CN113219272A - Method and system for predicting household appliance aging based on non-invasive monitoring - Google Patents

Method and system for predicting household appliance aging based on non-invasive monitoring Download PDF

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CN113219272A
CN113219272A CN202110367746.6A CN202110367746A CN113219272A CN 113219272 A CN113219272 A CN 113219272A CN 202110367746 A CN202110367746 A CN 202110367746A CN 113219272 A CN113219272 A CN 113219272A
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value
active power
aging
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predicting
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CN113219272B (en
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夏国芳
黄奇峰
田正其
段梅梅
徐晴
赵双双
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State Grid Jiangsu Electric Power Co ltd Marketing Service Center
State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
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State Grid Jiangsu Electric Power Co ltd Marketing Service Center
State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
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Abstract

A method and system for predicting household appliance aging based on non-invasive monitoring. The method comprises the steps of firstly, calculating an aging threshold value and a normal value of an electric appliance for judging the aging degree of the electric appliance by collecting the active power of the electric appliance which is of the same type and used to the average age limit of the electric appliance and the efficiency of the electric appliance which is of the same type when working. And then, collecting the active power of the electric appliance to be monitored, and predicting the average value of the active power of the refrigerator at the future moment by calculating the optimal estimated average value of the average measured value of the active power so as to judge the aging degree of the refrigerator. The invention can realize the on-line evaluation of the aging state of the household refrigerator and predict the future state.

Description

Method and system for predicting household appliance aging based on non-invasive monitoring
Technical Field
The invention belongs to the technical field of household appliances, and particularly relates to a method and a system for predicting household appliance aging based on non-invasive monitoring.
Background
Household appliances inevitably age during use. Aged appliances typically operate at a lower efficiency, causing economic losses to the user. Meanwhile, the aging of the household appliances can increase the probability of fire and electric power events, thereby causing dangerous hidden dangers. It becomes important to evaluate and predict the aging of the home appliance.
The aging of an electrical appliance can affect the electrical parameters of the appliance during operation. With the development of the smart power grid and the appearance of the non-invasive intelligent sensing electric meter, some electric parameters of the electric appliance can be directly given by the smart electric meter without additionally acquiring and processing signals. For an electric appliance, the aging degree of the electric appliance can be evaluated and predicted by analyzing the aging mechanism of the electric appliance, wherein the aging mechanism mainly refers to the aging degree of a compressor, and the compressor usually accounts for 80-100% of the energy consumption of the whole refrigerator.
Taking a household refrigerator as an example, the research and analysis on the aging mechanism of a compressor inside the household refrigerator mainly comprises the following aspects: air suction valve plate of compressor, air suction overheat and mechanical friction. The suction valve plate plays a crucial role in the reciprocating compressor. In the air suction process of the compressor, an air suction valve plate is opened to allow a refrigerant to flow into the cylinder; in the exhaust process of the compressor, the suction valve plate is closed to prevent the refrigerant from flowing into the suction cavity, and the valve seat is impacted at the closing moment. Under the action of cyclic reciprocating stress, the head area of the suction valve plate generates common impact fatigue, so that the performance of the compressor is reduced. Compressor suction superheat has a significant impact on compressor efficiency and some technique is typically employed to reduce suction superheat in compressors. If the heat insulation technology is adopted: the temperature difference of the air suction and exhaust adopts a heat insulation cylinder head structure, so that the heat transfer of the exhaust temperature to the air suction can be effectively reduced, and the air suction is reduced. Or a fixed-point heat dissipation technology is adopted, and the temperature of the cylinder hole of the crankcase and the temperature of the piston are obviously reduced through a fixed-point heat dissipation structure, so that the heated temperature of the low-temperature refrigerant in the cylinder hole is obviously reduced. Along with the increase of the working time of the compressor, the heat insulation and heat dissipation effects are reduced, so that the suction and overheating of the compressor are caused, and the efficiency of the compressor is influenced. The frictional work affects the efficiency of the compressor. In the compression process, the two ends of the piston are influenced by the pressure difference between air suction and air exhaust and the shape of a part, and the gaps between the piston and a cylinder hole are not uniformly distributed on the circumference, so that the radial pressure is unbalanced, and the radial force is generated. As the compressor operating time increases, boundary friction and dry friction are generated, resulting in increased power loss and a consequent reduction in compressor efficiency. The ageing of a domestic refrigerator is thus characterized by the following electrical parameters: with the aging degree of the household refrigerator, the working efficiency of the refrigerator is reduced, and the active power of the refrigerator during working is gradually increased.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a method and a system for predicting the aging of a household appliance based on non-invasive monitoring.
A method for predicting household appliance aging based on non-intrusive monitoring, the method for predicting household appliance aging comprising the steps of:
step 1, collecting the active power of t electrical appliances which have the same type and are used to the average age of the t electrical appliances when working and the active efficiency of n brand-new electrical appliances which have the same type as the t electrical appliances when working;
step 2, calculating the average value of the active efficiency of the t electric appliances in the step 1 as the aging threshold value P of the electric appliancest(ii) a Calculating the average value of the active efficiency of the n electric appliances in the step 1 as the normal value P of the electric appliancesn；
Step 3, setting a time interval, and collecting the actual active power { z of the electrical appliance to be detected for L days1,z2…zLCombine with P obtained in step 2nAnd PtCalculating the active power of the current day to judge the aging degree delta of the electrical applianceLE.g. deltaLE [0, 1)), then enter step 4, otherwise output "completely aged";
and 4, step 4: dividing the active power collected in the step 3 by taking the time interval set in the step 3 as a unit to calculate phi average values to obtain { Z1,Z2…Zφ}；
And 5: the average measured value Z of the active power in the step 4 is measured1Taking the average measured value of the active power at the previous moment as a reference value, and calculating the optimal estimated average value P of the average measured value of the active power at the current moment2|2(ii) a Then, the time is regarded as the last time, and the next time is regarded as the time, i.e. based on Z2And P2|2Is calculated to obtain P3|3Thus can be based on Zk-1And Pk-1|k-1Calculate all P in turnk|k，k＝1,2,…φ；
Step 6: taking phi as the last moment, and calculating P in step 5φ|φAs the optimal estimation value of the active power at the previous moment, the new moment in the future is taken as the moment, the step 5 is repeated to carry out the optimal estimation calculation, namely prediction, on the average value of the active power in m time intervals in the future, and the obtained P is obtainednew|newThen the predicted value of the future new time is new ═ phi +1, phi +2, … phi + m;
and 7: p obtained according to step 2nAnd PtCalculating P in step 6new|newCorresponding degree of ageing deltanew；
And 8: delta as obtained in step 7new∈[0,1]Returning to the step 3, repeating the contents from the step 3 to the step 7, namely continuously acquiring active power data of a new time interval and predicting active power at a future moment; otherwise, outputting the time new at the moment, namely the predicted earliest time of the complete aging of the electrical appliance.
In step 1 and step 3, the device used for collecting the effective power of the refrigerator is a non-invasive intelligent sensing electric meter, and the load decomposition is realized by obtaining the electric power information signal of the refrigerator.
Active power is the average power level of high levels in the appliance operating waveform.
In step 3, the value of the time interval is at least 30 days; the value range of L in the first round of prediction is [30, ∞ ]; the time interval is measured in each round of prediction later;
the frequency of acquisition being once a day, i.e. zLActive power collected on day L.
In step 3, the actual active power z of the refrigeratorLFrom the normal value PnDifference value of (D) and electric appliance aging threshold value PtAnd a normal value PnThe ratio of the difference is deltaLI.e. by
Figure BDA0003008120540000031
δL∈[0,∞)，δL> 1 indicates complete aging.
In the step 4, the process is carried out,
Figure BDA0003008120540000032
the positive integer after the remainder is removed.
In step 5, the average measured value Z of the collected active powerkThe method can be expressed by a theoretical value of active power, namely, the following conditions are met:
Figure BDA0003008120540000033
wherein xkThe system state at the moment k is the theoretical active power value at the moment k; x is the number ofk-1The system state at the moment k-1 is the theoretical active power value at the moment k-1; a is system parameter, A is 1+ a, a is more than 0 and less than 0.001; h is a system measurement parameter, here 1; w is akAnd vkRepresenting process and measurement noise, respectively, Q and R being w, respectivelykAnd vkThe covariance of (a); measurement noise v herekThrough ZkAnd HxkAnd (4) obtaining.
Step 5 comprises the following steps:
step 501: and using the optimal estimated value of the active power at the previous moment to perform prior state estimation on the active power at the current moment through the following formula:
Pk|k-1＝APk-1|k-1
wherein, Pk-1|k-1Is the optimal estimation value of the average value of the active power at the last moment.
Step 502: the covariance of the prior state estimate is found by the following formula:
Gk|k-1＝AGk-1|k-1AT+Q
Gk|k-1is Pk|k-1Corresponding covariance, Gk-1|k-1Is Pk-1|k-1The corresponding covariance.
Step 503: from the covariance of step 502, the gain value K is found by the following formulagThe gain value is an optimal proportion of the fusion active power measured value to the estimated value:
Figure BDA0003008120540000041
step 504: updating the posterior estimated covariance of the time by the following formula:
Gk|k＝(I-KgH)Gk|k-1
where I is a matrix with elements all 1. When the system enters the time k +1, Gk|kIs G in step 502k-1|k-1。
Step 505: and finally, correcting the prior estimation of the residual error between the actual measured value of the active power and the optimally estimated active power and the gain value by using the following formula, and solving the posterior estimation of the active power at this time, namely the optimal estimated value of the active power at the moment k:
Pk|k＝Pk|k-1+Kg(Zk-HPk|k-1)
step 506: repeating steps 501 to 505 to obtainOptimum active power estimated value P when k is phiφ|φ；
In step 6, when
Figure BDA0003008120540000042
When m is 1;
when in use
Figure BDA0003008120540000043
When m is 2;
when in use
Figure BDA0003008120540000044
When m is 3;
when in use
Figure BDA0003008120540000045
When m is 4;
when in use
Figure BDA0003008120540000046
When m is 5;
when in use
Figure BDA0003008120540000051
When m is 6;
m is the largest and is equal to 6,
Figure BDA0003008120540000052
positive integer + previous round after remainder removal
Figure BDA0003008120540000054
Value, for the first prediction round
Figure BDA0003008120540000053
The value of the previous round is 0.
In step 6, since there is no corresponding average value of the collected active power at a future time, the optimal estimation value at the previous time is used as the measurement value at the current time.
The invention also discloses a system for monitoring and predicting the aging of the household appliances based on the method for monitoring and predicting the aging of the household appliances in a non-invasive way, which comprises a data acquisition module, an appliance aging degree judgment module, an optimal estimation average value calculation module and an appliance aging prediction module, and is characterized in that:
the data acquisition module acquires the power information signal of the refrigerator by using a non-invasive intelligent sensing ammeter and realizes load decomposition on the power information signal;
the electric appliance aging degree judging module judges whether the electric appliance is aged or not according to the active power data acquired by the data acquisition module; judging whether the electric appliance is aged at the predicted moment according to the active power data predicted by the electric appliance aging prediction module;
the optimal estimated average value calculation module calculates the optimal estimated average value of each average value according to the acquired active power average value;
and the electrical appliance aging prediction module predicts the aging degree of the electrical appliance at the future time according to the most recent optimal estimation average value calculated by the optimal estimation average value calculation module and inputs the result to the electrical appliance aging degree judgment module for judgment.
The method has the advantages that compared with the prior art, the method is a non-invasive real-time online refrigerator aging monitoring method, effective characteristic data are obtained by the intelligent ammeter based on a non-invasive household appliance load intelligent sensing technology, the aging degree of the refrigerator can be monitored in real time, and refrigerator aging monitoring and prediction accuracy is improved.
Drawings
FIG. 1 is a flow chart of a method for online evaluation and prediction of the aging state of a household appliance according to the present invention;
FIG. 2 is a waveform of the operation of the refrigerator in winter for one day;
FIG. 3 is a waveform of the operation of the refrigerator in a day of summer;
fig. 4 is a basic schematic diagram of the intelligent sensing electric meter for detecting the state of the electric appliance.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Step 1, collecting the active power of t electrical appliances which have the same type and are used to the average age of the t electrical appliances when working and the active efficiency of n brand-new electrical appliances which have the same type as the t electrical appliances when working.
In this embodiment, the appliance is a refrigerator.
In step 1, the non-invasive intelligent sensing electric meter is used for collecting active power, and the process of detecting the state of the electric appliance by the non-invasive intelligent sensing electric meter is shown in fig. 4. It obtains the signals of voltage, current and the like of the total load by measuring and carrying power information, and the information contains information of different characteristic load components. By extracting the characteristic information of the electrical quantities, the system can realize load decomposition and obtain the type and the operation condition of a single load from a load cluster.
And detecting the average power of the refrigerator at a high level in the working waveform when the active power of the refrigerator is detected by the non-invasive intelligent sensing electric meter. As can be seen from the operating power waveform of the refrigerator in one day shown in fig. 2, the refrigerator is not in an operating state at all times, but is in a switch mode. The waveform resembles a PWM signal. Comparing fig. 2 and fig. 3, the only difference is that the external temperature is different, and it can be known that the power of the two devices is similar when they are operated because the aging degree is close, and the operation time of one day is different, i.e. the total high level time is different. Therefore, the energy consumption of the refrigerator in summer and winter is different, which is consistent with the life experience. Namely, the total width of the operating waveform of the refrigerator is affected by the external temperature, and the operating height, namely, the active power is affected by the aging degree. Although the refrigerator aging increases the power consumption, the external temperature also affects the power consumption, so that the degree of aging cannot be simply estimated by the power consumption, but active power is used. Since the real power is only related to the degree of ageing.
Step 2, calculating the average value of the active efficiency of the t electric appliances in the step 1 as the aging threshold value P of the electric appliancest(ii) a Calculating the average value of the active efficiency of the n electric appliances in the step 1 as the normal value P of the electric appliancesn；
Step 3, setting a time interval, and collecting the actual active power { z of the electrical appliance to be detected for L days1,z2…zLCombine with P obtained in step 2nAnd PtCalculating the active power of the current day to judge the aging degree delta of the electrical applianceLE.g. deltaLE [0, 1)), then enter step 4, otherwise output "completely aged";
and 3, collecting active power by using the non-invasive intelligent sensing electric meter which is the same as that in the step 1, wherein the working principle of the non-invasive intelligent sensing electric meter is the same as that of the intelligent sensing electric meter in the step 1.
The time interval takes at least 30 days.
In this embodiment, the acquisition frequency is once a day, then zLRepresenting active power harvested on day L.
The value range of L in the first round of prediction is [30, ∞ ]; each subsequent prediction round is the size of the time interval, which in this embodiment is 30.
Defining the actual active power z of the refrigeratorLFrom the normal value PnDifference value of (D) and electric appliance aging threshold value PtAnd a normal value PnThe ratio of the difference is deltaL. Namely, it is
Figure BDA0003008120540000071
δLE [0, ∞). Passing through deltaLTo evaluate the ageing degree of the appliance, deltaL> 1 indicates complete aging.
And 4, step 4: dividing the active power collected in the step 3 by taking the time interval set in the step 3 as a unit to calculate phi average values to obtain { Z1,Z2…Zφ}；
Figure BDA0003008120540000072
The positive integer after the remainder is removed.
In this embodiment, the time interval is 30 days, so during the first prediction,
Figure BDA0003008120540000073
the positive integer after the remainder is removed. Then Z1Representing the collected electricity from day 1 to day 30Mean measurement of active power of the machine, Z2Represents the average measurement value of the active power of the electric appliance collected from 31 th to 60 th day, and so on.
And 5: the average measured value Z of the active power in the step 4 is measured1Taking the average measured value of the active power at the previous moment as a reference value, and calculating the optimal estimated average value P of the average measured value of the active power at the current moment2|2(ii) a Then, the time is regarded as the last time, and the next time is regarded as the time, i.e. based on Z2And P2|2Is calculated to obtain P3|3All P's are thus calculated in sequencek|k，k＝1,2,…φ；
In the first round of prediction, for Z1Its optimum estimated mean value P1|1Namely the user is the user himself; the remaining optimum estimated mean value Pk|kThe average measurement value Z of the active power according to the last momentk-1And the best estimated mean value P of the previous momentk-1|k-1The calculation method is as follows:
after modeling the aging process of the electric appliance, respectively adding process noise w to a theoretical equation and an actual measurement equation of the electric appliancekAnd measuring the noise vkThe two noises conform to normal distribution with mean value of 0 and covariance matrix of Q, R respectively, and the collected average measured value Z of active powerkThe method can be expressed by a theoretical value of active power, namely, the following conditions are met:
Figure BDA0003008120540000081
wherein xkIs the system state at time k, i.e. the theoretical active power value at time k, xk-1And the system state at the moment k-1, namely the theoretical active power value at the moment k-1, wherein A is a system parameter, A is 1+ a, and a is more than 0 and less than 0.001. H is the system measurement parameter, here taken as 1. w is akAnd vkRespectively representing theoretical process noise and measurement noise, Q and R being wkAnd vkThe covariance of (a); n represents a normal distribution; measurement noise v herekThrough ZkAnd HxkAnd (4) obtaining.
Step 501: and using the optimal estimated value of the active power at the previous moment to perform prior state estimation on the active power at the current moment through the following formula:
Pk|k-1＝APk-1|k-1
wherein, Pk-1|k-1Is the optimal estimation value of the average value of the active power at the last moment.
Step 502: the covariance of the prior state estimate is found by the following formula:
Gk|k-1＝AGk-1|k-1AT+Q
Gk|k-1is Pk|k-1Corresponding covariance, Gk-1|k-1Is Pk-1|k-1The corresponding covariance.
Step 503: from the covariance of step 502, the gain value K is found by the following formulagThe gain value is an optimal proportion of the fusion active power measured value to the estimated value:
Figure BDA0003008120540000082
step 504: updating the posterior estimated covariance of the time by the following formula:
Gk|k＝(I-KgH)Gk|k-1
where I is a matrix with elements all 1. When the system enters the time k +1, Gk|kIs G in step 502k-1|k-1。
Step 505: and finally, correcting the prior estimation of the residual error between the actual measured value of the active power and the optimally estimated active power and the gain value by using the following formula, and solving the posterior estimation of the active power at this time, namely the optimal estimated value of the active power at the moment k:
Pk|k＝Pk|k-1+Kg(Zk-HPk|k-1)
step 506: repeating steps 501 to 505 to obtain the optimal active power estimated value P when k is equal to phiφ|φ；
Step 6: taking phi as the last moment of time,p calculated in step 5φ|φAs the optimal estimation value of the active power at the previous moment, the new moment in the future is taken as the moment, the step 5 is repeated to carry out the optimal estimation calculation, namely prediction, on the average value of the active power in m time intervals in the future, and the obtained P is obtainednew|newThen new +1, phi +2,. phi + m is the predicted value for the future new time.
When in use
Figure BDA0003008120540000091
When m is 1;
when in use
Figure BDA0003008120540000092
When m is 2;
when in use
Figure BDA0003008120540000093
When m is 3;
when in use
Figure BDA0003008120540000094
When m is 4;
when in use
Figure BDA0003008120540000095
When m is 5;
when in use
Figure BDA0003008120540000096
When m is 6;
m is maximum 6 and represents the mth time interval.
Figure BDA0003008120540000097
Positive integer + previous round after remainder removal
Figure BDA0003008120540000099
Value, for the first prediction round
Figure BDA00030081205400000910
The value of the previous round is 0.
And because the average value of the active power measurement does not correspond to the average value of the active power measurement at the future moment, the optimal estimation value at the previous moment is used as the measurement value at the current moment.
And 7: p obtained according to step 2nAnd PtCalculating P in step 6new|newCorresponding degree of ageing deltanew；
δnewIs calculated by
Figure BDA0003008120540000098
δnew∈[0,∞)，δnew> 1 indicates complete aging.
And 8: delta as obtained in step 7new∈[0,1]Returning to the step 3, repeating the contents from the step 3 to the step 7, namely continuously acquiring active power data of a new time interval and predicting active power at a future moment; otherwise, outputting the time new at the moment, namely the predicted earliest time of the complete aging of the electrical appliance.
When the w-th round of prediction is performed in the step 3, the size of L is the size of the time interval set by the first round of prediction, which is 30 in this embodiment; phi is 1, and the active power measurement average value Z of the round1The optimal estimated average value of (1) is P of the previous round of predictionφ+m|φ+m。
If in the first round of prediction, active power is collected for 300 days, namely L is 300; time interval 30, thus during the first prediction round
Figure BDA0003008120540000101
m is 1, P is predictednew|newIs namely Pφ+1|φ+1＝P11|11Meaning the average value of the active power from day 301 to day 330.
In the second round of prediction, the size of L is equal to the size of the time interval, i.e., L is 30, and data of a new 30 days after the first round of prediction, i.e., data from day 301 to day 330, are collected. At this time
Figure BDA0003008120540000102
Figure BDA0003008120540000103
m is 2, Z of this wheel1P for the last round of prediction11|11P of this round of predictionnew|newIs new Pφ+1|φ+1And Pφ+2|φ+2Mean the average value of the active power from day 331 to day 360 and the average value of the active power from day 361 to day 390. And so on.
The invention also discloses a system for monitoring and predicting the aging of the household appliances based on the method for monitoring and predicting the aging of the household appliances in a non-invasive way, which comprises a data acquisition module, an appliance aging degree judgment module, an optimal estimation average value calculation module and an appliance aging prediction module, and is characterized in that:
the data acquisition module acquires the power information signal of the refrigerator by using a non-invasive intelligent sensing ammeter and realizes load decomposition on the power information signal;
the electric appliance aging degree judging module judges whether the electric appliance is aged or not according to the active power data acquired by the data acquisition module; judging whether the electric appliance is aged at the predicted moment according to the active power data predicted by the electric appliance aging prediction module;
the optimal estimated average value calculation module calculates the optimal estimated average value of each average value according to the acquired active power average value;
and the electrical appliance aging prediction module predicts the aging degree of the electrical appliance at the future time according to the most recent optimal estimation average value calculated by the optimal estimation average value calculation module and inputs the result to the electrical appliance aging degree judgment module for judgment.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (11)

1. A method for predicting household appliance aging based on non-intrusive monitoring, the method for predicting household appliance aging comprising the steps of:
step 1, collecting the active power of t electrical appliances which have the same type and are used to the average age of the t electrical appliances when working and the active efficiency of n brand-new electrical appliances which have the same type as the t electrical appliances when working;
step 2, calculating the average value of the active efficiency of the t electric appliances in the step 1 as the aging threshold value P of the electric appliancest(ii) a Calculating the average value of the active efficiency of the n electric appliances in the step 1 as the normal value P of the electric appliancesn；
Step 3, setting a time interval, and collecting the actual active power { z of the electrical appliance to be detected for L days1,z2…zLCombine with P obtained in step 2nAnd PtCalculating the active power of the current day to judge the aging degree delta of the electrical applianceLE.g. deltaLE [0, 1)), then enter step 4, otherwise output "completely aged";
and 4, step 4: dividing the active power collected in the step 3 by taking the time interval set in the step 3 as a unit to calculate phi average values to obtain { Z1,Z2…Zφ}；
And 5: the average measured value Z of the active power in the step 4 is measured1Taking the average measured value of the active power at the previous moment as a reference value, and calculating the optimal estimated average value P of the average measured value of the active power at the current moment2|2(ii) a Then, the time is regarded as the last time, and the next time is regarded as the time, i.e. based on Z2And P2|2Is calculated to obtain P3|3Thus can be based on Zk-1And Pk-1|k-1Calculate all P in turnk|k，k＝1,2,…φ；
Step 6: taking phi as P calculated in the step 6 at the last momentφ|φAs the optimal estimation value of the active power at the previous moment, taking the future new moment as the moment, and repeating the step 5 to the optimal estimation value algorithm of the active power at the momentThe method carries out optimal estimation calculation, namely prediction, on the average value of the active power of m time intervals in the future to obtain Pnew|newThen the predicted value of the future new time is new ═ phi +1, phi +2, … phi + m;
and 7: p obtained according to step 2nAnd PtCalculating P in step 5new|newCorresponding degree of ageing deltanew；
And 8: delta as obtained in step 7new∈[0,1]Returning to the step 3, repeating the contents from the step 3 to the step 7, namely continuously acquiring active power data of a new time interval and predicting active power at a future moment; otherwise, outputting the time new at the moment, namely the predicted earliest time of the complete aging of the electrical appliance.
2. Method for predicting the ageing of a household appliance according to claim 1, characterized in that:
in the steps 1 and 3, the device used for collecting the effective power of the electrical appliance is a non-invasive intelligent sensing electric meter, and the device is used for load decomposition by obtaining the electric power information signal of the electrical appliance.
3. Method of predicting the ageing of a household appliance according to claim 1 or 2, characterized in that:
the active power is the average power of high level in the working waveform of the electric appliance.
4. Method of predicting the ageing of a household appliance according to claim 3, characterized in that:
in the step 3, the value of the time interval is at least 30 days; the value range of L in the first round of prediction is [30, ∞ ]; the time interval is measured in each round of prediction later;
the frequency of acquisition being once a day, i.e. zLActive power collected on day L.
5. Method of predicting the ageing of a household appliance according to claim 4, characterized in that:
at the placeIn the step 3, the actual active power z of the electric applianceLFrom the normal value PnDifference value of (D) and electric appliance aging threshold value PtAnd a normal value PnThe ratio of the difference is deltaLI.e. by
Figure FDA0003008120530000021
δL> 1 indicates complete aging.
6. Method of predicting the ageing of a household appliance according to claim 5, characterized in that:
in the step 4, the process is carried out,
Figure FDA0003008120530000022
the positive integer after the remainder is removed.
7. Method of predicting the ageing of a household appliance according to claim 6, characterized in that:
in the step 5, the average measured value Z of the collected active powerkThe method can be expressed by a theoretical value of active power, namely, the following conditions are met:
Figure FDA0003008120530000023
wherein xkThe system state at the moment k is the theoretical active power value at the moment k; x is the number ofk-1The system state at the moment k-1 is the theoretical active power value at the moment k-1; a is system parameter, A is 1+ a, a is more than 0 and less than 0.001; h is a system measurement parameter, here 1; w is akAnd vkRepresenting process and measurement noise, respectively, Q and R being w, respectivelykAnd vkThe covariance of (a); measurement noise v herekThrough ZkAnd HxkAnd (4) obtaining.
8. Method of predicting the ageing of a household appliance according to claim 7, characterized in that:
the step 5 comprises the following steps:
step 501: and using the optimal estimated value of the active power at the previous moment to perform prior state estimation on the active power at the current moment through the following formula:
Pk|k-1＝APk-1|k-1
wherein, Pk-1|k-1Is the optimal estimation value of the average value of the active power at the last moment.
Step 502: the covariance of the prior state estimate is found by the following formula:
Gk|k-1＝AGk-1|k-1AT+Q
Gk|k-1is Pk|k-1Corresponding covariance, Gk-1|k-1Is Pk-1|k-1The corresponding covariance.
Step 503: from the covariance of step 502, the gain value K is found by the following formulagThe gain value is an optimal proportion of the fusion active power measured value to the estimated value:
Figure FDA0003008120530000031
step 504: updating the posterior estimated covariance of the time by the following formula:
Gk|k＝(I-KgH)Gk|k-1
where I is a matrix with elements all 1. When the system enters the time k +1, Gk|kIs G in step 502k-1|k-1。
Step 505: and finally, correcting the prior estimation of the residual error between the actual measured value of the active power and the optimally estimated active power and the gain value by using the following formula, and solving the posterior estimation of the active power at this time, namely the optimal estimated value of the active power at the moment k:
Pk|k＝Pk|k-1+Kg(Zk-HPk|k-1)
step 506: repeating steps 501 to 505 to obtain the optimal active power estimated value P when k is equal to phiφ|φ；
9. Method of predicting the ageing of a household appliance according to claim 8, characterized in that:
in said step 6, when
Figure FDA0003008120530000032
When m is 1;
when in use
Figure FDA0003008120530000033
When m is 2;
when in use
Figure FDA0003008120530000034
When m is 3;
when in use
Figure FDA0003008120530000035
When m is 4;
when in use
Figure FDA0003008120530000036
When m is 5;
when in use
Figure FDA0003008120530000041
When m is 6;
m is the largest and is equal to 6,
Figure FDA0003008120530000042
positive integer + previous round after remainder removal
Figure FDA0003008120530000043
Value, for the first prediction round
Figure FDA0003008120530000044
The value of the previous round is 0.
10. Method of predicting the ageing of a household appliance according to claim 9, characterized in that:
in step 6, since there is no corresponding average value of the collected active power at a future time, the optimal estimation value at the previous time is used as the measurement value at the current time.
11. A system for monitoring and predicting household appliance aging by using the method for non-intrusive monitoring and predicting household appliance aging according to any one of claims 1 to 10, comprising a data acquisition module, an appliance aging degree determination module, an optimal estimation average calculation module and an appliance aging prediction module, wherein:
the data acquisition module acquires electric power information signals of the electric appliance by using a non-invasive intelligent sensing ammeter and realizes load decomposition on the electric power information signals;
the electric appliance aging degree judging module judges whether the electric appliance is aged or not according to the active power data acquired by the data acquisition module; judging whether the electric appliance is aged at the predicted moment according to the active power data predicted by the electric appliance aging prediction module;
the optimal estimated average value calculation module calculates the optimal estimated average value of each average value according to the acquired active power average value;
and the electrical appliance aging prediction module predicts the aging degree of the electrical appliance at the future time according to the most recent optimal estimation average value calculated by the optimal estimation average value calculation module and inputs the result to the electrical appliance aging degree judgment module for judgment.
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