Non-invasive load sensing intelligent ammeter fault judgment method and system
阅读说明：本技术 一种非侵入式负荷感知智能电表故障判断方法及系统 (Non-invasive load sensing intelligent ammeter fault judgment method and system ) 是由 胡文凯 于 2021-07-21 设计创作，主要内容包括：本发明提供一种非侵入式负荷感知智能电表故障判断方法及系统,获取周期T内非侵入式负荷感知智能电表的总负荷功率以及电表测量电量k；对所述总负荷功率进行分解,得到周期T内的功率-时间分布数据；根据历史数据确定基准功率w,并将功率w-(i)除以基准功率w,得到归一化功率时间分布数据,计算归一化后的总能耗将总能耗p与历史能耗p`进行对比,得到能耗变化量r-(p),将电表测量电量k与历史电表测量电量k`对比,得到电量数据变化量r-(k),比较r-(p)与r-(k),如果差异大于阈值则表明电表存在故障。本发明可解决无法高效确认智能电表是否异常的问题。(The invention provides a fault judgment method and a fault judgment system for a non-invasive load sensing intelligent ammeter, which are used for acquiring the total load power of the non-invasive load sensing intelligent ammeter and the measured electric quantity k of the ammeter in a period T; decomposing the total load power to obtain power-time distribution data in a period T; determining a reference power w according to historical data, and determining the power w i Dividing the power by the reference power w to obtain normalized power time distribution data, and calculating the normalized total energy consumption Comparing the total energy consumption p with the historical energy consumption p' to obtain the energy consumption variable r p Comparing the electric quantity k measured by the electric meter with the electric quantity k' measured by the historical electric meter to obtain the electric quantity data variable quantity r k Comparison of r p And r k And if the difference is larger than the threshold value, indicating that the electric meter has a fault. The method and the device can solve the problem that whether the intelligent electric meter is abnormal or not cannot be efficiently confirmed.)
1. A non-intrusive load sensing intelligent ammeter fault judgment method is characterized by comprising the following steps:
step S101, acquiring total load power of the non-intrusive load sensing intelligent ammeter and ammeter measured electric quantity k in a period T;
step S102, decomposing the total load power to obtain the power-time score in the period TCloth data ((w)1，t1)，(w2，t2)……(wnTn)), where wnIs power, tnN is the total number of decomposed powers;
step S103, determining reference power w according to historical data, and determining power wiDividing the power by the reference power w to obtain normalized power time distribution data ((w)1`，t1)，(w2`，t2)……(wn`，tn) Calculate the normalized total energy consumptionWherein i is the ith power;
step S104, comparing the total energy consumption p with the historical energy consumption p' to obtain the energy consumption variable rpComparing the electric quantity k measured by the electric meter with the electric quantity k' measured by the historical electric meter to obtain the electric quantity data variable quantity rkComparison of rpAnd rkAnd if the difference is larger than the threshold value, indicating that the electric meter has a fault.
2. The non-intrusive load sensing smart meter fault determination method of claim 1, wherein: the time for which the used amount of electricity regularly changes periodically is a period T.
3. The non-intrusive load sensing smart meter fault determination method of claim 1, wherein: the decomposing of the total load power is specifically to determine the equipment power according to the rising edge and the falling edge of the total load power curve.
4. The non-intrusive load sensing smart meter fault determination method of claim 1, wherein: and the reference power w is the lowest power obtained by decomposing the total load power and/or the power with the longest duration.
5. The non-intrusive load sensing smart meter fault determination method of claim 1, wherein: the historical energy consumption p 'is the average energy consumption of the previous N days, and the measured electric quantity k' of the historical electric meter is the average electric quantity of the previous N days.
6. A non-intrusive load sensing intelligent ammeter fault judgment system is characterized by comprising the following modules:
the acquisition module is used for acquiring the total load power of the non-invasive load sensing intelligent ammeter and the ammeter measured electric quantity k in the period T;
a decomposition module for decomposing the total load power to obtain power-time distribution data ((w) in the period T1，t1)，(w2，t2)……(wnTn)), where wnIs power, tnN is the total number of decomposed powers;
a calculation module for determining a reference power w according to the historical data and calculating the power wiDividing the power by the reference power w to obtain normalized power time distribution data ((w)1`，t1)，(w2`，t2)……(wn`，tn) Calculate the normalized total energy consumptionWherein i is the ith power;
a fault determining module for comparing the total energy consumption p with the historical energy consumption p' to obtain the energy consumption variation rpComparing the electric quantity k measured by the electric meter with the electric quantity k' measured by the historical electric meter to obtain the electric quantity data variable quantity rkAnd comparing rp with rk, and if the difference is larger than a threshold value, indicating that the electric meter has a fault.
7. The system of claim 6, wherein the system comprises: the time for which the used amount of electricity regularly changes periodically is a period T.
8. The system of claim 6, wherein the system comprises: the decomposing of the total load power is specifically to determine the equipment power according to the rising edge and the falling edge of the total load power curve.
9. The system of claim 6, wherein the system comprises: and the reference power w is the lowest power obtained by decomposing the total load power and/or the power with the longest duration.
10. The system of claim 6, wherein the system comprises: the historical energy consumption p 'is the average energy consumption of the previous N days, and the measured electric quantity k' of the historical electric meter is the average electric quantity of the previous N days.
Technical Field
The invention relates to the field of ammeter fault detection, in particular to a non-intrusive load sensing intelligent ammeter fault judgment method and system.
Background
At present, intelligent electric meters are gradually popularized, but the main structure of the electric meter still consists of a voltage coil, a current coil, a rotating disc, a rotating shaft, a braking magnet, a gear, a counter and the like, and the problem of inaccurate metering of the electric meter can be caused by faults such as aging and damage of devices. Such as reduced internal resistance due to breakdown of a device within the meter, too fast a meter running, aging of the meter device, increased internal resistance, too slow a meter running, or inadvertent alteration of the electromagnetic environment around the meter by a user, resulting in an unprepared meter, etc.
The current solution is that the resident usually finds the household electricity consumption abnormity, the power supply department carries out the on-site detection according to the requirement of the resident, or the power supply department actively carries out the on-site detection when finding the abnormal fluctuation of the electricity consumption. However, these detection means have inaccurate problems, such as the increase of high-power electric appliances leads to the sharp increase of power consumption, the poor owner leads to the sharp decrease of power consumption, and the like, and if these abnormal power consumption are all detected on the spot, the workload of the power supply department is greatly increased.
Disclosure of Invention
In order to solve the problem, the application provides a non-intrusive load sensing intelligent ammeter fault judgment method.
According to one aspect of the invention, the non-intrusive load sensing intelligent ammeter fault judgment method is characterized by comprising the following steps: step S101, acquiring total load power of the non-intrusive load sensing intelligent ammeter and ammeter measured electric quantity k in a period T; step S102, decomposing the total load power to obtain power-time distribution data ((w) in the period T1，t1)，(w2，t2)……(wnTn)), where wnIs power, tnN is the total number of decomposed powers; step S103, determining reference power w according to historical data, and determining power wiDividing the power by the reference power w to obtain normalized power time distribution data ((w)1`，t1)，(w2`，t2)……(wn`，tn) Calculate the normalized total energy consumptionWherein i is the ith power; step S104, comparing the total energy consumption p with the historical energy consumption p' to obtain the energy consumption variable rpComparing the electric quantity k measured by the electric meter with the electric quantity k' measured by the historical electric meter to obtain the electric quantity data variable quantity rkComparison of rpAnd rkIndicating that the difference is greater than the thresholdThe electric meter has a fault.
According to one aspect of the invention, the time at which the electricity usage is regularly changed periodically is the period T.
According to one aspect of the invention, decomposing the total load power is embodied as determining the device power according to the rising and falling edges of the total load power curve.
According to an aspect of the invention, the reference power w is the lowest power and/or the longest power duration obtained by decomposing the total load power.
According to an aspect of the invention, the historical power consumption p 'is the average power consumption of the previous N days, and the historical power meter measures the power k' as the average power of the previous N days.
The invention also provides a non-invasive load sensing intelligent ammeter fault judgment system, which comprises the following modules: the acquisition module is used for acquiring the total load power of the non-invasive load sensing intelligent ammeter and the ammeter measured electric quantity k in the period T; a decomposition module for decomposing the total load power to obtain power-time distribution data ((w) in the period T1，t1)，(w2，t2)……(wnTn)), where wnIs power, tnN is the total number of decomposed powers; a calculation module for determining a reference power w according to the historical data and calculating the power wiDividing the power by the reference power w to obtain normalized power time distribution data ((w)1`，t1)，(w2`，t2)……(wn`，tn) Calculate the normalized total energy consumptionWherein i is the ith power; a fault determining module for comparing the total energy consumption p with the historical energy consumption p' to obtain the energy consumption variation rpComparing the electric quantity k measured by the electric meter with the electric quantity k' measured by the historical electric meter to obtain the electric quantity data variable quantity rkAnd comparing rp with rk, and if the difference is larger than a threshold value, indicating that the electric meter has a fault.
According to one aspect of the invention, the time at which the electricity usage is regularly changed periodically is the period T.
According to one aspect of the invention, decomposing the total load power is embodied as determining the device power according to the rising and falling edges of the total load power curve.
According to an aspect of the invention, the reference power w is the lowest power and/or the longest power duration obtained by decomposing the total load power.
According to an aspect of the invention, the historical power consumption p 'is the average power consumption of the previous N days, and the historical power meter measures the power k' as the average power of the previous N days.
In the technical scheme provided by the invention, according to the principle that the total load power and the electric quantity are in a pure relation, the total load power is normalized, the absolute error in measurement is eliminated, and the measured electric quantity is judged according to the absolute error to determine whether the measured electric quantity is abnormal or not, so that whether the electric meter is abnormal or not is judged, and the efficiency of judging the abnormality of the electric meter is greatly improved.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a total load power curve;
FIG. 3 is an example of power decomposition;
FIG. 4 is a linear relationship between total load power and the measured electrical quantity of the electricity meter;
FIG. 5 is a block diagram of the system of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
In one implementation, the present application provides a non-intrusive load sensing smart meter fault determination method, which includes the following steps as shown in fig. 1.
Step S101, acquiring total load power of the non-intrusive load sensing intelligent electric meter and electric meter measurement electric quantity k in a period T.
The invention is applied to a Non-invasive load perception intelligent ammeter, a Non-invasive load monitoring NILM (Non-invasive load monitoring) is a novel load monitoring means, compared with the traditional invasive load monitoring means, the Non-invasive load monitoring NILM can obtain the independent power utilization condition of each load device of a user by only monitoring the power information at a user bus without adding an additional monitoring device at the device side. The non-intrusive load sensing smart meter can obtain indoor power change through periodic sampling at a user power service line, load data of the non-intrusive load sensing smart meter, namely data of total load power detected by the smart meter in a period T, is a total load power curve of a user within a certain time, as shown in fig. 2, when a power consumption device is turned on, a rising edge is generated on the curve, and when the power consumption device is turned off, a falling edge is generated on the curve.
In this embodiment, the period T may be a fixed time, such as a day, with the start time and the end time from 0 o 'clock to 24 o' clock of each day, or a week with the start time being 0 o 'clock of Monday and the end time being 24 o' clock of the Sunday. The invention is not limited to specific time, the smaller the period T value is, the more sensitive the value is, but the larger the error is, and the skilled in the art can carry out the value taking according to the actual requirement.
In a preferred implementation, the period T may be determined according to historical electricity consumption data of the user, in particular, according to the electricity consumption law of the user, and the time for which the electricity consumption regularly and periodically changes is selected as the period T. If user a sleeps at 22 points per day and the power usage is suddenly decreased after 22 points per day, then the period T may be determined to be 22 points per day to 22 points on the next day. The user B regularly goes to work on a monday-friday basis, the power consumption is very low, more electric appliances are used at home on a saturday, the power consumption is increased sharply, and the period T of the user B can be determined to be one week.
The method is characterized in that the electric meter measures electric quantity k, namely the user electricity consumption number measured by the electric meter in a period T, and the method is the same as the conventional electric meter reading rule in the field, if the electric meter counts for 1000 watts at the beginning of the period T and 1005 watts at the end of the period T, the electric meter measures the electric quantity k for 5 watts in the period T.
Step S102, decomposing the total load power to obtain power-time distribution data ((w) in the period T1，t1)，(w2，t2)……(wn，tn) W) of whichnIs power, tnThe duration of the corresponding power.
As shown in fig. 2, a switch of an electrical appliance forms a rising edge or a falling edge on the total load power curve, and since the power of the electrical appliance is generally fixed, the electrical appliance using the power can be determined by the rising edge and the falling edge in the total load power curve.
For example, the power of the television is 80W, at 10 o 'clock on the television, there is a rising edge of 80W power at 10 o' clock in the total load power curve, at 11 o 'clock off the television, there is a falling edge of 80W at 11 o' clock in the total load power curve, and the service time of the television is 60 minutes. When a rising edge of 80W appears at 19, the rising edge can be judged to be the same electrical appliance as the electrical appliance at 10 am, and when a falling edge of 80W appears at 19.30, the television is turned off, and the service time of the television in the period is 30 minutes. Analyzing the total load power, when a first 80W rising edge occurs, a device which is marked as 80W is turned on, when a 80W falling edge occurs, a device which is marked as 80W is turned off, repeating the steps until the period is ended, recording the total operation time of the power device, and then analyzing the total load powerExample load data decomposition may result in (80, 90), i.e. an 80W appliance runs for 90 minutes today. Similarly, the running time of the n power electrical appliances in the time period T can be obtained by decomposing all the falling edges below the rising edge, and the power-time distribution data ((w) in the period T can be obtained1，t1)，(w2，t2)……(wi，ti)……(wn，tn) W) of whichiIs the power of the ith device, tiThe total time for the i-th device to turn on, and n is the total number of detected devices, as shown in fig. 3, broken down into 4 power devices.
It should be noted that, in the prior art, various powers are further identified, for example, 80W is identified as a television, 1800W is identified as an induction cooker, etc., but the present invention mainly aims to determine the power consumption, and therefore, the present invention does not concern specific electric appliances, and therefore, the computational complexity is very small, and the television in the above example is also only an example for convenience of description.
Step S103, determining reference power w according to historical data, and determining power wiDividing the power by the reference power w to obtain normalized power time distribution data ((w)1`，t1)，(w2`，t2)……(wn`，tn) Calculate the normalized total energy consumption
Generally, the same components are used for the shell load sensing and the electricity metering in the non-intrusive load sensing smart electricity meter, so that the problem of inaccurate reading can also exist, for example, a television with the power of 80W in the last period only recognizes 60W in the present period, and if absolute values are directly used, the fault still cannot be recognized, so that the reference power needs to be determined.
In a preferred embodiment, the reference power may be the power of the appliance with the longest power-on time. If the router is normally powered on for 24 hours, if the power of the router is 4W, the router is broken down to be a straight line of 4W. Further, when the duration is approximate, the reference power with the minimum power is selected. For example, the refrigerator is also in 24-hour standby, the standby power is about 8W, but the smaller the power is, the more stable the power value is, for example, the power of the router is usually not changed greatly, but the larger the electric appliance, such as the refrigerator, etc., although the standby power is stable, the larger the power fluctuation is when the refrigerator compressor works, and the larger the power fluctuation is, the larger the electric appliance is used, the larger the standby power is.
Dividing the power wi by the reference power w to obtain normalized power time distribution data ((w)1`，t1)，(w2`，t2)……(wn`，tn) ) is to eliminate measurement errors. For example, when the power is 4W for the router as the reference power and the power is measured to be 3W for the normal time and the power is 80W for the tv for the normal time, the measured power of the tv at this time may be 60W due to the linear relationship among the resistance, the voltage and the current as shown in fig. 4. If the router is used as the reference power, the normalized power of the router is 4w/4w and is 1 unit in normal time, the normalized power of the router is 3w/3w and is still 1 unit in abnormal time, the normalized power of the television is 80w/4w and is 20 units in normal time, and the normalized power of the television is 60w/3w and is still 20 units in abnormal time, so the problem of inaccurate measurement is solved through normalization.
After normalization, the data can be passedCalculating the total energy consumption p of the user
As shown in the above table, the energy consumption p is 1 × 1440+20 × 90+50 × 60 ═ 6240
Step S104, the total energy consumption p and the historical energy consumption are comparedp' are compared to obtain the energy consumption variable rpComparing the electric quantity k measured by the electric meter with the electric quantity k' measured by the historical electric meter to obtain the electric quantity data variable quantity rkComparison of rpAnd rkAnd if the difference is larger than the threshold value, indicating that the electric meter has a fault.
Since the load power and the electricity meter measured electricity amount are both indicative of the electricity usage amount of the user, there is a linear relationship between the two, as shown in fig. 3.
Device
Device 1
Device 2
Device 3
Device 4
Normalized power
1
20
50
400
Duration of time
1440
90
60
120
When a high-power device is added, although the power consumption is greatly increased, the ratio of the energy consumption P to the power consumption is still in a certain stable range.
Because the energy consumption p eliminates the problem of inaccurate measurement, when the measured value of the electric meter is abnormal, the ratio of the energy consumption p to the measured value of the electric meter is abnormal, and accordingly, the abnormality judgment can be carried out.
After the total energy consumption is obtained, the comparison can be carried out with the historical data, and fault confirmation can be carried out, such as the previous data or historical average data.
Preferably, the historical data may be average data for the first N cycles, average power consumption for the first 5 cycles, and average power consumption. Such as the following data
Period of time
Period 1
Period 2
Period 3
Period 4
Period 5
Energy consumption p
5000
4500
6000
8500
6500
Electric quantity k
50
45
63
89
65
Then the historical power consumption p ═ (5000+4500+6000+8500+6500)/5 ═ 6100
The historical electric meter measures the electric quantity k ═ 50+45+63+89+65)/5 ═ 62.4
At this time, rp＝(p-p`)/p`＝(6240-6100)/6100＝0.023
Assuming that the electric quantity data at this time is 65, rkIf the threshold is set to 0.1, (0.042-0.023) ═ 0.019 < 0.1, (65-62.4)/62.4 ═ 0.042, the electric meter is considered to be normal at this time.
In another example, if the electricity meter is 50, then rkIf the value is equal to (k-k')/k ═ 50-62.4)/62.4 ═ 0.199, then the value is equal to (| (-0.199-0.023) | -0.2 > 0.1, then the electricity meter is considered to be abnormal.
In one implementation, the present application provides a non-intrusive load sensing smart meter fault determination system, which includes the following modules as shown in fig. 5.
And the obtaining module is used for obtaining the total load power of the non-intrusive load sensing intelligent electric meter and the electric meter measurement electric quantity k in the period T.
The invention is applied to a Non-invasive Load perception intelligent ammeter, a Non-invasive Load Monitoring NILM (Non-invasive Load Monitoring) is a novel Load Monitoring means, compared with the traditional invasive Load Monitoring means, the Non-invasive Load Monitoring NILM can obtain the independent power utilization condition of each Load device of a user by only Monitoring the power information at a user bus without adding an additional Monitoring device at the device side. The non-intrusive load sensing smart meter can obtain indoor power change through periodic sampling at a user power service line, load data of the non-intrusive load sensing smart meter, namely data of total load power detected by the smart meter in a period T, is a total load power curve of a user within a certain time, as shown in fig. 2, when a power consumption device is turned on, a rising edge is generated on the curve, and when the power consumption device is turned off, a falling edge is generated on the curve.
In this embodiment, the period T may be a fixed time, such as a day, with the start time and the end time from 0 o 'clock to 24 o' clock of each day, or a week with the start time being 0 o 'clock of Monday and the end time being 24 o' clock of the Sunday. The invention is not limited to specific time, the smaller the period T value is, the more sensitive the value is, but the larger the error is, and the skilled in the art can carry out the value taking according to the actual requirement.
In a preferred implementation, the period T may be determined according to historical electricity consumption data of the user, in particular, according to the electricity consumption law of the user, and the time for which the electricity consumption regularly and periodically changes is selected as the period T. If user a sleeps at 22 points per day and the power usage is suddenly decreased after 22 points per day, then the period T may be determined to be 22 points per day to 22 points on the next day. The user B regularly goes to work on a monday-friday basis, the power consumption is very low, more electric appliances are used at home on a saturday, the power consumption is increased sharply, and the period T of the user B can be determined to be one week.
The method is characterized in that the electric meter measures electric quantity k, namely the user electricity consumption number measured by the electric meter in a period T, and the method is the same as the conventional electric meter reading rule in the field, if the electric meter counts for 1000 watts at the beginning of the period T and 1005 watts at the end of the period T, the electric meter measures the electric quantity k for 5 watts in the period T.
A decomposition module for decomposing the total load power to obtain power-time distribution data ((w) in the period T1，t1)，(w2，t2)……(wn，tn) W) of whichnIs power, tnThe duration of the corresponding power.
As shown in fig. 2, a switch of an electrical appliance forms a rising edge or a falling edge on the total load power curve, and since the power of the electrical appliance is generally fixed, the electrical appliance using the power can be determined by the rising edge and the falling edge in the total load power curve.
For example, the power of the television is 80W, the television is turned on at 10 points, a rising edge of 80W power exists at 10 points in the total load power curve, the television is turned off at 11 points, and the power of the total load power curveAt 11, an 80W falling edge occurs, and the tv has a use time of 60 minutes. When a rising edge of 80W appears at 19, the rising edge can be judged to be the same electrical appliance as the electrical appliance at 10 am, and when a falling edge of 80W appears at 19.30, the television is turned off, and the service time of the television in the period is 30 minutes. Analyzing the total load power, when the first 80W rising edge occurs, the device which is marked as 80W is turned on, when the 80W falling edge occurs, the device which is marked as 80W is turned off, repeating the steps until the period is over, and recording the total operation time of the power device, the load data decomposition of the example can obtain (80, 90), namely that an 80W electric appliance operates for 90 minutes today. Similarly, the running time of the n power electrical appliances in the time period T can be obtained by decomposing all the falling edges below the rising edge, and the power-time distribution data ((w) in the period T can be obtained1，t1)，(w2，t2)……(wi，ti)……(wn，tn) W) of whichiIs the power of the ith device, tiThe total time for the i-th device to turn on, and n is the total number of detected devices, as shown in fig. 3, broken down into 4 power devices.
It should be noted that, in the prior art, various powers are further identified, for example, 80W is identified as a television, 1800W is identified as an induction cooker, etc., but the present invention mainly aims to determine the power consumption, and therefore, the present invention does not concern specific electric appliances, and therefore, the computational complexity is very small, and the television in the above example is also only an example for convenience of description.
A calculation module for determining a reference power w according to the historical data and calculating the power wiDividing the power by the reference power w to obtain normalized power time distribution data ((w)1`，t1)，(w2`，t2)……(wn`，tn) Calculate the normalized total energy consumption
Generally, the same components are used for the shell load sensing and the electricity metering in the non-intrusive load sensing smart electricity meter, so that the problem of inaccurate reading can also exist, for example, a television with the power of 80W in the last period only recognizes 60W in the present period, and if absolute values are directly used, the fault still cannot be recognized, so that the reference power needs to be determined.
In a preferred embodiment, the reference power may be the power of the appliance with the longest power-on time. If the router is normally powered on for 24 hours, if the power of the router is 4W, the router is broken down to be a straight line of 4W. Further, when the duration is approximate, the reference power with the minimum power is selected. For example, the refrigerator is also in 24-hour standby, the standby power is about 8W, but the smaller the power is, the more stable the power value is, for example, the power of the router is usually not changed greatly, but the larger the electric appliance, such as the refrigerator, etc., although the standby power is stable, the larger the power fluctuation is when the refrigerator compressor works, and the larger the power fluctuation is, the larger the electric appliance is used, the larger the standby power is.
The power wi is divided by the reference power w to obtain normalized power time distribution data ((w1 ', t1), (w2 ', t2) … … (wn ', tn)) in order to eliminate measurement errors. For example, when the power is 4W for the router as the reference power and the power is measured to be 3W for the normal time and the power is 80W for the tv for the normal time, the measured power of the tv at this time may be 60W due to the linear relationship among the resistance, the voltage and the current as shown in fig. 4. If the router is used as the reference power, the normalized power of the router is 4w/4w and is 1 unit in normal time, the normalized power of the router is 3w/3w and is still 1 unit in abnormal time, the normalized power of the television is 80w/4w and is 20 units in normal time, and the normalized power of the television is 60w/3w and is still 20 units in abnormal time, so the problem of inaccurate measurement is solved through normalization.
After normalization, the data can be passedCalculating the total energy consumption p of the user
Device
Device 1
Device 2
Device 3
Normalized power
1
20
50
Duration of time
1440
90
60
As shown in the above table, the energy consumption p is 1 × 1440+20 × 90+50 × 60 ═ 6240
And the fault determining module is used for comparing the total energy consumption p with the historical energy consumption p 'to obtain an energy consumption variation rp, comparing the electric quantity k measured by the electric meter with the historical electric quantity k' measured by the electric meter to obtain an electric quantity data variation rk, comparing the rp with the rk, and if the difference is greater than a threshold value, indicating that the electric meter has a fault.
Since the load power and the electricity meter measured electricity amount are both indicative of the electricity usage amount of the user, there is a linear relationship between the two, as shown in fig. 3.
Device
Device 1
Device 2
Device 3
Device 4
Normalized power
1
20
50
400
Duration of time
1440
90
60
120
When a high-power device is added, although the power consumption is greatly increased, the ratio of the energy consumption P to the power consumption is still in a certain stable range.
Because the energy consumption p eliminates the problem of inaccurate measurement, when the measured value of the electric meter is abnormal, the ratio of the energy consumption p to the measured value of the electric meter is abnormal, and accordingly, the abnormality judgment can be carried out.
After the total energy consumption is obtained, the comparison can be carried out with the historical data, and fault confirmation can be carried out, such as the previous data or historical average data.
Preferably, the historical data may be average data for the first N cycles, average power consumption for the first 5 cycles, and average power consumption. Such as the following data
Period of time
Period 1
Period 2
Period 3
Period 4
Period 5
Energy consumption p
5000
4500
6000
8500
6500
Electric quantity k
50
45
63
89
65
Then the historical power consumption p ═ (5000+4500+6000+8500+6500)/5 ═ 6100
The historical electric meter measures the electric quantity k ═ 50+45+63+89+65)/5 ═ 62.4
At this time, rp ═ (p-p')/p ═ 6240-
If the electric quantity data at this time is 65, rk ═ (k-k')/k ═ (65-62.4)/62.4 ═ 0.042, (0.042-0.023) ═ 0.019 < 0.1 if the threshold is set to 0.1, and the electric meter is considered to be normal at this time if the threshold is not exceeded.
In another example, if the electric quantity of the electric meter is 50, rk ═ (k-k')/k ═ (50-62.4)/62.4 ═ 0.199, and | (-0.199-0.023) | (0.2 > 0.1, the electric meter is considered to be abnormal.
In this application, the term "plurality" means two or more unless explicitly defined otherwise. The terms "mounted," "connected," "fixed," and the like are to be construed broadly, and for example, "connected" may be a fixed connection, a removable connection, or an integral connection; "coupled" may be direct or indirect through an intermediary. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In the description herein, the description of the terms "one embodiment," "some embodiments," "specific embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.