CN105337574A - Robust-regression-based distributed photovoltaic generating electricity-stealing identification method - Google Patents
Robust-regression-based distributed photovoltaic generating electricity-stealing identification method Download PDFInfo
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Abstract
The invention discloses a robust-regression-based distributed photovoltaic generating electricity-stealing identification method. The method comprises the following steps: (1), establishing a historical information database; (2), carrying out determination and filtering on abnormal data existing in historical data; (3), carrying out processing by using a robust regression model algorithm to obtain an irradiation power curve; (4), carrying out operation to obtain a corresponding photovoltaic generation power; (5), carrying out calculation to obtain a theoretic generating capacity; and (6), carrying out determination. The method has the following beneficial effects: (1), with the robust regression model algorithm, the influence on the model precision by the abnormal data can be reduced and concrete modeling of a photovoltaic system inversion model and a photovoltaic conversion model can be avoided; and (2), electricity-stealing suspicion determination is carried out based on three-layer screening architecture, so that accuracy of abnormal determination of the photovoltaic electric quantity and the electricity-stealing determination reliability are high and the electricity-stealing checking pertinency is improved.
Description
Technical field
The present invention relates to a kind of distributed photovoltaic power generation stealing recognition methods based on robustness regression, belong to photovoltaic generation monitoring technical field.
Background technology
Current common distributed photovoltaic power generation stealing electricity method has the boosting of commercial power rectification inversion method, civil power reconfiguration method, photovoltaic meter scale method, photovoltaic meter scale up-flow method.Inversion method in commercial power rectification directly utilizes rectifying device that commercial power rectification is become direct current, is parallel to the DC side of photovoltaic generating system, changes into alternating current online through photovoltaic combining inverter.Use this scheme, user even can not install photovoltaic battery panel, directly pretends to be photovoltaic cell component to generate electricity with rectifying device.Civil power reconfiguration method is that user passes through to adjust its back panel wiring mode, and be the inlet wire of civil power by the inlet wire reconfiguration of photovoltaic meter scale, now the continuous data of photovoltaic meter scale is the power consumption of home appliance.The voltage that photovoltaic meter scale boosting method utilizes additional step-up transformer to construct a virtual height is linked into photovoltaic meter scale, and circular speed is accelerated, and many meter photovoltaic power generation quantities are to gain the subsidy of national electricity price by cheating.The voltage regulator that photovoltaic meter scale up-flow method utilizes is equivalent to the transformer of a secondary short circuit, a very little voltage only need be added on former limit, secondary can induce larger current, be equivalent to an additional empty electric current on the current circuit of photovoltaic meter scale, circular speed is accelerated, and many meter energy output are to gain the subsidy of national electricity price by cheating.
In recent years, along with photovoltaic generation industry is constantly risen, based on distributed photovoltaic power generation its special generation mode and subsidy policy, distributed photovoltaic is anti-electricity-theft causes people to pay close attention to gradually, someone proposes a kind of anti-electricity-theft monitoring method based on photovoltaic power generation power prediction, mainly comprises the following steps: 1) calculate the theoretical output power curve of photovoltaic cell according to meteorological data and photovoltaic cell parameter information; 2) the factor prediction photovoltaic power generation grid-connecting power affecting photovoltaic power generation grid-connecting power output is considered; 3) power ratio that the grid-connected power predicted and photovoltaic generation metering ammeter are uploaded comparatively, judges that whether power is abnormal; 4) the photovoltaic meter electrometer of stealing may be there is by abnormal information statistical decision, carry out anti-electricity-theft monitoring.There is certain deficiency in this monitoring method, first when the method carries out photovoltaic power prediction, the impact on photovoltaic power output such as photovoltaic cell loss, inverter losses and the grid-connected loss of AC need be considered, wherein photovoltaic cell loss comprises battery temperature performance losses, component matching loss, the loss of photovoltaic module superficial dust, the ageing loss of photovoltaic cell own and direct current line loss, inverter losses comprises maximal power tracing energy loss and reversals loss, and the grid-connected loss of AC comprises alternating current circuit loss and transformer loss.Owing to affecting the many factors of photovoltaic power generation grid-connecting power output, the photovoltaic power obtained certainly exists certain error of calculation to utilize the method to predict.Secondly, the method utilizes instantaneous output as judging stealing whether foundation, and meteorological sampling time and power samples time likely there will be deviation physically, thus cause rated output and sampled power Time Inconsistency, if interval range is chosen improper, the situation of erroneous judgement can be caused.
Summary of the invention
For solving the deficiencies in the prior art, the object of the present invention is to provide a kind of distributed photovoltaic power generation stealing recognition methods based on robustness regression, adopt robustness regression algorithm, to evade in modeling process each influencing factor to the impact of computational accuracy, and using electricity as stealing foundation, because electricity is the accumulated value of power, reduce single power points error to the impact of judged result.
In order to realize above-mentioned target, the present invention adopts following technical scheme:
Based on a distributed photovoltaic power generation stealing recognition methods for robustness regression, it is characterized in that, comprise the steps:
1) distributed photovoltaic power generation system cloud gray model historical information database is set up;
2) according to photovoltaic effect characteristic and celestial body irradiation feature, judgement is carried out to the abnormal data existed in historical data and filters;
3) to the irradiation after filtration and power data, robustness regression model algorithm is adopted to obtain irradiation power curve;
4) according to numerical weather forecast irradiance data, utilize the irradiation power curve generated, try to achieve corresponding photovoltaic generation power;
5) utilize power electricity conversion relation, exerting oneself of Computation distribution formula photovoltaic plant different time, obtain theoretical energy output;
6) ground floor judgement is carried out: based on step 5) the theoretical energy output that obtains, judge whether normally to the actual power generation measured;
7) to step 6) in be judged to be that the data of normal condition carry out second layer judgement: hourage effectively will be utilized to convert with each photovoltaic plant daily generation in region; If effectively utilize hourage to be all greater than the power station day that ground floor result of determination is " normal condition " other photovoltaic plant days of same region effectively utilize hourage, then judge that this photovoltaic plant is abnormal, there is stealing suspicion;
8) to step 7) in the normal data of photovoltaic plant carry out third layer judgement: by analysis distribution formula photovoltaic owner " generating-electricity consumption " relation, carry out the judgement of stealing suspicion, if its photovoltaic power generation quantity and local load power consumption quite or local load power consumption and historical data base power consumption contrast empirically value power consumption obviously rise, then judge that this distributed photovoltaic power station is abnormal, there is stealing suspicion.
The aforesaid distributed photovoltaic power generation stealing recognition methods based on robustness regression, it is characterized in that, described step 1) in database comprise photovoltaic plant installed capacity, generated output, energy output and the same period meteorological data and the real-time power output data of photovoltaic plant of correspondence thereof.
The aforesaid distributed photovoltaic power generation stealing recognition methods based on robustness regression, is characterized in that, described step 2) in, abnormal data comprises 1) irradiation equals zero, power is non-vanishing value; 2) irradiation is greater than definite value and power is the value of zero; 3) value of now sun maximum permissible exposure is exceeded.
The aforesaid distributed photovoltaic power generation stealing recognition methods based on robustness regression, is characterized in that, described step 6) in, be judged to be that the number range of the actual power generation of normal condition is included in 70% ~ 130% of theoretical energy output numerical value.
The beneficial effect that the present invention reaches: 1) adopt robustness regression algorithm, reduce abnormal data to the impact of model accuracy, and avoid the concrete modeling to photovoltaic system inversion model and opto-electronic conversion model, distributed photovoltaic output calculation precision is high; 2) utilize area equivalent principle, be theoretical energy output by the conversion of photovoltaic output calculation model calculation value, and then contrast with photovoltaic plant actual power generation, for stealing identification provides strong theoretical foundation; 3) adopt three layers of screening framework to carry out the judgement of stealing suspicion, photovoltaic electricity abnormality juding accuracy is high, and stealing judges with a high credibility, improves stealing inspection specific aim.
Accompanying drawing explanation
Fig. 1 is the flow chart that the present invention calculates theoretical energy output;
Fig. 2 is differentiation flowage structure schematic diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.Following examples only for technical scheme of the present invention is clearly described, and can not limit the scope of the invention with this.
For the stealing electricity phenomenon that current distributed photovoltaic power generation exists, the invention provides a kind of stealing recognition methods based on robustness regression algorithm, discriminatory analysis is carried out to the distributed photovoltaic owner that there is stealing suspicion, for stealing inspection provides theoretical foundation.
The present invention considers that whole energy of photovoltaic generating system generation electricity come from solar irradiation, so the size of the solar irradiation intensity that photovoltaic battery array receives directly affects the size that photovoltaic cell is exerted oneself, irradiation intensity is larger, power output is larger, presents very strong positive correlation therebetween.In addition, solar cell exports and goes back temperature influence, and then affects distributed photovoltaic output of power station.The present invention passes through the homologous thread of matching irradiation and power, calculates distributed photovoltaic output of power station; Draw different matched curves respectively by spring, summer, autumn, winter Various Seasonal, carry out the impact that reaction temperature is exerted oneself on photovoltaic plant.
Distributed photovoltaic output calculation model provided by the invention, based on statistics, first sets up distributed photovoltaic power generation system cloud gray model historical information database, database comprise photovoltaic plant installed capacity, generated output, energy output and the same period meteorological data.The sample data that historical information database provides should contain spring, summer, autumn, the weather information in the four seasons in winter and the real-time power output of photovoltaic plant of correspondence thereof.Wherein, weather information and electric energy meter intelligence sample frequency higher, model is more accurate.
The present invention adopts M to estimate robust regression analysis method, the method can evade directly on middle original paper loss each in the modeling of distributed photovoltaic power station on the impact of computational accuracy.Reducing the impact of exceptional value by giving the less weight of exceptional value, obtaining irradiation power matched curve comparatively accurately.
M estimates that the basic thought of robustness regression adopts iteration weighted least square regression coefficient, minimizes one and increases progressively the lower residual error function sum of efficiency, according to the power w of the size determination each point of regression residuals
i, to reach sane object.
Regression model is as follows:
The target function that robustness regression is optimized is as follows:
in formula, n is sample number, weight w
ihuber method is adopted to carry out the calculating of residual error estimation:
Because photovoltaic power output is change at random amount, comparatively large by the meteorological condition such as irradiation, temperature variable effect, single-point does not have reference value, is to improve distributed photovoltaic stealing discrimination, and the present invention adopts theoretical photovoltaic power generation quantity as stealing whether distinguishing rule.Based on robustness regression distributed photovoltaic output calculation model framework chart as shown in Figure 1.
Distributed photovoltaic power station energy output expression formula is:
in formula, P is distributed photovoltaic power generation power, and W is photovoltaic power generation quantity.When temperature and irradiation change at random, can be similar to and think that irradiation and temperature are single peak functions, this single peak function can be replaced with the pulse of a series of wide not constant amplitude, i.e. area equivalent principle.
According to area equivalent principle, in certain hour section, can be steady state value by nonlinear curve Approximate Equivalent.By this method, within the scope of one day 24h, interval 10min gathers 144 points, regards steady state value as, obtain approximate for each sampled point constant photovoltaic array power output, then carry out energy output calculating according to formula (4).By that analogy, obtain the energy output of 144 sampled points, add up, contrast with actual light overhead utility energy output after unit conversion.
Photovoltaic power generation quantity conversion method:
After calculating theoretical energy output by above-mentioned model, the energy output of actual measurement is contrasted., adopt three layers to screen structure and identify distributed photovoltaic electricity filching behavior in the present embodiment with daily generation as a reference, stealing suspicion three layers screening structure as shown in Figure 2.
Ground floor Screening to use photovoltaic plant day theoretical energy output, this value is calculated by the distributed photovoltaic output calculation model based on robustness regression and obtains.Consider the error of calculation of photovoltaic output calculation model, regulation, when [70%, 130%] theoretical energy output is dropped in photovoltaic plant actual power generation interval, thinks that this photovoltaic power station power generation is normal.When photovoltaic plant actual power generation is greater than 130% theoretical energy output, judge that this distributed photovoltaic power station exists stealing suspicion; Further, the percentage that actual power generation is greater than theoretical energy output is higher, and the stealing suspicion that this photovoltaic plant exists is larger.Screened by ground floor, substantially can identify the distributed photovoltaic owner that major part exists stealing suspicion.But because theoretical capacity interval arranges more wide in range, there is the situation of failing to judge unavoidably.Second layer screening is carried out in the power station being " normal condition " for ground floor result of determination,
Second layer Screening to use with other photovoltaic plants of region energy output information as a reference.For ease of contrast, hourage effectively will be utilized to convert with each photovoltaic plant daily generation in region.If effectively utilize hourage to be all greater than the power station day that ground floor result of determination is " normal condition " other photovoltaic plant days of same region effectively utilize hourage, then judge that this photovoltaic plant exists certain stealing suspicion.Owing to cannot ensure that other photovoltaic plants of same region are all normal power generation states, if wherein there is stealing power station, then it effectively utilizes hourage can be higher, and now second layer screening can exist the phenomenon of failing to judge, and therefore adds third layer screening.
Third layer stealing Screening to use owner power information, by analysis distribution formula photovoltaic owner " generating-electricity consumption " relation, carries out the judgement of stealing suspicion.Because distributed photovoltaic owner is when stealing, civil power may be utilized to carry out commutation inversion or directly utilize load power consumption to pretend to be photovoltaic power generation quantity.For the distributed photovoltaic user of the type, by analyzing its " generating-electricity consumption " relation, if its photovoltaic power generation quantity and local load power consumption quite or local load power consumption and historical data base power consumption contrast find power consumption obviously rise, then judge that this distributed photovoltaic power station exists stealing suspicion.
Because distributed photovoltaic power generation is comparatively large by such environmental effects, have uncertainty and uncontrollability, only utilizing the energy output information of one day to conclude, whether not too suitable there is stealing in certain photovoltaic plant.Three layers of screening structure of carrying out judging based on daily generation that the present invention provides, object is to provide reference, if judge there is stealing suspicion in certain photovoltaic plant, time scale should be strengthened emphasis monitoring is carried out to this photovoltaic plant, continuous monitoring one week or one month, carry out comprehensive stealing assessment, thus improve inspection accuracy.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from the technology of the present invention principle; can also make some improvement and distortion, these improve and distortion also should be considered as protection scope of the present invention.
Claims (4)
1., based on a distributed photovoltaic power generation stealing recognition methods for robustness regression, it is characterized in that, comprise the steps:
1) distributed photovoltaic power generation system cloud gray model historical information database is set up;
2) according to photovoltaic effect characteristic and celestial body irradiation feature, judgement is carried out to the abnormal data existed in historical data and filters;
3) to the irradiation after filtration and power data, robustness regression model algorithm is adopted to obtain irradiation power curve;
4) according to numerical weather forecast irradiance data, utilize the irradiation power curve generated, try to achieve corresponding photovoltaic generation power;
5) utilize power electricity conversion relation, exerting oneself of Computation distribution formula photovoltaic plant different time, obtain theoretical energy output;
6) ground floor judgement is carried out: based on step 5) the theoretical energy output that obtains, judge whether normally to the actual power generation measured;
7) to step 6) in be judged to be that the data of normal condition carry out second layer judgement: hourage effectively will be utilized to convert with each photovoltaic plant daily generation in region; If effectively utilize hourage to be all greater than the power station day that ground floor result of determination is " normal condition " other photovoltaic plant days of same region effectively utilize hourage, then judge that this photovoltaic plant is abnormal, there is stealing suspicion;
8) to step 7) in the normal data of photovoltaic plant carry out third layer judgement: by analysis distribution formula photovoltaic owner " generating-electricity consumption " relation, carry out the judgement of stealing suspicion, if its photovoltaic power generation quantity and local load power consumption quite or local load power consumption and historical data base power consumption contrast empirically value power consumption obviously rise, then judge that this distributed photovoltaic power station is abnormal, there is stealing suspicion.
2. the distributed photovoltaic power generation stealing recognition methods based on robustness regression according to claim 1, it is characterized in that, described step 1) in database comprise photovoltaic plant installed capacity, generated output, energy output and the same period meteorological data and the real-time power output data of photovoltaic plant of correspondence thereof.
3. the distributed photovoltaic power generation stealing recognition methods based on robustness regression according to claim 1, is characterized in that, described step 2) in, abnormal data comprises 1) irradiation equals zero, power is non-vanishing value; 2) irradiation is greater than definite value and power is the value of zero; 3) value of now sun maximum permissible exposure is exceeded.
4. the distributed photovoltaic power generation stealing recognition methods based on robustness regression according to claim 1, it is characterized in that, described step 6) in, be judged to be that the number range of the actual power generation of normal condition is included in 70% ~ 130% of theoretical energy output numerical value.
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| CN105911321A (en) * | 2016-07-08 | 2016-08-31 | 华北电力大学 | Electrical parameter transmission relationship-based T-type connection line electricity stealing identification for power distribution network |
| CN105956394A (en) * | 2016-04-28 | 2016-09-21 | 南京理工大学 | Detection method of photovoltaic simulator inflated power based on least square method |
| CN105956713A (en) * | 2016-05-18 | 2016-09-21 | 中国电力科学研究院 | New energy annual/monthly electric quantity plan making method |
| CN108984484A (en) * | 2018-06-28 | 2018-12-11 | 河海大学常州校区 | The method for assessing two-sided double glass photovoltaic module irradiation gains based on regression analysis |
| CN110649659A (en) * | 2019-11-19 | 2020-01-03 | 浙江正泰新能源开发有限公司 | Photovoltaic inverter control method, device, equipment and computer storage medium |
| CN110969539A (en) * | 2019-11-28 | 2020-04-07 | 温岭市非普电气有限公司 | Photovoltaic electricity stealing discovery method and system based on curve morphological analysis |
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| CN110969539A (en) * | 2019-11-28 | 2020-04-07 | 温岭市非普电气有限公司 | Photovoltaic electricity stealing discovery method and system based on curve morphological analysis |
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Effective date of registration: 20160926 Address after: 210009 weft two road, hi tech Development Zone, Jiangsu, Nanjing Applicant after: State Grid Corporation of China Applicant after: State Grid Hebei Electric Power Company Applicant after: Nanjing Nanrui Solar Energy Technology Co., Ltd. Applicant after: Nanjing Nari Co., Ltd. Applicant after: NARI Technology Development Co., Ltd. Address before: 210009 weft two road, hi tech Development Zone, Jiangsu, Nanjing Applicant before: State Grid Corporation of China Applicant before: Nanjing Nari Co., Ltd. Applicant before: Nanjing Nanrui Solar Energy Technology Co., Ltd. |
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