CN-121978394-A - Load monitoring method of electric energy meter

CN121978394ACN 121978394 ACN121978394 ACN 121978394ACN-121978394-A

Abstract

The application relates to the technical field of electrical measurement, in particular to a load monitoring method of an electric energy meter, which comprises the steps of synchronously collecting a power grid voltage signal and a load current signal; the method comprises the steps of carrying out phase difference estimation processing on a power grid voltage signal and a load current signal to obtain a fundamental wave phase difference, calculating a power factor according to the fundamental wave phase difference, a fundamental wave current effective value and a total current effective value of the load current signal, calculating active power based on the power grid voltage signal and the load current signal, and judging the running state of target monitoring equipment based on the active power and the power factor, wherein the running state comprises a standby state or an equipment turn-off event. The application can improve the practicability and accuracy of the ammeter load monitoring function.

Inventors

LI JIAYI
LI DIXING
YANG YANG
SUN YU
ZHANG CHUANG
CHEN YUE
WANG HONGBO
GUO LONGDI
SUN LU
GUO SHUAI

Assignees

黑龙江省电工仪器仪表工程技术研究中心有限公司

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (10)

1. A method of load monitoring of an electrical energy meter, the method comprising: synchronously collecting a power grid voltage signal and a load current signal; performing phase difference estimation processing on the power grid voltage signal and the load current signal to obtain a fundamental wave phase difference; Calculating a power factor according to the fundamental wave phase difference, the fundamental wave current effective value and the total current effective value of the load current signal; calculating active power based on the grid voltage signal and the load current signal; And judging the running state of the target monitoring equipment based on the active power and the power factor, wherein the running state comprises a standby state or an equipment turn-off event.
2. The method according to claim 1, wherein determining the fundamental current effective value and the total current effective value of the load current signal comprises: Intercepting current data corresponding to a plurality of continuous grid fundamental wave periods from the load current signal; Performing frequency domain decomposition on each current data to obtain a fundamental component amplitude and each subharmonic component amplitude of the load current signal; calculating the fundamental current effective value based on the fundamental component amplitude of the load current signal; And calculating the total current effective value based on the fundamental component amplitude and each subharmonic component amplitude.
3. The method of claim 2, wherein determining the operational state of the target monitoring device based on the active power and the power factor, specifically comprises: For each subharmonic component, calculating the harmonic component amplitude change rate of each subharmonic component between adjacent grid fundamental wave periods to obtain a change rate sequence; generating dynamic harmonic distribution characteristics reflecting the stability of the harmonic components of the load current signal based on each change rate sequence; And based on the active power, the power factor and the dynamic harmonic distribution characteristics, determining whether a device shutdown event occurs in the operation state of the target monitoring device.
4. The method according to claim 2, wherein the phase difference estimation process is performed on the grid voltage signal and the load current signal to obtain a fundamental phase difference, and the method specifically includes: Respectively carrying out phase difference estimation processing on a power grid voltage signal and a load current signal in a plurality of continuous power grid fundamental wave periods to obtain a plurality of original delay correlation functions and preliminary phase differences corresponding to each original delay correlation function; For each preliminary phase difference, determining the confidence coefficient weight of the preliminary phase difference according to the deviation degree between the preliminary phase difference and the preliminary phase difference of the adjacent period, wherein the smaller the deviation degree is, the higher the confidence coefficient weight is; The plurality of original delay correlation functions are weighted and overlapped according to the confidence weights corresponding to the plurality of original delay correlation functions, and a weighted delay correlation function is obtained; And extracting a delay quantity corresponding to a main peak value from the weighted delay correlation function, and converting the delay quantity into a fundamental wave phase difference based on the current fundamental wave frequency, wherein the current fundamental wave frequency is used as a frequency reference for phase difference calculation.
5. The method of claim 4, wherein the phase difference estimation process is performed for the power grid voltage signal and the load current signal in each power grid fundamental wave period to obtain an original delay correlation function of the power grid fundamental wave period, and the method specifically comprises: constructing a continuous weighting function for focusing fundamental wave frequency bands by taking the nominal fundamental wave frequency of the power grid as an initial center; Calculating a cross power spectrum of a power grid voltage signal and a load current signal in a fundamental wave period of the power grid; weighting the cross power spectrum by using the continuous weighting function to obtain a weighted cross power spectrum; and performing inverse Fourier transform on the weighted cross power spectrum to obtain an original time delay correlation function of the fundamental wave period of the power grid.
6. The method of claim 3, wherein determining the operational state of the target monitoring device based on the active power and the power factor, specifically comprises: Extracting complex components of fundamental wave and preset subharmonic based on the fundamental wave component amplitude and each subharmonic component amplitude of the load current signal; matching the fundamental complex component and the harmonic complex component with a pre-established typical electric equipment harmonic characteristic template library, and calculating similarity scores of all equipment templates; If the similarity score of a certain equipment template exceeds a preset similarity threshold value and the deviation between the theoretical fundamental wave phase corresponding to the equipment template and the fundamental wave phase difference is smaller than a preset phase tolerance, marking the equipment as candidate operation equipment; Based on the information entropy value and the dynamic harmonic distribution characteristics, performing cross verification on the candidate operation equipment; and judging the operation state of the candidate operation equipment based on the active power and the power factor only when the cross verification is passed, and taking the operation state of the candidate operation equipment as the operation state of the target monitoring equipment.
7. The method of claim 6, wherein determining the operational state of the candidate operational device based on the active power and the power factor comprises: when the active power is below a first power threshold and the power factor is below a second power factor threshold, calculating a regularity information entropy value used for characterizing a weighted delay related function waveform: if the information entropy value is lower than a third entropy threshold value, judging that the candidate operation equipment is in a standby state; And if the information entropy value is higher than a fourth entropy threshold, judging that the candidate operation equipment generates an equipment shutdown event, wherein the third entropy threshold is smaller than the fourth entropy threshold.
8. The method of claim 2, wherein after said generating a dynamic harmonic distribution signature reflecting the stability of the harmonic content of the load current signal, the method further comprises a frequency update step comprising: calculating the frequency spectrum gravity center offset of each power grid fundamental wave period based on the dynamic harmonic distribution characteristics aiming at each power grid fundamental wave period in the continuous multiple power grid fundamental wave periods to obtain a frequency spectrum gravity center offset sequence; if the continuous multiple frequency spectrum gravity center offsets in the frequency spectrum gravity center offset sequence exceed a preset offset threshold value and the offset directions are consistent, calculating an average value of the continuous multiple frequency spectrum gravity center offsets; And performing gradual change type update on the current fundamental wave frequency according to the average value to obtain an updated current fundamental wave frequency, wherein the updated current fundamental wave frequency does not exceed a preset safety interval taking the nominal fundamental wave frequency of the power grid as the center.
9. The method according to claim 1, wherein the phase difference estimation process is performed on the grid voltage signal and the load current signal to obtain a fundamental phase difference, and the method specifically includes: Generating a digital local oscillator signal synchronous with the current fundamental wave frequency, wherein the digital local oscillator signal comprises an in-phase component and a quadrature component; multiplying the load current signal with an in-phase component and a quadrature component of the digital local oscillator signal respectively to obtain a fundamental in-phase component and a fundamental quadrature component of the load current signal; multiplying the power grid voltage signal with an in-phase component and a quadrature component of the digital local oscillator signal respectively to obtain a fundamental wave in-phase component and a fundamental wave quadrature component of the power grid voltage signal; determining a fundamental wave phase of the grid voltage based on the fundamental wave inphase component and the fundamental wave quadrature component of the grid voltage signal; Determining a load current fundamental phase based on a fundamental in-phase component and a fundamental quadrature component of the load current signal; and calculating the difference between the fundamental wave phase of the grid voltage and the fundamental wave phase of the load current to obtain the fundamental wave phase difference.
10. The method according to claim 4 or 9, wherein obtaining the current fundamental frequency comprises: Detecting zero crossing points of the power grid voltage signals to obtain rough power frequency period estimation; taking the rough power frequency period estimation as an observation input, performing state estimation through a recursive filtering algorithm, and outputting a smoothed instantaneous frequency estimation; And estimating the instantaneous frequency as the current fundamental frequency.

Description

Load monitoring method of electric energy meter Technical Field The application relates to the technical field of electrical measurement, in particular to a load monitoring method of an electric energy meter. Background The electric energy meter is used as key metering equipment at the tail end of the power grid, and the basic function of the electric energy meter is to accurately record the accumulated electricity consumption of a user. With the development of smart grids and advanced measurement systems, the functions of modern smart electric energy meters have been gradually expanded from pure electric quantity measurement to online sensing and analysis of user load characteristics so as to support advanced applications such as demand side response, electricity consumption safety early warning and refined energy management. At present, partial advanced electric energy meters can collect instantaneous electric quantities such as voltage, current and the like through built-in metering chips, and derivative parameters such as active power, reactive power, power factors and the like are calculated. Based on these parameters, some schemes attempt to identify the macroscopic operating state of the downstream consumer, for example, by setting a fixed low power threshold to determine whether the device is in a standby or off state. However, the above-described method faces significant challenges in practical applications. Because of the large difference in electrical characteristics of different electrical devices, the standby power consumption can range from a few tenths of a watt to tens of watts, and many nonlinear loads (such as a switching power supply) can generate serious current harmonics in standby, so that the power factor is extremely low. This makes it difficult to easily generate erroneous judgment in a complex and changeable real electricity scenario depending on only a single, fixed power threshold or a simple power factor criterion, thereby limiting the practicality and accuracy of the load monitoring function. Disclosure of Invention Based on the above, it is necessary to provide a load monitoring method of an electric energy meter for improving the accuracy of the load monitoring function of the intelligent electric energy meter. The application provides a load monitoring method of an electric energy meter, which comprises the following steps: synchronously collecting a power grid voltage signal and a load current signal; performing phase difference estimation processing on the power grid voltage signal and the load current signal to obtain a fundamental wave phase difference; Calculating a power factor according to the fundamental wave phase difference, the fundamental wave current effective value and the total current effective value of the load current signal; calculating active power based on the grid voltage signal and the load current signal; And judging the running state of the target monitoring equipment based on the active power and the power factor, wherein the running state comprises a standby state or an equipment turn-off event. The invention has the beneficial effects that 1) the invention takes the phase characteristic (inductive/capacitive/resistive) and the waveform distortion degree (harmonic content) of the load into consideration by introducing the power factor as a second criterion. Standby devices (e.g., switching power supplies) typically exhibit "low power consumption + low power factor" while real shutdown events exhibit "zero power consumption + meaningless power factor". The two-dimensional criterion combining the quantity and the quality describes the difference of two states more comprehensively from physical essence, and the theoretical completeness and the degree of distinction of judgment are obviously improved. 2) The invention can effectively capture the current waveform distortion caused by harmonic wave by accurately calculating the real power factor (rather than the simple displacement power factor). This allows the system to rely no longer on a ubiquitous but fragile fixed threshold, but rather to dynamically evaluate on the basis of the instantaneous, real power usage characteristics of each device, thus enhancing the adaptability and robustness to diverse, complex nonlinear loads. 3) The invention builds the final state decision on two independent but complementary parameters of active power and power factor, and sets up definite logic rules. This cross-validation mechanism effectively filters false positives that may be generated by a single parameter due to transient disturbances (e.g., a transient fluctuation in power due to a grid voltage sag). Meanwhile, as the two parameters are calculated based on the original voltage and current signals collected synchronously, the data sources are consistent, and the consistency and stability of the inside of the judgment logic are ensured, so that a high-reliability monitoring result is provided in actual operat