CN106680637A - Monitoring method of non-invasive household load switching event in real time - Google Patents
Monitoring method of non-invasive household load switching event in real time Download PDFInfo
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- CN106680637A CN106680637A CN201710021034.2A CN201710021034A CN106680637A CN 106680637 A CN106680637 A CN 106680637A CN 201710021034 A CN201710021034 A CN 201710021034A CN 106680637 A CN106680637 A CN 106680637A
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- active power
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
Abstract
Disclosed is a monitoring method of a non-invasive household load switching event in real time. The monitoring method of the non-invasive household load switching event in real time comprises the control of the parameters initialization, the calculation of the active power value, the judgment of the steady-state of the active power and the judgment of the load switching event. The monitoring method of the non-invasive household load switching event in real time, without the intrusion into the load inside, has the technical characteristics of high monitoring precision of a bound of the steady and transient state of the load event, strong fluctuation robustness of the active power, a small amount of calculation, a flexible configuration. The monitoring method of the non-invasive household load switching event in real time can provide a reliable basis for all kinds of the non-invasive load identification technologies and the classification measurement technologies.
Description
Technical field
Present invention relates particularly to a kind of non-intrusion type household loads switching event method of real-time.
Background technology
Energy issue of world is increasingly serious, and energy-saving and emission-reduction become already the main points of various circles of society's concern.It is with domestic consumer
Example, Google researchs show, if domestic consumer can in time understand the detailed power information of house electric apparatus, can just make it monthly
Electricity charge spending declines 5% ~ 15%.The efficient classification and Detection of power consumption load can be optimization power mode, improve consumption habit offer
Important reference frame, on the premise of its orthobiosis is not affected, reduces electric energy consumption spending.
Non-intrusion type load technology of identification (Non-intrusive Load Monitoring, NILM) is in order to installing
Use, data acquisition becomes power consumption load classification detection study hotspot with information analysiss.NILM can be subdivided into data acquisition, data
Filtering, event monitoring, load judges five most of with classification metering, wherein load switching event monitoring be follow-up load judge and
The basis of classification metering.Traditional power difference method is poor to the robustness of power swing, easily causes error hiding；And its other party
Rule there is a problem of low with transient state border monitoring accuracy to stress event stable state.
The content of the invention
It is an object of the invention to provide a kind of based on active power fluctuation index, monitoring accuracy is high, and reliability is high
Non-intrusion type household loads switching event method of real-time.
This non-intrusion type household loads switching event method of real-time that the present invention is provided, comprises the steps：
S1. control parameter initialization；
S2. the voltage sample data and current sampling data of domestic electric system are cached, active power value is calculated；
S3. stable state judgement is carried out to the active power described in step S2：
If it is determined that result is unstable state, then repeat step S2 ~ step S3 is until current state is judged to stable state；
S4. a series of active power values for being obtained according to step S3 carry out the judgement of load switching event.
Control parameter initialization described in step S1, specifically includes setting voltage sample frequency, current sample frequency, single
Active power is counted out, the active average of preamble stable state window, current steady state window have in active power calculating step-length, stable state window
Work(average and power transition detection threshold value.
Calculating active power value described in step S2, specially calculates step value and calculates wattful power according to single active power
Rate, until the interior active power of caching is counted out, active power is counted out in the stable state window for reach setting.
Stable state judgement is carried out to active power described in step S3, is specially judged using following rule：
1）Calculate the standard deviation of active power data in active power data window：
If standard deviation is less than predetermined threshold value, data judging is stable；
2）The active power data in active power data window are judged using following three formula：It is as follows that and if only if
When three formulas all meet, data are just judged to stable state, are otherwise judged to unstable state；
LLavg*AVGj≤AVGi≤ULavg*AVGjAnd i ≠ j, 1≤i, j≤Navg
LLstd*STDj≤STDi≤ULstd *STDjAnd i ≠ j, 0≤i, j≤Nstd
LLvpp*VPPj≤VPPi≤ULvpp*VPPjAnd i ≠ j, 0≤i, j≤Nvpp
Active power data window division number when Navg is detected for average in formula, ULavg be average upper limit of detection value, LLavg
For average Monitoring lower-cut value, AVGxFor the active power average of x-th segment data in active power data window；Nstd is side
Active power data window division number during difference detection, ULstd is variance upper limit of detection value, and LLstd is variance Monitoring lower-cut value,
STDxFor the active power variance of x-th segment data in active power data window；Wattful power when Nvpp is detected for peak-to-peak value
Rate data window division number, ULvpp be peak-to-peak value upper limit of detection value, LLvpp be peak-to-peak value Monitoring lower-cut value, VPPxFor active
The active power peak-to-peak value of segmentation x in power data window.
The judgement for carrying out load switching event described in step S4, is specially judged using following rule：
A. it is defined as follows variable：Pact is preamble stable state window active power average；Cact is current steady state window active power
Average；Δ Pthd is active power transition detection threshold value, and Δ Pthd>0；Δ Pcur is neighbouring stable state window power difference；
B. neighbouring stable state window power difference DELTA Pcur=Pact-Cact is calculated；
If C. Δ Pcur >=Δ Pthd, is judged to effectively put into event；
If Δ Pcur≤- Δ Pthd, is judged to effectively cut off event；
Otherwise, it is invalid detection.
This non-intrusion type household loads switching event method of real-time that the present invention is provided, without the need for invading in load
Portion, high with stress event stable state and transient state border monitoring accuracy, active power fluctuation strong robustness, amount of calculation is little, flexibly may be used
The technical characterstics such as configuration, can provide reliable basis for all kinds of non-intrusion type load technology of identification with classification measurement technology.
Description of the drawings
Fig. 1 is the flow chart of the inventive method.
Specific embodiment
It is illustrated in figure 1 the flow chart of the inventive method：This non-intrusion type household loads switching thing that the present invention is provided
Part method of real-time, comprises the steps：
S1. control parameter initialization, specifically include setting voltage sample frequency, current sample frequency, single effective power meter
Calculate step-length, in stable state window active power count out, the active average of preamble stable state window, the active average of current steady state window and work(
Rate transition detection threshold value；
S2. the voltage sample data and current sampling data of domestic electric system are cached, active power value is calculated；Specially basis
Single power calculation step value calculate active power, until caching in active power count out reach setting stable state window it is active
Power is counted out；
S3. stable state judgement is carried out to the active power described in step S2：
If active power calculating value is unstable state, repeat step S2 ~ step S3 is until active power value is stable state；
Described carries out stable state judgement to active power, is specially judged using following rule：
1）Calculate the standard deviation of active power data in active power data window：
If standard deviation is less than predetermined threshold value, data judging is stable；
2）The active power data in active power data window are judged using following three formula：It is as follows that and if only if
When three formulas all meet, data are just judged to stable state, are otherwise judged to unstable state；
LLavg*AVGj≤AVGi≤ULavg*AVGjAnd i ≠ j, 1≤i, j≤Navg
LLstd*STDj≤STDi≤ULstd *STDjAnd i ≠ j, 0≤i, j≤Nstd
LLvpp*VPPj≤VPPi≤ULvpp*VPPjAnd i ≠ j, 0≤i, j≤Nvpp
Active power data window division number when Navg is detected for average in formula, ULavg be average upper limit of detection value, LLavg
For average Monitoring lower-cut value, AVGxFor the active power average of x-th segment data in active power data window；Nstd is side
Active power data window division number during difference detection, ULstd is variance upper limit of detection value, and LLstd is variance Monitoring lower-cut value,
STDxFor the active power variance of x-th segment data in active power data window；Wattful power when Nvpp is detected for peak-to-peak value
Rate data window division number, ULvpp be peak-to-peak value upper limit of detection value, LLvpp be peak-to-peak value Monitoring lower-cut value, VPPxFor active
The active power peak-to-peak value of segmentation x in power data window；
S4. a series of active power values for being obtained according to step S3 carry out the judgement of load switching event；Specially using as follows
Rule is judged：
A. it is defined as follows variable：Pact is preamble stable state window active power average；Cact is current steady state window active power
Average；Δ Pthd is active power transition detection threshold value, and Δ Pthd>0；Δ Pcur is neighbouring stable state window power difference；
B. neighbouring stable state window power difference DELTA Pcur=Pact-Cact is calculated；
If C. Δ Pcur >=Δ Pthd, is judged to effectively put into event；
If Δ Pcur≤- Δ Pthd, is judged to effectively cut off event；
Otherwise, it is invalid detection.
Claims (5)
1. a kind of non-intrusion type household loads switching event method of real-time, comprises the steps：
S1. control parameter initialization；
S2. the voltage sample data and current sampling data of domestic electric system are cached, active power value is calculated；
S3. stable state judgement is carried out to the active power described in step S2：
If it is determined that result is unstable state, then repeat step S2 ~ step S3 is until current state is judged to stable state；
S4. a series of active power values for being obtained according to step S3 carry out the judgement of load switching event.
2. non-intrusion type household loads switching event method of real-time according to claim 1, it is characterised in that step
Control parameter initialization described in S1, specifically includes setting voltage sample frequency, current sample frequency, single power calculation step
Long, stable state window active power is counted out, the active average of preamble stable state window, the active average of current steady state window and power saltus step
Detection threshold value.
3. non-intrusion type household loads switching event method of real-time according to claim 2, it is characterised in that step
Calculating active power value described in S2, specially calculates active power according to single power calculation step value, until having in caching
Work(power is counted out and reaches the stable state window active power of setting and count out.
4. non-intrusion type household loads switching event method of real-time according to claim 3, it is characterised in that step
Stable state judgement is carried out to active power described in S3, is specially judged using following rule：
1）Calculate the standard deviation of active power data in active power data window：
If standard deviation is less than predetermined threshold value, data judging is stable；
2）The active power data in active power data window are judged using following three formula：It is as follows that and if only if
When three formulas all meet, data are just judged to stable state, are otherwise judged to unstable state；
LLavg*AVGj≤AVGi≤ULavg*AVGjAnd i ≠ j, 1≤i, j≤Navg
LLstd*STDj≤STDi≤ULstd *STDjAnd i ≠ j, 0≤i, j≤Nstd
LLvpp*VPPj≤VPPi≤ULvpp*VPPjAnd i ≠ j, 0≤i, j≤Nvpp
Active power data window division number when Navg is detected for average in formula, ULavg be average upper limit of detection value, LLavg
For average Monitoring lower-cut value, AVGxFor the active power average of x-th segment data in active power data window；Nstd is side
Active power data window division number during difference detection, ULstd is variance upper limit of detection value, and LLstd is variance Monitoring lower-cut value,
STDxFor the active power variance of x-th segment data in active power data window；Wattful power when Nvpp is detected for peak-to-peak value
Rate data window division number, ULvpp be peak-to-peak value upper limit of detection value, LLvpp be peak-to-peak value Monitoring lower-cut value, VPPxFor active
The active power peak-to-peak value of segmentation x in power data window.
5. non-intrusion type household loads switching event method of real-time according to claim 4, it is characterised in that step
The judgement for carrying out load switching event described in S4, is specially judged using following rule：
A. it is defined as follows variable：Pact is preamble stable state window active power average；Cact is current steady state window active power
Average；Δ Pthd is active power transition detection threshold value, and Δ Pthd>0；Δ Pcur is neighbouring stable state window power difference；
B. neighbouring stable state window power difference DELTA Pcur=Pact-Cact is calculated；
If C. Δ Pcur >=Δ Pthd, is judged to effectively put into event；
If Δ Pcur≤- Δ Pthd, is judged to effectively cut off event；
Otherwise, it is invalid detection.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107247201A (en) * | 2017-06-09 | 2017-10-13 | 东南大学 | Electric oven non-intruding discrimination method based on power and time response |
| CN107390043A (en) * | 2017-06-09 | 2017-11-24 | 东南大学 | One kind is based on active power fluctuation and periodic washing machine non-intruding discrimination method |
| CN108594035A (en) * | 2018-03-23 | 2018-09-28 | 中国海洋大学 | A kind of load testing method and system |
| CN109116157A (en) * | 2018-09-19 | 2019-01-01 | 广东卓维网络有限公司 | A kind of non-intrusion type frequency conversion equipment state change event monitoring system and method |
| CN109521296A (en) * | 2018-11-23 | 2019-03-26 | 四川长虹电器股份有限公司 | A kind of non-intrusion type electrical load under steady state condition identifies optimization algorithm |
| CN110261693A (en) * | 2019-04-16 | 2019-09-20 | 南京华盾电力信息安全测评有限公司 | A kind of online thermal test method and system based on stable state sampling technique |
| CN110426554A (en) * | 2019-08-09 | 2019-11-08 | 威胜集团有限公司 | Household loads electric characteristic extracting method, device and computer readable storage medium |
| CN110488112A (en) * | 2019-05-23 | 2019-11-22 | 杭州海兴电力科技股份有限公司 | Classification metering method non-intrusion type load identification and its realized based on recognition result |
| CN110751385A (en) * | 2019-10-08 | 2020-02-04 | 威胜集团有限公司 | Non-invasive load identification method, terminal device and storage medium |
| CN111160798A (en) * | 2019-12-31 | 2020-05-15 | 华南理工大学 | Non-invasive household appliance load identification method based on bee colony algorithm |
| CN111191908A (en) * | 2019-12-25 | 2020-05-22 | 国网江苏省电力有限公司电力科学研究院 | Load event detection method, system and storage medium |
| CN111208375A (en) * | 2020-03-04 | 2020-05-29 | 威胜集团有限公司 | Real-time monitoring method and device for starting of fixed-frequency electric appliance and storage medium |
| CN111665390A (en) * | 2020-06-15 | 2020-09-15 | 威胜集团有限公司 | Non-invasive load detection method, terminal device and readable storage medium |
| CN111830347A (en) * | 2020-07-17 | 2020-10-27 | 四川大学 | Two-stage non-invasive load monitoring method based on event |
| CN113131489A (en) * | 2021-04-08 | 2021-07-16 | 华南理工大学 | Non-invasive load identification method based on improved artificial fish swarm clustering algorithm |
| CN113238092A (en) * | 2021-05-25 | 2021-08-10 | 南京工程学院 | Non-invasive detection method based on machine learning |
| CN113899944A (en) * | 2021-09-30 | 2022-01-07 | 广东电网有限责任公司 | Detection method and device for power load switching point |
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Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107390043A (en) * | 2017-06-09 | 2017-11-24 | 东南大学 | One kind is based on active power fluctuation and periodic washing machine non-intruding discrimination method |
| CN107390043B (en) * | 2017-06-09 | 2019-07-30 | 东南大学 | Based on active power fluctuation and periodic washing machine non-intruding discrimination method |
| CN107247201A (en) * | 2017-06-09 | 2017-10-13 | 东南大学 | Electric oven non-intruding discrimination method based on power and time response |
| CN108594035A (en) * | 2018-03-23 | 2018-09-28 | 中国海洋大学 | A kind of load testing method and system |
| CN109116157A (en) * | 2018-09-19 | 2019-01-01 | 广东卓维网络有限公司 | A kind of non-intrusion type frequency conversion equipment state change event monitoring system and method |
| CN109521296B (en) * | 2018-11-23 | 2020-09-01 | 四川长虹电器股份有限公司 | Non-invasive power load identification optimization algorithm under steady-state working condition |
| CN109521296A (en) * | 2018-11-23 | 2019-03-26 | 四川长虹电器股份有限公司 | A kind of non-intrusion type electrical load under steady state condition identifies optimization algorithm |
| CN110261693A (en) * | 2019-04-16 | 2019-09-20 | 南京华盾电力信息安全测评有限公司 | A kind of online thermal test method and system based on stable state sampling technique |
| CN110488112A (en) * | 2019-05-23 | 2019-11-22 | 杭州海兴电力科技股份有限公司 | Classification metering method non-intrusion type load identification and its realized based on recognition result |
| CN110426554A (en) * | 2019-08-09 | 2019-11-08 | 威胜集团有限公司 | Household loads electric characteristic extracting method, device and computer readable storage medium |
| CN110751385A (en) * | 2019-10-08 | 2020-02-04 | 威胜集团有限公司 | Non-invasive load identification method, terminal device and storage medium |
| CN111191908B (en) * | 2019-12-25 | 2021-09-21 | 国网江苏省电力有限公司电力科学研究院 | Load event detection method, system and storage medium |
| CN111191908A (en) * | 2019-12-25 | 2020-05-22 | 国网江苏省电力有限公司电力科学研究院 | Load event detection method, system and storage medium |
| CN111160798A (en) * | 2019-12-31 | 2020-05-15 | 华南理工大学 | Non-invasive household appliance load identification method based on bee colony algorithm |
| CN111160798B (en) * | 2019-12-31 | 2023-11-21 | 华南理工大学 | Non-invasive household appliance load identification method based on bee colony algorithm |
| CN111208375A (en) * | 2020-03-04 | 2020-05-29 | 威胜集团有限公司 | Real-time monitoring method and device for starting of fixed-frequency electric appliance and storage medium |
| CN111208375B (en) * | 2020-03-04 | 2022-03-18 | 威胜集团有限公司 | Real-time monitoring method and device for starting of fixed-frequency electric appliance and storage medium |
| CN111665390A (en) * | 2020-06-15 | 2020-09-15 | 威胜集团有限公司 | Non-invasive load detection method, terminal device and readable storage medium |
| CN111830347A (en) * | 2020-07-17 | 2020-10-27 | 四川大学 | Two-stage non-invasive load monitoring method based on event |
| CN113131489B (en) * | 2021-04-08 | 2023-04-25 | 华南理工大学 | Non-invasive load identification method based on improved artificial fish swarm clustering algorithm |
| CN113131489A (en) * | 2021-04-08 | 2021-07-16 | 华南理工大学 | Non-invasive load identification method based on improved artificial fish swarm clustering algorithm |
| CN113238092A (en) * | 2021-05-25 | 2021-08-10 | 南京工程学院 | Non-invasive detection method based on machine learning |
| CN113238092B (en) * | 2021-05-25 | 2022-07-01 | 南京工程学院 | Non-invasive detection method based on machine learning |
| CN113899944A (en) * | 2021-09-30 | 2022-01-07 | 广东电网有限责任公司 | Detection method and device for power load switching point |
| CN113899944B (en) * | 2021-09-30 | 2023-11-10 | 广东电网有限责任公司 | Detection method and device for power load switching point |
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