Gateway electric energy metering monitoring method based on Beidou PPS frequency multiplication synchronous sampling

Abstract

The application relates to the technical field of measuring electric variables, and discloses a gateway electric energy metering monitoring method based on Beidou PPS frequency multiplication synchronous sampling, which comprises the following steps: collecting a PPS second pulse signal of Beidou, determining a linear offset component, a sudden jump component and a random diffusion component of a collection time, and constructing a signal jitter vector of the collection time; clustering the signal jitter vectors of all collection times, obtaining the membership of the collection time and the clustering cluster where the collection time is located, adopting a PI controller as a loop filter, determining whether the proportional term and the integral term are adjusted according to the clustering cluster where the collection time is located, adjusting according to the signal jitter vector, generating a clock signal, synchronously starting the sampling of voltage and current of the power grid by the clock signal, and calculating gateway electric energy. The application aims to realize Beidou PPS frequency multiplication synchronous sampling and improve the electric energy metering accuracy.

Description

Technical Field

[0001] This application relates to the field of electrical variable measurement technology, specifically to a gate power metering and monitoring method based on BeiDou PPS frequency doubling synchronous sampling. Background Technology

[0002] The construction of new power systems places higher demands on the accuracy and reliability of energy metering at key points. The PPS (Pulse Per Second) signal provided by the BeiDou Navigation Satellite System possesses excellent time synchronization capabilities, enabling the power system to have a unified, self-controllable time reference. Deeply integrating BeiDou's high-precision time information into the energy metering sampling link, overcoming the phase inaccuracy bottleneck caused by traditional local crystal oscillator drift, has become a key technological direction for improving the reliability of key point metering.

[0003] However, current technologies for applying PPS second pulse signals are mostly limited to "time stamping" or "periodic time synchronization." They merely use the real-time clock of PPS correction software to add timestamps to data frames, while voltage and current sampling is still driven by local crystal oscillator frequency division. The phase at the sampling moment is entirely dependent on the long-term stability of the crystal oscillator. Because crystal oscillators may have initial frequency deviation, temperature drift, and aging issues, even if they stabilize in the short term, phase errors will accumulate linearly over time, leading to serious inaccuracies in active power calculations. More importantly, since the physical sampling time deviates from the actual grid phase, this error cannot be corrected by post-processing time stamping. Therefore, there is an urgent need for a power metering synchronous sampling method that can optimize the quality of the PPS second pulse signal, prevent the accumulation of phase errors across seconds, and achieve high-precision synchronous sampling. Summary of the Invention

[0004] This application provides a gate-based power metering and monitoring method based on BeiDou PPS frequency doubling synchronous sampling to solve the problem of power metering inaccuracy caused by the accumulation of phase error across seconds in the PPS second pulse signal. The specific technical solution adopted is as follows: One embodiment of this application provides a method for monitoring and metering of energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling. The method includes the following steps: The PPS second pulse signal of the Beidou satellite navigation system is collected and converted into a clock signal. The voltage and current of the power grid are sampled synchronously by the clock signal, and the power energy at the threshold is calculated. The specific process of converting the clock signal is as follows: Based on the differences and trends in the time intervals between adjacent acquisition moments, the sampling time difference and time difference threshold between adjacent acquisition moments are determined, as well as the linear offset component at the acquisition moment. The linear offset component is used to characterize the degree of linear offset. Based on the numerical distribution of the sampling time difference between adjacent acquisition moments and the difference between the sampling time difference and the time difference threshold, the burst jump component and random diffusion component at the acquisition moment are determined. The burst jump component is used to characterize the degree of instantaneous fluctuation, and the random diffusion component is used to characterize the degree of random diffusion and irregular fluctuation. Combined with the linear offset component, the signal jitter vector at the acquisition moment is constructed. Cluster the signal jitter vectors at each acquisition time to obtain the membership degree of the acquisition time and the cluster in which the acquisition time belongs. Use a PI controller as a loop filter to determine whether to adjust the proportional and integral terms based on the cluster in which the acquisition time belongs. Adjust the proportional and integral terms based on the signal jitter vector at the acquisition time to generate a clock signal.

[0005] Furthermore, the sampling time difference between adjacent acquisition moments is the time interval between adjacent acquisition moments.

[0006] Furthermore, the method for determining the time difference threshold is as follows: Establish a sampling time difference sequence at the acquisition time, adaptively divide all sampling time differences in the sampling time difference sequence, obtain the division threshold and record it as the time difference threshold of the corresponding acquisition time.

[0007] Furthermore, the method for determining the linear offset component at the acquisition time is as follows: The maximum value of the proportion of positive numbers and the proportion of negative numbers in the first-order difference sequence of the sampling time difference sequence at the time of acquisition is denoted as the consistency of the change trend at the time of acquisition. The sampling time difference sequence is fitted with a straight line to obtain the sampling time difference fitting line at the acquisition time. The slope of the sampling time difference fitting line at the acquisition time is denoted as the sampling time difference slope at the acquisition time. The standard deviation of the sampling time difference between all two adjacent sampling times in the preset number of sampling times before the sampling time is recorded as the first standard deviation of the sampling time. The ratio of the first standard deviation of the sampling time to the standard deviation of all sampling time differences in the sampling time difference sequence of the sampling time is recorded as the first ratio of the sampling time. The negative correlation processing result of the first ratio of the sampling time is recorded as the relative time difference difference of the sampling time. The positive correlation between the consistency of the trend of change at the acquisition time, the slope of the sampling time difference, and the difference in relative time difference is recorded as the linear offset component at the acquisition time.

[0008] Furthermore, the method for obtaining the sudden transition component is as follows: The kurtosis of the sampling time difference sequence at the acquisition time is denoted as the sampling time difference concentration at the acquisition time. The difference between the sampling time difference between the sampling time and the previous adjacent sampling time and the time difference threshold is recorded as the first difference of the sampling time. When the first difference of the sampling time is less than 0, the first difference of the sampling time is assigned the value of 0. The normalized value of the first difference of the sampling time and the minimum value of the number 1 are recorded as the first minimum value of the sampling time. The positive correlation between the concentration of sampling time difference at the acquisition time and the first minimum value is recorded as the sudden jump component at the acquisition time.

[0009] Furthermore, the method for obtaining the random diffusion component is as follows: The absolute value of the difference between the proportion of positive numbers and the proportion of negative numbers in the first-order difference sequence of the sampling time difference sequence at the acquisition time is denoted as the trend difference at the acquisition time. The negative correlation processing result of the trend difference at the acquisition time is denoted as the relative trend difference at the acquisition time. The product of the first ratio at the time of acquisition and the difference in the relative trend is denoted as the random diffusion component at the time of acquisition.

[0010] Furthermore, the signal jitter vector at the acquisition time is a vector composed of the linear offset component, the sudden jump component, and the random diffusion component at the acquisition time, which are normalized and then arranged in sequence.

[0011] Furthermore, the specific steps for clustering the signal jitter vectors at each acquisition time to obtain the membership degree of the acquisition time and the cluster to which the acquisition time belongs are as follows: Cluster the signal jitter vectors at the acquisition time and all previous acquisition times, obtain the membership degree of the four clusters and the signal jitter vector at each acquisition time, calculate the mean of all values ​​contained in the signal jitter vectors at all acquisition times within the cluster, and denote the cluster with the smallest mean as the normal cluster. Based on the mean values ​​of the linear offset component, burst jump component, and random diffusion component in the signal jitter vector at all acquisition times within the remaining clusters and within the same cluster, the clusters are divided into linear offset clusters, burst jump clusters, and random diffusion clusters.

[0012] Furthermore, the method for determining whether to adjust the proportional and integral terms is as follows: When the cluster in which the signal jitter vector at the acquisition time belongs is a normal cluster, the proportional and integral terms of the PI controller are not adjusted; otherwise, the proportional and integral terms of the PI controller are adjusted.

[0013] Furthermore, the specific method for adjusting the proportional and integral terms based on the signal jitter vector at the acquisition time includes: Calculate the proportional and integral components of the linear offset component, the sudden jump component, and the random diffusion component at the acquisition time, respectively; The sum of the products of the proportional term component and the preset default value of the proportional term determined by the three components at the time of acquisition, and the corresponding membership degree of each cluster, is denoted as the adaptive proportional term at the time of acquisition. The sum of the products of the integral term components determined by the three components at the acquisition time and the preset default values ​​of the integral term with their corresponding membership degrees is denoted as the adaptive integral term at the acquisition time.

[0014] The beneficial effects of this application are: Considering that the signal jitter problem caused by disturbances during the propagation of the PPS second pulse signal is mainly divided into three categories: linear offset, sudden jump, and random diffusion, the degree of linear offset, instantaneous fluctuation, and continuous and irregular fluctuation caused by random diffusion at the acquisition time are evaluated respectively. The linear offset component, sudden jump component, and random diffusion component at the acquisition time are obtained, and the signal jitter vector at the acquisition time is established. Then, the signal jitter vectors at each acquisition time are clustered, and the acquisition time is mapped to normal, linear offset, sudden jump, and random diffusion respectively. The proportional and integral terms of the PI controller at the acquisition time corresponding to linear offset, sudden jump, and random diffusion are adaptively adjusted. The PI controller is used as a loop filter to generate a clock signal. The clock signal synchronously starts the sampling of the voltage and current of the power grid, realizing the gate power metering based on Beidou PPS frequency doubling synchronous sampling, solving the problem of phase error accumulation of PPS second pulse signal across seconds, which leads to inaccurate power metering. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of a gate power metering and monitoring method based on BeiDou PPS frequency doubling synchronous sampling, provided as an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Please see Figure 1 The diagram illustrates a flowchart of a gate power metering and monitoring method based on BeiDou PPS frequency doubling synchronous sampling according to an embodiment of this application. The method includes the following steps: Step S001: Collect the PPS second pulse signal of the Beidou satellite navigation system and convert it into a clock signal. Use the clock signal to synchronously start the sampling of the voltage and current of the power grid and calculate the threshold power.

[0019] First, the 1Hz PPS second pulse signal output by the BeiDou satellite navigation system is isolated and filtered for noise. The signal is then connected to the input of an optocoupler to eliminate electromagnetic interference from the power system. Finally, the residual noise is removed by an RC low-pass filter circuit to obtain a stable BeiDou 1Hz PPS second pulse signal.

[0020] The local high-frequency clock exhibits extremely high short-term frequency stability within the PPS second pulse interval (1 second), making it a suitable benchmark for measuring the time-domain jitter of the PPS signal. Simultaneously, the local high-frequency crystal oscillator demonstrates high short-term stability, ensuring the PPS signal has no long-term cumulative error. By using the local high-frequency clock as a high-frequency counter, the arrival time interval between adjacent PPS second pulses is measured, allowing for the extraction of the short-term time-domain jitter characteristics of the PPS signal.

[0021] Subsequently, the stable BeiDou 1Hz PPS second pulse signal was subjected to closed-loop phase-locked frequency multiplication to convert it into a 6.4kHz stable clock signal, which was used as a unified time reference. The 6.4kHz stable clock signal was then driven by a transistor and used as a unified trigger source to synchronously start the sampling of the grid voltage and current, achieving strict time alignment of voltage and current, with a sampling rate of 6.4kSPS.

[0022] The system processes the collected voltage and current data from the power grid, calculates the power factor, accumulates electrical energy, and finally outputs high-precision metering results to obtain the energy at the critical point. The electrical energy metering process is a well-known technology and will not be elaborated further.

[0023] This completes the synchronous sampling of voltage and current based on the BeiDou satellite navigation system.

[0024] Step S002, the specific process of converting the clock signal is as follows: Based on the difference and trend of the time interval between adjacent acquisition moments, determine the sampling time difference and time difference threshold between adjacent acquisition moments, as well as the linear offset component of the acquisition moment. The linear offset component is used to characterize the degree of linear offset. Based on the numerical distribution of the sampling time difference between adjacent acquisition moments, and the difference between the sampling time difference and the time difference threshold, determine the burst jump component and random diffusion component of the acquisition moment. The burst jump component is used to characterize the degree of instantaneous fluctuation, and the random diffusion component is used to characterize the degree of random diffusion and irregular fluctuation. Combined with the linear offset component, construct the signal jitter vector of the acquisition moment.

[0025] Accurate electricity metering is crucial for the stable operation of power systems. Existing methods for synchronously sampling voltage and current in power grids rely on local crystal oscillator frequency division, and the sampling accuracy is constrained by the long-term stability of the crystal oscillator. Although PPS second pulse signals have been introduced, they are mostly used only for "time marking" or "periodic time synchronization," still resulting in significant error accumulation and prominent synchronization deviations. In fact, PPS second pulse signals possess strong time synchronization capabilities. If they can be deeply coupled into the sampling clock generation stage, synchronization deviations can be effectively reduced, significantly improving the accuracy of electricity metering.

[0026] Introducing the PPS second pulse signal into synchronous sampling can avoid the accumulation of errors from the local crystal oscillator, thus its stability becomes crucial to ensuring sampling accuracy. Theoretically, the PPS second pulse signal is stable and has no error accumulation. However, in practical applications, it is affected by propagation disturbances such as ionospheric scintillation, multipath effects, and strong electromagnetic interference, causing jitter in the PPS second pulse signal, which leads to a short-term decrease in accuracy. Therefore, its stability needs to be quantitatively analyzed.

[0027] Common disturbances in the propagation of PPS second pulse signals include thermal noise, multipath effects, ionospheric or tropospheric scintillation, strong electromagnetic interference, and inherent system delay drift caused by hardware aging or temperature drift. Different disturbances result in different signal jitter characteristics, which can be mainly divided into three categories: linear offset, sudden jump, and random diffusion.

[0028] Under normal circumstances, the rising edge of the PPS second pulse signal is strictly aligned with the ideal integer second, with minimal time deviation and no obvious trend. The interval between adjacent pulses is stable, and the time error is concentrated. Linear offset, however, is caused by slow, time-varying interference such as ionospheric disturbances and hardware aging. It manifests as a gradual increase or decrease in time deviation, remaining stable in the short term but amplifying in the long term, exhibiting a clear trend. Sudden jumps originate from multipath reflections and sudden strong electromagnetic interference, manifesting as individual PPS second pulse signals deviating significantly from their normal positions while the remaining pulses remain stable. These isolated anomalies in the time series can easily mislead phase-locked loop (PLL) correction. Random diffusion is caused by short-term unstable interference such as receiver internal noise and satellite clocks. It manifests as irregular fluctuations in time deviation within a small range, with unpredictable direction and amplitude. Short-term stability decreases, but there is no unidirectional offset trend.

[0029] The time interval between two adjacent acquisition moments is taken as the sampling time difference between the two adjacent acquisition moments. Any acquisition moment is designated as the target acquisition moment. The sampling time differences between the target acquisition moment and all adjacent acquisition moments before the target acquisition moment are arranged sequentially to obtain the sampling time difference sequence of the target acquisition moment. The sampling time differences in the sampling time difference sequence of the target acquisition moment are used as the dependent variable, and the order of the sampling time differences in the sampling time difference sequence is used as the independent variable. Linear fitting is performed to obtain the fitting line for the sampling time difference of the target acquisition moment. All sampling time differences in the sampling time difference sequence of the target acquisition moment are adaptively divided, and the division threshold is obtained and denoted as the time difference threshold of the target acquisition moment.

[0030] In this embodiment, the least squares method is used to achieve linear fitting, and a method based on the 3-sigma criterion is used for adaptive partitioning to obtain the partitioning threshold. Linear fitting and adaptive partitioning of numerical values ​​are well-known techniques and will not be described in detail here.

[0031] The maximum value of the proportion of positive and negative numbers in the first-order difference sequence of the sampling time difference sequence at the target acquisition time is denoted as the consistency of the trend at the target acquisition time. The slope of the fitted line of the sampling time difference at the target acquisition time is denoted as the slope of the sampling time difference at the target acquisition time. The standard deviation of the sampling time difference between the target acquisition time and all adjacent sampling times in the preset number of sampling times before the target acquisition time is denoted as the first standard deviation of the target acquisition time. The ratio of the first standard deviation of the target acquisition time to the standard deviation of all sampling time differences in the sampling time difference sequence at the target acquisition time is denoted as the first ratio of the target acquisition time. The negative correlation processing result of the first ratio of the target acquisition time is denoted as the relative time difference difference at the target acquisition time.

[0032] In this embodiment, the preset number is set to 20. During the ratio calculation, to avoid the denominator being zero, a preset value is added to the denominator; in this embodiment, the preset value is 0.01.

[0033] It is understood that negative correlation processing is applied to the first ratio of the target acquisition time, that is, to ensure that the first ratio of the target acquisition time is negatively correlated with the relative time difference of the target acquisition time. It is understood that the negative correlation in this application refers to the relationship between the independent variable and the dependent variable, where the independent variable is the first ratio of the target acquisition time, and the dependent variable is the relative time difference of the target acquisition time. The negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases), and can be an inverse relationship, a subtraction relationship, etc.

[0034] Preferably, as an embodiment of this application, the negative of the first ratio of the target acquisition time is taken as the exponent of an exponential function with the natural constant as the base, and the calculated value of the exponential function is recorded as the relative time difference of the target acquisition time.

[0035] The positive correlation between the consistency of the target acquisition time change trend, the slope of the sampling time difference, and the difference in relative time difference is recorded as the linear offset component of the target acquisition time.

[0036] It is understood that positive correlation processing is applied to the consistency of the trend of change at the target acquisition time, the slope of the sampling time difference, and the difference in relative time difference. This ensures that the consistency of the trend of change at the target acquisition time, the slope of the sampling time difference, and the difference in relative time difference are positively correlated with the linear offset component of the target acquisition time. It is understood that the positive correlation in this application refers to the relationship between the independent and dependent variables. The independent variables are the consistency of the trend of change at the target acquisition time, the slope of the sampling time difference, and the difference in relative time difference; the dependent variable is the linear offset component of the target acquisition time. The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and can be an additive or multiplicative relationship.

[0037] Preferably, as an embodiment of this application, the product of the consistency of the change trend of the target acquisition time, the slope of the sampling time difference, and the difference of the relative time difference is denoted as the linear offset component of the target acquisition time.

[0038] In the first-order difference sequence of the sampling time difference sequence, positive and negative numbers can be used to determine the monotonicity of the change in the time interval between adjacent sampling times before the target sampling time. The slope of the fitting line of the sampling time difference of the target sampling time can be used to determine the trend and fluctuation magnitude of the change in the time interval between adjacent sampling times before the target sampling time. The first ratio of the target sampling time can be used to determine the relative fluctuation difference of the time interval between adjacent sampling times before the target sampling time in the short and long term.

[0039] The linear offset component at the target acquisition time is used to evaluate the degree of linear offset at the target acquisition time.

[0040] The kurtosis of the sampling time difference sequence at the target acquisition time is denoted as the sampling time difference concentration at the target acquisition time. The difference between the sampling time difference between the target acquisition time and the previous adjacent acquisition time and the time difference threshold is denoted as the first difference at the target acquisition time. When the first difference at the target acquisition time is less than 0, the first difference at the target acquisition time is assigned the value of 0. The normalized value of the first difference at the target acquisition time and the minimum value among the numbers 1 are denoted as the first minimum value at the target acquisition time. The positive correlation result between the sampling time difference concentration at the target acquisition time and the first minimum value is denoted as the burst jump component at the target acquisition time.

[0041] Preferably, as an embodiment of this application, the product of the sampling time difference concentration at the target acquisition time and the first minimum value is recorded as the sudden jump component at the target acquisition time.

[0042] It should be noted that this embodiment uses the maximum-minimum normalization method to calculate the normalized value. In practical applications, implementers may use other methods of existing technology, such as the tanh function or the sigmoid function, to calculate the normalized value, which is not limited here. The calculation of the kurtosis of the sequence is a well-known technique and will not be described in detail here.

[0043] Kurtosis of a sequence is used to evaluate the steepness of the data distribution and the difference between the tail of the distribution and the normal distribution. Its core function is to measure the concentration of data around the mean and the distribution characteristics of extreme values. The normalized value of the first difference at the target acquisition time is used to measure the degree of abrupt change in the time interval corresponding to the target acquisition time. The sudden jump component at the target acquisition time is used to evaluate the degree of instantaneous fluctuation at the target acquisition time.

[0044] The absolute value of the difference between the proportion of positive numbers and the proportion of negative numbers in the first-order difference sequence of the sampling time difference sequence at the target acquisition time is denoted as the trend difference at the target acquisition time. The negative correlation processing result of the trend difference at the target acquisition time is denoted as the relative trend difference at the target acquisition time. The product of the first ratio at the target acquisition time and the relative trend difference is denoted as the random diffusion component at the target acquisition time.

[0045] Preferably, as an embodiment of this application, the negative number of the difference in the trend of change at the target acquisition time is taken as the exponent of an exponential function with the natural constant as the base, and the calculated value of the exponential function is recorded as the difference in the relative trend of change at the target acquisition time.

[0046] The random diffusion component at the target acquisition time is used to evaluate the degree of continuous and irregular fluctuations caused by random diffusion at the target acquisition time.

[0047] The linear offset component, sudden jump component, and random diffusion component at the target acquisition time are normalized and then arranged in sequence to obtain the signal jitter vector at the target acquisition time.

[0048] The same method can be used to obtain the signal jitter vector at any acquisition time.

[0049] At this point, the signal jitter vector at the acquisition time is obtained.

[0050] Step S003: Cluster the signal jitter vectors at each acquisition time, obtain the membership degree of the acquisition time and the cluster in which the acquisition time is located, use a PI controller as a loop filter, determine whether to adjust the proportional and integral terms according to the cluster in which the acquisition time is located, adjust the proportional and integral terms according to the signal jitter vectors at the acquisition time, and generate a clock signal.

[0051] A fuzzy C-means clustering algorithm is used to cluster the signal jitter vectors at the current acquisition time and all previous acquisition times. The membership degree of the signal jitter vector at each acquisition time is obtained into four clusters. The mean of all values ​​contained in the signal jitter vectors at all acquisition times within a cluster is calculated. The cluster with the smallest mean is designated as the normal cluster. The mean of the linear offset component and the mean of the burst jump component in the signal jitter vectors at all acquisition times within the remaining clusters and within the same cluster are calculated and designated as the linear offset mean and burst jump mean of the cluster, respectively. The cluster with the largest linear offset mean is designated as the linear offset cluster. The cluster with the largest burst jump mean among the remaining clusters is designated as the burst jump cluster. The remaining clusters are designated as the random diffusion clusters.

[0052] In the clustering process using the fuzzy C-means clustering algorithm, the fuzzy index is set to 2, and the iteration stopping threshold is set to... The maximum number of iterations is set to 100; fuzzy C-means clustering is a well-known technique and will not be elaborated further.

[0053] In acquiring the BeiDou 1Hz PPS second pulse signal, the clock signal is generated through frequency multiplication and conversion using a digital phase-locked loop (DPLL). Specifically, the phase error signal is obtained by the digital converter and phase detector in the DPLL, filtered by a loop filter, and then the clock signal is generated. The loop filter technique is well-known and will not be elaborated further.

[0054] Since the phase error signal is obtained based on the PPS second pulse signal, the jitter error of the PPS second pulse signal will be synchronously transmitted to the phase error signal. Therefore, the filtering parameters of the loop filter can be adaptively adjusted by combining the jitter characteristics of the PPS second pulse signal to improve the filtering quality.

[0055] This embodiment uses a PI controller as a loop filter, achieving adaptive control of the filtering effect by adjusting the proportional and integral terms of the PI controller. It is understood that adjusting the proportional term of the PI controller controls the response speed; an excessively large value can introduce noise. Adjusting the integral term of the PI controller controls anti-interference capability; an excessively large value can lead to long-term errors. It should be noted that during the initial system startup or when clustering has not yet output effective classification results, the loop filter's proportional and integral terms are operated using preset default values. These preset values ​​are pre-set by those skilled in the art. In this embodiment, the initial system startup is set to the first 20 seconds of PPS second pulse signal acquisition, and the preset default values ​​for the proportional and integral terms are set to 0.05 and 0.0005, respectively.

[0056] To address the different jitter characteristics of PPS signals, the proportional and integral terms of the PI controller are adaptively adjusted. The specific strategies are as follows: For linear offset, since linear offset changes smoothly in the short term but gradually amplifies over long periods, the proportional term can be increased to improve response speed when using a loop filter. Simultaneously, the integral term is increased to enhance the loop's ability to eliminate steady-state phase errors and track slow-changing trends. For sudden jumps, which exhibit large instantaneous fluctuations but remain relatively stable overall, the proportional term needs to be reduced to decrease response speed and improve noise suppression. Simultaneously, the integral term is reduced to enhance noise smoothing. For random diffusion, which causes continuous and irregular signal fluctuations, smaller proportional and integral terms are needed to effectively suppress noise while avoiding the introduction of additional noise.

[0057] Identify the cluster in which the signal jitter vector at the acquisition time belongs. When the cluster in which the signal jitter vector at the acquisition time belongs is a normal cluster, no adjustment is made to the proportional and integral terms of the PI controller. When the cluster in which the signal jitter vector at the acquisition time belongs is any one of the linear offset cluster, the sudden jump cluster, and the random diffusion cluster, the proportional and integral terms of the PI controller are adjusted.

[0058] Before adjusting the proportional and integral terms of the PI controller, it is necessary to identify the adjustment scenarios for the proportional and integral terms. These scenarios include both increasing and decreasing. Specifically: when the signal jitter vector at the acquisition time belongs to a linear offset cluster, both the proportional and integral terms of the PI controller should be increased; when the signal jitter vector at the acquisition time belongs to a sudden jump cluster, both the proportional and integral terms of the PI controller should be decreased; and when the signal jitter vector at the acquisition time belongs to a random diffusion cluster, both the proportional and integral terms of the PI controller should be decreased.

[0059] Based on the linear offset component, sudden jump component, and random diffusion component at the acquisition time, the adaptive scaling term and adaptive integral term at the acquisition time are calculated. The specific process is as follows: in, The value is or ,when The value is hour, Represents the proportion term, when The value is hour, Represents the integral term. Indicates the first The first data collection time Each component determines the proportional component. Indicates the first The first data collection time The integral term components are determined by each component. The values ​​of are 1, 2, 3, and the th When the values ​​of each component are 1, 2, and 3, they correspond to the normalized processing results of the linear offset component, the sudden jump component, and the random diffusion component at the acquisition time, respectively. Indicates the default value for the ratio term; Indicates the default value for the integral term; Indicates the first The first data collection time One component; An increase indicates an adjustment to the proportional or integral term. The term indicating an adjustment to the proportional or integral term is a decrease; This represents the maximum value function, which calculates the maximum value among all comma-separated values ​​within parentheses.

[0060] It should be noted that this embodiment uses the maximum-minimum normalization method to achieve normalization. In practical applications, implementers may use other methods of existing technology, such as the tanh function or the sigmoid function, to achieve normalization, which is not limited here.

[0061] The three components at the acquisition time are selected to determine the proportional term component and the preset default value of the proportional term, for a total of four values. The sum of the products of the four selected values ​​and the corresponding membership degrees of each cluster is recorded as the adaptive proportional term at the acquisition time. The three components at the acquisition time are selected to determine the integral term component and the preset default value of the integral term, for a total of four values. The sum of the products of the four selected values ​​and the corresponding membership degrees is recorded as the adaptive integral term at the acquisition time.

[0062] When adjusting the proportional and integral terms of the PI controller, the adaptive proportional and adaptive integral terms at the acquisition time are used as the values ​​of the proportional and integral terms, respectively. The PI controller is used as a loop filter, and the filtered signal is input to the NCO numerically controlled oscillator to generate a 6.4kHz clock signal.

[0063] It should be noted that when the number of samples is less than the preset threshold at the initial stage of system startup, the PI controller will operate with preset default parameters and will not start adaptive adjustment. The specific duration of the initial stage of system startup shall be set by those skilled in the art.

[0064] Thus, based on BeiDou PPS frequency doubling synchronous sampling, the metering of electrical energy at the gateway is realized.

[0065] In order to realize the monitoring of energy metering at the gateway under BeiDou PPS frequency doubling synchronous sampling, different modules and units are set up in the energy metering and monitoring process at the gateway, including: BeiDou PPS second pulse receiving module, PPS frequency doubling circuit module, ADC sampling trigger module, voltage / current sampling unit, and metering processing unit. The modules are connected to each other through electrical signals.

[0066] The Beidou PPS second pulse receiving module is used to realize the isolated transmission and noise filtering of Beidou PPS signals. The 1Hz PPS second pulse signal output by the Beidou satellite navigation system is first connected to the input terminal of the optocoupler. The optocoupler isolates and eliminates the influence of electromagnetic interference from the power system on the reference signal, thereby improving the signal purity. The isolated signal is then filtered by an RC low-pass filter circuit to eliminate residual noise during transmission, resulting in a stable isolated signal, which is finally input to the phase-locked loop unit pin of the PPS frequency multiplication circuit module.

[0067] The PPS frequency multiplier circuit module is used to convert the BeiDou 1Hz PPS signal into a stable 6.4kHz clock signal through closed-loop phase-locked loop frequency multiplication, providing a unified time reference for sampling triggering.

[0068] In the ADC sampling trigger module, the trigger signal input terminal is associated with the collector of the transistor, and the trigger signal output terminal is connected to the ADC sampling trigger pins of the voltage and current sampling units respectively. The 6.4kHz standard pulse driven by the transistor is used as a unified trigger source to synchronously drive the ADC modules of the two sampling units to start sampling, so as to achieve strict time alignment of voltage and current signals and a sampling rate of 6.4kSPS.

[0069] The voltage / current sampling unit includes independent voltage and current acquisition units. The ADC sampling trigger terminals are all connected to the output terminal of the ADC sampling trigger module. Under the control of a unified 6.4kHz trigger signal, the unit continuously acquires grid voltage and current signals and transmits the real-time sampled data to the metering processing unit.

[0070] The data input terminal of the metering processing unit is connected to the output terminal of the voltage / current sampling unit to receive continuous synchronous sampling data, perform calculations on the continuous sampling data, accurately calculate the power factor and complete the energy accumulation, and output high-precision metering results.

[0071] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for monitoring and metering energy at key points based on BeiDou PPS frequency doubling synchronous sampling, characterized in that, The method includes the following steps: The PPS second pulse signal of the Beidou satellite navigation system is collected and converted into a clock signal. The voltage and current of the power grid are sampled synchronously by the clock signal, and the power energy at the threshold is calculated. The specific process of converting the clock signal is as follows: Based on the differences and trends in the time intervals between adjacent acquisition moments, the sampling time difference and time difference threshold between adjacent acquisition moments are determined, as well as the linear offset component at the acquisition moment. The linear offset component is used to characterize the degree of linear offset. Based on the numerical distribution of the sampling time difference between adjacent acquisition moments and the difference between the sampling time difference and the time difference threshold, the burst jump component and random diffusion component at the acquisition moment are determined. The burst jump component is used to characterize the degree of instantaneous fluctuation, and the random diffusion component is used to characterize the degree of random diffusion and irregular fluctuation. Combined with the linear offset component, the signal jitter vector at the acquisition moment is constructed. Cluster the signal jitter vectors at each acquisition time, obtain the membership degree of the acquisition time and the cluster in which the acquisition time belongs, use a PI controller as a loop filter, determine whether to adjust the proportional and integral terms according to the cluster in which the acquisition time belongs, adjust the proportional and integral terms according to the signal jitter vector at the acquisition time, and generate a clock signal. The method for obtaining the sudden transition component is as follows: Establish a sampling time difference sequence at the acquisition time, and denote the kurtosis of the sampling time difference sequence at the acquisition time as the sampling time difference concentration at the acquisition time. The difference between the sampling time difference between the sampling time and the previous adjacent sampling time and the time difference threshold is recorded as the first difference of the sampling time. When the first difference of the sampling time is less than 0, the first difference of the sampling time is assigned the value of 0. The normalized value of the first difference of the sampling time and the minimum value of the number 1 are recorded as the first minimum value of the sampling time. The positive correlation between the concentration of sampling time difference at the acquisition time and the first minimum value is recorded as the sudden jump component at the acquisition time. The method for obtaining the random diffusion component is as follows: The absolute value of the difference between the proportion of positive numbers and the proportion of negative numbers in the first-order difference sequence of the sampling time difference sequence at the acquisition time is denoted as the trend difference at the acquisition time. The negative correlation processing result of the trend difference at the acquisition time is denoted as the relative trend difference at the acquisition time. The product of the first ratio at the time of acquisition and the difference in the relative trend is denoted as the random diffusion component at the time of acquisition.

2. The method for monitoring and metering energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling according to claim 1, characterized in that, The sampling time difference between adjacent sampling times is the time interval between adjacent sampling times.

3. The method for monitoring and metering energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling according to claim 1, characterized in that, The method for determining the time difference threshold is as follows: Adaptively divide all sampling time differences in the sampling time difference sequence, obtain the division threshold, and record it as the time difference threshold of the corresponding sampling time.

4. The method for monitoring and metering energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling according to claim 3, characterized in that, The method for determining the linear offset component at the acquisition time is as follows: The maximum value of the proportion of positive numbers and the proportion of negative numbers in the first-order difference sequence of the sampling time difference sequence at the time of acquisition is denoted as the consistency of the change trend at the time of acquisition. The sampling time difference sequence is fitted with a straight line to obtain the sampling time difference fitting line at the acquisition time. The slope of the sampling time difference fitting line at the acquisition time is denoted as the sampling time difference slope at the acquisition time. The standard deviation of the sampling time difference between all two adjacent sampling times in the preset number of sampling times before the sampling time is recorded as the first standard deviation of the sampling time. The ratio of the first standard deviation of the sampling time to the standard deviation of all sampling time differences in the sampling time difference sequence of the sampling time is recorded as the first ratio of the sampling time. The negative correlation processing result of the first ratio of the sampling time is recorded as the relative time difference difference of the sampling time. The positive correlation between the consistency of the trend of change at the acquisition time, the slope of the sampling time difference, and the difference in relative time difference is recorded as the linear offset component at the acquisition time.

5. The method for monitoring and metering energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling according to claim 1, characterized in that, The signal jitter vector at the acquisition time is a vector composed of the linear offset component, the sudden jump component, and the random diffusion component at the acquisition time, which are normalized and then arranged in sequence.

6. The method for monitoring and metering energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling according to claim 1, characterized in that, The specific steps involved in clustering the signal jitter vectors at each acquisition time to obtain the membership degree of each acquisition time and the cluster to which the acquisition time belongs are as follows: Cluster the signal jitter vectors at the acquisition time and all previous acquisition times, obtain the membership degree of the four clusters and the signal jitter vector at each acquisition time, calculate the mean of all values ​​contained in the signal jitter vectors at all acquisition times within the cluster, and denote the cluster with the smallest mean as the normal cluster. Based on the mean values ​​of the linear offset component, burst jump component, and random diffusion component in the signal jitter vector at all acquisition times within the remaining clusters and within the same cluster, the clusters are divided into linear offset clusters, burst jump clusters, and random diffusion clusters.

7. The method for monitoring and metering energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling according to claim 6, characterized in that, The method for determining whether to adjust the proportional and integral terms is as follows: When the cluster in which the signal jitter vector at the acquisition time belongs is a normal cluster, the proportional and integral terms of the PI controller are not adjusted; otherwise, the proportional and integral terms of the PI controller are adjusted.

8. The method for monitoring and metering energy at the gateway based on BeiDou PPS frequency doubling synchronous sampling according to claim 1, characterized in that, The specific method for adjusting the proportional and integral terms based on the signal jitter vector at the acquisition time includes: Calculate the proportional and integral components of the linear offset component, the sudden jump component, and the random diffusion component at the acquisition time, respectively; The sum of the products of the proportional term component and the preset default value of the proportional term determined by the three components at the time of acquisition, and the corresponding membership degree of each cluster, is denoted as the adaptive proportional term at the time of acquisition. The sum of the products of the integral term components determined by the three components at the acquisition time and the preset default values ​​of the integral term with their corresponding membership degrees is denoted as the adaptive integral term at the acquisition time.

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