CN105823948A - Non-invasive resident load identification method - Google Patents

Non-invasive resident load identification method Download PDF

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Publication number
CN105823948A
CN105823948A CN201610330951.4A CN201610330951A CN105823948A CN 105823948 A CN105823948 A CN 105823948A CN 201610330951 A CN201610330951 A CN 201610330951A CN 105823948 A CN105823948 A CN 105823948A
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China
Prior art keywords
load
resident
residential households
voltage
recognition methods
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CN201610330951.4A
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Chinese (zh)
Inventor
潘爱强
林顺富
赵伦加
刘庆强
杨秀
刘蓉晖
汤波
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State Grid Shanghai Municipal Electric Power Co
East China Power Test and Research Institute Co Ltd
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State Grid Shanghai Municipal Electric Power Co
East China Power Test and Research Institute Co Ltd
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Application filed by State Grid Shanghai Municipal Electric Power Co, East China Power Test and Research Institute Co Ltd filed Critical State Grid Shanghai Municipal Electric Power Co
Priority to CN201610330951.4A priority Critical patent/CN105823948A/en
Publication of CN105823948A publication Critical patent/CN105823948A/en
Pending legal-status Critical Current

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/15Correlation function computation including computation of convolution operations

Abstract

The invention relates to a non-invasive resident load identification method comprising the following steps: (1) getting the load characteristic indexes of all appliances in a resident household, and building a resident household load characteristic database; (2) sampling the voltage and current of a load to be identified at a main power inlet end of the resident household, and preprocessing the sampled data; (3) judging whether there is a resident load switching event according to the voltage and current sampling values, performing step (4) if there is a resident load switching event, and returning to step (2) if there is no resident load switching event; (4) extracting the characteristic index of the load to be identified according to the voltage and current sampling values; and (5) judging the type of a load where a switching event happens according to the built resident household load characteristic database and the characteristic index of the load to be identified, and getting the working status information of the appliances in the resident household. Compared with the prior art, the method has the advantages of being simple, convenient, accurate, strong in anti-interference performance, and the like.

Description

A kind of non-intervention formula resident load recognition methods
Technical field
The present invention relates to a kind of load recognition methods, especially relate to a kind of non-intervention formula resident load recognition methods.
Background technology
Along with the development of society, the proportion that residential households power consumption accounts for power distribution network total electricity consumption is increasing, according to statistics, and this proportion up to 40%.Further, along with the propelling of China's urbanization, within following time, the growth of urban population, housing demand will continue to increase.The sustainable development tool of China is of great significance by the benefit therefore reduced discharging household energy conservation.
Energy consumption monitoring is the basis carrying out energy conservation, and research shows, obtains electrical equipment real-time power consumption information and can effectively reduce the energy consumption of 20%.Traditional monitoring system mainly uses isolated sensor to monitor each electrical equipment, although isolated sensor can be in high precision, monitor the running status of each electrical equipment expeditiously, obtain the power information of each electrical equipment, but its system hardware cost is high, communication network architecture is complicated and is not easy to user's maintenance, non-intervention formula monitoring system can solve the difficult point of traditional monitoring system well, non-intervention formula monitoring technology is by installing monitoring device in end of incoming cables, utilize part throttle characteristics distinguishing indexes and intelligent algorithm that total energy data is decomposed to individual equipment rank, thus obtain the electric energy service condition of each electrical equipment, it is low that this system has hardware cost, install and the advantage such as easy to maintenance.
NILM technology is that George's .W. professor Hart of nineteen eighty-two MIT proposes, and has obtained the concern of numerous scholars after proposition.Develop through years of researches, propose a lot of intelligent algorithms for NILM technology, such as BP neural network algorithm, genetic algorithm, S-transformation etc..Domestic study in terms of Household Appliance on-line monitoring less, though though non-intervention formula system now solves the problem of traditional monitoring system, but there is the shortcomings such as algorithm accuracy of identification is low, error is big.
Summary of the invention
Defect that the purpose of the present invention is contemplated to overcome above-mentioned prior art to exist and provide a kind of simple, convenient, accurately, the non-intervention formula resident load recognition methods of strong interference immunity.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of non-intervention formula resident load recognition methods, for monitoring load type and the duty of low-voltage network residential households electrical equipment, comprises the following steps:
1) obtain the Load characteristics index of all resident's electrical equipment in residential households, and build residential households load characteristic database;
2) at residential households electric power total input-wire end, load to be identified carried out voltage and current sample, and the data after sampling are carried out pretreatment;
3) according to judging whether resident load switch events occurs by voltage and current sample value, the most then step 4 is carried out), if it is not, then return step 2);
4) according to the characteristic index being extracted load to be identified by voltage and current sample value;
5) according to the residential households load characteristic database set up and the characteristic index of load to be identified, it is determined that the load type of switch events occurs, and obtains the work state information of household electrical appliance in this residential households.
Described step 1) specifically include following steps:
Low-voltage network residential households switchs resident's electrical equipment one by one, extracts the load characteristic index of each resident's electrical equipment at residential households electric power total input-wire end, after collecting, set up residential households load characteristic database.
In described residential households, all resident's electrical equipment include air-conditioning, water dispenser, computer, water heater, washing machine, microwave oven, electric refrigerator, television set and/or illuminating lamp.
Described Load characteristics index includes the geometric properties of current effective value, current harmonics, active power, reactive power and/or voltage-current curve.
Described geometric properties includes centrage slope, closed area area and cross point number.
Described step 3) according to bilateral accumulation summation method judge whether resident load switch events occurs, comprise the following steps:
31) initialize Sudden Changing Rate Δ and noise level β, calculate threshold value h;
32) read block, it is judged that whether current time, in sliding window, is, carries out step 33), otherwise jump to step 32) read subsequent data chunk;
33) carry out switch events monitoring to judge.First determine whether that the monitoring moment, whether in sliding window, is to carry out step 34), otherwise carry out step 32);
34) Rule of judgment calculates and judges, the satisfied then output switch event time parameter of condition also jumps to step 2, otherwise will determine that the moment adds 1, then carries out step 33).
Described step 5) in judge to occur the load type of switch events by 0-1 Novel Algorithm, the model objective function of load identification is:
Y=Y'+ ε=Ψ X+ ε
Constraints is:
Σ i = 1 N x i ≤ 1 , x i = { 0 , 1 }
Wherein, Y is rank, M × 1 vectors, and M is certain type load distinguishing indexes number corresponding;X is rank, N × 1 state vectors, and N is database load sum;ε=[ε12,...,εM]TFor rank, M × 1 vector;Ψ is M × N rank load distinguishing indexes value matrixs.
Owing to Y is the measurement of redundancy, therefore can not directly carry out solving that (discounting for error, object function is without solving, because the number of equation is more than the number of unknown number according to above-mentioned equation group, but solution immediate with object function can be found, so that above-mentioned ε=[ε12,...,εM]TVariance minimum determines parameter.
Therefore the problems referred to above are converted into 0-1 quadratic programming problem, and its mathematical model is as follows:
min J = Y T Y - 2 Y T Ψ X + 1 2 ( X T Ψ T 2 Ψ X ) Σ i = 1 N x i ≤ 1 x i = { 0 , 1 }
Owing to constraints is that one-zero programming problem can only use discrete method so solving above-mentioned planning problem.Discrete logarithm is the discrete feature direct solution integer programming from design variable.Traditional discrete method belongs to combinational algorithm mostly, such as the method for exhaustion, implicit enumeration method etc..This kind of algorithm can correctly find problem globally optimal solution, but along with the increase of problem scale, its calculation cost is the biggest.Another kind is discrete heuritic approach, such as genetic algorithm. the major defect of this kind of method is can not to process constraint well, and is easy to Premature Convergence problem occur.
Compared with prior art, the invention have the advantages that
Load recognition methods of the present invention is a kind of non-intervention formula method, produces impact thus without on residential households normal power supply;And present system low cost, install and safeguard simple, convenient, it is only necessary to monitoring device is installed at end of incoming cables, applies the inventive method, it is possible to easily grasp the function situation of household electrical appliance.Present invention is suitably applied in non-intervention formula load identification system, it is achieved the centralized monitoring of residential electricity consumption.
Present invention also offers a kind of non-intervention formula load recognizer, this algorithm is optimized process on the basis of conventional discrete algorithm, it is achieved the serialization of discrete logarithm, solves the series of problems of discrete logarithm.New load recognizer identification load energy consumption effect is preferable, has certain capacity of resisting disturbance, and identifies that load species number is many.
Accompanying drawing explanation
Fig. 1 is non-intervention formula load identification schematic diagram.
Fig. 2 is single family non-intervention formula load identification schematic diagram.
Fig. 3 is load identification process figure.
Fig. 4 is experimentation RMS curve.
Fig. 5 is experiment electric switch monitoring result.
Fig. 6 is bilateral CUSUM detection of change-point algorithm evolution process figure based on sliding window.
Fig. 7 is sliding window bilateral CUSUM algorithm flow chart.
Detailed description of the invention
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment:
Fig. 1 is load identification schematic diagram, by monitoring end of incoming cables voltage, current signal, identifies household electrical appliance job information in a day, and according to this information, resident is it is apparent that the energy consumption condition of each electrical equipment.Therefore, resident can formulate corresponding Energy Saving Strategy with the use of reasonable arrangement electrical equipment, can buy energy-saving equipment for highly energy-consuming electrical equipment, reach energy-conservation purpose.
Fig. 2 is single family non-intervention formula load identification detailed maps, wherein, before non-intervention formula load identification device is not limited to be installed on control panel, it is also possible to be integrated in control panel.
Fig. 3 is the idiographic flow utilizing intelligent algorithm to carry out load identification:
Step 301: start;
Step 302: the Load characteristics index of each electrical equipment in reading database;
The primary work of resident load non-intervention formula identification is to set up residential households electrical operating characteristics data base.The part throttle characteristics (current curve characteristic, harmonic characterisitic, PQ characteristic, instantaneous power characteristic, V-I characteristic) that electrical work voltage and current information is provided is the basis of load identification.According to different part throttle characteristics, extract respective distinguishing indexes.As a example by V-I curve, according to V-I curvilinear characteristic, can be with the V-I curve of following index identification difference electrical equipment: centrage slope, closed area area, cross point number, for other part throttle characteristics, also make same process, finally set up load characteristic database.
Step 303: read the end of incoming cables data gathered;
Relative to traditional load monitor system, the present invention only need to install one group of monitoring device in end of incoming cables, and each electrical equipment need not be installed monitoring device, greatly reduces system cost and installs complexity.
Step 304: gather data prediction;
Step 305: switch events is monitored;
By the pretreated data of CUSUM Algorithm Analysis, obtaining whether this moment exists switch events, if there is switch events, performing step 306;Otherwise, continue the monitoring of switch events, comprise the following steps:
When the installation completing monitoring device, it is necessary to set up household electrical appliance distinguishing indexes data base in whole family, the foundation of data base is that the mode manually registered completes.When detection starts, from data base, read the distinguishing indexes of each electrical equipment, set up distinguishing indexes matrix.Detect switch events by CUSUM algorithm, when switch events being detected, according to change amount signal before and after the voltage collected, current signal acquisition switch events, extract every distinguishing indexes value, set up matrix of differences Y '.By solving the matrix X in following formula, i.e. may recognize that the household electrical appliance that on off state changes this moment;
min J = Y T Y - 2 Y T Ψ X + 1 2 ( X T Ψ T 2 Ψ X ) Σ i = 1 P x i ≤ 1 Σ i = 1 P a i ( x i - x i 2 ) = 0 0 ≤ x i ≤ 1 .
Step 306: extract part throttle characteristics;
Step 307: load recognizer;
Load recognizer is the core of load identification, according to the real time data gathered, it is judged that whether switch events occurs, if be detected that switch events, intelligent algorithm, based on data base, identifies the equipment in this switch events.The present invention proposes a kind of new algorithm being applicable to load identification.This algorithm is to be optimized process on the basis of discrete logarithm, and solve discrete logarithm solves the problems such as difficulty.
Step 308: recognition result;
In order to be better described, extracting method is applied to feasibility and the accuracy of load identification, and applicant combines example and is further illustrated:
The use of resident's electrical equipment is relevant with behavioural habits with residents'living habit to a great extent, it is believed that the use of electrical equipment is random distribution.Carry out item experiment for thousands of kinds of switch conditions to be difficult to.The present invention is verified on the basis of emulation and experiment.Separately below accuracy of identification and capacity of resisting disturbance are carried out test specification.
Build experiment porch in laboratory environments.Platform includes that five kinds of electrical equipment are respectively as follows: fan, water dispenser, desk lamp, microwave oven and electric heater, and its operation includes that 10 kinds of switch events, table 1 are switch events sequence altogether.As shown in Figure 4, wherein, OE (OperationEvent) i.e. represents facility switching event to the active power RMS curve of experimentation.
110 kinds of switch events of table
Wherein: ↑ represent opening of device;↓ represent that equipment is closed
The bilateral accumulation of market demand and (CUCUM) algorithm to Fig. 4 carry out event generation and moment monitoring, extract part validity feature information, event is identified by the load recognizer that application is proposed, and each equipment carries out identification and follows the tracks of its switching manipulation, and result is as shown in Figure 5.
As shown in Figure 6, figure is the vivid declarative procedure of bilateral accumulative summation.In figure, light blue window is mean value calculation window WM, and white window is transient process detection window WD.When in transient process detection window WD, transient event detection algorithm has been not detected by transient event generation, mean value calculation window WM and transient process detection window WD slides into the right new sampled point.When transient event detection algorithm has detected that transient event occurs in transient process detection window WD, new mean value calculation window WM and transient process detection window WD slides into the height moment that transient event detection algorithm detects, as it is shown in fig. 7, figure is sliding window bilateral CUSUM algorithm flow chart.
Gather the electricity consumption data of 100 groups of each loads, and it is hot-tempered to apply matlab to add data, build the data containing hot-tempered 1%;Finally using matlab to write model solution program, above-mentioned data are carried out identification, its result is as shown in table 2.
Table 2 load recognizer identification result
Note: CW represents current characteristics;HAR represents harmonic characterisitic;PQ represents PQ characteristic;V-I represents V-I characteristic;IPW represents instantaneous power characteristic
For the test devices in system species number impact on algorithm accuracy of identification, expand device type kind, increase fan, washing machine, vacuum cleaner and television set, kind p of tested equipment is by 4 kinds to 10 kinds, given accuracy is the statistic analysis result carrying out 100*p test in the case of distinct device kind, and test result is as shown in table 3.
Load recognizer identification result under table 3 variety classes equipment
By table 2 and table 3 it can be seen that serialization 0-1 quadratic programming recognizer is satisfactory for result, there is certain capacity of resisting disturbance, and identify that device category number is bigger.
In sum, this method can identify load operation situation exactly, and possesses capacity of resisting disturbance.This algorithm is suitably applied in NILM system, it is achieved the centralized monitoring of residential electricity consumption.

Claims (7)

1. a non-intervention formula resident load recognition methods, for monitoring load type and the duty of low-voltage network residential households electrical equipment, it is characterised in that comprise the following steps:
1) obtain the Load characteristics index of all resident's electrical equipment in residential households, and build residential households load characteristic database;
2) at residential households electric power total input-wire end, load to be identified carried out voltage and current sample, and the data after sampling are carried out pretreatment;
3) according to judging whether resident load switch events occurs by voltage and current sample value, the most then step 4 is carried out), if it is not, then return step 2);
4) according to the characteristic index being extracted load to be identified by voltage and current sample value;
5) according to the residential households load characteristic database set up and the characteristic index of load to be identified, it is determined that the load type of switch events occurs, and obtains the work state information of household electrical appliance in this residential households.
One the most according to claim 1 non-intervention formula resident load recognition methods, it is characterised in that described step 1) specifically include following steps:
Low-voltage network residential households switchs resident's electrical equipment one by one, extracts the load characteristic index of each resident's electrical equipment at residential households electric power total input-wire end, after collecting, set up residential households load characteristic database.
One the most according to claim 1 non-intervention formula resident load recognition methods, it is characterized in that, in described residential households, all resident's electrical equipment include air-conditioning, water dispenser, computer, water heater, washing machine, microwave oven, electric refrigerator, television set and/or illuminating lamp.
One the most according to claim 1 non-intervention formula resident load recognition methods, it is characterised in that described Load characteristics index includes the geometric properties of current effective value, current harmonics, active power, reactive power and/or voltage-current curve.
One the most according to claim 4 non-intervention formula resident load recognition methods, it is characterised in that described geometric properties includes centrage slope, closed area area and cross point number.
One the most according to claim 1 non-intervention formula resident load recognition methods, it is characterised in that described step 3) according to bilateral accumulation summation method judge whether resident load switch events occurs.
One the most according to claim 1 non-intervention formula resident load recognition methods, it is characterised in that described step 5) in judge to occur the load type of switch events by 0-1 Novel Algorithm.
CN201610330951.4A 2016-05-18 2016-05-18 Non-invasive resident load identification method Pending CN105823948A (en)

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CN106802379A (en) * 2017-03-06 2017-06-06 中国海洋大学 A kind of load switch detection of adaptive threshold and recognition methods and system
CN106815677A (en) * 2016-12-09 2017-06-09 国网北京市电力公司 The recognition methods of non-intrusion type load and device
CN108021943A (en) * 2017-12-06 2018-05-11 北京上格云技术有限公司 The method and apparatus for detecting electromechanical equipment power supply source
CN108872666A (en) * 2018-09-20 2018-11-23 广东石油化工学院 A kind of load switch event detecting method and system
CN109782086A (en) * 2018-12-25 2019-05-21 武汉中原电子信息有限公司 A kind of non-intruding load recognition methods based on the analysis of various dimensions signal
CN110196354A (en) * 2019-04-23 2019-09-03 广东石油化工学院 A kind of detection method and device of the switch events of load
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
CN111708925A (en) * 2020-08-20 2020-09-25 国网浙江省电力有限公司湖州供电公司 Intelligent household appliance load identification system and method for residential user

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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106815677A (en) * 2016-12-09 2017-06-09 国网北京市电力公司 The recognition methods of non-intrusion type load and device
CN106762594A (en) * 2017-01-12 2017-05-31 威胜集团有限公司 Compressor apparatus start method of real-time
CN106762594B (en) * 2017-01-12 2018-09-14 威胜集团有限公司 Compressor apparatus starts method of real-time
CN106802379A (en) * 2017-03-06 2017-06-06 中国海洋大学 A kind of load switch detection of adaptive threshold and recognition methods and system
CN108021943A (en) * 2017-12-06 2018-05-11 北京上格云技术有限公司 The method and apparatus for detecting electromechanical equipment power supply source
CN108021943B (en) * 2017-12-06 2020-06-16 北京上格云技术有限公司 Method and device for detecting power supply of electromechanical device
CN108872666A (en) * 2018-09-20 2018-11-23 广东石油化工学院 A kind of load switch event detecting method and system
CN109782086A (en) * 2018-12-25 2019-05-21 武汉中原电子信息有限公司 A kind of non-intruding load recognition methods based on the analysis of various dimensions signal
CN109782086B (en) * 2018-12-25 2020-12-18 武汉中原电子信息有限公司 Non-intrusive load identification method based on multi-dimensional signal analysis
CN110196354A (en) * 2019-04-23 2019-09-03 广东石油化工学院 A kind of detection method and device of the switch events of load
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
CN111708925A (en) * 2020-08-20 2020-09-25 国网浙江省电力有限公司湖州供电公司 Intelligent household appliance load identification system and method for residential user
CN111708925B (en) * 2020-08-20 2021-01-05 国网浙江省电力有限公司湖州供电公司 Intelligent household appliance load identification system and method for residential user

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