CN106655160A - Non-intrusion electric power load decomposition identification decision method and system - Google Patents
Non-intrusion electric power load decomposition identification decision method and system Download PDFInfo
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- CN106655160A CN106655160A CN201610955736.3A CN201610955736A CN106655160A CN 106655160 A CN106655160 A CN 106655160A CN 201610955736 A CN201610955736 A CN 201610955736A CN 106655160 A CN106655160 A CN 106655160A
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- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 16
- 238000012544 monitoring process Methods 0.000 claims abstract description 9
- 230000001052 transient effect Effects 0.000 claims description 15
- 230000005611 electricity Effects 0.000 claims description 7
- 230000010354 integration Effects 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
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- 238000005265 energy consumption Methods 0.000 claims description 3
- 210000002569 neuron Anatomy 0.000 claims description 3
- 230000000452 restraining effect Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 2
- 238000000605 extraction Methods 0.000 abstract description 4
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Classifications
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- H02J3/005—
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The invention discloses a non-intrusion electric power load decomposition identification decision method and system in an electric power load decomposition technology field. The system comprises a load monitoring unit, wherein the load monitoring unit is in electrical output connection with a data acquisition unit, the data acquisition unit is in electrical output connection with a characteristic extraction module, the characteristic extraction module is in electrical output connection with a non-intrusion load identification system, the non-intrusion load identification system is in electrical output connection with an identification decision unit, and the identification decision unit is in electrical output connection with an identification result output. The system is advantaged in that comprehensive decision is carried out through employing multiple algorithms, non-intrusion electric power load decomposition identification precision is improved, the quantity of extra hardware is small, assembling and debugging are convenient, maintenance is easy, manpower and material resources are saved, an original configuration at a load side can be prevented from being damaged, influence of newly-added electric power equipment can be avoided, easy development is realized, daily life of users is not influenced, and the system can be easily accepted by the users.
Description
Technical field
The present invention relates to electric load decomposition technique field, the identification that specially a kind of non-intrusive electrical load decomposes is determined
Plan method and system.
Background technology
Electric load decomposes the device for generally using intrusive mood and non-intrusion type again, and invasive device requires every in subscriber household
Install sensor on one electrical equipment, with the working condition of testing equipment and related power information, its is relatively costly, Er Qiean
Dress trouble, to the use of user great inconvenience is brought.The electric load decomposer low cost of non-intrusion type, it is easy for installation
And be easy to be easily accepted by a user, but its some existing non-intrusive electrical load decomposer generally adopts single method pair
Electric power carries out load decomposition, it is easy to is affected by external factor and reduces its accuracy of identification, so will result in load side
Equipment error in judgement, so as to the electricity consumptions such as the peak regulation paddy after affecting scheduling, not only affect user side electricity consumption experience, it is also unfavorable
In energy-saving.
Improving non-intrusive electrical load decomposition method at present can not meet the demand of real system, for this purpose, we send out
The identification decision-making technique and system for understanding a kind of non-intrusive electrical load decomposition comes into operation, to solve the above problems.
The content of the invention
It is an object of the invention to provide the identification decision-making technique and system of a kind of non-intrusive electrical load decomposition, to solve
The single recognition methods proposed in certainly above-mentioned background technology is vulnerable to the impact of extraneous factor so as to cause its accuracy of identification low
Problem.
For achieving the above object, the present invention provides following technical scheme：The identification that a kind of non-intrusive electrical load decomposes
Decision-making technique, the method specifically includes following steps：
S1：Using the voltage of electrical equipment, current signal, current harmonics feature under electrical equipment normal operating conditions is extracted
And active power feature；
S2：By common apparatus model and energy consumption data, and the concrete model of individual equipment is set up, with reference to extension
Fast algorithm, for the Energy Decomposition of electric load；
S3：At set intervals the incompatible estimation equation group of the belt restraining to being formed by stable state carries out Optimal calculation,
Realize load decomposition；
S4：Electric current, harmonic characteristic and the active power of electric load are analyzed using multilayer neural network, so as to set to electricity consumption
It is standby effectively to be recognized.
Preferably, in step S1, real-time tracking total electricity load active power harmony wave characteristic analyzes electric load
Stable state and transient information, while obtaining detailed power information using the method for many piecewise analysis.
Preferably, in step S2, the fast algorithm of extension is specifically represented by：
P (Ci | X) ＞ P (Cj | X) 1≤J≤m, J ≠ i
Wherein, P (X) is constant to all classes, and maximizing posterior probability P (Ci | X) can be converted into maximization prior probability P
(X|Ci)P(Ci)。
Preferably, in step S3, optimal algorithm according to current harmonics and the superimposed characteristics of active power, and by excellent
Change method is cut, and its object function is：
Wherein, ImkK-th harmonic component of the total current of porch is represented, | |, | | represent L2Norm, ai represents equipment
Switching.
Preferably, in step S4, the specific algorithm of multilayer neural network is：
The output of k-th output layer neuron is embodied as, wherein d is characterized dimension, nHFor the number of hidden nodes, k is normal
It is set to 3.
Present invention also offers the identification decision system that a kind of non-intrusive electrical load decomposes, including load monitoring list
Unit, the load monitoring unit electrically exports connection data acquisition unit, and the data acquisition unit electrically exports connection features
Extraction module, the characteristic extracting module electrically exports connection non-intrusion type load identification system, and the non-intrusion type load is distinguished
Knowledge system electrically exports connection identification decision package, and the identification decision package electrically exports connection identification result output.
Preferably, the non-intrusion type load identification system includes non-intrusion type integration algorithm unit, the non-intrusion type
Integration algorithm unit is electrically bi-directionally connected grader, and the grader is electrically bi-directionally connected respectively steady state characteristic and transient characteristic,
Respectively electrically output connects transient state characteristic, harmonic characteristic and active reactive feature, the transient characteristic difference to the steady state characteristic
Electrically output connects voltage noise, duration harmony wave energy.
Preferably, the transient state characteristic includes instantaneous power, instantaneous admittance and current waveform.
Compared with prior art, the invention has the beneficial effects as follows：The present invention by carrying out integrated decision-making using many algorithms,
The identification precision of non-intrusive electrical load decomposition is improve, its extra hardware installed is few, and assembly and adjustment is convenient, easy care,
Use manpower and material resources sparingly, will not the original framework in failing load side, and do not affected by newly-increased power equipment, it is easy to expand, not shadow
Ring the daily life of user, it is easy to be easily accepted by a user.
Description of the drawings
Fig. 1 is principle of the invention block diagram；
Fig. 2 is non-intrusion type load identification system theory diagram of the present invention.
In figure：1 load monitoring unit, 2 data acquisition units, 3 characteristic extracting modules, 4 non-intrusion type load identification systems,
40 non-intrusion type integration algorithm units, 41 graders, 42 steady state characteristics, 43 transient characteristics, 44 transient state characteristics, 45 harmonic characteristics,
46 active reactive features, 47 voltage noises, 48 duration, 49 harmonic energies, 5 identification decision packages, the output of 6 identification results.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than the embodiment of whole.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Fig. 1-2 is referred to, the present invention provides a kind of technical scheme：The identification decision-making that a kind of non-intrusive electrical load decomposes
Method, the method specifically includes following steps：
S1：Using the voltage of electrical equipment, current signal, current harmonics feature under electrical equipment normal operating conditions is extracted
And active power feature, real-time tracking total electricity load active power harmony wave characteristic, analyze the stable state and temporarily of electric load
State information, while obtaining detailed power information using the method for many piecewise analysis；
S2：By common apparatus model and energy consumption data, and the concrete model of individual equipment is set up, with reference to extension
Fast algorithm, for the Energy Decomposition of electric load, the fast algorithm of extension is specifically represented by：
P (Ci | X) ＞ P (Cj | X) 1≤J≤m, J ≠ i
Wherein, P (X) is constant to all classes, and maximizing posterior probability P (Ci | X) can be converted into maximization prior probability P
(X|Ci)P(Ci)；
S3：At set intervals the incompatible estimation equation group of the belt restraining to being formed by stable state carries out Optimal calculation,
Load decomposition is realized, optimal algorithm is cut according to current harmonics and the superimposed characteristics of active power by optimization method,
Its object function is：
Wherein, ImkK-th harmonic component of the total current of porch is represented, | |, | | represent L2Norm, ai represents equipment
Switching；
S4：Electric current, harmonic characteristic and the active power of electric load are analyzed using multilayer neural network, so as to set to electricity consumption
Standby effectively to be recognized, the specific algorithm of multilayer neural network is：
The output of k-th output layer neuron is embodied as, wherein d is characterized dimension, nHFor the number of hidden nodes, k is normal
It is set to 3.
Present invention also offers the identification decision system that a kind of non-intrusive electrical load decomposes, including load monitoring unit
1, electrically output connects data acquisition unit 2 to the load monitoring unit 1, and electrically output connection is special for the data acquisition unit 2
Extraction module 3 is levied, electrically output connects non-intrusion type load identification system 4 to the characteristic extracting module 3, and the non-intrusion type is born
Electrically output connection recognizes decision package 5 to lotus identification system 4, and electrically output connection identification result is defeated for the identification decision package 5
Go out 6.
Wherein, the non-intrusion type load identification system 4 includes non-intrusion type integration algorithm unit 40, the non-intrusion type
Integration algorithm unit 40 is electrically bi-directionally connected grader 41, and the grader 41 is electrically bi-directionally connected respectively steady state characteristic 42 and temporarily
State feature 43, respectively electrically output connects transient state characteristic 44, harmonic characteristic 45 and active reactive feature 46 to the steady state characteristic 42,
Respectively electrically output connects voltage noise 47, the harmonious wave energy 49 of duration 48, the transient state characteristic to the transient characteristic 43
44 include instantaneous power, instantaneous admittance and current waveform.
Although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
Understanding can carry out various changes, modification, replacement to these embodiments without departing from the principles and spirit of the present invention
And modification, the scope of the present invention be defined by the appended.
Claims (8)
1. the identification decision-making technique that a kind of non-intrusive electrical load decomposes, it is characterised in that：The method specifically includes following step
Suddenly：
S1：Using the voltage of electrical equipment, current signal, extract under electrical equipment normal operating conditions current harmonics feature and
Active power feature；
S2：By common apparatus model and energy consumption data, and the concrete model of individual equipment is set up, with reference to the quick of extension
Algorithm, for the Energy Decomposition of electric load；
S3：At set intervals the incompatible estimation equation group of the belt restraining to being formed by stable state carries out Optimal calculation, realizes
Load decomposition；
S4：Electric current, harmonic characteristic and the active power of electric load are analyzed using multilayer neural network, so as to enter to electrical equipment
Row effectively identification.
2. the identification decision-making technique that a kind of non-intrusive electrical load according to claim 1 decomposes, it is characterised in that：Institute
In stating step S1, real-time tracking total electricity load active power harmony wave characteristic analyzes the stable state and transient information of electric load,
Simultaneously detailed power information is obtained using the method for many piecewise analysis.
3. the identification decision-making technique that a kind of non-intrusive electrical load according to claim 1 decomposes, it is characterised in that：Institute
In stating step S2, the fast algorithm of extension is specifically represented by：
P (Ci | X) ＞ P (Cj | X) 1≤J≤m, J ≠ i
Wherein, P (X) is constant to all classes, maximize posterior probability P (Ci | X) can be converted into maximization prior probability P (X |
Ci)P(Ci)。
4. the identification decision-making technique that a kind of non-intrusive electrical load according to claim 1 decomposes, it is characterised in that：Institute
In stating step S3, optimal algorithm is cut according to current harmonics and the superimposed characteristics of active power by optimization method, its
Object function is：
S.t.ai ∈ 0,1 }
Wherein, ImkK-th harmonic component of the total current of porch is represented, | |, | | represent L2Norm, ai represents the throwing of equipment
Cut.
5. the identification decision-making technique that a kind of non-intrusive electrical load according to claim 1 decomposes, it is characterised in that：Institute
In stating step S4, the specific algorithm of multilayer neural network is：
The output of k-th output layer neuron is embodied as, wherein d is characterized dimension, nHFor the number of hidden nodes, k is set up as 3.
6. the identification decision system that a kind of non-intrusive electrical load decomposes, including load monitoring unit (1), it is characterised in that：
The load monitoring unit (1) electrically output connection data acquisition unit (2), electrically output connects the data acquisition unit (2)
Characteristic extracting module (3) is connect, the characteristic extracting module (3) electrically output connection non-intrusion type load identification system (4) is described
Non-intrusion type load identification system (4) electrically output connection identification decision package (5), identification decision package (5) is electrically defeated
Go out to connect identification result output (6).
7. the identification decision system that a kind of non-intrusive electrical load according to claim 6 decomposes, it is characterised in that：Institute
Non-intrusion type load identification system (4) is stated including non-intrusion type integration algorithm unit (40), the non-intrusion type integration algorithm list
First (40) are electrically bi-directionally connected grader (41), and the grader (41) is electrically bi-directionally connected respectively steady state characteristic (42) and transient state
Feature (43), respectively electrically output connects transient state characteristic (44), harmonic characteristic (45) and active reactive to the steady state characteristic (42)
Feature (46), respectively electrically output connects voltage noise (47), duration (48) harmony wave energy to the transient characteristic (43)
(49)。
8. the identification decision system that a kind of non-intrusive electrical load according to claim 7 decomposes, it is characterised in that：Institute
Transient state characteristic (44) is stated including instantaneous power, instantaneous admittance and current waveform.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108062627A (en) * | 2017-12-16 | 2018-05-22 | 广西电网有限责任公司电力科学研究院 | A kind of demand response analysis method based on non-intrusion type electricity consumption data |
| CN109239494A (en) * | 2018-09-21 | 2019-01-18 | 无锡风繁伟业科技有限公司 | A kind of non-intrusive electrical load alert detecting method and system |
| CN109407544A (en) * | 2017-08-17 | 2019-03-01 | 凌华科技股份有限公司 | System module of simulation machine operation picture of non-invasive data extraction system |
| CN109599952A (en) * | 2019-01-11 | 2019-04-09 | 曹雄志 | A kind of distribution network monitoring management system and method based on the 4G communication technology |
| CN109813978A (en) * | 2018-12-25 | 2019-05-28 | 武汉中原电子信息有限公司 | A kind of non-intruding load-type recognition methods of variation characteristic between comprehensive transient characteristic and stable state |
| CN110018344A (en) * | 2019-02-21 | 2019-07-16 | 国网山东省电力公司临沂供电公司 | The electric energy metering device for having identification power load function |
| CN110333404A (en) * | 2019-06-24 | 2019-10-15 | 江门云天电力设计咨询有限公司 | A kind of load monitoring method, apparatus, equipment and the storage medium of non-intrusion type |
| CN111722028A (en) * | 2019-03-19 | 2020-09-29 | 华北电力大学 | Load identification method based on high-frequency data |
| CN113033775A (en) * | 2021-03-10 | 2021-06-25 | 南方电网数字电网研究院有限公司 | Non-invasive load identification network architecture based on supervised learning |
| CN113158446A (en) * | 2021-04-07 | 2021-07-23 | 国网江苏省电力有限公司信息通信分公司 | Non-invasive electric load identification method |
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| CN114598722A (en) * | 2022-03-15 | 2022-06-07 | 北京汇智博艺科技有限公司 | Non-invasive load identification and energy consumption monitoring system of Internet of things and implementation method thereof |
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Cited By (20)
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|---|---|---|---|---|
| CN109407544B (en) * | 2017-08-17 | 2021-07-30 | 凌华科技股份有限公司 | System module of simulation machine operation picture of non-invasive data extraction system |
| CN109407544A (en) * | 2017-08-17 | 2019-03-01 | 凌华科技股份有限公司 | System module of simulation machine operation picture of non-invasive data extraction system |
| CN108062627B (en) * | 2017-12-16 | 2022-01-07 | 广西电网有限责任公司电力科学研究院 | Demand response analysis method based on non-invasive electricity consumption data |
| CN108062627A (en) * | 2017-12-16 | 2018-05-22 | 广西电网有限责任公司电力科学研究院 | A kind of demand response analysis method based on non-intrusion type electricity consumption data |
| CN109239494A (en) * | 2018-09-21 | 2019-01-18 | 无锡风繁伟业科技有限公司 | A kind of non-intrusive electrical load alert detecting method and system |
| CN109813978A (en) * | 2018-12-25 | 2019-05-28 | 武汉中原电子信息有限公司 | A kind of non-intruding load-type recognition methods of variation characteristic between comprehensive transient characteristic and stable state |
| CN109813978B (en) * | 2018-12-25 | 2021-04-20 | 武汉中原电子信息有限公司 | Non-intrusive load type identification method integrating transient characteristic and change characteristic between stable states |
| CN109599952A (en) * | 2019-01-11 | 2019-04-09 | 曹雄志 | A kind of distribution network monitoring management system and method based on the 4G communication technology |
| CN109599952B (en) * | 2019-01-11 | 2023-03-31 | 曹雄志 | 4G communication technology-based power distribution network monitoring management system and method |
| CN110018344A (en) * | 2019-02-21 | 2019-07-16 | 国网山东省电力公司临沂供电公司 | The electric energy metering device for having identification power load function |
| CN111722028A (en) * | 2019-03-19 | 2020-09-29 | 华北电力大学 | Load identification method based on high-frequency data |
| CN110333404A (en) * | 2019-06-24 | 2019-10-15 | 江门云天电力设计咨询有限公司 | A kind of load monitoring method, apparatus, equipment and the storage medium of non-intrusion type |
| CN110333404B (en) * | 2019-06-24 | 2022-02-01 | 江门云天电力设计咨询有限公司 | Non-invasive load monitoring method, device, equipment and storage medium |
| CN113033775B (en) * | 2021-03-10 | 2023-08-18 | 南方电网数字电网研究院有限公司 | Non-invasive load identification network architecture based on supervised learning |
| CN113033775A (en) * | 2021-03-10 | 2021-06-25 | 南方电网数字电网研究院有限公司 | Non-invasive load identification network architecture based on supervised learning |
| CN113158446A (en) * | 2021-04-07 | 2021-07-23 | 国网江苏省电力有限公司信息通信分公司 | Non-invasive electric load identification method |
| CN113158446B (en) * | 2021-04-07 | 2024-06-11 | 国网江苏省电力有限公司信息通信分公司 | Non-invasive electrical load identification method |
| CN113378655B (en) * | 2021-05-24 | 2022-04-19 | 电子科技大学 | Antagonistic energy decomposition method based on deep neural network |
| CN113378655A (en) * | 2021-05-24 | 2021-09-10 | 电子科技大学 | Antagonistic energy decomposition method based on deep neural network |
| CN114598722A (en) * | 2022-03-15 | 2022-06-07 | 北京汇智博艺科技有限公司 | Non-invasive load identification and energy consumption monitoring system of Internet of things and implementation method thereof |
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