CN104348413A - Data-analysis-based adaptive photovoltaic electricity stealing prevention method - Google Patents
Data-analysis-based adaptive photovoltaic electricity stealing prevention method Download PDFInfo
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- CN104348413A CN104348413A CN201410539709.9A CN201410539709A CN104348413A CN 104348413 A CN104348413 A CN 104348413A CN 201410539709 A CN201410539709 A CN 201410539709A CN 104348413 A CN104348413 A CN 104348413A
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- photovoltaic
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- 230000005611 electricity Effects 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000007405 data analysis Methods 0.000 title claims abstract description 15
- 230000003044 adaptive effect Effects 0.000 title abstract description 4
- 230000002265 prevention Effects 0.000 title abstract 3
- 238000011156 evaluation Methods 0.000 claims abstract description 19
- 230000002159 abnormal effect Effects 0.000 claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 238000003745 diagnosis Methods 0.000 claims abstract description 10
- 238000010248 power generation Methods 0.000 claims abstract description 7
- 238000012544 monitoring process Methods 0.000 claims abstract description 6
- 230000006978 adaptation Effects 0.000 claims description 13
- 238000012795 verification Methods 0.000 claims description 12
- 230000005856 abnormality Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 abstract 1
- 238000012937 correction Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
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- Photovoltaic Devices (AREA)
Abstract
The invention discloses a data-analysis-based adaptive photovoltaic electricity stealing prevention method, which comprises the following steps of 1) extracting photovoltaic running and power generation condition data from a photovoltaic running monitoring database, and preprocessing the data; 2) analyzing an abnormal photovoltaic power generation state by virtue of an electricity stealing diagnosis model, generating a plurality of abnormal state evaluation indexes, and listing a photovoltaic user more suspected to steal electricity into an electricity stealing risk list; 3) performing data checking on the photovoltaic user in the electricity stealing risk list to generate a checking worksheet; 4) checking a photovoltaic power generation condition on the spot, performing electricity stealing processing and recording electricity stealing information if an electricity stealing behavior exists, and if the photovoltaic user is checked not to steal electricity, feeding back an on-spot checking condition; 5) performing misjudgment analysis by a professional, perfecting an electricity stealing diagnosis system, and optimizing the diagnosis model. The photovoltaic power generation data analysis-based electricity stealing prevention diagnosis method is provided to solve the problems of low installed capacity, massive data and electricity stealing supervision and control difficulty of distributed photovoltaic power generation rapidly developed in recent years.
Description
Technical field
The present invention relates to photovoltaic generation anti-theft electricity technology field, be particularly applicable to the anti-electricity-theft diagnosis of distributed photovoltaic power generation.
Background technology
Conventional fossil energy reduces day by day, the worry of energy security and ecological deterioration makes to be that the extensive regenerative resource of representative is generated electricity by way of merging two or more grid systems and become the important component part of future source of energy system with photovoltaic generation.National Energy Board's planning newly-increased photovoltaic installation 14GW in 2014, surface power station is 5.6GW, and distributed power station is 8.4GW, distributed photovoltaic accounting 60%, and following distributed photovoltaic accounting is increasing.
Due to photovoltaic generation subsidy policy that is national and local government's great number, likely exist and be subject to the distributed photovoltaic project owner of profit temptation for gaining subsidy by cheating, make a desperate move, take various stealing electricity method to cause the situation of photovoltaic electric energy meter virtual height.
Tradition electricity anti-theft method finds stealing mainly through means such as regular visit, periodic check ammeter, user's report, and conventional method exists people's dependence strong, the shortcomings such as object is indefinite, and workload is large.Distributed photovoltaic has that networking threshold is low, capacity is little, quantity is large, distribute the feature such as wide, and the low-voltage-grade of distributed photovoltaic makes stealing easily implement, and stealing mode is many, inspection difficulty.Anti-electricity-theft from traditional anti-electricity-theft different of photovoltaic are not in addition anti-from electrical network stealing, but anti-photovoltaic electric energy meter virtual height, the photovoltaic generation electricity gaining virtual height part by cheating is worth.At present for the new feature of photovoltaic stealing, also do not realize the photovoltaic electricity anti-theft method of quick and precisely locating stealing suspicion user.
Summary of the invention
Quick and precisely cannot locate the object of photovoltaic stealing suspicion user and metering fault in order to solve traditional anti-theft electricity technology, the invention provides a kind of self adaptation photovoltaic electricity anti-theft method based on data analysis.In order to solve the problem, the technical solution used in the present invention is:
This anti-electricity-theft diagnostic method comprises the following steps: 1) extract the data such as photovoltaic generation type, installed capacity, running status, energy output and power from photovoltaic operation monitoring database, and do preliminary treatment to data; 2) analyzed the abnormality of photovoltaic generation by stealing diagnostic model, generate multiple evaluation indexes such as electricity, power, warning, history stealing coefficient, photovoltaic generation user larger for stealing suspicion coefficient is listed in stealing risk list; 3) stealing data check is carried out to the photovoltaic in stealing risk list, generate after confirming and verify work order; 4) on-site verification photovoltaic generation situation, confirms that there is electricity filching behavior then does stealing process and record stealing information; Verify the non-stealing of photovoltaic then feed back on-site verification situation; 5) carrying out analysis of misjudgement to verifying non-stealing situation, improving stealing analyzing and diagnosing system, continuing to optimize diagnostic model.
Aforesaid a kind of self adaptation photovoltaic electricity anti-theft method based on data analysis, it is characterized in that: in step 1) in, the data such as photovoltaic generation type, installed capacity, running status, energy output, realtime power and correction factor are extracted from photovoltaic operation monitoring database, and the missing values in data, exceptional value are removed, smoothly, the preliminary treatment such as interpolation, improve the quality of data.
Aforesaid a kind of self adaptation photovoltaic electricity anti-theft method based on data analysis, it is characterized in that: in step 2) in, based on step 1) preprocessed data by stealing diagnostic model in conjunction with stealing evaluation index, draw multiple stealing suspicion evaluation index such as electricity, power, warning, history stealing coefficient, improve the accuracy of stealing diagnostic analysis.
Aforesaid a kind of self adaptation photovoltaic electricity anti-theft method based on data analysis, it is characterized in that: in step 2) in stealing diagnostic model to be photovoltaic preprocessed data with photovoltaic compare with reference to the data that generate electricitys draws evaluation index, the photovoltaic reference data that generate electricity are that normal light overhead utility data acquisition weighted average near 4 produces.
Aforesaid a kind of self adaptation photovoltaic electricity anti-theft method based on data analysis, it is characterized in that: in step 5) carry out analysis of misjudgement to verifying non-stealing situation, and revise diagnostic model, form one and steal electrodiagnostic closed loop new mechanism, improve constantly the accuracy of diagnostic model.
Compared with prior art, remarkable advantage of the present invention is:
1) after preliminary treatment being carried out to photovoltaic generation data, the availability of system and strong adaptability.
2) photovoltaic generation stealing is analyzed multiple diagnosis index, and photovoltaic generation abnormality juding accuracy is high;
3) verify according to the stealing suspicion user that exists that surreptitiously electrodiagnostic result identifies, reduce suspicion scope, improve inspecting efficiency;
4) the anti-electricity-theft diagnostic model of photovoltaic adopts closed loop mechanism, and anti-electricity-theft diagnostic model will be more and more accurate.
Accompanying drawing explanation
Fig. 1 is the self adaptation photovoltaic electricity anti-theft method overall construction drawing based on data analysis;
Fig. 2 is the anti-electricity-theft evaluation index of photovoltaic;
Fig. 3 is the anti-electricity-theft diagnostic flow chart of photovoltaic;
Fig. 4 is photovoltaic anti-electricity-theft diagnosis adaptive correction figure.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with accompanying drawing, the present invention is in depth described in detail.Should be appreciated that concrete case study on implementation described herein is only in order to explain the present invention, and be not used in restriction invention.
As shown in Figure 1, a kind of self adaptation photovoltaic electricity anti-theft method based on data analysis, comprise the following steps: 1) extract the data such as the photovoltaic generation type of user, installed capacity, running status, energy output and power from photovoltaic operation monitoring database, and the missing values in data and exceptional value are removed, the preliminary treatment such as level and smooth and interpolation, improve the availability of data.
2) to be photovoltaic preprocessed data with photovoltaic compare with reference to the data that generate electricitys stealing diagnostic model draws evaluation index, and photovoltaic is that normal light overhead utility data acquisition weighted average near 4 produces with reference to the data that generate electricity.Photovoltaic with reference to generating data with the unit generated output of photovoltaic plant and electricity for benchmark, 4 photovoltaic plants are by distance respectively with 0.1, and 0.2,0.3,0.4 sues for peace for coefficient obtains reference units generated output and the electricity of photovoltaic.Because the component type of user's photovoltaic, inverter performance and system effectiveness are variant, there is the correction factor that generates electricity for each user of this difference.The reference units generated output of photovoltaic and electricity are multiplied by user and generate electricity correction factor, then are multiplied by the reference generating data of installed capacity and photovoltaic.
P
reference=(0.1*P
1+ 0.2*P
2+ 0.3*P
3+ 0.4*P
4) * W (1)
Q
reference=(0.1*Q
1+ 0.2*Q
2+ 0.3*Q
3+ 0.4*Q
4) * W (2)
Wherein P
referenceand Q
referencebe respectively reference realtime power and the energy output of photovoltaic user, W is the installed capacity of photovoltaic user, P
1, P
2, P
3, P
4be respectively the unit reference realtime power of photovoltaic plant, Q
1, Q
2, Q
3, Q
4be respectively the unit reference energy output of photovoltaic plant.
3) user's photovoltaic data and the abnormality with reference to generating data is analyzed by stealing diagnostic model, generate multiple stealing suspicion evaluation indexes such as electricity, power, warning, history stealing coefficient, the photovoltaic generation that stealing suspicion is larger lists stealing risk list in.Anti-electricity-theft evaluation index comprises electricity class index, power class index, warning class index and specific indexes four class, specifically as shown in Figure 2.Be the subitem setting coefficient under four class evaluation indexes, electricity and power index temporally yardstick arrange coefficient, and other indexs arrange coefficient by importance.By the stealing suspicion coefficient that the addition of all evaluation index coefficients is exactly user, this value exceedes stealing suspicion coefficient threshold, it is determined that the presence of stealing suspicion, and puts into stealing risk list, by occurring that abnormal evaluation index is listed, facilitates next step data check.
4) carry out abnormal index data check to the photovoltaic user in stealing risk list, whether the stealing evaluation index that verification model analysis draws is correct.Evaluation index confirms errorless, lists this user in verification list, provides abnormal index, specify on-site verification object, and generates verification work order.
5) according to on-site verification user photovoltaic feedback, confirm that there is electricity filching behavior then does stealing process and record stealing information; Verify the non-stealing of photovoltaic and then list on-site verification situation and stealing evaluation abnormal information in next step analysis of misjudgement.
6) professional carries out analysis of misjudgement to on-site verification situation and stealing evaluation abnormal information, finds evaluation index erroneous judgement reason.Then the index erroneous judgement of update the system, improves stealing analyzing and diagnosing system, continues to optimize diagnostic model.
To sum up, the self adaptation photovoltaic electricity anti-theft method based on abnormal data analysis comprises stealing diagnosis and stealing diagnosis adaptive correction two parts, as shown in Figure 3 and Figure 4, is one and forms the anti-electricity-theft analytical method of closed loop.
More than show and describe general principle of the present invention, principal character and advantage.The technical staff of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and specification just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection range is defined by appending claims and equivalent thereof.
Claims (4)
1. the self adaptation photovoltaic electricity anti-theft method based on data analysis, it is characterized in that the method comprises the following steps: 1) extract the data such as photovoltaic generation type, installed capacity, running status, energy output and power from photovoltaic operation monitoring database, and preliminary treatment is done to data; 2) analyzed the abnormality of photovoltaic generation by stealing diagnostic model, generate multiple evaluation index, list photovoltaic generation user larger for stealing suspicion coefficient in stealing risk list; 3) stealing data check is carried out to the photovoltaic in stealing risk list, generate after confirming and verify work order; 4) on-site verification photovoltaic generation situation, confirms that there is electricity filching behavior then does stealing process and record stealing information; Verify the non-stealing of photovoltaic then feed back on-site verification situation; 5) carrying out analysis of misjudgement to verifying non-stealing situation, improving stealing analyzing and diagnosing system, continuing to optimize diagnostic model.
2. the self adaptation photovoltaic electricity anti-theft method based on data analysis according to claim 1, it is characterized in that step 2) in by stealing diagnostic model generate electricity, power, warning, the multiple evaluation index of history stealing coefficient, list photovoltaic generation user larger for stealing suspicion coefficient in stealing risk list.
3. the self adaptation photovoltaic electricity anti-theft method based on data analysis according to claim 2, it is characterized in that carrying out abnormal index data check to the photovoltaic in stealing risk list in step 3), verify after confirming and provide abnormal index and required verification content, generate and verify work order.
4. the anti-electricity-theft monitoring method based on photovoltaic power generation power prediction according to claim 3, it is characterized in that carrying out analysis of misjudgement to verifying non-stealing situation in step 5), improve stealing analyzing and diagnosing system, Optimized Diagnosis model, form the self adaptation electricity anti-theft method of a closed loop.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104793030A (en) * | 2015-04-24 | 2015-07-22 | 国家电网公司 | Supervision method of distributed photovoltaic power generation electric larceny |
| CN105139275A (en) * | 2015-08-17 | 2015-12-09 | 国家电网公司 | Method for establishing distributed photovoltaic power stealing cost benefit evaluation model |
| CN105160595A (en) * | 2015-08-24 | 2015-12-16 | 国家电网公司 | Distributed photovoltaic electricity-stealing supervising method based on multi-time scale output estimation |
| CN107067155A (en) * | 2017-02-23 | 2017-08-18 | 武汉烽火技术服务有限公司 | Antitheft electric management system and method based on work order |
| CN107085653A (en) * | 2017-03-29 | 2017-08-22 | 国网上海市电力公司 | A kind of anti-electricity-theft real-time diagnosis method of data-driven |
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Patent Citations (4)
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| CN101917137A (en) * | 2010-07-06 | 2010-12-15 | 上海淘科网络技术有限公司 | Network monitor and management platform of solar photovoltaic generation system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104793030A (en) * | 2015-04-24 | 2015-07-22 | 国家电网公司 | Supervision method of distributed photovoltaic power generation electric larceny |
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| CN105139275B (en) * | 2015-08-17 | 2018-09-11 | 国家电网公司 | A method of establishing distributed photovoltaic stealing Cost benefit assessment model |
| CN105160595A (en) * | 2015-08-24 | 2015-12-16 | 国家电网公司 | Distributed photovoltaic electricity-stealing supervising method based on multi-time scale output estimation |
| CN107067155A (en) * | 2017-02-23 | 2017-08-18 | 武汉烽火技术服务有限公司 | Antitheft electric management system and method based on work order |
| CN107085653A (en) * | 2017-03-29 | 2017-08-22 | 国网上海市电力公司 | A kind of anti-electricity-theft real-time diagnosis method of data-driven |
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Application publication date: 20150211 |