CN110855241B - Photovoltaic system fault diagnosis method and device - Google Patents
Photovoltaic system fault diagnosis method and device Download PDFInfo
- Publication number
- CN110855241B CN110855241B CN201911229791.4A CN201911229791A CN110855241B CN 110855241 B CN110855241 B CN 110855241B CN 201911229791 A CN201911229791 A CN 201911229791A CN 110855241 B CN110855241 B CN 110855241B
- Authority
- CN
- China
- Prior art keywords
- photovoltaic
- string
- power data
- target
- preset
- 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.)
- Active
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 91
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000002159 abnormal effect Effects 0.000 claims abstract description 114
- 230000005856 abnormality Effects 0.000 claims abstract description 61
- 238000012423 maintenance Methods 0.000 claims abstract description 38
- 230000001960 triggered effect Effects 0.000 claims abstract description 20
- 238000011065 in-situ storage Methods 0.000 claims description 16
- 238000012935 Averaging Methods 0.000 claims description 11
- 238000010248 power generation Methods 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000002093 peripheral effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 238000007667 floating Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
- H02S50/10—Testing of PV devices, e.g. of PV modules or single PV cells
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Landscapes
- Photovoltaic Devices (AREA)
Abstract
The embodiment of the invention discloses a method and a device for diagnosing faults of a photovoltaic system, wherein the method comprises the following steps: acquiring first electric power data of each photovoltaic string in the photovoltaic system at a plurality of preset moments, wherein the first electric power data comprise normalized string current or normalized string average power, and the first electric power data are related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string; when a diagnosis event is triggered and started, determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string, and marking the target photovoltaic group string with the potential abnormality; when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic group strings located at the periphery of the target photovoltaic group string at the same preset moment, it is determined that the target photovoltaic group string is abnormal. The photovoltaic system fault diagnosis method and device provided by the embodiment of the invention can improve the diagnosis accuracy and reduce the increase of operation and maintenance cost caused by false diagnosis.
Description
Technical Field
The embodiment of the invention relates to a photovoltaic power generation technology, in particular to a photovoltaic system fault diagnosis method and device.
Background
In a photovoltaic power station, photoelectric conversion is performed through photovoltaic modules and photovoltaic string strings, and the photoelectric conversion efficiency of the whole photovoltaic string is affected by an inefficient photovoltaic module in the photovoltaic string, so how to rapidly identify the inefficient photovoltaic modules, reduce the loss of generated energy, and improve the operation and maintenance efficiency and the power generation efficiency of the photovoltaic power station is very important work.
At present, the problem of low diagnosis accuracy exists in the existing photovoltaic system fault diagnosis method, and the operation and maintenance efficiency and the power generation efficiency of a photovoltaic power station are influenced.
Disclosure of Invention
The embodiment of the invention provides a photovoltaic system fault diagnosis method and device, which aim to improve the diagnosis accuracy and reduce the increase of operation and maintenance cost caused by false diagnosis.
In a first aspect, an embodiment of the present invention provides a method for diagnosing a fault of a photovoltaic system, where the method includes:
acquiring first electric power data of each photovoltaic string in the photovoltaic system at a plurality of preset moments, wherein the first electric power data comprise normalized string current or normalized string average power, and the first electric power data are related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string;
when a diagnosis event is triggered and started, determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string, and marking the target photovoltaic group string with the potential abnormality;
when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic group strings located at the periphery of the target photovoltaic group string at the same preset moment, it is determined that the target photovoltaic group string is abnormal.
Optionally, obtaining first power data of each photovoltaic group string at a plurality of preset times in the photovoltaic system includes:
acquiring second power data of each photovoltaic module at preset intervals, wherein the second power data are the module current or module power of the photovoltaic module corresponding to the first power data;
averaging second electric power data of each photovoltaic module in any photovoltaic group string at any preset moment to obtain third electric power data of the photovoltaic group strings at a plurality of preset moments;
and normalizing the third power data based on the inclination angle coefficient, the orientation coefficient and the characteristic coefficient of the photovoltaic string to obtain first power data.
Optionally, a calculation formula of the first power data of the photovoltaic string at any preset time is as follows:
Q'string＝α*β*x*QString；
Wherein, Q'StringRepresenting first power data; qStringRepresenting a string current or a string average power of the photovoltaic string corresponding to the first power data; alpha represents the inclination angle coefficient of the photovoltaic string; beta represents an orientation coefficient of the photovoltaic string; x represents the characteristic coefficient of the photovoltaic string.
Optionally, triggering the initiation of a diagnostic event includes:
averaging first power data of each photovoltaic group string in the photovoltaic system at any preset moment to obtain fourth power data of the photovoltaic system at a plurality of preset moments;
determining whether the fourth power data is greater than a startup diagnostic threshold;
if the fourth power data is greater than the startup diagnostic threshold, it is determined that a startup diagnostic event is triggered.
Optionally, determining a target photovoltaic group string with a potential abnormality at a preset time in each photovoltaic group string includes:
judging whether the value of the first power data of the photovoltaic string lower than the corresponding fourth power data exceeds a preset percentage or not;
and if the first electric power data of the photovoltaic string is lower than the corresponding fourth electric power data and exceeds a first preset percentage, judging that the photovoltaic string has potential abnormality, and taking the photovoltaic string as a target photovoltaic string.
Optionally, determining that no potential abnormal mark exists in the photovoltaic string located around the target photovoltaic string at the same preset time includes:
determining photovoltaic group strings positioned at the periphery of the target photovoltaic group string according to the preset serial numbers of the photovoltaic group strings;
acquiring target marks of photovoltaic group strings positioned at the periphery of the target photovoltaic group string at the same preset moment;
and if the target mark of any photovoltaic group string positioned at the periphery of the target photovoltaic group string does not exist or is not a preset mark, determining that no potential abnormal mark exists in the photovoltaic group strings positioned at the periphery of the target photovoltaic group string.
Optionally, the method further includes:
when a target photovoltaic group string is in a non-maintenance state, if it is determined that at least one photovoltaic group string located at the periphery of the target photovoltaic group string at the same preset moment has a potential abnormal mark, adding 1 to the count of the potential abnormal marks of the target photovoltaic group string;
and if the count of the continuous potential abnormal marks of the target photovoltaic string at a plurality of preset moments exceeds a count threshold, judging that the target photovoltaic string is abnormal.
Optionally, the method further includes:
and eliminating the potential abnormal marks of the target photovoltaic group string when the target photovoltaic group string is in the maintenance state.
Optionally, the method further includes:
acquiring fifth power data of each photovoltaic module at preset intervals, wherein the fifth power data comprise module voltage or module power;
averaging the fifth power data of each photovoltaic module at any preset moment to obtain sixth power data of the photovoltaic module string at a plurality of preset moments;
when the sixth power data is larger than the judgment threshold, if the fifth power data of the photovoltaic module is lower than the corresponding sixth power data by more than a second preset percentage, judging that the photovoltaic module is abnormal.
In a second aspect, an embodiment of the present invention further provides a photovoltaic system fault diagnosis apparatus, where the apparatus includes:
the photovoltaic power generation system comprises a first power data acquisition module, a second power data acquisition module and a control module, wherein the first power data acquisition module is used for acquiring first power data of each photovoltaic string in the photovoltaic system at a plurality of preset moments, the first power data comprises normalized string current or normalized string average power, and the first power data is related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string;
the abnormal group string marking module is used for determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string when a diagnosis event is triggered to start, and marking the target photovoltaic group string with potential abnormality;
and the abnormal string judgment module is used for judging that the target photovoltaic string is abnormal if the photovoltaic strings around the target photovoltaic string at the same preset moment are determined to have no potential abnormal mark when the target photovoltaic string is in a non-maintenance state.
In a third aspect, the embodiment of the invention further provides a photovoltaic system fault diagnosis system, which comprises an in-situ diagnosis device and a cloud server, wherein the in-situ diagnosis device is in communication connection with the cloud server;
the in-situ diagnosis equipment is used for calculating first electric power data of each photovoltaic string in the photovoltaic system at a plurality of preset moments and sending the first electric power data to the cloud server, wherein the first electric power data comprise normalized string current or normalized string average power, and the first electric power data are related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string;
the cloud server is used for determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string when a diagnosis event is triggered and started, and marking the target photovoltaic group string with the potential abnormality; when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic group strings located at the periphery of the target photovoltaic group string at the same preset moment, it is determined that the target photovoltaic group string is abnormal.
The embodiment of the invention provides a photovoltaic system fault diagnosis method and device, wherein first electric power data of each photovoltaic string in a photovoltaic system at a plurality of preset moments are obtained, wherein the first electric power data comprise normalized string current or normalized string average power, and the first electric power data are related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string; when a diagnosis event is triggered and started, determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string, and marking the target photovoltaic group string with the potential abnormality; when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic group strings located at the periphery of the target photovoltaic group string at the same preset moment, it is determined that the target photovoltaic group string is abnormal. According to the photovoltaic system fault diagnosis method and device provided by the embodiment of the invention, the first power data is subjected to normalization processing, the inclination angle coefficient, the orientation coefficient, the characteristic coefficient and the like of the photovoltaic string are considered, abnormal diagnosis false alarm is avoided, the temporary normal low value caused by weather, human factors and the like is filtered by combining the diagnosis state of the photovoltaic string at the periphery of the photovoltaic string, the factors influencing the power generation of the assembly and the string are fully considered, the judgment method is simple and convenient, the diagnosis accuracy is improved, and the operation and maintenance cost rise caused by false diagnosis is reduced.
Drawings
Fig. 1 is a flowchart of a method for diagnosing a fault of a photovoltaic system according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for diagnosing a fault of a photovoltaic system according to a second embodiment of the present invention;
fig. 3 is a schematic numbering diagram of a photovoltaic string according to a second embodiment of the present invention;
fig. 4 is a flowchart of a method for diagnosing a fault of a photovoltaic system according to a third embodiment of the present invention;
fig. 5 is a block diagram of a photovoltaic system fault diagnosis apparatus according to a fourth embodiment of the present invention;
fig. 6 is a structural block diagram of a photovoltaic system fault diagnosis system provided in the fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a photovoltaic system fault diagnosis method according to an embodiment of the present invention, and referring to fig. 1, the determining method specifically includes the following steps:
step 110, acquiring first power data of each photovoltaic group string in the photovoltaic system at a plurality of preset moments.
Specifically, the first power data includes a normalized string current or a normalized string average power, and the first power data is related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string; the calculation formula of the first power data of the photovoltaic string at any preset moment is as follows:
Q'string＝α*β*x*QString
Wherein, Q'StringRepresenting first power data; qStringRepresenting a string current or a string average power of the photovoltaic string corresponding to the first power data; alpha represents the inclination angle coefficient of the photovoltaic string; beta represents an orientation coefficient of the photovoltaic string; x represents the characteristic coefficient of the photovoltaic string. Meter for first power data from photovoltaic stringThe calculation formula can obtain first power data of each photovoltaic group string in the photovoltaic system at a plurality of preset moments, the preset moments can be preset fixed moments, and time intervals between adjacent preset moments can be equal or unequal; the preset times can also be determined by the initial time for collecting the power data of the photovoltaic module related to the first power data and the collection time interval, for example, data is collected every 1 minute, and the preset times can be set according to actual conditions, and are not limited specifically.
And step 120, when the diagnosis event is triggered to be started, determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string, and marking the target photovoltaic group string with the potential abnormality.
Specifically, when the first power data is greater than a start-up diagnosis threshold, a start-up diagnosis event is triggered, where the start-up diagnosis threshold may be 25% of a rated current or a rated power of the photovoltaic string, and the start-up diagnosis threshold may be set according to an actual requirement, which is not limited herein. When a trigger diagnostic event is triggered, if QStringWhen the difference value of the first power data and the second power data is lower than the first power data and exceeds a preset percentage of the first power data, determining that the photovoltaic string is a target photovoltaic string with potential abnormality, and marking the target photovoltaic string with the potential abnormality, wherein the preset percentage may be 3% -5%, and is not limited specifically.
Step 130, when the target photovoltaic string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic strings around the target photovoltaic string at the same preset time, it is determined that the target photovoltaic string is abnormal.
Specifically, when the target photovoltaic string is in a non-maintenance state, i.e., a working state, if no potential abnormal mark exists in the photovoltaic strings around the target photovoltaic string at the same preset time, it is determined that the target photovoltaic string is abnormal; if the photovoltaic strings positioned at the periphery of the target photovoltaic string at the same preset moment have potential abnormal marks, adding 1 to the potential abnormal count of the target photovoltaic string, and judging that the target photovoltaic string is abnormal when the continuous potential abnormal count exceeds an M value (the M value can be 30-60, and actually takes a value according to the local weather condition); and if the target photovoltaic group string is in a maintenance state, eliminating the potential abnormal mark of the target photovoltaic group string. When the target photovoltaic string is judged to be abnormal, the abnormal target photovoltaic string can be added into the abnormal warning queue, and the abnormal photovoltaic string can be searched in the abnormal warning queue.
According to the photovoltaic system fault diagnosis method provided by the embodiment, first power data of each photovoltaic group string in the photovoltaic system at a plurality of preset moments is obtained, when a diagnosis event is triggered to start, a target photovoltaic group string with potential abnormality at one preset moment in each photovoltaic group string is determined, potential abnormality marks are made on the target photovoltaic group string, and when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormality mark exists in photovoltaic group strings around the target photovoltaic group string at the same preset moment, it is determined that the target photovoltaic group string is abnormal. According to the photovoltaic system fault diagnosis method provided by the embodiment, the first power data is subjected to normalization processing, the inclination angle coefficient, the orientation coefficient, the characteristic coefficient and the like of the photovoltaic string are considered, abnormal diagnosis false alarm is avoided, the temporary normal low value caused by weather, human factors and the like is filtered by combining the diagnosis state of the photovoltaic string at the periphery of the photovoltaic string, the factors influencing the power generation of the assembly and the string are fully considered, the judgment method is simple and convenient, the diagnosis accuracy is improved, and the increase of operation and maintenance cost caused by false diagnosis is reduced.
Example two
Fig. 2 is a flowchart of a photovoltaic system fault diagnosis method according to a second embodiment of the present invention, where the present embodiment is optimized based on the foregoing embodiment, and with reference to fig. 2, the method specifically includes the following steps:
Specifically, the first power data may be acquired by the following method: collecting second electric power data of each photovoltaic module at preset time intervals; averaging second electric power data of each photovoltaic module in any photovoltaic group string at any preset moment to obtain third electric power data of the photovoltaic group strings at a plurality of preset moments; and normalizing the third power data based on the inclination angle coefficient, the orientation coefficient and the characteristic coefficient of the photovoltaic string to obtain first power data.
And the second power data is the component current or the component power of the photovoltaic component corresponding to the first power data. One photovoltaic string includes a plurality of photovoltaic modules connected in series, the module current of a photovoltaic module is the string current of the photovoltaic string, and the module currents of the photovoltaic modules in different photovoltaic strings may be different.
Specifically, the third power data includes a string current and a string average power, Q, of the photovoltaic string prior to normalizationStringFor example, for string average power, the average power of a string can be obtained by dividing the sum of the device powers of the photovoltaic devices in a photovoltaic string by the number of devices. Wherein the normalization process can be obtained according to the formula for calculating the first power data, i.e. according to Q'StringAnd QStringThe first power data may be obtained from the relationship of (a).
Step 220, determining whether to trigger a start diagnostic event.
The method for judging whether the trigger is triggered is as follows: averaging first power data of each photovoltaic group string in the photovoltaic system at any preset moment to obtain fourth power data of the photovoltaic system at a plurality of preset moments; it is determined whether the fourth power data is greater than a startup diagnostic threshold.
Specifically, the first power data of each photovoltaic group string are summed, and the sum of the obtained first power data is divided by the number of the photovoltaic group strings in the photovoltaic system, so that fourth power data of the photovoltaic system at a plurality of preset moments can be obtained, wherein the fourth power data comprises the average current and the average power of the photovoltaic system. Wherein the startup diagnostic threshold may be 25% of the rated current or rated power of the photovoltaic module. If the fourth power data is greater than the startup diagnostic threshold, it is determined that a startup diagnostic event is triggered.
And step 230, if the starting diagnosis event is not triggered, the photovoltaic system fault diagnosis operation is not executed.
Step 240, if the diagnosis event is triggered to be started, whether a target photovoltaic string with potential abnormality exists in the photovoltaic string at a preset moment is judged.
Specifically, whether a target photovoltaic string with potential abnormality exists in the photovoltaic string at a preset moment is judged by judging whether the value of the first electric power data of the photovoltaic string lower than the corresponding fourth electric power data exceeds a preset percentage.
Specifically, it is first determined whether the first power data is lower than the corresponding fourth power data, and it is determined that a ratio of a difference between the first power data and the corresponding fourth power data to the corresponding fourth power data exceeds a preset percentage.
And step 250, if no target photovoltaic group string with potential abnormality exists in the photovoltaic group strings at a preset moment, not marking the target photovoltaic group string with potential abnormality.
And step 260, if a target photovoltaic string with potential abnormality exists in the photovoltaic string at a preset moment, marking the target photovoltaic string with potential abnormality.
Specifically, if the first power data of the photovoltaic string is lower than the corresponding fourth power data by more than a first preset percentage, it is determined that the photovoltaic string is potentially abnormal, and the photovoltaic string is taken as a target photovoltaic string.
The first preset percentage may be 3% to 5%, and when the normalized string current of the photovoltaic string is lower than the average current of the photovoltaic system or the normalized string average power of the photovoltaic string is lower than the average power of the photovoltaic system, it is determined that the photovoltaic string has a potential abnormality.
And step 270, judging whether the target photovoltaic string is in a maintenance state.
Step 280, if the target photovoltaic string is in a non-maintenance state, determining the photovoltaic strings around the target photovoltaic string according to the preset serial numbers of the photovoltaic strings.
Specifically, fig. 3 is a schematic diagram of numbering photovoltaic string provided in the second embodiment of the present invention, and referring to fig. 3, the rows of photovoltaic string are numbered sequentially from south to north, and each row of photovoltaic string is numbered sequentially from west to east, so as to determine the preset number of each photovoltaic string. For example, SWn-m may represent the nth row mth string of photovoltaic groups. If the photovoltaic string with the number SW2-2 is the target photovoltaic string, the peripheral string is determined to be SW1-2\ SW3-2\ SW2-1\ SW 2-3.
It should be noted that fig. 3 is only a schematic illustration of numbers of the photovoltaic string, and the number of components in the photovoltaic string, the number of photovoltaic strings in each row and each column, and the number of rows and columns of the photovoltaic strings are not particularly limited.
And 290, judging whether the photovoltaic string around the target photovoltaic string has no potential abnormal mark at the same preset time.
Specifically, a target mark of a photovoltaic group string located at the periphery of the target photovoltaic group string at the same preset time can be obtained; judging whether a target mark of any photovoltaic string positioned at the periphery of the target photovoltaic string exists or not and whether the target mark is a preset mark or not; and if the target mark of any photovoltaic group string positioned at the periphery of the target photovoltaic group string does not exist or is not a preset mark, determining that no potential abnormal mark exists in the photovoltaic group strings positioned at the periphery of the target photovoltaic group string.
Specifically, each photovoltaic string corresponds to a mark segment at any preset time, when it is determined that the photovoltaic string is potentially abnormal, a potential abnormal mark (any set symbol) is made in the mark segment, and when the photovoltaic string is not potentially abnormal, no mark is made in the mark segment. It is also possible to mark a symbol, such as "1", in the mark segment when it is determined that there is a potential abnormality in the photovoltaic string, that is, mark "1" as a potential abnormality mark, and mark another symbol, such as "0" and "0", in the mark segment when there is no potential abnormality in the photovoltaic string, that indicates that there is no potential abnormality.
And 291, if it is determined that at least one photovoltaic string in the photovoltaic string around the target photovoltaic string at the same preset time has a potential abnormal mark, adding 1 to the count of the potential abnormal marks of the target photovoltaic string.
Specifically, the pv strings around the target pv string refer to pv strings adjacent to the target pv string in at least one of the four directions of east, west, south, and north, for example, the pv string numbered SW2-2 in fig. 3 has a potential abnormality, which is the target pv string, and if there is a potential abnormality flag in at least one of the 4 pv strings SW1-2\ SW3-2\ SW2-1\ SW2-3 around the target pv string SW2-2, the count of the potential abnormality flag in SW2-2 is increased by 1.
Step 293, if the count of the continuous potential anomaly marks of the target photovoltaic string at a plurality of preset times exceeds the count threshold, determining that the target photovoltaic string is abnormal.
Wherein, the count threshold value is M, and the value of M can take the value 30 ~ 60, actually comes the value according to local weather condition. At least one photovoltaic group string positioned at the periphery of the target photovoltaic group string has a potential abnormal mark at the continuous (M-1) preset moments, and if the count of the continuous potential abnormal marks of the target photovoltaic group string at the plurality of preset moments exceeds M, the target photovoltaic group string is judged to be abnormal. And if the count is less than M, waiting for the next judgment, and avoiding a temporary normal low value caused by floating cloud, human factors and the like.
And 294, if the target photovoltaic string is in a maintenance state or whether none of the photovoltaic strings around the target photovoltaic string at the same preset time has the potential abnormal mark, eliminating the potential abnormal mark of the target photovoltaic string.
Specifically, if the target photovoltaic string is in a maintenance state such as a maintenance state, the determined potential abnormality is invalid, the potential abnormality flag of the target photovoltaic string needs to be cleared, and the count value of the abnormality flag can be cleared.
According to the photovoltaic system fault diagnosis method provided by the embodiment, the third power data is subjected to normalization processing, the inclination angle coefficient, the orientation coefficient, the characteristic coefficient and the like of the photovoltaic string are considered, abnormal diagnosis false alarm is avoided, the diagnosis state of the photovoltaic string around the photovoltaic string is combined to filter temporary normal low values caused by weather, human factors and the like, factors influencing power generation of the assembly and the string are fully considered, the judgment method is simple and convenient, the diagnosis accuracy is improved, and the increase of operation and maintenance cost caused by false diagnosis is reduced.
EXAMPLE III
Fig. 4 is a flowchart of a photovoltaic system fault diagnosis method provided in a third embodiment of the present invention, which may be based on the foregoing embodiments, and with reference to fig. 4, the method specifically includes the following steps:
and 310, collecting fifth power data of each photovoltaic module at preset time intervals.
Wherein the fifth power data includes a component voltage or a component power.
And 320, averaging the fifth power data of each photovoltaic module at any preset moment to obtain sixth power data of the photovoltaic module string at a plurality of preset moments.
Specifically, the average of the component voltages or the component powers of the photovoltaic components in the photovoltaic string is obtained, that is, the average voltage or the average power of the photovoltaic string is obtained, and the sixth power data includes the average voltage or the average power of the photovoltaic string.
And 330, when the sixth power data is larger than the judgment threshold, if the fifth power data of the photovoltaic module is lower than the corresponding sixth power data by more than a second preset percentage, judging that the photovoltaic module is abnormal.
The starting judgment threshold value can be 25% of the rated voltage or rated power of the photovoltaic module corresponding to the sixth power data, and the second preset percentage can be 10% -20%. If the voltage of a certain photovoltaic module in the photovoltaic string is lower than the average voltage of the photovoltaic string by more than 20%, the photovoltaic module can be judged to be abnormal.
According to the photovoltaic system fault diagnosis method provided by the embodiment, whether the photovoltaic module is abnormal or not is judged according to the relation between the fifth power data and the sixth power data, and a more accurate judgment result can be obtained.
Example four
Fig. 5 is a block diagram of a photovoltaic system fault diagnosis apparatus according to a fourth embodiment of the present invention, where the apparatus includes: a first power data acquisition module 410, an abnormal group string marking module 420 and an abnormal group string determination module 430; wherein,
the first power data acquisition module 410 is configured to acquire first power data of each photovoltaic string in the photovoltaic system at a plurality of preset times, where the first power data includes a normalized string current or a normalized string average power, and the first power data is related to an inclination angle coefficient, an orientation coefficient, and a characteristic coefficient of the photovoltaic string;
the abnormal group string marking module 420 is configured to determine, when a start diagnosis event is triggered, a target photovoltaic group string in each photovoltaic group string, which has a potential abnormality at a preset time, and mark the target photovoltaic group string with the potential abnormality;
the abnormal string determining module 430 is configured to determine that the target photovoltaic string is abnormal if it is determined that none of the photovoltaic strings located around the target photovoltaic string at the same preset time has a potential abnormal flag when the target photovoltaic string is in a non-maintenance state.
On the basis of the above embodiment, the first power data obtaining module 410 may include an acquiring unit, a calculating unit and a normalization processing unit, where the acquiring unit is configured to acquire second power data of each photovoltaic module at preset time intervals, where the second power data is a module current or a module power of the photovoltaic module corresponding to the first power data; the calculating unit is used for averaging second electric power data of each photovoltaic assembly in any photovoltaic group string at any preset moment to obtain third electric power data of the photovoltaic group strings at a plurality of preset moments; the normalization processing unit is used for performing normalization processing on the third electric power data based on the inclination angle coefficient, the orientation coefficient and the characteristic coefficient of the photovoltaic string to obtain first electric power data.
In one embodiment, the photovoltaic system fault diagnosis method further includes a trigger starting module, where the trigger starting module includes a calculating unit, a judging unit and a triggering unit, and the calculating unit is configured to average first power data of each photovoltaic group string in the photovoltaic system at any preset time to obtain fourth power data of the photovoltaic system at multiple preset times; the judging unit is used for judging whether the fourth power data is larger than a starting diagnosis threshold value; the triggering unit is used for judging that a starting diagnosis event is triggered if the fourth power data is larger than a starting diagnosis threshold value.
Preferably, the abnormal string determination module 430 includes a determination unit and an abnormal determination unit, where the determination unit is configured to determine whether a value of the first power data of the photovoltaic string, which is lower than the corresponding fourth power data, exceeds a preset percentage; the abnormity determining unit is used for determining that the photovoltaic string has potential abnormity if the first electric power data of the photovoltaic string is lower than the corresponding fourth electric power data and exceeds a first preset percentage, and taking the photovoltaic string as a target photovoltaic string.
In one embodiment, the abnormal group string determining module 430 includes a determining unit, an obtaining unit, and a potential abnormal marking unit, where the determining unit is configured to determine, according to a preset number for each photovoltaic group string, a photovoltaic group string located around a target photovoltaic group string; the acquisition unit is used for acquiring a target mark of a photovoltaic group string positioned at the periphery of the target photovoltaic group string at the same preset moment; the potential abnormal marking unit is used for determining that no potential abnormal mark exists in any photovoltaic string around the target photovoltaic string if the target mark of any photovoltaic string around the target photovoltaic string does not exist or is not a preset mark.
In an embodiment, the abnormal string determination module 430 includes a counting unit and a determination unit, where the counting unit is configured to, when the target pv string is in a non-maintenance state, add 1 to a count of potential abnormal marks of the target pv string if it is determined that at least one pv string in the pv strings around the target pv string at the same preset time has a potential abnormal mark; the judgment unit is used for judging that the target photovoltaic group string is abnormal if the count of the continuous potential abnormal marks of the target photovoltaic group string at a plurality of preset moments exceeds a count threshold value.
Preferably, the photovoltaic system fault diagnosis method further includes an elimination module, and the elimination module is used for eliminating the potential abnormal mark of the target photovoltaic string when the target photovoltaic string is in the maintenance state.
Preferably, the photovoltaic system fault diagnosis method further comprises an acquisition module, a mean value calculation module and an abnormality determination module, wherein the acquisition module is used for acquiring fifth electric power data of each photovoltaic module at preset time intervals, and the fifth electric power data comprises module voltage or module power; the average value calculation module is used for averaging the fifth power data of each photovoltaic module at any preset moment to obtain sixth power data of the photovoltaic module string at a plurality of preset moments; the abnormity determining module is used for determining that the photovoltaic module is abnormal if the fifth power data of the photovoltaic module is lower than the corresponding sixth power data by more than a second preset percentage when the sixth power data is larger than the judgment threshold.
The photovoltaic system fault diagnosis device provided by the embodiment has the corresponding beneficial effects of the photovoltaic system fault diagnosis method.
EXAMPLE five
Fig. 6 is a block diagram of a photovoltaic system fault diagnosis system according to a fifth embodiment of the present invention, and referring to fig. 6, the system includes an in-situ diagnosis device 10 and a cloud server 20, and the in-situ diagnosis device 10 is in communication connection with the cloud server 20; wherein,
the in-situ diagnosis device 10 is configured to calculate first power data of each photovoltaic string 30 in the photovoltaic system at a plurality of preset times, and send the first power data to the cloud server 20, where the first power data includes a normalized string current or a normalized string average power, and the first power data is related to an inclination angle coefficient, an orientation coefficient, and a characteristic coefficient of the photovoltaic string 30;
the cloud server 20 is configured to determine, when a start diagnosis event is triggered, a target photovoltaic string with a potential anomaly at a preset time in each photovoltaic string 30, and mark the target photovoltaic string with the potential anomaly; when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic group strings located at the periphery of the target photovoltaic group string at the same preset moment, it is determined that the target photovoltaic group string is abnormal.
Specifically, for each photovoltaic string in the photovoltaic system, referring to fig. 3, the module current, the string current, the module average power, the string average power, the inclination angle coefficient α of the photovoltaic string 30, the orientation coefficient β of the photovoltaic string 30, the characteristic coefficient x of the photovoltaic string 30, the number of the photovoltaic string 30, the current operation and maintenance state of the photovoltaic string 30, and the potential anomaly count of the photovoltaic string 30 are obtained.
Wherein, the in-situ diagnosis device 10 collects the voltage or power of each photovoltaic module 40 in the photovoltaic string 30 once at a time interval (for example, 1 minute), averages the voltage or power, and sends the average to the cloud server 20, referring to fig. 3, if there are photovoltaic modules 22 in the photovoltaic string numbered SW2-2, the voltage of each photovoltaic module at a time T is U respectively1、U2…U22Then the average voltage of SW2-2 can be obtained as U(SW2-2)＝(U1+U2+…+U22)/22。
Specifically, the in-situ diagnostics device 10 first makes a voltage start-up diagnostic determination when the voltage U is present(SW2-2)Starting judgment is started only when the voltage is larger than a starting judgment threshold value, weak light diagnosis errors are avoided, and the starting judgment threshold value can be 25% of the rated voltage of the photovoltaic module 40; and then judging the abnormality of the photovoltaic modules 40, and marking the abnormality of a certain photovoltaic module when the voltage of the photovoltaic module is lower than the average voltage Q% (Q takes the value of 10-20). The in-situ diagnostic device 10 may also make a power-on diagnostic decision when the power P is(SW2-2)Starting judgment is started only when the power is larger than a starting judgment threshold value, weak light diagnosis errors are avoided, and the starting judgment threshold value can be 25% of rated power of the photovoltaic module 40; and then judging the abnormality of the photovoltaic modules 40, and marking the abnormality of a certain photovoltaic module when the power of the photovoltaic module is lower than the average power Q% (Q takes the value of 10-20).
The in-situ diagnostics device 10 normalizes the string current of the photovoltaic string, which is I if the photovoltaic string numbered SW2-2 is usedSkewer (SW2-2)＝I(SW2-2)*α*β*x，I(SW2-2)For the string current of the photovoltaic string acquired by the in-situ diagnostic device 10, the α, β, and x coefficients are sent to the in-situ diagnostic device 10 by the cloud server 20, the α, β, and x coefficients can be manually adjusted, and the cloud server 20 may resend the adjusted values to the diagnostic device 10 each time.
The in-situ diagnostics device 10 will normalize the abnormal photovoltaic module and the normalized time T ISkewer (SW2-2)The normalized string power P at time T may also be sent to the cloud server 20 by the in-situ diagnostics device 10Skewer (SW2-2)Sent to the cloud server 20, the normalized string power is calculated in the same manner as the current,normalized group string power PSkewer (SW2-2)＝P(SW2-2)*α*β*x。
The string current normalized at the time T of each photovoltaic string is transmitted to the cloud server 20, and the cloud server 20 recalculates the average current, I, at the time T of each photovoltaic stringStation＝[ISkewer (SW1-1)+ISkewer (SW1-2)+…+ISkewer (SW2-1)+…+ISkewer (SWn-m)+…+ISkewer (SWX-Y)]N, N-m is the number of the photovoltaic string, N represents the row where the photovoltaic string is located, m represents the digit of the row where the photovoltaic string is located, X represents the total row number of the photovoltaic string, Y represents the total digit of the photovoltaic string in the X-th row, SWX-Y represents the last photovoltaic string in the last row, and N is the number of the photovoltaic string, and meanwhile, the abnormal component information of the photovoltaic string uploaded by the in-situ diagnosis device 10 is directly added into the abnormal alarm queue. If the power is calculated in the last step, calculating the average power of each photovoltaic string at the T moment, PStation＝[PSkewer (SW1-1)+PSkewer (SW1-2)+…+PSkewer (SW2-1)+…+PSkewer (SWn-m)+…+PSkewer (SWX-Y)]/N。
If IStationIf the threshold value is larger than the starting diagnosis threshold value S, the following diagnosis is calculated, otherwise, the diagnosis is not carried out, and the cutoff threshold value S can be about 25% of the rated current of the photovoltaic component. The cloud server 20 makes an abnormality diagnosis for the photovoltaic string, ISkewer (SWn-m)Is less than IStationAnd (3) marking potential abnormality marks on the photovoltaic strings exceeding mu% (mu can take a value of 3-5), and judging for 2 times to determine whether the abnormal state exists.
Judging one: and judging the current operation and maintenance state of the photovoltaic string, and if the photovoltaic string is in the operation and maintenance state, ignoring the current potential abnormal mark.
And II, judging: judging whether the photovoltaic group strings around the photovoltaic group string number are marked with potential abnormality or not, if the photovoltaic group strings around the photovoltaic group string number are not marked with potential abnormality, confirming that the photovoltaic group strings are set as abnormal alarm and adding the abnormal alarm into an abnormal alarm queue; and if the peripheral photovoltaic string is marked as potential abnormality, adding 1 to the potential abnormality count of the photovoltaic string, and when the continuous potential abnormality count exceeds the M value (the M value can be preferably 30-60, and the value is actually obtained according to the local weather condition), setting the photovoltaic string as an abnormality alarm and adding the abnormality alarm into an abnormality alarm queue.
Referring to fig. 3, taking the pv string numbered SW2-2 as an example, if SW2-2 is marked as a potentially abnormal string, then, it is determined whether the photovoltaic strings around SW2-2 are also marked as potential abnormal strings, the surrounding strings are SW1-2\ SW3-2\ SW2-1\ SW2-3, if none of the peripheral 4 strings are marked, SW2-2 is set to an exception alert and added to the exception alert queue, if at least one of the peripheral 4 photovoltaic strings is marked as a potential abnormity, judging a potential abnormity count, if the count is greater than or equal to M, SW2-2 is set to an exception alarm and added to the exception alarm queue, and if the potential abnormal count is less than M, adding 1 to the potential abnormal count, waiting for the next judgment, and avoiding a temporary normal low value caused by floating clouds, human factors and the like.
Specifically, in the actual operation and maintenance inspection, if the abnormal photovoltaic string is found to be normal and no abnormal phenomenon exists, the values α, β, and x may be manually adjusted, the abnormal value of the photovoltaic string is adjusted to the normal value, usually, the value x is corrected, and after the correction, the cloud server 20 issues the corrected value to the local diagnostic device 10 again.
The photovoltaic system fault diagnosis system provided by the embodiment has the corresponding beneficial effects of the photovoltaic system fault diagnosis method.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for diagnosing faults of a photovoltaic system is characterized by comprising the following steps:
acquiring first electric power data of each photovoltaic string in a photovoltaic system at a plurality of preset moments, wherein the first electric power data comprise normalized string current or normalized string average power, and the first electric power data are related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string;
when a diagnosis event is triggered to start, determining a target photovoltaic group string with potential abnormality at one preset moment in each photovoltaic group string, and marking the target photovoltaic group string with potential abnormality;
when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic group strings located at the periphery of the target photovoltaic group string at the same preset moment, judging that the target photovoltaic group string is abnormal;
when the target photovoltaic group string is in a non-maintenance state, if it is determined that at least one photovoltaic group string located at the periphery of the target photovoltaic group string at the same preset time has a potential abnormal mark, adding 1 to the count of the potential abnormal marks of the target photovoltaic group string;
and if the count of the continuous potential abnormal marks of the target photovoltaic string at the preset moments exceeds a count threshold, judging that the target photovoltaic string is abnormal.
2. The method for diagnosing the faults of the photovoltaic system according to claim 1, wherein the step of acquiring first power data of each photovoltaic group string in the photovoltaic system at a plurality of preset moments comprises the following steps:
acquiring second electric power data of each photovoltaic module at preset intervals, wherein the second electric power data are the module current or module power of the photovoltaic module corresponding to the first electric power data;
averaging second power data of each photovoltaic assembly in any one photovoltaic group string at any preset moment to obtain third power data of the photovoltaic group string at the preset moments;
and normalizing the third power data based on the inclination angle coefficient, the orientation coefficient and the characteristic coefficient of the photovoltaic string to obtain the first power data.
3. The photovoltaic system fault diagnosis method according to claim 1 or 2, wherein the calculation formula of the first power data of the photovoltaic string at any one of the preset times is as follows:
Q'string＝α*β*x*QString；
Wherein, Q'StringRepresenting the first power data; qStringRepresenting a string current or a string average power of the photovoltaic string corresponding to the first power data; a represents a tilt angle coefficient of the photovoltaic string; β represents an orientation coefficient of the photovoltaic string; x represents a characteristic coefficient of the photovoltaic string.
4. The photovoltaic system fault diagnosis method according to claim 1, characterized in that triggering a start-up diagnosis event comprises:
averaging first power data of each photovoltaic group string in the photovoltaic system at any preset time to obtain fourth power data of the photovoltaic system at the preset times;
determining whether the fourth power data is greater than a startup diagnostic threshold;
if the fourth power data is greater than the start-up diagnostic threshold, determining to trigger a start-up diagnostic event.
5. The method according to claim 4, wherein determining a target photovoltaic string with a potential abnormality at the preset time in each photovoltaic string comprises:
judging whether the value of the first power data of the photovoltaic string lower than the corresponding fourth power data exceeds a preset percentage or not;
and if the first power data of the photovoltaic string is lower than the corresponding fourth power data and exceeds a first preset percentage, judging that the photovoltaic string has potential abnormality, and taking the photovoltaic string as the target photovoltaic string.
6. The method for diagnosing faults of a photovoltaic system according to claim 1, wherein determining that no potential abnormal mark exists in the photovoltaic strings located around the target photovoltaic string at the same preset time includes:
determining the photovoltaic group strings positioned at the periphery of the target photovoltaic group string according to the preset number of each photovoltaic group string;
acquiring a target mark of the photovoltaic group string positioned at the periphery of the target photovoltaic group string at the same preset moment;
and if the target mark of any one photovoltaic group string positioned at the periphery of the target photovoltaic group string does not exist or is not a preset mark, determining that no potential abnormal mark exists in the photovoltaic group string positioned at the periphery of the target photovoltaic group string.
7. The photovoltaic system fault diagnosis method according to claim 1, further comprising:
eliminating potential anomaly markings of the target photovoltaic string when the target photovoltaic string is in a maintenance state.
8. The photovoltaic system fault diagnosis method according to claim 1, further comprising:
acquiring fifth electric power data of each photovoltaic module in the photovoltaic group string at preset intervals, wherein the fifth electric power data comprise module voltage or module power;
averaging fifth power data of each photovoltaic module in the photovoltaic string at any preset moment to obtain sixth power data of the photovoltaic string at the preset moments;
when the sixth power data is larger than a starting judgment threshold value, if the fifth power data of the photovoltaic module is lower than the corresponding sixth power data by more than a second preset percentage, judging that the photovoltaic module is abnormal.
9. A photovoltaic system fault diagnosis apparatus, comprising:
the photovoltaic power generation system comprises a first power data acquisition module, a second power data acquisition module and a control module, wherein the first power data acquisition module is used for acquiring first power data of each photovoltaic string in the photovoltaic system at a plurality of preset moments, the first power data comprises normalized string current or normalized string average power, and the first power data is related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string;
the abnormal group string marking module is used for determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string when a diagnosis event is triggered to start, and marking the target photovoltaic group string with potential abnormality;
an abnormal string determining module, configured to determine that the target photovoltaic string is abnormal if it is determined that none of the photovoltaic strings located around the target photovoltaic string at the same preset time has a potential abnormal mark when the target photovoltaic string is in a non-maintenance state;
the photovoltaic string management module is further configured to, when the target photovoltaic string is in a non-maintenance state, add 1 to the count of the potential abnormal marks of the target photovoltaic string if it is determined that at least one of the photovoltaic strings located around the target photovoltaic string at the same preset time has a potential abnormal mark;
and if the count of the continuous potential abnormal marks of the target photovoltaic string at the preset moments exceeds a count threshold, judging that the target photovoltaic string is abnormal.
10. The photovoltaic system fault diagnosis system is characterized by comprising an on-site diagnosis device and a cloud server, wherein the on-site diagnosis device is in communication connection with the cloud server;
the in-situ diagnosis equipment is used for calculating first electric power data of each photovoltaic string in a photovoltaic system at a plurality of preset moments and sending the first electric power data to the cloud server, wherein the first electric power data comprise normalized string current or normalized string average power, and the first electric power data are related to an inclination angle coefficient, an orientation coefficient and a characteristic coefficient of the photovoltaic string;
the cloud server is used for determining a target photovoltaic group string with potential abnormality at a preset moment in each photovoltaic group string when a diagnosis event is triggered and started, and marking the target photovoltaic group string with potential abnormality; when the target photovoltaic group string is in a non-maintenance state, if it is determined that no potential abnormal mark exists in the photovoltaic group strings located at the periphery of the target photovoltaic group string at the same preset moment, judging that the target photovoltaic group string is abnormal;
when the target photovoltaic group string is in a non-maintenance state, if it is determined that at least one photovoltaic group string located at the periphery of the target photovoltaic group string at the same preset time has a potential abnormal mark, adding 1 to the count of the potential abnormal marks of the target photovoltaic group string;
and if the count of the continuous potential abnormal marks of the target photovoltaic string at the preset moments exceeds a count threshold, judging that the target photovoltaic string is abnormal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911229791.4A CN110855241B (en) | 2019-12-04 | 2019-12-04 | Photovoltaic system fault diagnosis method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911229791.4A CN110855241B (en) | 2019-12-04 | 2019-12-04 | Photovoltaic system fault diagnosis method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110855241A CN110855241A (en) | 2020-02-28 |
| CN110855241B true CN110855241B (en) | 2022-04-08 |
Family
ID=69607652
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911229791.4A Active CN110855241B (en) | 2019-12-04 | 2019-12-04 | Photovoltaic system fault diagnosis method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110855241B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113242018B (en) * | 2021-06-17 | 2023-08-04 | 阳光智维科技股份有限公司 | Photovoltaic equipment fault diagnosis method and application device thereof |
| CN113708490B (en) * | 2021-08-18 | 2023-12-19 | 阳光智维科技股份有限公司 | Abnormality detection method and device for photovoltaic power generation tracking system and storage medium |
| CN113872524B (en) * | 2021-09-13 | 2024-06-04 | 阳光智维科技股份有限公司 | Method and device for positioning low-efficiency group strings and computer storage medium |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102707213B (en) * | 2012-05-08 | 2015-01-21 | 常州天合光能有限公司 | Detection method for failure of encapsulated cells |
| CN103701410B (en) * | 2013-12-31 | 2016-09-21 | 阳光电源股份有限公司 | A kind of photovoltaic group string fault recognition method, Apparatus and system |
| CN103985060A (en) * | 2014-04-25 | 2014-08-13 | 云南省电力设计院 | Method for calculating photovoltaic power station generating capacity based on light metering data |
| CN104767481B (en) * | 2015-04-28 | 2017-01-25 | 北京铂阳顶荣光伏科技有限公司 | Method and system for monitoring working state of solar photovoltaic power station |
| CN104779914B (en) * | 2015-04-28 | 2017-01-25 | 北京铂阳顶荣光伏科技有限公司 | Conflux detection method and system for conflux boxes and solar power station |
| CN105184404B (en) * | 2015-08-31 | 2018-12-18 | 中国科学院广州能源研究所 | Output power classification forecasting system suitable for photovoltaic system Life cycle |
| CN106100580A (en) * | 2016-08-05 | 2016-11-09 | 江阴海润太阳能电力有限公司 | A kind of method that photovoltaic plant equipment fault monitors in real time |
| CN109298228A (en) * | 2018-09-13 | 2019-02-01 | 安徽天尚清洁能源科技有限公司 | A kind of Intelligence Diagnosis method and system based on photovoltaic group string current anomaly |
-
2019
- 2019-12-04 CN CN201911229791.4A patent/CN110855241B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110855241A (en) | 2020-02-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110855241B (en) | Photovoltaic system fault diagnosis method and device | |
| JP5584622B2 (en) | Fault detection method for photovoltaic system | |
| JP5736530B1 (en) | A method of predicting the future current value or the amount of power generation decline of a photovoltaic power generation system | |
| CN112924471A (en) | Equipment fault diagnosis system and diagnosis method thereof | |
| KR20160064451A (en) | A Photovoltaic Modular Abnormal Condition Effective Diagnosis System and Method thereof | |
| CN104485889A (en) | Failure detection method for photovoltaic power generation units with multiple same mounting dip angles | |
| CN108131321B (en) | A kind of axial fan stall fault monitoring method | |
| CN117609739B (en) | Structure on-line monitoring method based on multi-point deformation data joint analysis | |
| JP2013239686A (en) | Failure detection method and monitoring system for solar power generation | |
| CN116317166B (en) | Photovoltaic module fault positioning analysis method based on cloud computing real-time online monitoring | |
| CN116667783A (en) | Distributed photovoltaic power station maintenance system | |
| CN113010394A (en) | Machine room fault detection method for data center | |
| CN104849654B (en) | Circuit breaker online monitoring method and system | |
| JP2019109059A (en) | Abnormality diagnosis apparatus and abnormality diagnosis method | |
| CN115902483A (en) | Power consumption measurement monitoring early warning device among electric power system | |
| CN113361849B (en) | Determination method and device of override probability model, and risk assessment method and device | |
| CN113848347A (en) | Health state detection method for wind meter of wind driven generator | |
| CN118071178B (en) | Method and device for evaluating abnormal state of power station, electronic equipment and storage medium | |
| CN112630522A (en) | Photovoltaic grid-connected power station fault type detection device based on temperature lattice measurement | |
| CN112381423B (en) | Power grid cascading failure risk assessment method considering relay protection refusal influence | |
| WO2016170558A1 (en) | Diagnostic device, diagnostic system, and diagnostic method | |
| CN117614381A (en) | Abnormality detection method and device for photovoltaic string, storage medium and computer equipment | |
| CN117060574B (en) | Visual monitoring platform for electric power monitoring | |
| CN118590001A (en) | Photovoltaic array online health evaluation method and device and terminal equipment | |
| CN117471174A (en) | Low-voltage transformer area line abnormal impedance detection method and system based on acquisition terminal |
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 | ||
| CB02 | Change of applicant information |
Address after: High tech Zone of Hefei city of Anhui Province in 230088 Lake Road No. 2 Applicant after: Sunshine New Energy Development Co.,Ltd. Address before: 230088 6th floor, R & D center building, no.1699 Xiyou Road, high tech Zone, Hefei City, Anhui Province Applicant before: HEFEI SUNGROW RENEWABLE ENERGY SCI. & TECH. Co.,Ltd. Address after: High tech Zone of Hefei city of Anhui Province in 230088 Lake Road No. 2 Applicant after: Sunshine New Energy Development Co.,Ltd. Address before: High tech Zone of Hefei city of Anhui Province in 230088 Lake Road No. 2 Applicant before: Sunshine New Energy Development Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |