Electric energy quality monitoring method based on intelligent electric energy meter

Abstract

The invention relates to the technical field of data processing, in particular to an electric energy quality monitoring method based on an intelligent electric energy meter, and the method comprises the steps: obtaining two kinds of electric power data of the electric energy meter, determining a reference time period, and calculating the change degree of any kind of electric power data at the current moment; calculating the noise degree at the current moment by combining the correlation of the change degree sequences of the two kinds of power data in the reference time period and the sampling time period; correcting any kind of electric power data at the current moment, wherein the weight at the current moment is negatively correlated with the noise degree; and Fourier decomposition is carried out on the two kinds of corrected power data in the sampling time period, so that the power quality is monitored according to the average proportion of harmonic components in the two kinds of frequency domain signals obtained through decomposition. The electric energy quality of the electric energy meter can be accurately monitored.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology. More specifically, the present invention relates to a method for monitoring power quality based on a smart power meter. Background Art

[0002] In actual power systems, due to nonlinear loads (such as high-power frequency conversion equipment and load overload short circuit, etc.), the frequency domain signals of voltage and current may be distorted, affecting the power quality and thus the stable operation of the power system, such as reducing equipment performance, increasing losses, interfering with relay protection, etc. Therefore, it is very important to monitor the power quality of smart energy meters.

[0003] In the related art, a current quality assessment method is disclosed in a patent application document with publication number CN104850751A, which includes: first, Fourier decomposition of the voltage and current measured at the PCC is performed, and the voltage subset and current subset obtained after the decomposition are divided into two parts: harmonics and interharmonics; secondly, for the harmonic part, based on the direction of the harmonic power at each frequency, the current components at each frequency are calculated from the harmonic voltage and the harmonic current, and the active current, scattered current, reactive current, unbalanced current, zero-sequence current and scattered current can be decomposed; finally, the required current quality assessment index is calculated from each current component.

[0004] However, the above scheme does not consider the influence of noise when performing Fourier decomposition on voltage and current. The presence of noise may cause the normal frequency domain signal of voltage or current to be misjudged as distortion, thereby affecting the results of harmonic analysis. Specifically, noise will interfere with the harmonic components of the frequency domain signal, reducing the accuracy of harmonic decomposition, which in turn leads to deviations in the current quality assessment results and affects the accuracy of the assessment. Summary of the invention

[0005] In order to solve the problem of being unable to accurately evaluate power quality, the present invention provides a power quality monitoring method based on a smart power meter. The method comprises: Obtain two types of power data during the working process of the power meter, the power data are voltage and current; The period other than the current time in the sampling period is taken as the reference period, and the degree of change of any type of power data at the current time is calculated. The degree of change is positively correlated with the difference between the current time and the previous time of the power data, and negatively correlated with the range of the power data in the reference period; Calculate the noise level at the current moment , , are the correlations of the change degree series of the two types of power data in the reference period and the sampling period, is the standard deviation of the difference between the two power data changes during the reference period, is the absolute value symbol, is the hyperbolic tangent function; Correct any type of power data at the current moment, the correction value is the weighted sum of the power data at the current moment and the previous moment, and the weight at the current moment is negatively correlated with the noise level; The two corrected power data within the sampling period are subjected to Fourier transform, and the power quality is monitored according to the average proportion of the harmonic components in the two obtained frequency domain signals.

[0006] Before using Fourier transform to convert time series data into frequency domain signals for analysis, the present invention first corrects the voltage and current data according to the noise level at each moment, thereby avoiding the interference of noise on the frequency domain signals of voltage and current, and preventing normal frequency domain signals from being misjudged as distorted, thereby ensuring the accuracy of the proportion of harmonic components in the voltage and current frequency domain signals, making it possible to accurately evaluate the power quality and improve the accuracy of power quality monitoring.

[0007] Preferably, the difference between the power data at the current moment and the previous moment is a comprehensive difference, and a method for obtaining the comprehensive difference includes: Calculate the average value of any type of power data within the reference period, as well as the difference between the current moment and the previous moment of the power data; The absolute value of the ratio of the difference to the average value is taken as the comprehensive difference of the power data at the current moment and the previous moment.

[0008] The present invention can accurately measure the variation range of any type of power data at any time within a sampling period.

[0009] Preferably, a method for obtaining the degree of change of any type of power data at the current moment includes: Calculate the maximum value of any type of power data in the reference period and the difference between the minimum value of the power data in the reference period to obtain the range of the power data in the reference period; The degree of change of any type of power data at the current moment satisfies the following relationship: ; In the formula, is the degree of change of any type of power data at the current moment; is the extreme value of the power data in the reference period; It is the comprehensive difference between the current moment and the previous moment of the power data; The natural constant An exponential function with base .

[0010] The present invention measures the stability of historical data by calculating the range of historical data, and uses the stability as the credibility of evaluating the change range of power data at each moment, thereby ensuring the accuracy of the degree of change.

[0011] Preferably, any type of power data at the current moment is corrected to satisfy the following relationship: ; In the formula, It is the correction value of any kind of power data at the current moment; , are the values ​​of the power data at the current moment and the previous moment respectively; is the noise level at the current moment.

[0012] Preferably, any type of power data at the current moment is corrected to satisfy the following relationship: ; In the formula, It is the correction value of any kind of power data at the current moment; , are the values ​​of the power data at the current moment and the previous moment respectively; is the noise level at the current moment.

[0013] The present invention can adjust the data correction direction according to the noise level of the power data. When the noise level at a certain moment is high, the power data at that moment can be corrected to the value of the previous moment; conversely, when the noise level is low, the power data at that moment will be kept near its original value.

[0014] Preferably, before calculating the average proportion, the method further comprises: Obtain the fundamental frequency and harmonic frequency in any frequency domain signal, and count the fundamental component frequency and harmonic component frequency in the frequency domain signal; The cumulative sum of the fundamental component frequency and the harmonic component frequency is calculated, and the ratio of the fundamental component frequency to the cumulative sum is used as the proportion of the harmonic component in the frequency domain signal.

[0015] Preferably, monitoring the power quality according to the average proportion of the harmonic components in the two obtained frequency domain signals includes: The opposite number of the average proportion is used as the power of the natural exponential function, and the power quality of the electric energy meter is obtained by performing power operation; When the power quality is less than the preset quality threshold, the power quality of the power meter is judged to be unqualified and an alarm is issued. When the power quality is greater than or equal to the quality threshold, the power quality of the power meter is judged to be qualified.

[0016] The present invention utilizes the characteristic that the more harmonic components there are in the frequency domain signal, the greater the interference to the frequency domain signal will be, and can accurately evaluate the quality of the voltage and current within the sampling period.

[0017] Preferably, the method for obtaining the correlation between the change degree series of the two kinds of power data in the reference period and the sampling period includes: The Pearson correlation coefficient of the change degree series of the two power data in the reference period and the sampling period is calculated to obtain the correlation of the change degree series of the two power data in the reference period and the sampling period.

[0018] The present invention has the following effects: 1. The present invention corrects the voltage and current according to the noise level to avoid misjudging the normal signal frequency domain as distortion due to the influence of noise, thereby ensuring the accuracy of the proportion of harmonic components in the frequency domain signal, thereby being able to accurately evaluate the power quality and improve the accuracy of power quality monitoring.

[0019] 2. When calculating the noise level at each moment in the sampling period, the present invention combines various data to ensure the accuracy of the noise level.

[0020] 3. The present invention can adjust the data correction direction according to the noise level of the power data, thereby ensuring the accuracy of the voltage and current correction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 The present invention is a schematic diagram of a step flow chart of a method for monitoring power quality based on a smart power meter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Reference Figure 1 , a method for monitoring power quality based on a smart power meter, comprising steps S1 to S5, specifically as follows: S1: Acquire two kinds of power data during the working process of the electric energy meter, the two kinds of power data are voltage data and current data respectively.

[0025] Specifically, the readings of the current sensor and the voltage sensor built into the smart energy meter can be read at a certain frequency, such as once per second, to detect the voltage and current of the power line and obtain two kinds of power data during the operation of the energy meter. This embodiment does not specifically limit the frequency of reading data.

[0026] S2: Take the period other than the current moment in the sampling period as the reference period, and calculate the degree of change of any type of power data at the current moment. The degree of change is positively correlated with the difference between the power data at the current moment and the previous moment, and negatively correlated with the extreme value of the power data in the reference period.

[0027] It should be noted that in the normal use of electricity, the load changes of power equipment are usually slow and stable to ensure the stability of the power system. That is, under normal operating conditions, the changes in voltage and current recorded by the electric energy meter at adjacent moments are relatively stable. If the difference in voltage or current at adjacent moments is large, it indicates that an abnormal situation may have occurred in the power system, such as load overload or short circuit, resulting in data deviation. Therefore, the present invention uses this feature to calculate the degree of change of voltage and current, and can evaluate whether the voltage and current have fluctuated greatly in a short period of time, thereby effectively capturing the instantaneous changes of the power system and identifying potential abnormalities.

[0028] In an exemplary embodiment of the present invention, the difference of any type of power data at adjacent moments is a comprehensive difference, and the comprehensive difference can be determined by the following steps: Specifically, the comprehensive difference of any type of power data at adjacent moments satisfies the following relationship: ; In the formula, It is the comprehensive difference between the current moment and the previous moment of the power data; , are the values ​​of the power data at the current moment and the previous moment respectively; is the average value of the power data in the reference period; is the absolute value symbol.

[0029] in, Characterizes the change in the value of any type of power data at the current moment compared to the value at historical moments.

[0030] In an exemplary embodiment of the present invention, the determination of the degree of change of any type of power data can be achieved by the following steps: Calculate the maximum value of any kind of power data in the reference period, and the difference between the minimum value of the power data in the reference period, and obtain the range of the power data in the reference period; the degree of change of any kind of power data at the current moment satisfies the following relationship: ; In the formula, is the degree of change of any type of power data at the current moment; is the extreme value of the power data in the reference period; It is the comprehensive difference between the current moment and the previous moment of the power data; The natural constant An exponential function with base .

[0031] in, The smaller the value, the more stable the change of the power data in the reference period. When it is larger, it means that the value of this type of power data at the current moment has a larger change range than the value at the historical moment, and the degree of change of this type of power data at the current moment is relatively large.

[0032] Further, step S2 is repeated to obtain the degree of change of the two types of power data at each moment in the sampling period.

[0033] S3: Calculate the noise level at the current moment.

[0034] It should be noted that in normal power consumption, there is usually a positive correlation between voltage and current; however, when the load is too high or a short circuit is caused by external factors, the resistance in the circuit will drop sharply, causing the current to increase rapidly and the voltage near the short circuit point to drop significantly. At this time, there is a negative correlation between voltage and current. Therefore, whether in normal operation or abnormal fault conditions, there is usually a strong correlation between the various electrical parameters of electrical equipment.

[0035] It should be further explained that when the smart energy meter monitors the voltage and current of the power line, it is often affected by electromagnetic interference and environmental factors, thereby introducing noise. These noises make it difficult to accurately determine whether the sudden changes in voltage and current are caused by fluctuations caused by noise or by real abnormal conditions. The presence of noise will significantly weaken or even eliminate the normal correlation between voltage and current. Therefore, the present invention uses this feature to effectively analyze and distinguish the impact of noise and real abnormalities, and measure the impact of noise at each moment in the sampling period.

[0036] Specifically, the noise level at the current moment satisfies the following relationship: ; In the formula, is the noise level at the current moment; , are the correlations of the change degree series of the two types of power data in the reference period and the sampling period, is the standard deviation of the difference between the two power data changes during the reference period, is the absolute value symbol; is the hyperbolic tangent function.

[0037] in, It reflects the influence of the change degree of the two power data at the current moment on the change degree sequence of the two power data in the sampling period; when the value is greater than zero, it means that the two power data at the current moment reduce or even eliminate the correlation of the change degree sequence of the two power data in the reference period, and the two power data at the current moment are more likely to be noise. On the contrary, when the value is less than or equal to zero, it means that the two power data at the current moment increase or do not change the correlation of the change degree sequence of the two power data in the reference period, and the two power data at the current moment are less likely to be noise.

[0038] The smaller it is, the more stable the change range of the two power data in the reference period is, and the higher the credibility of the correlation between the change series of the two power data in the reference period is. The higher the credibility.

[0039] Optional, due to There are negative results, therefore, the present invention uses Normalization function limits the value to the range of 0-1.

[0040] In an exemplary embodiment of the present invention, the correlation between the change degree sequences of the two power data in the reference period and the sampling period can be determined by the following steps: The Pearson correlation coefficient of the change degree series of the two power data in the reference period and the sampling period is calculated to obtain the correlation of the change degree series of the two power data in the reference period and the sampling period.

[0041] It should be noted that the calculation of the Pearson correlation coefficient between data sequences is a prior art and will not be described in detail in this embodiment.

[0042] In another embodiment, the correlation between the change degree series of the two power data in the reference period and the sampling period can also be determined by other methods, such as using the Spearman rank correlation coefficient or Kendall rank correlation coefficient of the change degree series of the two power data in the reference period and the sampling period to determine the correlation.

[0043] Further, step S3 is repeated to obtain the noise level at each moment in the sampling period.

[0044] S4: Correct any type of power data at the current moment, the correction value is the weighted sum of the power data at the current moment and the previous moment, and the weight at the current moment is negatively correlated with the noise level.

[0045] Specifically, the correction value of any type of power data at the current moment satisfies the following relationship: ; In the formula, It is the correction value of any kind of power data at the current moment; , are the values ​​of the power data at the current moment and the previous moment respectively; is the noise level at the current moment. It should be noted that the sum of the weight at the current moment and the weight at the previous moment is 1.

[0046] in, is the weight at the current moment, when When it is larger, it means that the value of any kind of power data at the current moment is greatly affected by noise, and it is necessary to correct the value of the power data at the current moment to be closer to the value of the power data at the previous moment.

[0047] Optional, when When it is smaller, it means that the value of any kind of power data at the current moment is less affected by noise, then it is necessary to correct the value of the power data at the current moment in the direction close to the value of the power data at the current moment, so as to ensure the accuracy of the correction value.

[0048] In another embodiment, the correction value of any type of power data at the current moment also satisfies the following relationship: ; In the formula, It is the correction value of any kind of power data at the current moment; , are the values ​​of the power data at the current moment and the previous moment respectively; is the noise level at the current moment.

[0049] Further, step S4 is repeated to obtain the correction values ​​of the two types of power data at each moment in the sampling period.

[0050] S5: Perform Fourier transform on the two corrected power data within the sampling period, and monitor the power quality according to the average proportion of the harmonic components in the two obtained frequency domain signals.

[0051] It should be noted that Fourier Transform (FT) is a mathematical transformation used to convert signals from the time domain (time or space) to the frequency domain (frequency). Through Fourier transform, a complex signal can be decomposed into the sum of different frequency components, thereby analyzing the frequency characteristics of the signal. However, due to the influence of electromagnetic interference and environmental factors, noise may exist in the monitored voltage and current. When the waveform distortion rate of the frequency domain signal is used to judge abnormal conditions, the distortion caused by noise may be misjudged as an abnormal condition, thereby affecting the monitoring effect of the power quality. For this reason, the present invention corrects the voltage and current before applying the Fourier transform to reduce noise interference and ensure the accuracy of the frequency domain conversion results, so as to accurately monitor the power quality of the smart meter.

[0052] It should be further explained that in the actual power consumption process, there may be a variety of nonlinear loads, which will cause the frequency domain signal of voltage or current to be distorted, and produce other frequency components (i.e. harmonic components) besides the fundamental frequency. The presence of harmonics will not only reduce equipment efficiency and increase energy loss, but may also interfere with the communication system, thus causing serious impact on power quality. Therefore, the power quality of smart power meters can be measured by the proportion of harmonic components in the frequency domain signal.

[0053] Optionally, the two corrected power data in the sampling period are converted into frequency domain signals using discrete Fourier transform, or fast Fourier transform. This embodiment does not specifically limit the type of Fourier transform selected.

[0054] In an exemplary embodiment of the present invention, before calculating the average proportion of the harmonic components in the two frequency domain signals, the proportion of the harmonic components in any one of the frequency domain signals may be determined by the following steps: Obtain the fundamental frequency and harmonic frequency in any frequency domain signal, count the fundamental component frequency and the harmonic component frequency in the frequency domain signal; calculate the cumulative sum of the fundamental component frequency and the harmonic component frequency, and take the ratio of the fundamental component frequency to the cumulative sum as the proportion of the harmonic component in the frequency domain signal.

[0055] Among them, the fundamental frequency refers to the main frequency component of the frequency domain signal, usually the lowest frequency component of the frequency domain signal; the harmonic frequency refers to the other frequency components in the frequency domain signal except the fundamental frequency, and the harmonic frequency is an integer multiple of the fundamental frequency.

[0056] Specifically, the proportion of harmonic components in any frequency domain signal satisfies the following relationship: ; In the formula, is the proportion of harmonic components in any frequency domain signal; is the frequency of the fundamental component in the frequency domain signal; It should be noted that when the proportion of harmonic components in any frequency domain signal is large, it means that there are many harmonic components in the frequency domain signal, the distortion degree of the frequency domain signal is large, and the corresponding power quality is poor.

[0057] In an exemplary embodiment of the present invention, the monitoring of power quality can be achieved by the following steps: Step 1: Take the inverse of the average proportion as the power of the natural exponential function, and perform power operation to obtain the power quality of the electric energy meter; The natural exponential function refers to the natural constant An exponential function with base . Specifically, the power quality of the power meter satisfies the following relationship: ; In the formula, The power quality of the energy meter; is the proportion of harmonic components in the first frequency domain signal; is the proportion of harmonic components in the second frequency domain signal; The natural constant An exponential function with base .

[0058] Step 2: When the power quality is less than the preset quality threshold, the power quality of the power meter is determined to be unqualified and an alarm is issued. When the power quality is greater than or equal to the quality threshold, the power quality of the power meter is determined to be qualified.

[0059] Optionally, the quality threshold can be set to 0.7. When the power quality is greater than or equal to 0.7, it indicates that the power quality of the power meter is qualified; on the contrary, when the power quality is less than 0.7, the power quality of the power meter is determined to be unqualified, and an alarm is issued, so that the operator can take appropriate measures to improve the power quality of the power meter in a timely manner, thereby realizing the monitoring of the power quality of the power meter. Of course, the quality threshold can also be set according to the specific situation, and the setting of the quality threshold is not particularly limited in this embodiment.

[0060] In another embodiment, the power quality may be monitored directly by comparing the average proportion of the harmonic components in the two frequency domain signals with a preset risk threshold.

[0061] Optionally, the risk threshold can be set to 0.6. When the average proportion of the harmonic components in the two frequency domain signals is greater than or equal to 0.6, the power quality of the electric energy meter is determined to be unqualified and an alarm is issued; when the average proportion of the harmonic components in the two frequency domain signals is less than 0.6, the power quality of the electric energy meter is determined to be qualified. Of course, a suitable risk threshold can also be set according to specific circumstances, and this embodiment does not specifically limit the risk threshold.

[0062] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0063] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring power quality based on a smart power meter, characterized in that: include: Acquire two kinds of power data during the operation of the electric energy meter, the two kinds of power data being voltage data and current data; The period other than the current moment in the sampling period is taken as the reference period, and the degree of change of any type of power data at the current moment is calculated, and the degree of change is positively correlated with the difference between the power data at the current moment and the previous moment, and negatively correlated with the extreme value of the power data in the reference period; Calculate the noise level at the current moment , , are the correlations of the change degree series of the two types of power data in the reference period and the sampling period, is the standard deviation of the difference between the two power data changes during the reference period, is the absolute value symbol, is the hyperbolic tangent function; Correcting any type of power data at the current moment, wherein the correction value is the weighted sum of the power data at the current moment and the previous moment, and the weight at the current moment is negatively correlated with the noise level; The two corrected power data within the sampling period are subjected to Fourier transform, and the power quality is monitored according to the average proportion of the harmonic components in the two obtained frequency domain signals.

2. The method for monitoring power quality based on a smart power meter according to claim 1, characterized in that: The difference between the power data at the current moment and the previous moment is a comprehensive difference, and a method for obtaining the comprehensive difference includes: Calculate the average value of any type of power data within the reference period, and the difference between the power data at the current moment and the previous moment; The absolute value of the ratio of the difference to the average value is taken as the comprehensive difference of the power data at the current moment and the previous moment.

3. The method for monitoring power quality based on a smart power meter according to claim 2, characterized in that: The method for obtaining the degree of change of any type of power data at the current moment includes: Calculate the difference between the maximum value of any type of power data in the reference period and the minimum value of the power data in the reference period to obtain the range of the power data in the reference period; The degree of change of any type of power data at the current moment satisfies the following relationship: ； In the formula, is the degree of change of any type of power data at the current moment; is the extreme value of the power data in the reference period; It is the comprehensive difference between the current moment and the previous moment of the power data; The natural constant An exponential function with base .

4. The method for monitoring power quality based on a smart power meter according to claim 1, characterized in that: The correction of any type of power data at the current moment satisfies the following relationship: ； In the formula, It is the correction value of any kind of power data at the current moment; , are the values ​​of the power data at the current moment and the previous moment respectively; is the noise level at the current moment.

5. The method for monitoring power quality based on a smart power meter according to claim 1, characterized in that: The correction of any type of power data at the current moment also satisfies the following relationship: ； In the formula, It is the correction value of any kind of power data at the current moment; , are the values ​​of the power data at the current moment and the previous moment respectively; is the noise level at the current moment.

6. The method for monitoring power quality based on a smart power meter according to claim 1, characterized in that: Before calculating the average proportion, the method further includes: Obtain the fundamental frequency and harmonic frequency in any frequency domain signal, and count the fundamental component frequency and harmonic component frequency in the frequency domain signal; The cumulative sum of the fundamental wave component frequency and the harmonic component frequency is calculated, and the ratio of the fundamental wave component frequency to the cumulative sum is used as the proportion of the harmonic component in the frequency domain signal.

7. The method for monitoring power quality based on a smart power meter according to claim 6, characterized in that: The method of monitoring the power quality according to the average proportion of the harmonic components in the two obtained frequency domain signals includes: Taking the inverse of the average proportion as the power of the natural exponential function, performing power operation to obtain the power quality of the electric energy meter; When the power quality is less than a preset quality threshold, the power quality of the power meter is determined to be unqualified and an alarm is issued. When the power quality is greater than or equal to the quality threshold, the power quality of the power meter is determined to be qualified.

8. The method for monitoring power quality based on a smart power meter according to claim 1, characterized in that: The method for obtaining the correlation between the change degree series of the two power data in the reference period and the sampling period includes: The Pearson correlation coefficient of the change degree series of the two kinds of power data in the reference period and the sampling period is calculated to obtain the correlation of the change degree series of the two kinds of power data in the reference period and the sampling period.

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