CN110969539A - Photovoltaic electricity stealing discovery method and system based on curve morphological analysis - Google Patents

Photovoltaic electricity stealing discovery method and system based on curve morphological analysis Download PDF

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CN110969539A
CN110969539A CN201911191055.4A CN201911191055A CN110969539A CN 110969539 A CN110969539 A CN 110969539A CN 201911191055 A CN201911191055 A CN 201911191055A CN 110969539 A CN110969539 A CN 110969539A
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陈海峰
应国德
曹杰
林超
叶一博
周晨牧
陈逸婧
潘成峰
金潮
项冰野
陈肖雄
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State Grid Zhejiang Wenling Power Supply Co ltd
Wenling Feipu Electric Co ltd
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Abstract

The invention relates to a photovoltaic electricity stealing discovery method and a photovoltaic electricity stealing discovery system based on curve form analysis, wherein the method comprises the following steps: setting a clustering center of a normal user set, a clustering center of a fault user set and a clustering center of a power stealing user set; calculating the variance of each clustering center; calculating the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of each clustering center, and classifying the users for the first time; calculating the distance between the variance of the actual curve and the fitting curve of the output descending section and the variance of each clustering center, and classifying the users for the second time; and judging whether the parameters of the photovoltaic equipment of the users left in the user set to be examined after the second classification exceed the threshold value or not, and performing the third classification. The invention designs a discovery algorithm of photovoltaic electricity stealing behavior, fully excavates data information of a photovoltaic curve, has less dependence on other information of equipment and users, and has higher universality.

Description

Photovoltaic electricity stealing discovery method and system based on curve morphological analysis
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a photovoltaic electricity stealing discovery method and system based on curve morphological analysis.
Background
Because subsidies enjoyed by distributed photovoltaic power generation mainly depend on self power generation amount, certain users enable the distributed photovoltaic on-line electricity meters to measure more power generation amount through certain technical means under the drive of benefits, and further obtain the risk of high-volume subsidies, and the behavior of cheating the subsidies is called as photovoltaic electricity stealing behavior. The behavior of cheating and subsidizing photovoltaic electricity stealing seriously affects the implementation of Chinese new energy planting policies, the fairness of the power generation market, huge potential safety hazards are brought to power supply and distribution due to private line switching of users due to electricity stealing, and the normal development of the photovoltaic power generation industry is affected.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses a photovoltaic electricity stealing discovery method and a photovoltaic electricity stealing discovery system based on curve form analysis.
The technical scheme adopted by the invention is as follows:
a photovoltaic electricity stealing discovery method based on curve form analysis is provided, wherein a clustering center of a normal user set, a clustering center of a fault user set and a clustering center of an electricity stealing user set are set; calculating the variance of each clustering center;
calculating the variance between an actual curve and a fitting curve in an output ascending section aiming at the output curves of the photovoltaic devices of all users, respectively calculating the distance between the variance between the actual curve and the fitting curve of the output ascending section and the variance of each clustering center, and classifying the users for the first time;
calculating the variance between the actual curve and the fitted curve of the output descending section aiming at the output curve of the photovoltaic equipment of the users in the user set to be examined in the first classification, respectively calculating the distance between the variance between the actual curve and the fitted curve of the output descending section and the variance of each clustering center, and classifying the users for the second time;
setting a highest power generation threshold value and a lowest power generation threshold value of the photovoltaic equipment in unit time, a first-order coefficient threshold value of Fourier series of an output ascending section and a first-order coefficient threshold value of Fourier series of an output descending section in an output curve; and judging whether the parameters of the photovoltaic equipment of the users left in the user set to be examined after the second classification exceed the corresponding threshold values or not, if so, classifying the users into an electricity stealing user set, and classifying the rest users into a normal user set.
The further technical scheme is that the Fourier series parameter calculation method of the output curve of the photovoltaic equipment in the output ascending section is as follows:
Figure BDA0002293584270000021
a is a zeroth order coefficient of a Fourier series of the output rising section; b is the first order coefficient of the Fourier series of the rising section of the output(ii) a n is a data number when data are collected; m is0Number of data corresponding to sunrise time, m1Numbering data corresponding to the time when the output value tends to be stable or rises or falls; ep(n) is the amount of power generated per unit time at point n; eps(n) is an output curve obtained by fitting a formula; ep(m0) Is the amount of electricity generated per unit time at sunrise time.
The further technical scheme is that the calculation method of the variance between the actual curve and the fitting curve of the output curve of the photovoltaic equipment in the output ascending section comprises the following steps:
Figure BDA0002293584270000022
Figure BDA0002293584270000023
σ is the variance of the actual curve and the fitted curve in the force rise section.
The further technical scheme is that the method for calculating the Fourier series parameters of the output curve of the photovoltaic equipment in the output descending section comprises the following steps:
Figure BDA0002293584270000031
a' is a zero-order coefficient of Fourier series of the output descending section; b' is a first-order coefficient of Fourier series of the output reduction section; n is a data number when data are collected in the output descending section; m is2The data number corresponding to the time when the output value starts to continuously decrease is numbered; m is3Numbering data corresponding to sunset time; ep(n) the table is the generated energy per unit time at the point n; e'ps(n) is an output curve obtained by fitting a formula; ep(m2) Is the amount of electricity generated per unit time at sunset time.
The further technical scheme is that the calculation method of the variance between the actual curve and the fitting curve of the output curve of the photovoltaic equipment in the output descending section comprises the following steps:
Figure BDA0002293584270000032
Figure BDA0002293584270000033
σ' is the variance of the actual curve and the fitted curve in the output dip section.
The further technical scheme is that the clustering center of the fault user set is set as follows:
Figure BDA0002293584270000034
the further technical scheme is that
Figure BDA0002293584270000035
When the power stealing users are in use, only one side with a value larger than sigma is provided with a clustering center of the power stealing user set; when in use
Figure BDA0002293584270000036
And is
Figure BDA0002293584270000037
And then, setting the clustering centers of the electricity stealing user sets on both sides of the sigma value.
In the first classification, calculating the variance between the actual curves and the fitted curves of the output ascending sections aiming at the output curves of the photovoltaic devices of all users, and respectively calculating the distance between the variance between the actual curves and the fitted curves of the output ascending sections and the variance of each clustering center; if the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the normal user set is shortest, the users are classified into the user set to be examined; if the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the fault user set is shortest, the users are classified into the fault user set; if the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the electricity stealing user set is shortest, the user samples are collected into the electricity stealing user set;
in the second classification, calculating the variance between the actual curve and the fitting curve of the output descending section aiming at the output curve of the photovoltaic equipment of the users classified in the user set to be examined in the first classification, and respectively calculating the distance between the variance between the actual curve and the fitting curve of the output descending section and the variance of each clustering center; if the distance between the variance of the actual curve and the fitting curve of the output descending section and the variance of the clustering center of the normal user set is shortest, the user is left in the user set to be inspected; if the distance between the variance of the actual curve and the fitting curve of the output descending section and the variance of the clustering center of the fault user set is shortest, the users are classified into the fault user set; and if the distance between the variance of the actual curve and the fitted curve of the output decline section and the variance of the clustering center of the electricity stealing user set is shortest, the users are classified into the electricity stealing user set.
A photovoltaic electricity stealing discovery system based on curve morphology analysis comprises:
the cluster center setting and variance calculating module is used for setting the cluster centers of the normal user set, the fault user set and the electricity stealing user set and calculating the variance of each cluster center;
the first classification module is used for calculating the variance between an actual curve and a fitting curve in the output ascending section aiming at the output curves of the photovoltaic devices of all users, calculating the distance between the variance between the actual curve and the fitting curve of the output ascending section and the variance of each clustering center respectively, and classifying the users for the first time;
the second classification module is used for calculating the variance between the actual curve and the fitted curve of the output descending section aiming at the output curve of the photovoltaic equipment of the users in the user set to be examined classified for the first time, calculating the distance between the variance between the actual curve and the fitted curve of the output descending section and the variance of each clustering center respectively, and classifying the users for the second time;
the third classification module is provided with a highest power generation threshold value and a lowest power generation threshold value of the photovoltaic equipment in unit time, a first-order coefficient threshold value of Fourier series of an output ascending section and a first-order coefficient threshold value of Fourier series of an output descending section in an output curve; and judging whether the parameters of the photovoltaic equipment of the users left in the user set to be examined after the second classification exceed the threshold value or not, if any one of the parameters exceeds the threshold value, collecting the users into an electricity stealing user set, and collecting the rest users into a normal user set.
The photovoltaic curve information acquisition and calculation module is used for acquiring and calculating parameters of Fourier series of the output curve of the photovoltaic equipment in the output rising section, the variance of an actual curve and a fitting curve of the output curve of the photovoltaic equipment in the output rising section, the Fourier series of the output curve of the photovoltaic equipment in the output falling section and the variance of the actual curve and the fitting curve of the output curve of the photovoltaic equipment in the output falling section.
The invention has the following beneficial effects:
according to the invention, through acquiring the photovoltaic curve information of the photovoltaic equipment and clustering analysis, a user sample is classified and judged for multiple times, a discovery algorithm of photovoltaic electricity stealing behavior is designed, and the method belongs to the first time in the industry.
The method fully excavates the data information of the photovoltaic curve, has less dependence on other information of equipment and users, and has higher universality.
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FIG. 1 is a flow chart of example 1 of the present invention.
Fig. 2 is a flowchart of embodiment 2 of the present invention.
Fig. 3 is a frame configuration diagram of embodiment 4 of the present invention.
Fig. 4 is a frame configuration diagram of embodiment 5 of the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
Example 1.
FIG. 1 is a flow chart of example 1 of the present invention. As shown in fig. 1, the photovoltaic electricity stealing discovery method based on the curve morphology analysis in embodiment 1 includes:
s101, setting a clustering center of a normal user set, a clustering center of a fault user set and a clustering center of a power stealing user set; calculating the variance of each clustering center;
s102, calculating the variance between an actual curve and a fitting curve in an output ascending section aiming at the output curves of the photovoltaic devices of all users, calculating the distance between the variance between the actual curve and the fitting curve of the output ascending section and the variance of each clustering center, and classifying the users for the first time: if the variance of the actual curve and the fitting curve of the output rising section and the normal user set SnThe distance between the variances of the cluster centers is shortest, and the users are collected into a user set S to be examinedu(ii) a If the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the fault user set is shortest, the users are classified into the fault user set Sb(ii) a If the distance between the variance of the actual curve and the fitting curve of the output rising section and the variance of the clustering center of the electricity stealing user set is shortest, the user samples are collected into the electricity stealing user set Ss。
S103, classifying the user set to be examined according to the first classification SuCalculating the variance between the actual curve and the fitting curve of the output descending section, respectively calculating the distance between the variance between the actual curve and the fitting curve of the output descending section and the variance of each clustering center, and classifying the users for the second time: if the variance of the actual curve and the fitting curve of the output decline section is equal to the normal user set SnThe distance between the variances of the cluster centers is the shortest, and the user is left in the user set S to be examinedu(ii) a If the variance of the actual curve and the fitting curve of the output decline section and the fault user set SbThe distance between the variances of the cluster centers is shortest, and the users are collected into a fault user set Sb(ii) a If the variance of the actual curve and the fitting curve of the output decline section and the electricity stealing user set SsThe distance between the variances of the cluster centers is shortest, and the users are collected into a power stealing user set Ss。
S104, setting the unit timeThe method comprises the following steps that a maximum power generation threshold value, a minimum power generation threshold value, a first-order coefficient threshold value of Fourier series of an output ascending section and a first-order coefficient threshold value of Fourier series of an output descending section in an output curve of the photovoltaic equipment are obtained; the second classification is judged and then the second classification is left in the user set S to be examinedsWhether the first order coefficient of Fourier series of the output ascending section and the first order coefficient of Fourier series of the output descending section in the output curve exceed the threshold value or not is judged, if any one of the parameters exceeds the threshold value, the users are collected into a power stealing user set SsAnd judging that the user has the behavior of stealing electricity. The photovoltaic equipment with the rest users as normal is counted into a normal user set Sn。
Example 1 shows the core flow of the clustering algorithm according to the present invention.
Example 2.
Fig. 2 is a flowchart of a method of embodiment 2, and as shown in fig. 2, in embodiment 2, a photovoltaic electricity stealing discovery method based on curve morphology analysis includes:
s201, aiming at the output curves of the photovoltaic devices of all users, calculating all parameters of Fourier series of an output ascending section in the force curve:
Figure BDA0002293584270000071
in the formula (1), a is a starting point parameter and is a zeroth order coefficient (average value) of Fourier series of the output ascending section; b is a first-order coefficient of Fourier series of the output rising section and is directly related to the fluctuation degree of the curve; n is the value of the abscissa of the force curve, the force curve is a discrete curve, the abscissa is a discrete point, n represents the data number when data are acquired, the number when data are acquired for the first time every day is 0, the number when data are acquired for the second time is 1, and so on. m is0Number of data corresponding to sunrise time, m1Numbering data corresponding to the time when the output value tends to be stable or rises or falls; ep(n) is the amount of power generated per unit time at point n; eps(n) is after fitting by formulaObtaining an output curve; ep(m0) Is the amount of electricity generated per unit time at sunrise time.
S202, variance of an actual curve and a fitted curve of an output curve of the photovoltaic equipment in an output ascending section is as follows:
Figure BDA0002293584270000072
in the formula (2), the reaction mixture is,
Figure BDA0002293584270000073
the average load of the force rise section is shown, and the sigma represents the variance of the actual curve and the fitted curve in the force rise section.
S203, calculating each parameter of Fourier series of a force descending section in the force curve according to the force curve of the photovoltaic equipment:
Figure BDA0002293584270000074
in the formula (3), a' is an initial point parameter and is a zero-order coefficient (average value) of Fourier series of the output reduction section; b' is a first-order coefficient of Fourier series of the output reduction section and is directly related to the fluctuation degree of the curve; n is a data number when data are collected; m is2The data number corresponding to the time when the output value starts to continuously decrease is numbered; m is3Numbering data corresponding to sunset time; epThe (n) table shows the amount of power generation per unit time at point n. (ii) a E'ps(n) is an output curve obtained by fitting a formula; ep(m2) Is the amount of electricity generated per unit time at sunset time.
S204, the variance of the actual curve and the fitting curve of the output curve in the output descending section is as follows:
Figure BDA0002293584270000081
in the formula (4), the reaction mixture is,
Figure BDA0002293584270000082
the mean load of the output dip is shown, and σ' represents the variance of the actual curve and the fitted curve in the output dip.
S205, calculating the highest power generation amount E of the photovoltaic equipment in unit time in one daypMaxMinimum power generation amount EpMin。
And S206, selecting the credible photovoltaic users as the clustering centers of the normal user set. And users who have long-term cooperation with the power station can be defined as credible photovoltaic users.
S207, setting the clustering center of the fault user set as follows:
Figure BDA0002293584270000083
s208. when
Figure BDA0002293584270000084
When the power stealing users are in use, only one side with a value larger than sigma is provided with a clustering center of the power stealing user set; when in use
Figure BDA0002293584270000085
And is
Figure BDA0002293584270000086
And then, setting the clustering centers of the electricity stealing user sets on both sides of the sigma value.
S209, examining the variance sigma of the actual curve and the fitting curve of the output curve of the photovoltaic equipment of the user in the output ascending section. The distances are defined as follows:
dσ＝|σ-σc| (5)
in the formula (5), dσIndicating the distance. SigmacIs the variance of the cluster centers of the individual user sets of the contributing rise segment.
Respectively calculating the distance d between the variance of the actual curve and the fitting curve of the output rising section and the variance of each clustering centerσ: if the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the normal user set is shortest, the users are classified into the users to be examinedSet of users Su(ii) a If the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the fault user set is shortest, the users are classified into the fault user set Sb(ii) a If the distance between the variance of the actual curve and the fitting curve of the output rising section and the variance of the clustering center of the electricity stealing user set is shortest, the user samples are collected into the electricity stealing user set Ss。
S210, in the examination step S209, a user set S to be examineduThe variance σ' of the actual curve of the output drop section of the photovoltaic curve of the photovoltaic device of the sample to the fitted curve. The distances are defined as follows:
dσ′＝|σ′-σ′c| (6)
in the formula (6), dσ′Indicating the distance. Sigma'cIs the variance σ' of the cluster centers of the individual user sets of the contribution falling segment.
For the classification into the investigation user set S in step S209uCalculating the variance between the actual curve and the fitting curve of the output descending section according to the output curve of the photovoltaic equipment of the user, and respectively calculating the distance between the variance between the actual curve and the fitting curve of the output descending section and the variance of each clustering center: if the distance between the variance of the actual curve and the fitting curve of the output descending section and the variance of the clustering center of the normal user set is shortest, the user is left in the user set S to be inspectedu(ii) a If the distance between the variance of the actual curve and the fitting curve of the output decline section and the variance of the clustering center of the fault user set is shortest, the users are classified into the fault user set Sb(ii) a If the distance between the variance of the actual curve and the fitted curve of the output decline section and the variance of the clustering center of the electricity stealing user set is shortest, the users are classified into the electricity stealing user set Ss。
S211, in the investigation step S210, a user set S to be examined is leftuThe user samples in (1); setting the highest generated energy threshold value, the lowest generated energy threshold value, the first-order coefficient threshold value of the Fourier series of the output ascending section and the first-order coefficient threshold value of the Fourier series of the output descending section of the photovoltaic equipment in unit timeA coefficient threshold; when the unit time is within the maximum power generation amount E of the photovoltaic equipmentpMaxMinimum power generation amount EpMinIf any one parameter of a first order coefficient b of Fourier series of an output ascending section and a first order coefficient b' of Fourier series of an output descending section in the output curve is higher than a corresponding threshold value, judging that the electricity stealing behavior exists, and classifying corresponding users into an electricity stealing user set Ss. In step S210, the user set S to be examined is leftuThe remaining user samples in (1) are considered as normal photovoltaic devices and are counted into a normal user set Sn。
Embodiment 2 is further detailed and perfected on the basis of embodiment 1, and specifically discloses a method and a calculation formula for acquiring each piece of information of a photovoltaic curve.
Example 3.
In example 3, the actual operation was verified on the basis of example 2. The procedure of example 3 was exactly the same as example 2. Embodiment 3 targets 386 photovoltaic users in greens city, zhejiang. The daily sampling frequency of the 386 photovoltaic users is 288 points. Among the users, one user has a long-term cooperation relationship with a power supply company on a greenish city and belongs to a credible user, so that the index of the user is set as a clustering center.
And (4) analyzing the data of 7 months in 2019, extracting parameters such as rising-segment Fourier parameters, falling-segment Fourier parameters, maximum and minimum generated energy in unit time and the like of each sample, and analyzing. In the Fourier parameter analysis link, the suspected electricity stealing behavior of the user 3 is found. And in the clustering link of the maximum and minimum generated energy, the suspected electricity stealing behavior of the 1 user is found. And (4) confirming the fact of the electricity stealing behavior of the household 4 through the verification of the staff at home. Example 3 directly demonstrates the reliability and convenient operability of the present invention.
Example 4.
Fig. 3 is a schematic diagram of the frame structure of embodiment 4 of the present invention. As shown in fig. 3, embodiment 4 is a photovoltaic electricity stealing discovery system based on curve morphology analysis, including:
the cluster center setting and variance calculating module is used for setting the cluster centers of the normal user set, the fault user set and the electricity stealing user set and calculating the variance of each cluster center;
the first classification module is used for calculating the variance between an actual curve and a fitting curve in the output ascending section aiming at the output curves of the photovoltaic devices of all users, calculating the distance between the variance between the actual curve and the fitting curve of the output ascending section and the variance of each clustering center respectively, and classifying the users for the first time;
the second classification module is used for calculating the variance between the actual curve and the fitted curve of the output descending section aiming at the output curve of the photovoltaic equipment of the users in the user set to be examined classified for the first time, calculating the distance between the variance between the actual curve and the fitted curve of the output descending section and the variance of each clustering center respectively, and classifying the users for the second time;
the third classification module is provided with a highest power generation threshold value and a lowest power generation threshold value of the photovoltaic equipment in unit time, a first-order coefficient threshold value of Fourier series of an output ascending section and a first-order coefficient threshold value of Fourier series of an output descending section in an output curve; and judging whether the parameters of the photovoltaic equipment of the user left in the user set to be examined after the second classification exceed the threshold value or not, if any parameter exceeds the corresponding threshold value, collecting the user into the electricity stealing user set, and judging that the electricity stealing behavior exists in the user.
Example 5.
Fig. 4 is a schematic diagram of the frame structure of embodiment 5 of the present invention. As shown in fig. 4, based on embodiment 4, embodiment 5 further includes a photovoltaic curve information obtaining and calculating module, configured to obtain and calculate a parameter of a fourier series of the output curve of the photovoltaic device in the output rising section, a variance between an actual curve and a fitted curve of the output curve of the photovoltaic device in the output rising section, a fourier series of the output curve of the photovoltaic device in the output falling section, and a variance between an actual curve and a fitted curve of the output curve of the photovoltaic device in the output falling section.
The foregoing description is illustrative of the present invention and is not to be construed as limiting thereof, the scope of the invention being defined by the appended claims, which may be modified in any manner without departing from the basic structure thereof.

Claims (10)

1. A photovoltaic electricity stealing discovery method based on curve morphological analysis is characterized by comprising the following steps: setting a clustering center of a normal user set, a clustering center of a fault user set and a clustering center of a power stealing user set; calculating the variance of each clustering center;
calculating the variance between an actual curve and a fitting curve in an output ascending section aiming at the output curves of the photovoltaic devices of all users, respectively calculating the distance between the variance between the actual curve and the fitting curve of the output ascending section and the variance of each clustering center, and classifying the users for the first time;
calculating the variance between the actual curve and the fitted curve of the output descending section aiming at the output curve of the photovoltaic equipment of the users in the user set to be examined in the first classification, respectively calculating the distance between the variance between the actual curve and the fitted curve of the output descending section and the variance of each clustering center, and classifying the users for the second time;
setting a highest power generation threshold value and a lowest power generation threshold value of the photovoltaic equipment in unit time, a first-order coefficient threshold value of Fourier series of an output ascending section and a first-order coefficient threshold value of Fourier series of an output descending section in an output curve; and judging whether the parameters of the photovoltaic equipment of the users left in the user set to be examined after the second classification exceed the corresponding threshold values or not, if so, classifying the users into an electricity stealing user set, and classifying the rest users into a normal user set.
2. The photovoltaic electricity stealing discovery method based on the curve morphology analysis of claim 1, wherein the parameter calculation method of the Fourier series of the output curve of the photovoltaic equipment in the output ascending section is as follows:
Figure FDA0002293584260000011
a is the zero order of the Fourier series of the rising section of the outputA coefficient; b is a first order coefficient of a Fourier series of the output rising section; n is a data number when data are collected; m is0Number of data corresponding to sunrise time, m1Numbering data corresponding to the time when the output value tends to be stable or rises or falls; ep(n) is the amount of power generated per unit time at point n; eps(n) is an output curve obtained by fitting a formula; ep(m0) Is the amount of electricity generated per unit time at sunrise time.
3. The photovoltaic electricity stealing discovery method based on the curve morphology analysis of claim 1, wherein the variance of the actual curve and the fitted curve of the output curve of the photovoltaic equipment in the output ascending section is calculated by:
Figure FDA0002293584260000021
Figure FDA0002293584260000022
σ is the variance of the actual curve and the fitted curve in the force rise section.
4. The photovoltaic electricity stealing discovery method based on the curve morphology analysis of claim 1, wherein the parameter calculation method of the Fourier series of the output curve of the photovoltaic equipment in the output descending section is as follows:
Figure FDA0002293584260000023
a' is a zero-order coefficient of Fourier series of the output descending section; b' is a first-order coefficient of Fourier series of the output reduction section; n is a data number when data are collected in the output descending section; m is2The data number corresponding to the time when the output value starts to continuously decrease is numbered; m is3Numbering data corresponding to sunset time; ep(n) the table is the generated energy per unit time at the point n; e'ps(n) is an output curve obtained by fitting a formula; ep(m2) Is the amount of electricity generated per unit time at sunset time.
5. The photovoltaic electricity stealing discovery method based on curve morphology analysis according to claim 1, characterized in that: the method for calculating the variance between the actual curve and the fitted curve of the output curve of the photovoltaic equipment in the output descending section comprises the following steps:
Figure FDA0002293584260000024
Figure FDA0002293584260000031
σ' is the variance of the actual curve and the fitted curve in the output dip section.
6. The photovoltaic electricity stealing discovery method based on curve morphology analysis according to claim 1, wherein the cluster center of the set of faulty users is set as:
Figure FDA0002293584260000032
7. the photovoltaic electricity stealing discovery method based on curve morphology analysis according to claim 1, characterized in that: when in use
Figure FDA0002293584260000033
When the power stealing users are in use, only one side with a value larger than sigma is provided with a clustering center of the power stealing user set; when in use
Figure FDA0002293584260000034
And is
Figure FDA0002293584260000035
When the user is in the power stealing state, the user sets are set on both sides of the sigma valueThe cluster center of (2).
8. The photovoltaic electricity stealing discovery method based on curve morphology analysis according to claim 1, characterized in that: in the first classification, calculating the variance between the actual curve and the fitting curve of the output ascending section aiming at the output curves of the photovoltaic devices of all users, and respectively calculating the distance between the variance between the actual curve and the fitting curve of the output ascending section and the variance of each clustering center; if the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the normal user set is shortest, the users are classified into the user set to be examined; if the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the fault user set is shortest, the users are classified into the fault user set; if the distance between the variance of the actual curve and the fitting curve of the output ascending section and the variance of the clustering center of the electricity stealing user set is shortest, the user samples are collected into the electricity stealing user set;
in the second classification, calculating the variance between the actual curve and the fitting curve of the output descending section aiming at the output curve of the photovoltaic equipment of the users classified in the user set to be examined in the first classification, and respectively calculating the distance between the variance between the actual curve and the fitting curve of the output descending section and the variance of each clustering center; if the distance between the variance of the actual curve and the fitting curve of the output descending section and the variance of the clustering center of the normal user set is shortest, the user is left in the user set to be inspected; if the distance between the variance of the actual curve and the fitting curve of the output descending section and the variance of the clustering center of the fault user set is shortest, the users are classified into the fault user set; and if the distance between the variance of the actual curve and the fitted curve of the output decline section and the variance of the clustering center of the electricity stealing user set is shortest, the users are classified into the electricity stealing user set.
9. A photovoltaic electricity stealing discovery system based on curve morphology analysis is characterized by comprising:
the cluster center setting and variance calculating module is used for setting the cluster centers of the normal user set, the fault user set and the electricity stealing user set and calculating the variance of each cluster center;
the first classification module is used for calculating the variance between an actual curve and a fitting curve in the output ascending section aiming at the output curves of the photovoltaic devices of all users, calculating the distance between the variance between the actual curve and the fitting curve of the output ascending section and the variance of each clustering center respectively, and classifying the users for the first time;
the second classification module is used for calculating the variance between the actual curve and the fitted curve of the output descending section aiming at the output curve of the photovoltaic equipment of the users in the user set to be examined classified for the first time, calculating the distance between the variance between the actual curve and the fitted curve of the output descending section and the variance of each clustering center respectively, and classifying the users for the second time;
the third classification module is provided with a highest power generation threshold value and a lowest power generation threshold value of the photovoltaic equipment in unit time, a first-order coefficient threshold value of Fourier series of an output ascending section and a first-order coefficient threshold value of Fourier series of an output descending section in an output curve; and judging whether the parameters of the photovoltaic equipment of the users left in the user set to be examined after the second classification exceed the threshold value or not, if any one of the parameters exceeds the threshold value, collecting the users into an electricity stealing user set, and collecting the rest users into a normal user set.
10. The photovoltaic electricity stealing discovery system based on curvilinear morphological analysis according to claim 9, further comprising a photovoltaic curve information acquisition and calculation module for acquiring and calculating parameters of the fourier series of the output curve of the photovoltaic device in the ascending section of output, the variance of the actual curve and the fitted curve of the output curve of the photovoltaic device in the ascending section of output, the fourier series of the output curve of the photovoltaic device in the descending section of output, and the variance of the actual curve and the fitted curve of the output curve of the photovoltaic device in the descending section of output.
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