CN112434822A - Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping - Google Patents

Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping Download PDF

Info

Publication number
CN112434822A
CN112434822A CN202011096516.2A CN202011096516A CN112434822A CN 112434822 A CN112434822 A CN 112434822A CN 202011096516 A CN202011096516 A CN 202011096516A CN 112434822 A CN112434822 A CN 112434822A
Authority
CN
China
Prior art keywords
abnormal
photovoltaic
users
value
user
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202011096516.2A
Other languages
Chinese (zh)
Inventor
费晓明
郑松松
徐俊
薛钦
沈勤卫
邢翼
周允
陈瑜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang Huayun Information Technology Co Ltd
Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Zhejiang Huayun Information Technology Co Ltd
Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang Huayun Information Technology Co Ltd, Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd filed Critical Zhejiang Huayun Information Technology Co Ltd
Priority to CN202011096516.2A priority Critical patent/CN112434822A/en
Publication of CN112434822A publication Critical patent/CN112434822A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation
    • G06Q30/0205—Location or geographical consideration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Accounting & Taxation (AREA)
  • Marketing (AREA)
  • Finance (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • Data Mining & Analysis (AREA)
  • Public Health (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Photovoltaic Devices (AREA)

Abstract

The invention discloses a household photovoltaic anomaly identification method based on intelligent electric meters and geographic information clustering, and relates to the field of photovoltaic anomaly identification. At present, abnormal photovoltaics are identified, the calculation amount is large, and the accuracy is low. According to the method, three normal users nearest to a fault user are found out, a photo-generated current value is calculated according to a single diode model formula of a photovoltaic module, then a power loss time sequence of the fault user in a single day is calculated, then the power loss can be fitted by using a least square method, and a fault diagnosis result is obtained according to a fitting result by combining the output characteristics of a photovoltaic array. The technical scheme has the advantages that comprehensive judgment is carried out in two modes, the accuracy is high, and the resource loss caused by recognition errors is reduced. Through the identification of the abnormal user, the range and the calculated amount of the abnormal diagnosis can be reduced, the time for the abnormal diagnosis is shortened, and the method has important significance for the subsequent maintenance and overhaul of the abnormal user; the income and the utilization ratio of equipment are improved.

Description

Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping
Technical Field
The invention relates to the field of photovoltaic anomaly identification, in particular to a household photovoltaic anomaly identification method based on intelligent electric meters and geographic information clustering.
Background
With the vigorous popularization of renewable clean new energy and the attention on natural ecology in China, the photovoltaic power generation is widely popularized and applied by the characteristics of no pollution, sustainability and the like. The subsidies of photovoltaic power generation policies are increased before and after 2013, large-scale photovoltaic power generation is gradually combined into a grid, distributed photovoltaic users are increased rapidly, and the installed photovoltaic capacity jumps to the first global position. After the photovoltaic is widely spread across the country and becomes stable, due to the particularity of the photovoltaic installation position, how to monitor the operation state of the photovoltaic and analyze the faults becomes the key point of the benign development of the photovoltaic.
At present, photovoltaic power generation is divided into two forms of centralized type and distributed type at home, wherein the centralized type mainly appears in a photovoltaic power station form, and the distributed type mainly appears in a household photovoltaic form. The photovoltaic power station is large in capacity and highly centralized in geographic position, and is generally provided with a centralized monitoring system, so that the work of information interaction, intelligent operation and maintenance, dispatching management and the like can be integrally completed. The household photovoltaic is installed by reporting of residents, the photovoltaic module is installed in a place (generally a roof) with sufficient sunlight at home, the photovoltaic module is provided with an inverter, direct current output by the photovoltaic module is inverted and boosted to be synchronous with a power grid, and meanwhile, the intelligent electric meter is installed on the output side of the inverter and used for recording electric energy generated by the photovoltaic module. The residential users can supply the electric energy to daily household electricity, and can also sell the electric energy to a power grid company in a grid-connected mode, so that benefits are obtained.
The household photovoltaic has the characteristics of large quantity, small single machine size compared with a photovoltaic power station, and high total capacity proportion. The installation position of the photovoltaic module is an open and shelterless outdoor environment, and the photovoltaic module can break down due to various reasons under the condition of no protection, so that the photovoltaic power generation efficiency is reduced. Due to the distributed characteristic of the photovoltaic power station, the low input-output ratio of the household positioning and operation and maintenance device, the photovoltaic power station does not have the operation and maintenance conditions of the photovoltaic power station, and the diagnosis can be relied on only by reading recorded by the intelligent electric meter and equipment information reserved by the household photovoltaic power station in a power grid company during installation. The data recorded by the intelligent electric meter are output voltage, output current, output power and daily generated energy of the photovoltaic array after being boosted by the inverter, the data reserved by a user photovoltaic user generally comprises the installation capacity, namely the rated power of the photovoltaic system under the standard test condition, and the geographic position of the user, namely the address and the longitude and latitude of the user, and meanwhile, the manufacturers and the models of the photovoltaic assembly and the inverter are recorded by part of the users.
The connection modes of most household photovoltaic modules are series-parallel connection, and the photovoltaic module has the advantages that under the condition of improving the output voltage, the stability of output is considered; meanwhile, a bypass diode is arranged in the junction box of each photovoltaic module, and the bypass diode is cut off from a circuit under the condition of certain shadow of the module, so that the output power is improved, and the function of protecting the module is also achieved. The faults on the direct current side include permanent short-circuit and open-circuit faults such as component layering, internal bubbles, unit yellowing, scratches, hot spots and the like, temporary faults caused by factors such as local shadow covering, dust pollution and the like, and abnormal aging faults occurring in the normal service life of the components, which can cause the reduction of the output power, efficiency and reliability of the photovoltaic of the household photovoltaic system. However, how to identify abnormal photovoltaics is large in calculation amount and low in accuracy.
The identification of the abnormal user is to reduce the range and the calculation amount of the abnormal diagnosis and shorten the time of the abnormal diagnosis, and has important significance for the subsequent maintenance and overhaul of the abnormal user; meanwhile, the identification of the abnormal user can also provide corresponding abnormal alarm information for the user, so that the photovoltaic user can find the abnormality in time, and the benefit and the utilization rate of equipment are improved.
Disclosure of Invention
The technical problem to be solved and the technical task to be solved are to perfect and improve the prior technical scheme, and provide a household photovoltaic abnormity identification method based on intelligent electric meters and geographic information grouping so as to achieve the purpose of accurate identification; therefore, the invention adopts the following technical scheme.
A household photovoltaic abnormity identification method based on intelligent electric meters and geographic information grouping comprises the following steps:
1) acquiring original data; the principle data comprise the geographic position of a user, and photovoltaic load and power generation amount recorded by the intelligent electric energy meter;
2) clustering according to the geographic position;
3) calculating the daily power generation amount of each photovoltaic user in unit capacity;
4) calculating the average value and the standard deviation of the daily generated energy of the unit capacity of the photovoltaic users in the divided sub-areas;
5) judging whether the daily power generation amount of each photovoltaic user unit capacity in the sub-area is an abnormal value or not according to the average value and the standard deviation;
6) when the abnormal value is detected, marking the abnormal value as a first abnormal value;
7) judging whether a first abnormal value exists in all the numerical values; if yes, entering step 8); if not, entering the step 10);
8) marking the photovoltaic users corresponding to the first abnormal values as abnormal users and removing the abnormal users; sorting to obtain the screened photovoltaic users in the area; and returning to the step 4) to recalculate the average value and the standard deviation;
9) outputting all the first abnormal values and corresponding abnormal users;
10) acquiring each household photovoltaic user, arranging the daily generated energy of the photovoltaic users in unit capacity in the divided sub-areas from small to large, and calculating the upper quartile and the lower quartile;
11) calculating the four-quadrant distance according to the quartile and determining an upper limit and a lower limit;
12) judging whether the daily power generation amount of the unit capacity of the photovoltaic users in the area is an abnormal value or not according to the upper limit and the lower limit, and if so, marking the abnormal value as a second abnormal value;
13) outputting all the second abnormal values and the corresponding abnormal users;
14) taking the intersection of the abnormal users corresponding to the first abnormal value and the second abnormal value as a final abnormal recognition output result; obtaining a final abnormal user;
15) sending abnormal warning information to a final abnormal photovoltaic user according to the abnormal recognition output result; and corresponding abnormal photovoltaic equipment is overhauled.
According to the technical scheme, firstly, clustering and grouping are carried out on all photovoltaic users according to geographical position information recorded by an installation file, the daily power generation amount of each photovoltaic user in unit capacity is calculated in each divided sub-area, abnormal values are identified according to two methods of 3-sigma and box type diagrams, and the intersection of the two methods is taken as a final abnormal identification result; the two methods are simple in calculation process, the required data amount and the calculated data amount are small, the data dimension obtained by relying on the intelligent electric meter is limited, the single identification method can have the problems of misjudgment and missed judgment, the two methods are used for comprehensive judgment, the accuracy is high, and the resource loss caused by identification errors is reduced. Through the identification of the abnormal user, the range and the calculated amount of the abnormal diagnosis can be reduced, the time for the abnormal diagnosis is shortened, and the method has important significance for the subsequent maintenance and overhaul of the abnormal user; meanwhile, the identification of the abnormal user provides corresponding abnormal alarm information for the user, so that the photovoltaic user can find the abnormality in time, and the benefit and the utilization rate of equipment are improved.
As a preferable technical means: in step 1), the obtained original information further includes the installation capacity of the user to be retained when the user is installed.
As a preferable technical means: in the step 2), clustering is carried out on all photovoltaic users by a large-scale distributed user photovoltaic space clustering method based on space correlation, so as to obtain the optimal clustering number and corresponding sub-region division; in the divided sub-regions, the meteorological conditions and the output of the household photovoltaic have consistency.
As a preferable technical means: in step 3), when the daily power generation amount of the photovoltaic users is calculated, corresponding conversion is carried out according to the difference between the system capacity and the service life, and the conversion is converted into the daily power generation amount of the unit capacity, wherein the conversion calculation formula is as follows:
Figure BDA0002723941160000051
wherein X is the daily generated energy of the converted unit capacity, X' is the daily generated energy collected by the intelligent ammeter, epsilon is the linear property maintenance value, and P is the power output of the intelligent ammetersetTo declare installation capacity.
As a preferable technical means: in step 4), calculating the average value of the daily power generation amount of the unit capacity of the photovoltaic users in the divided sub-areas
Figure BDA0002723941160000055
The calculation formula is shown in (2) and (3) with the standard deviation sigma:
Figure BDA0002723941160000052
Figure BDA0002723941160000053
and n is the total number of photovoltaic users in the sub-area.
As a preferable technical means: in step 5), when it is determined whether the daily power generation amount per unit capacity of each photovoltaic consumer is an abnormal value, if the value satisfies the formula (4), the value is determined as an abnormal value, which is not a random error but a coarse error:
Figure BDA0002723941160000054
judging whether abnormal values are identified or not, if so, marking the users corresponding to the abnormal values as abnormal users E1。
As a preferable technical means: in step 10), arranging the daily generated energy of the photovoltaic users in the divided sub-areas from small to large, and calculating the quartile Q on the daily generated energy3Lower quartile Q1(ii) a Upper quartile Q3The value at 75% of the positions after sorting, the lower quartile Q1For the value at 25% position after sorting, the calculation formula is shown in formulas (5) and (6):
Q3＝(1+[(i+1)/4]-(i+1)/4)X[(i+1)/4]+((i+1)/4-[(i+1)/4])X-(i+1)/4]+1 (5)
Q1＝(1+[3(i+1)/4]-3(i+1)/4)X[3(i+1)/4]+(3(i+1)/4-[3(i+1)/4])X[3(i+1)/4]+1 (6)
wherein [ x ] is a rounding function, and the function value is the maximum integer not exceeding the real number x; i is the total number of data X.
As a preferable technical means: in step 11), the quartile range IQR is the upper quartile Q3And lower quartile Q1The calculation formula of the upper limit and the lower limit, the quartile range IQR and the formula (7), (8) and (9) are shown as follows:
IQR＝Q3-Q1 (7)
upper limit of Q3+1.5IQR (8)
Lower limit of Q1-1.5IQR (9)
The upper limit and the lower limit are abnormal value interception points, and an inner limit is arranged between the upper limit and the lower limit; the data within the inner limit are normal values, and the data outside the inner limit are all abnormal values; marking the users corresponding to the abnormal values as abnormal users E2。
As a preferable technical means: taking the intersection of two abnormal recognition results as a final abnormal recognition output result E in the step 14):
E＝E1∩E2。
has the advantages that: according to the technical scheme, all photovoltaic users are clustered according to geographical position information recorded by an installation file, the daily power generation amount of each photovoltaic user in unit capacity is calculated in each divided sub-area, abnormal values are identified according to two methods, namely 3-sigma and a box type graph, and the intersection is taken as a final abnormal identification result by combining the results of the two methods. The two methods are simple in calculation process, the required data amount and the calculated data amount are small, the data dimension obtained by relying on the intelligent electric meter is limited, the single identification method can have the problems of misjudgment and missed judgment, the two methods are used for comprehensive judgment, the accuracy is high, and the resource loss caused by identification errors is reduced. Through the identification of the abnormal user, the range and the calculated amount of the abnormal diagnosis can be reduced, the time for the abnormal diagnosis is shortened, and the method has important significance for the subsequent maintenance and overhaul of the abnormal user; meanwhile, the identification of the abnormal user provides corresponding abnormal alarm information for the user, so that the photovoltaic user can find the abnormality in time, and the benefit and the utilization rate of equipment are improved.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 1, the present invention comprises the steps of:
step 1: acquiring original data; the method comprises data of a user photovoltaic user remaining in a power grid company during installation, and generally comprises the reporting capacity, the geographical position of the user, and photovoltaic load and power generation amount recorded by the intelligent electric meter.
Step 2: clustering according to the geographic position; the photovoltaic users are clustered by the large-scale distributed user photovoltaic space clustering method based on the space correlation for all the photovoltaic users, the optimal clustering number and the corresponding sub-region division can be obtained through the algorithm, and the weather conditions and the output of the photovoltaic users in the divided sub-regions can be considered to have better consistency.
And step 3: calculating the daily power generation amount of each photovoltaic user in unit capacity; the daily power generation amount of the photovoltaic users is correspondingly converted according to the difference between the system capacity and the service life, and is converted into the daily power generation amount of unit capacity, and the conversion calculation formula is as follows:
Figure BDA0002723941160000081
wherein X is the daily generated energy of the converted unit capacity, X' is the daily generated energy collected by the intelligent ammeter, epsilon is the linear property maintenance value, and P is the power output of the intelligent ammetersetTo declare installation capacity.
And 4, step 4: calculating the average value and the standard deviation of the daily generated energy of the unit capacity of the photovoltaic users in the divided sub-areas; calculating the average value of the daily generated energy of the unit capacity of the photovoltaic users in the divided sub-areas
Figure BDA0002723941160000082
The calculation formula is shown in (4) and (5) with the standard deviation sigma:
Figure BDA0002723941160000083
Figure BDA0002723941160000084
and n is the total number of photovoltaic users in the sub-area.
And 5: judging whether the daily power generation amount of each photovoltaic user unit capacity in the sub-area is an abnormal value or not according to the average value and the standard deviation; judging whether the daily power generation amount of each photovoltaic user unit capacity is an abnormal value or not, if the daily power generation amount accords with the following formula, determining that the daily power generation amount does not belong to a random error but is a coarse error, and determining that the daily power generation amount is the abnormal value:
Figure BDA0002723941160000085
thereby determining whether or not an abnormal value is recognized.
Step 6: when the abnormal value is detected, marking the abnormal value as a first abnormal value; marking the users corresponding to the abnormal values as abnormal users E1。
And 7: judging whether a first abnormal value exists in all the numerical values; if yes, entering step 8; if not, go to step 10.
And 8: marking the photovoltaic users corresponding to the first abnormal values as abnormal users and removing the abnormal users; sorting to obtain the screened photovoltaic users in the area; and returning to the step 4) to recalculate the average value and the standard deviation.
And step 9: outputting all the first abnormal values and the corresponding abnormal users E1。
Step 10: acquiring each photovoltaic user, arranging the daily generated energy of the photovoltaic users in unit capacity in the divided sub-areas from small to large, and calculating the quartile Q on the daily generated energy3Lower quartile Q1. Upper quartile Q3The value at 75% of the positions after sorting, the lower quartile Q1For the value at 25% position after sorting, the calculation formula is shown in formulas (5) and (6):
Q3＝(1+[(i+1)/4]-(i+1)/4)X[(i+1)/4]+((i+1)/4-[(i+1)/4])X[(i+1)/4]+1 (5)
Q1＝(1+[3(i+1)/4]-3(i+1)/4)X[3(i+1)/4]+(3(i+1)/4-[3(i+1)/4])X[3(i+1)/4]+1 (6)
wherein [ x ] is a rounding function, and the function value is the maximum integer not exceeding the real number x; i is the total number of data X.
Step 11: calculating the four-quadrant distance according to the quartile and determining an upper limit and a lower limit; the quartile range IQR is the upper quartile Q3And lower quartile Q1The calculation formula of the upper limit and the lower limit, the quartile range IQR and the formula (7), (8) and (9) are shown as follows:
IQR＝Q3-Q1 (7)
upper limit of Q3+1.5IQR (8)
Lower limit of Q1-1.5IQR (9)
The upper and lower limits are outlier cut points.
Step 12: and judging whether the daily power generation amount of the unit capacity of the photovoltaic users in the area is an abnormal value or not according to the upper limit and the lower limit, wherein the upper limit and the lower limit form an inner limit. The data within the inner limit are normal values, and the data outside the inner limit are second abnormal values; marking the users corresponding to the second abnormal values as abnormal users E2。
Step 13: outputting all the second abnormal values and the corresponding abnormal users E2。
Step 14: taking the intersection of the abnormal users corresponding to the first abnormal value and the second abnormal value as a final abnormal recognition output result; and obtaining a final abnormal user E:
E＝E1∩E2。
step 15: sending abnormal warning information to a final abnormal photovoltaic user according to the abnormal recognition output result; and corresponding abnormal photovoltaic equipment is overhauled.
The method for identifying photovoltaic anomaly of users based on smart meters and geographic information grouping shown in fig. 1 is a specific embodiment of the present invention, already embodies the substantial features and advances of the present invention, and can make equivalent modifications in shape, structure and the like according to the practical use requirements and under the teaching of the present invention, all of which are within the protection scope of the present scheme.

Claims (9)

1. A household photovoltaic abnormity identification method based on intelligent electric meters and geographic information grouping is characterized by comprising the following steps:
1) acquiring original data; the principle data comprise the geographic position of a user, and photovoltaic load and power generation amount recorded by the intelligent electric energy meter;
2) clustering according to the geographic position;
3) calculating the daily power generation amount of each photovoltaic user in unit capacity;
4) calculating the average value and the standard deviation of the daily generated energy of the unit capacity of the photovoltaic users in the divided sub-areas;
5) judging whether the daily power generation amount of each photovoltaic user unit capacity in the sub-area is an abnormal value or not according to the average value and the standard deviation;
6) when the abnormal value is detected, marking the abnormal value as a first abnormal value;
7) judging whether a first abnormal value exists in all the numerical values; if yes, entering step 8); if not, entering the step 10);
8) marking the photovoltaic users corresponding to the first abnormal values as abnormal users and removing the abnormal users; sorting to obtain the screened photovoltaic users in the area; and returning to the step 4) to recalculate the average value and the standard deviation;
9) outputting all the first abnormal values and corresponding abnormal users;
10) acquiring each household photovoltaic user, arranging the daily generated energy of the photovoltaic users in unit capacity in the divided sub-areas from small to large, and calculating the upper quartile and the lower quartile;
11) calculating the four-quadrant distance according to the quartile and determining an upper limit and a lower limit;
12) judging whether the daily power generation amount of the unit capacity of the photovoltaic users in the area is an abnormal value or not according to the upper limit and the lower limit, and if so, marking the abnormal value as a second abnormal value;
13) outputting all the second abnormal values and the corresponding abnormal users;
14) taking the intersection of the abnormal users corresponding to the first abnormal value and the second abnormal value as a final abnormal recognition output result; obtaining a final abnormal user;
15) sending abnormal warning information to a final abnormal photovoltaic user according to the abnormal recognition output result; and corresponding abnormal photovoltaic equipment is overhauled.
2. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 1, characterized in that: in step 1), the obtained original information further includes the installation capacity of the user to be retained when the user is installed.
3. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 2, characterized in that: in the step 2), clustering is carried out on all photovoltaic users by a large-scale distributed user photovoltaic space clustering method based on space correlation, so as to obtain the optimal clustering number and corresponding sub-region division; in the divided sub-regions, the meteorological conditions and the output of the household photovoltaic have consistency.
4. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 3, characterized in that: in step 3), when the daily power generation amount of the photovoltaic users is calculated, corresponding conversion is carried out according to the difference between the system capacity and the service life, and the conversion is converted into the daily power generation amount of the unit capacity, wherein the conversion calculation formula is as follows:
Figure FDA0002723941150000021
wherein X is the daily generated energy of the converted unit capacity, X' is the daily generated energy collected by the intelligent ammeter, epsilon is the linear property maintenance value, and P is the power output of the intelligent ammetersetTo declare installation capacity.
5. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 4, characterized in that: in step 4), calculating the average value of the daily power generation amount of the unit capacity of the photovoltaic users in the divided sub-areas
Figure FDA0002723941150000031
The calculation formula is shown in (2) and (3) with the standard deviation sigma:
Figure FDA0002723941150000032
Figure FDA0002723941150000033
and n is the total number of photovoltaic users in the sub-area.
6. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 5, characterized in that: in step 5), when it is determined whether the daily power generation amount per unit capacity of each photovoltaic consumer is an abnormal value, if the value satisfies the formula (4), the value is determined as an abnormal value, which is not a random error but a coarse error:
Figure FDA0002723941150000034
judging whether abnormal values are identified or not, if so, marking the users corresponding to the abnormal values as abnormal users E1。
7. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 6, characterized in that: in step 10), arranging the daily generated energy of the photovoltaic users in the divided sub-areas from small to large, and calculating the quartile Q on the daily generated energy3Lower quartile Q1(ii) a Upper quartile Q3The value at 75% of the positions after sorting, the lower quartile Q1For the value at 25% position after sorting, the calculation formula is shown in formulas (5) and (6):
Q3＝(1+[(i+1)/4]-(i+1)/4)X[(i+1)/4]+((i+1)/4-[(i+1)/4])X-(i+1)/4]+1 (5)
Q1＝(1+[3(i+1)/4]-3(i+1)/4)X[3(i+1)/4]+(3(i+1)/4-[3(i+1)/4])X[3(i+1)/4]+1 (6)
wherein [ x ] is a rounding function, and the function value is the maximum integer not exceeding the real number x; i is the total number of data X.
8. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 7, characterized in that: in step 11), the quartile range IQR is the upper quartile Q3And lower quartile Q1The calculation formula of the upper limit and the lower limit, the quartile range IQR and the formula (7), (8) and (9) are shown as follows:
IQR＝Q3-Q1 (7)
upper limit of Q3+1.5IQR (8)
Lower limit of Q1-1.5IQR (9)
The upper limit and the lower limit are abnormal value interception points, and an inner limit is arranged between the upper limit and the lower limit; the data within the inner limit are normal values, and the data outside the inner limit are all abnormal values; marking the users corresponding to the abnormal values as abnormal users E2。
9. The household photovoltaic anomaly identification method based on smart meters and geographic information clustering according to claim 8, characterized in that: taking the intersection of two abnormal recognition results as a final abnormal recognition output result E in the step 14):
E＝E1∩E2。
CN202011096516.2A 2020-10-14 2020-10-14 Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping Pending CN112434822A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011096516.2A CN112434822A (en) 2020-10-14 2020-10-14 Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011096516.2A CN112434822A (en) 2020-10-14 2020-10-14 Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping

Publications (1)

Publication Number Publication Date
CN112434822A true CN112434822A (en) 2021-03-02

Family

ID=74690570

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011096516.2A Pending CN112434822A (en) 2020-10-14 2020-10-14 Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping

Country Status (1)

Country Link
CN (1) CN112434822A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114039886A (en) * 2021-10-19 2022-02-11 国网山东省电力公司日照供电公司 Data correctness identification method and device based on four-differential and autocorrelation analysis
CN116961576A (en) * 2023-07-26 2023-10-27 国网山东省电力公司沂南县供电公司 Distributed photovoltaic power generation amount detection method based on peripheral detection data
CN116991964A (en) * 2023-09-25 2023-11-03 浙江电力交易中心有限公司 Method, system and storage medium for processing green electricity data of regional users
CN117078268A (en) * 2023-10-16 2023-11-17 国网浙江省电力有限公司宁波供电公司 Settlement method for distributed photovoltaic and financial robot

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114039886A (en) * 2021-10-19 2022-02-11 国网山东省电力公司日照供电公司 Data correctness identification method and device based on four-differential and autocorrelation analysis
CN116961576A (en) * 2023-07-26 2023-10-27 国网山东省电力公司沂南县供电公司 Distributed photovoltaic power generation amount detection method based on peripheral detection data
CN116991964A (en) * 2023-09-25 2023-11-03 浙江电力交易中心有限公司 Method, system and storage medium for processing green electricity data of regional users
CN116991964B (en) * 2023-09-25 2023-12-26 浙江电力交易中心有限公司 Method, system and storage medium for processing green electricity data of regional users
CN117078268A (en) * 2023-10-16 2023-11-17 国网浙江省电力有限公司宁波供电公司 Settlement method for distributed photovoltaic and financial robot

Similar Documents

Publication Publication Date Title
CN112434822A (en) Household photovoltaic anomaly identification method based on intelligent electric meters and geographic information grouping
Omran et al. A clustering-based method for quantifying the effects of large on-grid PV systems
Gallardo-Saavedra et al. Quantitative failure rates and modes analysis in photovoltaic plants
Wang et al. Implementation of a web-based real-time monitoring and control system for a hybrid wind-PV-battery renewable energy system
CN106230378B (en) Diagnosis method for group string fault of photovoltaic power station
CN113159601B (en) Photovoltaic power station operation state analysis method based on DBSCAN clustering algorithm
CN112671002B (en) Phase modulator configuration method and device for improving power transmission capacity of new energy high-occupancy power grid
CN103606107A (en) Wind power-solar power-energy storage combined power generation system equipment state assessment information system
Singh et al. Performance analysis of 58 kW grid-connected roof-top solar PV system
CN114943371A (en) Principal component analysis-decision tree-based photovoltaic power station power generation prediction method and device
CN112380695A (en) Method for acquiring reliability of current collection system of offshore wind farm considering real-time wind speed
CN114660381A (en) Power line branch monitoring terminal and impedance analysis method thereof
CN108696249A (en) Photovoltaic module Fault Quick Diagnosis method
Elhassene et al. Early degradation factors of solar 33 kV grid connected power plant, a comparative study
Wang et al. A linear integer programming model for fault diagnosis in active distribution systems with bi-directional fault monitoring devices installed
CN112290885B (en) DC side fault diagnosis method for household photovoltaic system
CN104767479A (en) Convergence detecting method and system for convergence box and solar power station
CN205230339U (en) Test system is gathered to smart electric meter total event based on virtual electric energy meter module
Cavalcante et al. Performance analysis of a solar photovoltaic power plant
CN114285073B (en) Grid-connected photovoltaic inverter performance analysis system
CN104779914A (en) Conflux detection method and system for conflux boxes and solar power station
Gomathy et al. Automatic monitoring and fault identification of photovoltaic system by wireless sensors
CN202495775U (en) Solar energy photovoltaic grid-connected power generation system
Gökgöz et al. Investigation of failures during commissioning and operation in photovoltaic power systems
Mariani et al. A Survey on Anomalies and Faults That May Impact the Reliability of Renewable-Based Power Systems

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination