampling point in the same phase is less than 0, then this sampling point at this moment is considered a zero-crossing point.

[0025] As a further improvement of the above-mentioned method for estimating power grid parameters of electric energy meters and power consumption and acquisition terminals based on statistical features, in step 2, after identifying the sampling point, based on the sampling time t current 、sampling value s current of this sampling point at this moment and the sampling time t last 、sampling value s last of the previous sampling point in the same phase, calculate the zero-crossing time:

[0026]

[0027] As a further improvement of the above-mentioned method for estimating power grid parameters of electric energy meters and power consumption and acquisition terminals based on statistical features, step 3 specifically includes:

[0028] Step 3-1: Taking the rated frequency as a reference value, calculate the expected mean of the zero-crossing intervals:

[0029]

[0030] where f s represents the sampling frequency, and f is the rated frequency of the signal;

[0031] Step 3-2: Calculate the variance of the zero-crossing interval sequence:

[0032]

[0033] where p i .time is the zero-crossing time time of the i-th element p i in the recording buffer;

[0034] Step 3-3: Compare the variance of the zero-crossing interval sequence with a threshold to judge the number of valid phases, thereby obtaining the open-phase judgment result:

[0035]

[0036] N Ph is the number of effective phases, and ε 1 and ε 2 are thresholds.

[0037] As a further improvement of the above-mentioned method for estimating power grid parameters of electric energy meters and power consumption and acquisition terminals based on statistical features, the thresholds ε 1 and ε 2 are valued as:

[0038]

[0039] As a further improvement of the above-mentioned method for estimating power grid parameters of electric energy meters and power consumption and acquisition terminals based on statistical features, step 4 specifically includes:

[0040] Step 4-1: Construct an inverse phase sequence R[N] with the same length as the recording buffer according to the phase label p 0 .phase of the first zero-crossing point in the recording buffer:

[0041]

[0042] Step 4-2: Use the phase labels corresponding to all zero-crossing points in the recording buffer to construct a recording phase sequence P[N]:

[0043] P[N] = [p 0 .phase, p 1 .phase, p 2 .phase, …, p N-1 .phase];

[0044] Judge according to the number of effective phases. When N Ph = 3, jump to step 4-3; otherwise, jump to step 4-4;

[0045] Step 4-3: Calculate the Pearson correlation coefficient between the recording phase sequence P[N] and the inverse phase sequence R[N]:

[0046]

[0047] where P i is the i-th element in the recording phase sequence P[N], is the mean value of the recording phase sequence P[N], and R i is the i-th element in the inverse phase sequence R[N], is the mean value of the inverse phase sequence R[N];

[0048] If the correlation coefficient r RPIf it is equal to 1, it indicates an inverse phase sequence. Otherwise, it should be a positive phase sequence, and step 4 ends;

[0049] Step 4-4: There is a phase loss in the current working condition. When there is a phase loss, there is no need to judge the inverse phase sequence, and it is defaulted to the positive phase sequence.

[0050] As a further improvement of the above-mentioned method for estimating power grid parameters of an electric energy meter and a power consumption and acquisition terminal based on statistical features, step 5 specifically includes:

[0051] Step 5-1: Establish three zero-crossing time arrays m A , m B , M C , which are used to store the zero-crossing times of phase A, phase B, and phase C respectively;

[0052] Step 5-2: Traverse the recording buffer, and place the zero-crossing times in each element into the corresponding array according to the following rules:

[0053]

[0054] For the i-th element p of the recording buffer i , if its belonging phase p i .phase = 0, then place it into the zero-crossing time array m corresponding to phase A A , if its belonging phase p i .phase = 1, then place it into the zero-crossing time array M corresponding to phase B B , if its belonging phase p i .phase = 2, then place it into the zero-crossing time array M corresponding to phase C C ;

[0055] Step 5-3: Calculate the power grid frequency for each zero-crossing time array respectively;

[0056] Suppose a zero-crossing time array M = [m 0 , m 1 , m 2 , …, m L-1 ;

[0057] Among them, L represents the number of elements in the zero-crossing time array M, then the frequency estimation value f * of its corresponding phase is calculated as follows:

[0058]

[0059] Among them, f S is the sampling frequency.

[0060] As a further improvement of the above-mentioned method for estimating power grid parameters of an electric energy meter and a power consumption and acquisition terminal based on statistical features, step 6 specifically includes:

[0061] Step 6-1: If the number of effective phases N Ph = 1, then the phase angle φ AB , φ BC , φ AC are all equal to 0°, end the calculation, otherwise jump to Step 6-2;

[0062] Step 6-2: Establish a three-phase zero-crossing array K of length 3 = [k 0 , k 1 , k 2 to store the times of the three-phase zero-crossings. Among them, k 0 is used to store the A-phase zero-crossing time, k 1 is used to store the B-phase zero-crossing time, and k 2 is used to store the C-phase zero-crossing time; initialize each element in K to -1;

[0063] Step 6-3: Write the zero-crossing times into the three-phase zero-crossing array K according to the following rules:

[0064] When N Ph = 2,

[0065] K[p N-2 .phase] = p N-2 .time;

[0066] K[p N-1 .phase] = p N-1 .time;

[0067] When N Ph = 3,

[0068] K[p N-3 .phase] = p N-3 .time;

[0069] K[p N-2 .phase] = p N-2 .time;

[0070] K[p N-1 .phase] = p N-1 .time;

[0071] Among them, K[j] represents the j-th element of the three-phase zero-crossing array K;

[0072] Step 6-4: Calculate the phase angles according to the three-phase zero-crossing array K.

[0073] As a further improvement of the above-mentioned method for estimating power grid parameters of an electric energy meter and a power consumption and acquisition terminal based on statistical features, the method for calculating the phase angle values in Step 4 is:

[0074]

[0075]

[0076] wherein, φ AB is the phase angle between phase A and phase B, φ BC is the phase angle between phase B and phase C, φ AC is the phase angle between phase A and phase C, f s is the sampling frequency, and f is the rated frequency of the signal.

[0077] Compared with the prior art, the present invention has the following positive effects:

[0078] 1. The present invention performs oscillogram caching for the zero-crossing points, records the times of the three-phase zero-crossing points in the order of the time axis, and then further analyzes their statistical characteristics. The fast judgment of phase loss is realized by variance calculation, and the fast judgment of phase sequence is realized by the correlation coefficient, which simplifies the judgment logic of related steps. At the same time, the present invention performs frequency estimation and phase angle estimation after transforming and reorganizing the zero-crossing point data, skillfully weakens the problem of selecting the reference phase, further simplifies the calculation logic, and realizes a significant improvement in calculation efficiency.

[0079] 2. Based on the zero-crossing oscillogram caching, the present invention combines the estimation calculations of different electrical parameters into one process through simple judgment of the statistical results, greatly reducing the resource consumption, reducing the calculation time of electrical parameters, and improving the calculation efficiency. Specific Embodiments

[0080] The technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0081] In this field, the calculation methods of voltage parameters and current parameters are the same, and it is easy to extend to the parameter calculation between voltage and current. Therefore, without special instructions, the following signals can refer to either voltage signals or current signals.

[0082] A method for estimating power grid parameters of an electric energy meter and a power acquisition terminal based on statistical characteristics, the steps include:

[0083] Step 1: Establish an oscillogram cache. The oscillogram cache is an array with a length of N, where N is an integer multiple of 3, and the i-th element in the oscillogram cache is denoted as p i . All elements in the oscillogram cache include two attributes: zero-crossing time time and phase mark phase. The phase mark phase takes values of 0, 1, or 2, where phase A is denoted as 0, phase B is denoted as 1, and phase C is denoted as 2.

[0084] Step 2: Sample the three-phase signals and select a certain sampling point as the reference zero point. In this embodiment, the first sampling point of phase A is used as the reference zero point. Starting from the reference zero point, sequentially determine whether each sampling point is a zero-crossing point in chronological order, encapsulate the obtained zero-crossing time and the phase to which the zero-crossing point belongs, and store them as new elements in the recording buffer until the recording buffer is full.

[0085] If the phase to which the i-th obtained zero-crossing point belongs is phase A, then p i .phase = 0. If it belongs to phase B, then p i .phase = 1. If it belongs to phase C, then p i .phase = 2.

[0086] Among them, the basis for judging the zero-crossing point is: if the value of the sampling point at a certain moment of a certain phase is greater than or equal to 0, and the value of the previous sampling point of this phase is less than 0, then it is considered that the sampling point at this moment is a zero-crossing point.

[0087] Furthermore, after identifying the sampling point, based on the sampling time t current of the sampling point at this moment, the sampling value s current and the sampling time t last corresponding to the previous sampling point of this phase, the sampling value s last calculate the zero-crossing time. In this embodiment, the linear interpolation method is used to calculate the zero-crossing time:

[0088]

[0089] According to the empirical value, the recording buffer records 12 zero-crossing points (4-cycle data) to be able to perform statistical feature analysis of the power grid parameters.

[0090] Step 3: Judge whether there is a phase loss in the three-phase signals according to the statistical features of the recording buffer.

[0091] Step 3-1: Taking the rated frequency as the reference value, calculate the expected mean of the zero-crossing intervals:

[0092]

[0093] Among them, f s represents the sampling frequency, and f is the rated frequency of the signal. The actual voltage rated frequency can generally be fixed at 50Hz or 60Hz.

[0094] Step 3-2: Calculate the variance of the zero-crossing interval sequence:

[0095]

[0096] Among them, N is the length of the recording buffer.

[0097] Step 3-3: Compare the variance of the zero-crossing interval sequence with a threshold to determine the number of effective phases, thereby obtaining the missing-phase judgment result.

[0098] Considering the frequency deviation and sampling error, the variance σ under different working conditions 2 will still show significant differences. Therefore, it can be judged through statistical characteristic thresholds. Taking a sampling rate of 6.4 kHz, a rated voltage frequency of 50 Hz, and the number of recorded wave points N = 12 as an example, the variances under different working conditions are calculated as shown in Table 1:

[0099] Table 1: Variances of the zero-crossing interval sequence under different working conditions.

[0100]

[0101] Table 1 only lists the variance values of some working conditions. After actual measurement, when the frequency is between 45 Hz and 55 Hz, in the case of reverse phase sequence or lack of other phases, the variance value σ 2 will not differ much from the values listed in the above table. Therefore, according to the above statistical characteristic table, two variance thresholds ε 1 and ε 2 can be specified, and they satisfy the following conditions with the number of effective phases N Ph :

[0102]

[0103] The empirical values of the thresholds ε 1 and ε 2 can be taken as:

[0104]

[0105] Step 4: Use the Pearson correlation coefficient to determine whether the current working condition is a reverse phase sequence.

[0106] Step 4-1: Construct an inverse phase sequence R[N] of the same length as the recorded wave cache according to the phase mark p 0 .phase of the first zero-crossing in the recorded wave cache:

[0107]

[0108] Step 4-2: Use the phase marks corresponding to all zero-crossings in the recorded wave cache to construct the recorded wave phase sequence P[N]:

[0109] P[N] = [p 0 .phase, p 1 .phase, p 2 .phase,..., p N-1 .phase].

[0110] Judge according to the effective number of phases obtained in step 3. When N Ph = 3, jump to step 4-3; otherwise, jump to step 4-4.

[0111] Step 4-3: Calculate the Pearson correlation coefficient between the recorded wave phase sequence P[N] and the inverse phase sequence R[N]:

[0112]

[0113] where P i is the i-th element in the recorded wave phase sequence P[N], is the mean value of the recorded wave phase sequence P[N], and R i is the i-th element in the inverse phase sequence R[N], is the mean value of the inverse phase sequence R[N].

[0114] If the correlation coefficient r RP = 1, it indicates that the phase sequence is the inverse phase sequence; otherwise, it should be the positive phase sequence, and step 4 ends.

[0115] Step 4-4: There is a missing phase in the current working condition. When there is a missing phase, there is no need to judge the inverse phase sequence, and it is defaulted to the positive phase sequence.

[0116] Step 5: Group the zero-crossing points recorded in the recording buffer according to the belonging phase, and calculate the estimated value of the power grid frequency respectively.

[0117] Step 5-1: Establish three zero-crossing time arrays M A , M B , M C to store the zero-crossing times of phase A, phase B, and phase C respectively.

[0118] Step 5-2: Traverse the recording buffer and place the zero-crossing times in each element into the corresponding array according to the following rules:

[0119]

[0120] For the i-th element p i of the recording buffer, if its belonging phase p i .phase = 0, put it into the zero-crossing time array M A corresponding to phase A; if its belonging phase p i .phase = 1, put it into the zero-crossing time array M B corresponding to phase B; if its belonging phase p i .phase = 2, put it into the zero-crossing time array M C .

[0121] Step 5-3: Calculate the power grid frequency for each zero-crossing time array respectively.

[0122] Let a zero-crossing time array M = [m 0 , m 1 , m 2 , …, m L-1 ;

[0123] Where L represents the number of elements in the zero-crossing time array M, and the calculation method of the corresponding phase frequency estimate value f is:

[0124]

[0125] Where f s is the sampling frequency.

[0126] Step 6: Establish a three-phase zero-crossing array based on the effective number of phases and the recording buffer, and estimate the phase angle between phases based on the three-phase zero-crossing array.

[0127] Step 6-1: If the effective number of phases N Ph = 1, the phase angle between phases cannot be calculated, and φ AB , φ BC , φ AC are all equal to 0°, and the calculation ends. Otherwise, jump to Step 6-2.

[0128] Step 6-2: Establish a three-phase zero-crossing array K of length 3 = [k 0 , k 1 , k 2 to store the zero-crossing times of the three phases. Among them, k 0 is used to store the zero-crossing time of phase A, k 1 is used to store the zero-crossing time of phase B, and k 2 is used to store the zero-crossing time of phase C. Initialize each element in K to -1.

[0129] Step 6-3: Write the zero-crossing times into the three-phase zero-crossing array K according to the following rules:

[0130] When N Ph = 2,

[0131] K[p N-2 .phase] = p N-2 .time;

[0132] K[p N-1 .phase] = p N-1 .time;

[0133] When N Ph = 3,

[0134] K[p N23 .phase] = pN-3 .time;

[0135] K[p N-2 .phase] = p N-2 .time;

[0136] K[p N-1 .phase] = p N-1 .time;

[0137] Wherein, K[j] represents the j-th element of the three-phase zero-crossing point array K.

[0138] Step 6-4: Since step 6-3 has rearranged the three-phase zero-crossing times in the order of A, B, and C phases, the phase angle values between phases can be calculated according to the following formula:

[0139]

[0140] Wherein, φ AB is the phase angle between phase A and phase B, φ BC is the phase angle between phase B and phase C, φ AC is the phase angle between phase A and phase C, f s is the sampling frequency, and f is the rated frequency of the signal.

[0141] After actual testing, when the electrical parameters are estimated with the same cycle number, the estimation time of the electrical parameter estimation method in the present invention can be reduced by 50% compared with the conventional estimation method.

[0142] It should be noted that for those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. The scope of the present invention is defined by the claims rather than the above description.

Claims

1. A method for estimating electric energy meter and power grid parameters of a terminal based on statistical characteristics, characterized in that the steps include: Step 1: Establish recording buffer; Step 2: Sample the three-phase signal, determine whether each sampling point is a zero-crossing point in chronological order, encapsulate the obtained zero-crossing time and the phase to which the zero-crossing point belongs, and store them in the recording buffer as new elements until the recording buffer is full; Step 3: Determine whether there is a phase loss in the three-phase signal based on the statistical characteristics of the recording buffer; Step 4: Use the Pearson correlation coefficient to determine whether the current operating condition is a reverse phase sequence; Step 5: Group the zero-crossing points recorded in the recording buffer according to the phases to which they belong, and calculate the estimated value of the power grid frequency respectively; Step 6: Establish a three-phase zero-crossing point array according to the number of effective phases and the recording buffer, and estimate the phase angle based on the three-phase zero-crossing point array.

2. The method for estimating electric energy meter and electric power grid parameters of a user terminal based on statistical characteristics according to claim 1, characterized in that: The recording buffer is an array of length N, where N is an integer multiple of 3. The i-th element in the recording buffer is denoted by p i ; All elements in the recording buffer contain two attributes: the zero-crossing time time and the phase mark phase; the phase mark phase takes the value of 0, 1 or 2, phase A is recorded as 0, phase B is recorded as 1, and phase C is recorded as 2.

3. The method for estimating electric energy meter and power grid parameters of a user terminal based on statistical characteristics according to claim 1, characterized in that: In step 2, the basis for determining the zero-crossing point is: if the value of a sampling point at a certain moment in a certain phase is greater than or equal to 0, and the value of a previous sampling point in the phase is less than 0, then the sampling point at that moment is considered to be a zero-crossing point.

4. The method for estimating electric energy meter and power grid parameters of a user terminal based on statistical characteristics according to claim 3, characterized in that: In step 2, after the sampling point is identified, the sampling time t of the sampling point at that moment is calculated. current , sampling value s current The sampling time t corresponding to the previous sampling point of this phase last , sampling value s last Calculate the zero-crossing time:

5. The method for estimating electric energy meter and power grid parameters of a user terminal based on statistical characteristics according to claim 2, characterized in that: Step 3 specifically includes: Step 3-1: Taking the rated frequency as the reference value, calculate the expected mean of the zero-crossing interval: Among them, f s represents the sampling frequency, f is the rated frequency of the signal; Step 3-2: Calculate the variance of the zero-crossing interval sequence: Among them, p i .time is the i-th element in the recording buffer, denoted as p i Step 3-3: Compare the variance of the zero-crossing interval sequence with the threshold to determine the number of valid phases, thereby obtaining the phase loss determination result: N Ph is the effective phase number, ε1 and ε2 are the thresholds.

6. The method for estimating electric energy meter and power grid parameters of a user terminal based on statistical characteristics according to claim 5, characterized in that: The thresholds ε1 and ε2 are as follows:

7. The method for estimating electric energy meter and power grid parameters of a user terminal based on statistical characteristics according to claim 2, characterized in that: Step 4 specifically includes: Step 4-1: Construct a reverse phase sequence R[N] with the same length as the recording buffer according to the phase mark p0.phase of the first zero-crossing point in the recording buffer: Step 4-2: Use the phase marks corresponding to all zero-crossing points in the recording buffer to construct the recording phase sequence P[N]: P[N]=[p0.phase,p1.phase,p2.phase,…,p N-1 .phase]; According to the effective number of phases, when N Ph =3, jump to step 4-3; otherwise, jump to step 4-4; Step 4-3: Calculate the Pearson correlation coefficient between the recorded phase sequence P[N] and the reverse phase sequence R[N]: Among them, P i is the i-th element in the waveform phase sequence P[N], is the mean value of the recorded phase sequence P[N], R i is the i-th element in the reverse phase sequence R[N], is the mean value of the reverse phase sequence R[N]; If the correlation coefficient r RP =1, it indicates that the phase sequence is reverse phase sequence, otherwise it should be positive phase sequence, and step 4 ends; Step 4-4: There is a phase loss in the current working condition. When there is a phase loss, there is no need to determine the reverse phase sequence. The default is the positive phase sequence.

8. The method for estimating electric energy meter and power grid parameters of a user terminal based on statistical characteristics according to claim 2, characterized in that: Step 5 specifically includes: Step 5-1: Create three zero-crossing time arrays M A ,M B ,M C , used to store the zero-crossing time of phase A, phase B, and phase C respectively; Step 5-2: Traverse the waveform buffer and put the zero-crossing time of each element into the corresponding array according to the following rules: For the i-th element p of the recording buffer i , if its phase p i .phase=0, then put A The corresponding zero-crossing time array M A , if its phase p i .phase=1, then put B The corresponding zero-crossing time array M B , if its phase p i .phase=2, then put C The corresponding zero-crossing time array M C ; Step 5-3: Calculate the grid frequency for each zero-crossing time array; Suppose a zero-crossing time array M = [m0,m1,m2,…,m L-1 ]; Where L represents the number of elements in the zero-crossing time array M, then the frequency estimate of the corresponding phase is Evaluation f * The calculation method is: Among them, f s is the sampling frequency.

9. The electric energy meter and the method for estimating the parameters of the electric power grid using the terminal based on statistical characteristics as claimed in claim 2, characterized in that: Step 6 specifically includes: Step 6-1: If the effective phase number N Ph =1, the phase angle cannot be calculated, φ AB ,φ BC ,φ AC If both are equal to 0°, the calculation ends; otherwise, jump to step 6-2; Step 6-2: Create a three-phase zero-crossing point array K = [k0, k1, k2] with a length of 3, which is used to store the time of the three-phase zero-crossing points, where k0 is used to store the zero-crossing time of phase A, k1 is used to store the zero-crossing time of phase B, and k2 is used to store the zero-crossing time of phase C; initialize each element in K to -1; Step 6-3: Write the zero-crossing time into the three-phase zero-crossing array K according to the following rules: When N Ph =2, K[p N-2 .phase]=p N-2 .time; K[p N-1 .phase]=p N-1 .time; When N Ph =3, K[p N-3 .phase]=p N-3 .time; K[p N-2 .phase]=p N-2 .time; K[p N-1 .phase]=p N-1 .time; Wherein, K[j] represents the jth element of the three-phase zero-crossing point array K; Step 6-4: Calculate the phase angle based on the three-phase zero-crossing point array K.

10. The method for estimating electric energy meter and electric power grid parameters of a user terminal based on statistical characteristics according to claim 9, characterized in that: The method for calculating the angle value between each phase in step 4 is: Among them, φ AB is the phase angle between phase A and phase B, φ BC is the phase angle between phase B and phase C, φ AC is the phase angle between phase A and phase C, f s is the sampling frequency, and f is the rated frequency of the signal.

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