CN107733357B - The fault detection algorithm of battery panel in a kind of large-sized photovoltaic power station - Google Patents
The fault detection algorithm of battery panel in a kind of large-sized photovoltaic power station Download PDFInfo
- Publication number
- CN107733357B CN107733357B CN201610945557.1A CN201610945557A CN107733357B CN 107733357 B CN107733357 B CN 107733357B CN 201610945557 A CN201610945557 A CN 201610945557A CN 107733357 B CN107733357 B CN 107733357B
- Authority
- CN
- China
- Prior art keywords
- value
- voltage
- algorithm
- current
- standard
- 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
- 238000001514 detection method Methods 0.000 title claims abstract description 50
- 230000032683 aging Effects 0.000 claims abstract description 37
- 230000007613 environmental effect Effects 0.000 claims abstract description 17
- 239000000428 dust Substances 0.000 claims abstract description 15
- 238000005286 illumination Methods 0.000 claims abstract description 15
- 238000013517 stratification Methods 0.000 claims abstract description 14
- 230000003044 adaptive effect Effects 0.000 claims abstract description 8
- 238000012544 monitoring process Methods 0.000 claims abstract description 7
- 238000010248 power generation Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 42
- 238000012360 testing method Methods 0.000 claims description 33
- 230000002159 abnormal effect Effects 0.000 claims description 18
- 238000005259 measurement Methods 0.000 claims description 16
- 238000012545 processing Methods 0.000 claims description 8
- 230000005611 electricity Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 241000196324 Embryophyta Species 0.000 description 3
- 230000004888 barrier function Effects 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 241001269238 Data Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 210000003850 cellular structure Anatomy 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000002688 persistence Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012105 stratification Analysis Methods 0.000 description 1
- 238000012956 testing procedure Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
-
- 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 invention belongs to technical field of photovoltaic power generation, it is related to a kind of fault detection algorithm of battery panel in large-sized photovoltaic power station, the present invention directly issues manipulation command using host computer, the electric current of the every branch of the moment that will acquire and the voltage of every piece of battery plate, and environmental parameter includes intensity of illumination and temperature, it is calculated after input data, wherein using the lateral comparison algorithm positioned to single failure panel, to the history comparison algorithm of power station entirety aging judgement, and the more new algorithm of the standard value as control, and the individual failure record sheet using aided algorithm, history mean value record sheet, and standard value look-up table, individual failure positioning is carried out simultaneously to judge with whole aging/dust stratification.Energy on-line real time monitoring of the present invention is easily achieved, is avoided that soft error bring is judged by accident, and constructing standard value look-up table using adaptive by the way of, adapts to different geographical and environmental condition, thus with stronger universality and higher accuracy.
Description
Technical field
The invention belongs to technical field of photovoltaic power generation, the fault detection for being related to battery panel in a kind of large-sized photovoltaic power station is calculated
A kind of method of battery plate fault detection positioning and whole aging or dust stratification judgement in method more particularly to large-sized photovoltaic array.
Background technique
With the continuous development of new energy technology, the application of solar energy power generating is more and more extensive.The prior art
The core component that photovoltaic array is photovoltaic generating system is disclosed, it is by several pieces of series-parallel photovoltaic battery panel groups
At.Data shows that photovoltaic plant common at present is made of the large-scale photovoltaic array of outdoor placement, by environmental factor shadow
Ring, very likely there is the case where individual failure, aging or dust stratification in photovoltaic panel, due to photovoltaic panel substantial amounts Chang Wufa and
When detection be out of order, cannot be replaced in time, seriously affect the generating efficiency of photovoltaic plant.
Based on above-mentioned status, those skilled in the art use the fault detection means of some photovoltaic panels, by acquiring photovoltaic
The parameters such as voltage, the electric current of panel are detected, and still, practice display collects the data of panel parameter, can not directly obtain
The conclusion for the situation that is out of order also needs to carry out rationally and effectively diagnostic analysis to data, can obtain accurate fault location and event
Hinder type.Following limitations are still had in the photovoltaic generating system fault detection algorithm of the prior art, as: one of classification
In technology, by the method judgement of current differential, (document<solar battery array Research on fault diagnosis method>, the 14th complete
State's equipment monitoring and diagnosis academic conference, 2010), found out by filtering out the excessive adjacent column of current differential containing faulty group
The branch of part, and the voltage value of each photovoltaic module of the branch is analyzed, position the photovoltaic cell component that is out of order；The algorithm
It can be realized by real-time measurement, and only need to measure voltage and current information, calculation amount is small, and it is easy to accomplish, still, still deposit
In problem: cannot effectively be judged soft error, be easy to produce erroneous judgement situations such as blocking to what is temporarily occurred, and wherein
Lateral comparison between carry out branch, lacks analysis of the combining environmental parameter to power station overall work situation, cannot reflect power station
Situations such as whole aging or dust stratification；Wherein in the technology of another classification, it is trained using given data, obtains partial data
Library, then the algorithm compared with measurement data, Chinese patent (application number 201410449777.6) disclose a kind of using fuzzy poly-
The method that class algorithm carries out breakdown judge regathers photovoltaic its main feature is that first passing through extensive work early period establishes fault knowledge library
Power failure warning message is as sample to be compared, by comparing degree of membership, and selects the highest fault type conduct of degree of membership
Sample to be tested represent fault type, the algorithm the problem is that: need to judge in advance a certain group of data be fault data,
Degree of membership algorithm could be used, can not directly judge failure by electric parameters such as electric current, the voltages of easy test；And the calculation
Religious services or rituals first needs a large amount of training data, is used to compare to establish a fault knowledge library, since failure mode is various, failure
It emulates complex, vast resources will be consumed；Also, since the collection in fault knowledge library is limitation, and fault knowledge library
Update needs long time, thus the case where be difficult to adapt to various geographical environments, after geographical environment change, fault knowledge library
Content will face inaccuracy problem；There are also in the technology of classification, the algorithm of breakdown judge is carried out using neural network model,
Join using electric parameters such as maximum power point electric current, maximum power point voltage, short circuit current, open-circuit voltages as the input of algorithm
Number obtains fault model output by BP neural network, to judge a kind of fault type (document " photovoltaic module of four parameters
On-line fault diagnosis method ", Proceedings of the CSEE, on May 5th, 2014, the 13rd phase of volume 34), (document " and based on BP mind
Diagnosing failure of photovoltaic array research through network ", electric power system protection and control, on August 16th, 2013, the 16th phase of volume 41),
The method directly can judge fault type by electric parameter, but the problem is that: algorithm itself is complex, calculation amount
Greatly, it and a large amount of fault datas is also needed is trained；It can not be real with open-circuit voltage additionally as the short circuit current of algorithm input
When measure, need that photovoltaic array is made to temporarily cease work, influence power station generating efficiency, etc..
In view of the limitation of the prior art, present inventor is quasi- provide it is a kind of can online fast real-time monitoring, be easy to
Realize, can carry out simultaneously individual failure positioning and whole aging judge and adapt to different geographical and environmental condition with stronger
The photovoltaic array fault detection algorithm of universality and more high accuracy.
Summary of the invention
The object of the present invention is in view of the defects existing in the prior art, battery panel in a kind of large-sized photovoltaic power station is provided
Fault detection algorithm more particularly to a kind of large-sized photovoltaic array in battery plate fault detection positioning judge with whole aging or dust stratification
Method.The method of the present invention energy on-line real time monitoring, be easily achieved, combining environmental information and electric parameter carry out simultaneously it is individual therefore
The photovoltaic array fault detection of barrier positioning and whole aging judgement.
Specifically, in a kind of large-sized photovoltaic power station of the invention battery panel fault detection algorithm, which is characterized in that its
Including, manipulation command is directly issued using host computer, will acquire equipment obtain every branch of the moment electric current and every piece
The environmental parameter (including intensity of illumination and temperature) that the voltage and enviromental monitoring equipment of battery plate obtain, as input data into
Row calculates, and realizes while carrying out the photovoltaic array fault detection of individual failure positioning with whole aging judgement；It wherein uses: to list
The lateral comparison algorithm of a failure panel positioning, to the history comparison algorithm of power station entirety aging judgement, and as control
Standard value more new algorithm, and searched using the individual failure record sheet of aided algorithm, history mean value record sheet and standard value
Table.
In the present invention, the photovoltaic array is the group string that multiple photovoltaic battery plates are constituted, and additionally strong equipped with can measure illumination
The environmental parameter monitoring device of degree and temperature；
In the present invention, the host computer generallys use the computer that can directly issue manipulation command；
Wherein, the correlation table that detection algorithm is preserved in host computer:
1) primary standard value look-up table can be changed: for comparing with institute's measured data, to judge whole aging or dust stratification feelings
Condition；
The standard value look-up table includes (a) light intensity-current standard value look-up table, for recording various intensity of illumination situations
Under corresponding current standard value, (b) temperature-voltage standard value look-up table records corresponding voltage standard under the conditions of various temperature
Value；Wherein, light intensity-current standard value look-up table, temperature-voltage standard value look-up table, preserve initially set in the initial state
The standard value set obtains one wherein every group of data are used under the premise of known light intensity value to a update flag bit is also corresponded to
A standard current value, the average current of each branch when as power station normal power generation, compares with the measurement average value of electric current,
It updates flag bit and indicates its corresponding data to whether in initial state, it, can be by when certain group data is to original state is in
It updates, after being updated once, corresponding flag bit is labeled, so that standard value is determined；
2) individual failure record sheet: the number occurred extremely for recording certain block battery plate current or voltage, to judge certain block
Whether battery plate breaks down comprising:
(a) position of failure battery plate can be indicated with number；
(b) failure indicates number, abnormal number occurs to record the battery plate current or voltage,
(c) electric current of the battery plate, voltage value size when breaking down；
(d) failure indicates the final updating time, is indicated with testing serial number, for screening out the soft error being likely to occur；
In the embodiment of the present invention, the individual failure record sheet includes:
The position of abnormal battery panel can be indicated, it is also possible to which two-dimensional coordinate is indicated with one-dimensional number；
The Reflector of abnormal battery panel records the battery panel and is detected abnormal number, for determining the face
Whether plate breaks down；
Electric current, electric voltage exception value are detected electric current and voltage value when exception every time for the battery plate, may include multiple groups
Data；The final updating time, when being detected abnormal for the battery plate the last time, corresponding test serial number is soft for excluding
The Reflector of mistake resets process；
In the embodiment of the present invention, the Reflector resets process, can be periodically to every in individual failure record sheet
The Reflector of one group of failure logging and final updating time carry out screening, by comparing last more row time and current time,
By Reflector, not increased failure logging is automatically deleted for a long time；
3) history mean value record sheet: record every time measurement when full power station average current and voltage, illumination when test is strong
Degree and temperature judge whether situations such as aging and dust stratification occur, wrap to analyze the overall variation trend of power station electricity generation situation
It includes:
(a) branch current mean value is the branch current to each fault-free group string in entire power station, striked average electricity
Stream；
(b) full battery plate average voltage is the battery plate to all fault-frees in entire power station mark, striked average voltage；
(c) serial number is tested, to indicate the time tested every time；
In the embodiment of the present invention, history mean value record sheet includes:
Electric current ensemble average value measures the average value of obtained each branch current every time；
Voltage ensemble average value measures the average value of obtained each battery panel voltage every time；
Intensity of illumination, temperature value, the i.e. illumination intensity value and temperature value of measurement record every time can be as environmental parameter
Corresponding voltage, current standard value are found in standard value look-up table；
Serial number is tested, for indicating the time sequencing of each test, wherein add 1 to it automatically when test every time, use is just whole
Number reflects the precedence relationship of each secondary test result.
In detection algorithm of the invention, main part includes that lateral comparison algorithm, standard value more new algorithm, history compare calculation
Method (whole aging judgement)；
Wherein, lateral comparison algorithm compares each series arm size of current, further extracts to the group string of current value exception
Voltage value is compared again, and the photovoltaic module position of voltage value exception is stored in the individual failure record sheet, if
Have the failure logging of the position in table, then increase the Reflector of the battery plate, when Reflector reaches it is a certain number of when, to
Outer interface shows that the battery panel is likely to occur failure and reports its position, while showing Current Voltage fault data for ginseng
It examines；
In the lateral comparison algorithm, individual failure record sheet is automatically deleted according to the final updating time of deposited element
Reflector unchanged battery plate information for a long time prevents erroneous judgement caused by as temporarily blocked equal soft errors；
In the embodiment of the present invention, the lateral comparison algorithm of single failure panel position includes:
To measure each branch current at moment, algorithm that each piece of battery panel voltage is constituted input parameter；
Transverse current comparison step compares the difference of each branch current and average value；
One current ratio coefficient set in advance, when the ratio between a certain branch current difference and average value are greater than the ratio
When coefficient, it is believed that the branch exists abnormal；
Lateral voltage comparison step compares the difference of each piece of panel voltage and average value for abnormal branch；
One voltage ratio coefficient set in advance, when the ratio between a certain panel voltage difference and average value are greater than the ratio
When coefficient, it is believed that the panel exists abnormal；
It needs to record individual failure when detecting a certain panel exception with the association process of individual failure record sheet
Table carries out corresponding modification to save the failure logging of the battery plate；
One fault index threshold set in advance, when certain corresponding Reflector of block battery panel be more than the threshold value,
It is considered as panel failure, and exports the fault message of the panel outward.
The history comparison algorithm (whole aging judgement) excludes existing in individual failure record sheet in each measurement
Battery panel, average voltage level is sought to the battery panel of normal work, current average is sought to the branch of normal work,
And it is stored in simultaneously with environmental parameter in history mean value record sheet；When storing data reaches certain amount, into whole aging
Judgement finds each measurement in electric current-standard of luminous intensity value look-up table and voltage-temperature standard value look-up table and records corresponding electricity
Stream, standard voltage value, and calculate the difference of measurement mean value and standard value；The song about the time is fitted for counted counted difference
Line, by analyze its variation tendency judge its whether aging；In the embodiment of the present invention, history comparison algorithm includes:
With the environmental parameter for measuring the intensity of illumination at moment, temperature value is constituted, with each branch current, each piece of panel voltage
The electric parameter of composition, the input parameter as algorithm；
With the association process of individual failure record sheet, by retrieval individual failure record sheet recorded in failure logging,
Electric current and voltage value abnormal in the electric parameter of input are excluded, to increase the accuracy of algorithm；
Electric current, voltage entirety average value processing merge the current value of all branches with the voltage value of all panels, use
The whole result that the average value of electric current and voltage is measured as this；
With the association process of history mean value record sheet, obtained whole result will be measured every time and is stored in history mean value record
In table；
With the association process of standard value look-up table, by light intensity and temperature value as search condition, in standard value look-up table
In obtain electric current and standard voltage value, be compared with measured value；
Curve fitting process, using test serial number as abscissa, electric current (voltage) and standard value difference as ordinate
Curve is fitted, indicates whole aging (or dust stratification) trend.
The standard value more new algorithm will execute after the data volume in history mean value record sheet reaches certain scale；If surveying
Same light intensity value frequency of occurrence reaches given threshold in examination, then the corresponding light intensity is found in light intensity-current standard value look-up table
The corresponding current measurement value of the light intensity value is averaged, replaces if it, which updates flag bit, shows that the point data not yet updates by the point of value
The current value of the point in former table is changed, later dirty bit, indicating the point data not allows to update again.Reality of the invention
It applies in example, it is different according to power station local environment and used equipment, electric current, voltage are adaptively updated during the test
The algorithm of standard value comprising:
The characteristic disposably updated, i.e. each group of standard value can only update once after algorithm operation, no longer change later；
With the association process of history mean value record sheet, adaptive updates stream is opened when data reach certain scale in table
Journey；
One electric current set in advance updates threshold value, when the identical record number of light intensity value is greater than in history mean value record sheet
When the threshold value, the update of this group of standard value is carried out；
One voltage set in advance updates threshold value, when the identical record number of temperature value is greater than in history mean value record sheet
When the threshold value, the update of this group of standard value is carried out；
With the association process of standard value look-up table, all standard values are stored in standard value look-up table, and standard value updates
Algorithm will directly be updated the data of the table.
Update in the present invention, to the more new technological process and light intensity-current standard value look-up table of temperature-voltage standard lookup table
Process is similar, i.e., after certain number occurs in same temperature value, the corresponding point for changing temperature value is found in a lookup table, if it is updated
Flag bit shows that the point data does not update also, after the corresponding voltage measuring value of the temperature value is averaged, replaces in former table
The voltage value of the point will no longer change the point data later.
In the present invention, the standard value more new algorithm makes described two standard value look-up tables with more adaptivity, does not adopt
With fixed data, for different environment, equipment, energy self-recision, increases power station overall operation state while detection
The precision of analysis.
The output result of photovoltaic array fault detection algorithm of the invention includes:
The position of the photovoltaic panel to break down, and its while breaking down corresponding electric current, voltage condition；
The aging of photovoltaic plant entirety or dust stratification state analysis result comprising recent work efficiency curve, overall electric current and
Voltage and corresponding environmental parameter situation；
And output will be provided to display end comprising individual failure unit information, whole aging conditions, and power generation quantitative change
Change the information such as curve, meanwhile, photovoltaic array fault detection algorithm of the invention reduces mistake for soft error, energy accurate judgement
Alarm.
The present invention carried out it is actually detected, the results show that photovoltaic array fault detection algorithm of the invention can online in real time
It monitors, be easily achieved, individual failure positioning and whole aging/dust stratification judgement can be carried out simultaneously, be avoided that soft error bring is missed
Sentence, and construct standard value look-up table by the way of adaptive, this algorithm is made to adapt to different geographical and environmental condition, thus
With stronger universality and higher accuracy.
In order to make it easy to understand, below will be former to a kind of large-sized photovoltaic array of the invention by specific drawings and examples
Barrier detection method is described in detail.It is important to note that specific example and attached drawing are merely to explanation, it is clear that ability
The those of ordinary skill in domain can according to illustrating herein, within the scope of the invention to the present invention make various amendments and
Change, these modifications and variations are also included in the scope of the present invention.In addition, the present invention refers to open source literature, these documents are
In order to more clearly describe the present invention, their entire contents are included in is referred to herein, just look like their full text
Repeated description herein is excessively.
Detailed description of the invention
Photovoltaic fault detection algorithm Overall data structure figure Fig. 1 of the invention.
Single panel fault detection algorithm flow chart in photovoltaic fault detection algorithm Fig. 2 of the invention.
The algorithm flow for excluding soft error is reset in single panel fault detection algorithm Fig. 3 of the invention using flag bit
Figure.
To the algorithm flow chart of whole aging conditions judgement in photovoltaic fault detection algorithm Fig. 4 of the invention.
The adaptive updates flow chart of photovoltaic fault detection algorithm Plays data look-up table Fig. 5 of the invention.
Specific implementation method
Embodiment 1
Fig. 1 is photovoltaic fault detection algorithm Overall data structure figure proposed by the present invention, including the inspection of system output
Commencing signal is surveyed, which is automatically sent to data acquisition equipment according to the preset testing time, and acquisition equipment is prompted to start to adopt
Collect and measured electric current, voltage, intensity of illumination and temperature information are sent back into processing system；
Further include the input data 101 sent back by acquisition equipment, by the current value of serial input, voltage value, and surveys
The intensity of illumination and temperature for trying the moment are constituted, since required electrical parameter includes the operating current and all photovoltaics of all serial branch
The operating voltage of battery plate, data volume is larger, therefore uses serial input mode, and the data inputted will initially enter decision system；
It further include three tables of data, by individual failure record sheet 112, history mean value record sheet 113, standard value look-up table
111 compositions, these tables of data by persistence in processing system, the criterion as comparison algorithm, wherein standard value tables of data
It further include light intensity-current standard lookup table 114 and temperature-voltage standard lookup table 115, this two tables storage size is fixed, and light
To no longer it change after setting with temperature value by force, Reflector record sheet is chain structure, and every one group of fault data of generation will
It is directly added after former table, to save memory space, history mean value record sheet can accommodate data volume and fix, when test data set number
The data measured earliest are deleted when more than record sheet capacity, and new data is inserted into corresponding position, and basic guarantee tests serial number
Continuity,
It further include the algorithm main body 102 that Algorithm constitution is updated by lateral comparison algorithm, history comparison algorithm and standard value.Its
Middle lateral comparison algorithm does not need ambient parameter information, it is only necessary to compare to each branch current, find current value exception
Branch, then the voltage value of battery plate each in branch is compared, the battery plate of voltage value exception is found out, and fault condition addition is existed
In individual failure record sheet 112, history comparison algorithm to obtained all branch current averageds are measured every time, to all
Battery plate voltage averaged, and intensity of illumination and temperature when recording test, as one group of test data, after addition test serial number
It stores to history mean value record sheet 113, after data volume reaches certain scale in the table, history comparison algorithm will extract number in table
According to, the corresponding current standard value of identical light intensity, mutually synthermal corresponding standard voltage value are found in standard value tables of data 111,
After carrying out difference operation, each group of data is analyzed according to the changing rule of time sequencing, judges whether whole aging or dust stratification occur
The problems such as,
It further include the output data 103 provided to external display, by individual failure unit information, whole aging conditions, hair
Electricity is constituted with corresponding ambient parameter information, and wherein individual failure unit information includes the battery plate position that may be broken down and work
For the Current Voltage of judgment basis, generated by lateral comparison algorithm.Whole aging conditions provide whether point of aging or dust stratification
Analysis, and using generated energy change curve as judgment basis, it is generated by history comparison algorithm.
Embodiment 2
Fig. 2 is single panel fault detection flow chart in single measurement of the present invention.
Firstly, algorithm needs to obtain the current value of all test branches and the voltage value (201) of every piece of battery plate；
The average value of all branch currents is calculated, then the current value of every branch and average value work is poor, obtains every branch
The difference (202) of road electric current and average current；
Using the difference as the criterion of fault branch, if a certain branch current is more than average current certain percentage (ratio
Value is set in advance), i.e., difference is excessive, then it is assumed that may have battery plate failure in the branch, and read under the entrance of battery plate voltage
The judgement (203) of one step, otherwise returns to (202), calculates next branch；
For a certain suspicious branch, calculate the mean value of each battery plate voltage in branch road, then by the voltage value of each battery plate with
It is poor that average value is made, and obtains the difference (205) of every piece of battery plate voltage and average voltage；
Using the difference as the criterion of failure battery plate, if a certain branch voltage is more than average voltage certain percentage (ratio
Value is set in advance), i.e., difference is excessive, then it is assumed that the block battery plate is likely to occur failure (206), otherwise returns to (204), to next piece
Battery plate is calculated；
The battery plate information is searched in individual failure record sheet 112, that is, judges whether the battery plate has been logged individual failure
Record sheet: if the battery plate has been labeled, adding 1 to its fault flag, the electric current and voltage value of battery plate when recording this time measurement
As failure criterion, and use the test serial number of this measurement as renewal time；If not being labeled, by the battery plate information
It is added to individual failure record sheet 112, including its location information, electric current, voltage value and the renewal time when this is measured
(test serial number), and Reflector is enabled to be equal to 1 (205)；
If the block battery plate Reflector is greater than setting threshold value after above-mentioned processing, then the battery plate is determined as that event may occur
The battery plate of barrier exports the fault message of the battery plate to display end, including the battery plate position and electric current, the electricity when every time measuring failure
Pressure value prompts staff to be manually checked (206) in time, otherwise returns to (204), calculate next piece of battery plate；
After all branches after (202) to the detection process of (206), terminate a lateral comparison (207).
Embodiment 3
After Fig. 3 is artificial replacement failure battery plate, or it is directed to soft error, certain block battery plate is deleted from individual failure record sheet 112
The flow chart removed, (301) to (306) are that the Reflector after replacing battery plate is reset, and (311) to (314) are the mark after soft error
Will position is reset；
After replacing battery plate, need to input the location information (301) of battery plate after having replaced；
To improve efficiency, additional testing procedure is avoided, next subsystem will be waited to detect automatically and started, is normally being examined
The working condition (302) of battery plate has been replaced in detection simultaneously in flow gauge；
After detection algorithm starts, single panel fault detection described in embodiment 2 will be entered, it after sensing can basis
Testing result updates individual failure record sheet 112 (303)；
After lateral comparison algorithm, is searched in individual failure record sheet and replaced failure corresponding to battery plate position
Data (304)；
At this point, if the corresponding Reflector of the battery plate is still identical as before replacement battery plate, then it represents that the position after replacement battery plate
It is no longer abnormal to locate Current Voltage, it is believed that battery plate is updated successfully, and by failure corresponding to the position in individual failure record sheet 112
Data dump (305)；
If the corresponding Reflector of the battery plate continues growing, then it represents that still faulty presence at this needs to further confirm that
With investigation, then system will be prompted at the position still faulty (306) by output；
The interval that regular Reflector clearing will be preset before system worked well, when the integral multiple of the test serial number value
When, after normal detection algorithm, system will additionally enter flag bit and reset process (311)；
To every group of fault data in individual failure record sheet 112, (test when last time updates of its final updating time is calculated
Serial number) and this detection test serial number difference (312)；
If serial number difference is greater than the threshold value of preset difference value, show that the failure identification of this group of fault data does not increase for a long time,
It may be only soft error caused by partial occlusion, shade etc., not battery plate breaks down, and will indicate the fault data of the battery plate
(314) are removed from individual failure record sheet 112.
Embodiment 4
Fig. 4 is whole aging judgement, the i.e. flow chart of history comparison algorithm.
The algorithm includes from the preprocessing part of acquisition the obtained data of equipment, due in standard value look-up table 111
Light intensity and temperature value are more discrete, and the light intensity value and temperature value obtained from enviromental monitoring equipment needs to carry out approximate processing, make ring
Border parameter mutually agrees with (401) with table store data inside；
Since whole aging judges electric current and voltage parameter based on all battery plates in power station, the battery plate of individual failure is because of its electricity
Parameter differs larger with normal operating conditions, may produce bigger effect to the result integrally judged, therefore first according to individual
The data stored in failure logging table 112 obtain the battery plate of possible breakdown found in closely measurement several times, by it from history ratio
Compared with excluding in the input data of algorithm, for the branch that other are worked normally, its average current value is sought, to all normal works
Battery plate, seek its average voltage (402)；
This time is tested into average current and average voltage obtained and pretreated light intensity and temperature value as one
Group data are stored in history mean value record sheet 113 (403) after adding this test serial number；
Due to needing the variation to power station overall work situation to analyze, the data volume of certain scale is needed just to can be carried out
It handles in next step, therefore judges whether already present valid data are greater than preset value (404) in history mean value record sheet；
If data volume carries out history comparison algorithm enough, to each group of data in history mean value record sheet 113, in light
The corresponding current standard value of identical light intensity value is searched in by force-current standard look-up table, is searched in temperature-voltage standard lookup table
Mutually synthermal corresponding standard voltage value (405)；
It is poor that the electric current mean value of this group of data in history mean value record sheet 113 and the current standard value searched are made, and obtains
Current differential, it is poor that average voltage and the standard voltage value searched are made, and obtains voltage difference, as a whole the one of aging conditions
Group criterion (406)；
After all obtaining one group of electric current, voltage difference to every group of data in history mean value record sheet 113, to test serial number (table
Show the time) it is abscissa, current differential-time and voltage difference-time graph (407) are fitted respectively；
The case where above-mentioned curve reflects electric current, the degree of voltage deviation standard value changes over time, it can be divided by the curve
The whole aging conditions in power station are precipitated, if being in positive ascendant trend than discovery curve, then obvious power station whole work efficiency reduces,
It is likely to occur whole aging or dust stratification, needs to check and clear up in time.What algorithm finally went out the curve binding analysis fitted
Possible situation is exported to outside and is shown, in order to which administrative staff more intuitively understand the working condition (408) in power station.
Embodiment 5
Fig. 5 show the adaptive updates process of standard value look-up table 111.To sentence whole aging as described in example 4
Disconnected to implement as early as possible, which has primary data before system operation, and primary data table can be used directly and carry out history comparison
Algorithm.Since primary data is based on obtained by priori knowledge and practical experience, in practice, due to power station operating condition not
Together, which may slightly have gap with actual standard value, more accurate to judge whole aging, need using adaptive
The standard value answered constantly improve standard value look-up table 111.
Standard scale realizes its adaptivity by providing more new logo for each group of data.
The data in history mean value record sheet 113 are waited to reach certain scale (500) first；
It is shown to the more new technological process such as (501) to (506) of light intensity-current standard lookup table:
Each group of data in mean value record sheet is ranked up by light intensity value, it is therefore an objective to be stored in the data of identical light intensity value
(501) together；
Order traversal reduced data, if the data for being found to have a certain identical light intensity value reach certain amount (502),
The corresponding point (503) of the light intensity value is then found in light intensity-current standard lookup table；
The more new logo (more new logo is 1 when system just starts operation) for reading the position, if being identified as 1, then it represents that
The point data is still primary data, can update (504)；
All current values corresponding to the light intensity value are extracted in ordering history mean data, find out its mean value, with
Original current value (505) in the mean value alternate standard look-up table；
The update that the point is modified in light intensity-current standard lookup table is identified as 0, indicates that the point data has been subjected to adaptively
Processing, cannot be modified (506) again.
It is similar with above-mentioned process to the more new technological process of temperature-voltage standard lookup table, such as shown in (511) to (516):
Each group of data in mean value record sheet is ranked up by temperature value, it is therefore an objective to be stored in the data of identical temperature value
(511) together；
Order traversal reduced data, if the data for being found to have a certain identical temperature value reach certain amount (512),
The corresponding point (513) of the light intensity value is then found in temperature-voltage standard lookup table；
The more new logo for reading the position, if being identified as 1, then it represents that the point data is still primary data, can be updated
(514)；
All voltage values corresponding to the temperature value are extracted in ordering history mean data, find out its mean value, with
Original voltage value (505) in the mean value alternate standard value look-up table 111；
The update that the point is modified in temperature-voltage standard lookup table is identified as 0, indicates that the point data has been subjected to adaptively
Processing, cannot be modified (506) again.
It should be pointed out that light intensity-current standard lookup table more new technological process (501) to (506) in above-described embodiment, with temperature
Sequence can be interchanged in degree-voltage standard look-up table more new technological process (511) to (516) process, has no effect on arithmetic result.
In addition, the present embodiment is in standard value look-up table 111, there are the more new technological process in the case of primary data, if searching
Without primary data in table, the adaptive updates of standard value can also be carried out according to this process, have only delayed 4 institute of embodiment
State the execution of whole aging judgement.
Claims (14)
1. the fault detection algorithm of battery panel in a kind of large-sized photovoltaic power station, which is characterized in that it includes, straight using host computer
Sending and receiving go out manipulation command, will acquire the voltage of the electric current and every piece of battery plate of test every branch of moment that equipment obtains, and
The environmental parameter that enviromental monitoring equipment obtains, including intensity of illumination and temperature are realized simultaneously as being calculated after input data
Carry out the photovoltaic array fault detection of individual failure positioning with whole aging judgement；It wherein uses: single failure panel is positioned
Lateral comparison algorithm, to the history comparison algorithm of power station entirety aging judgement, and the more new algorithm of the standard value as control,
And the individual failure record sheet using aided algorithm, history mean value record sheet and standard value look-up table.
2. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 1, which is characterized in that described
Individually the lateral comparison algorithm of failure panel position includes:
To measure each branch current at moment, algorithm that each piece of battery panel voltage is constituted input parameter；
Transverse current comparison step compares the difference of each branch current and average value；
One current ratio coefficient set in advance, when the ratio between a certain branch current difference and average value are greater than the proportionality coefficient
When, it is believed that the branch exists abnormal；
Lateral voltage comparison step compares the difference of each piece of panel voltage and average value for abnormal branch；
One voltage ratio coefficient set in advance, when the ratio between a certain panel voltage difference and average value are greater than the proportionality coefficient
When, it is believed that the panel exists abnormal；
With the association process of individual failure record sheet, when detecting a certain panel exception, need to individual failure record sheet into
Row is corresponding to be modified to save the failure logging of the battery plate；
One fault index threshold set in advance, when certain corresponding Reflector of block battery panel be more than the threshold value, just recognize
It breaks down for the panel, and exports the fault message of the panel outward.
3. the fault detection algorithm of battery panel in large-sized photovoltaic power station as described in claim 1 or 2, which is characterized in that institute
The individual failure record sheet stated includes:
The position of abnormal battery panel is indicated with one-dimensional number, or is indicated with two-dimensional coordinate；
The Reflector of abnormal battery panel records the battery panel and is detected abnormal number, for determining that the panel is
No failure；
Electric current, electric voltage exception value are detected electric current and voltage value when exception for the battery plate every time, or including multi-group data；
The final updating time, when being detected abnormal for the battery plate the last time, corresponding test serial number, for excluding soft error
Reflector accidentally resets process.
4. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 3, which is characterized in that described
Reflector resets Reflector and final updating time of the flow at regular intervals to each group of failure logging in individual failure record sheet
Screening is carried out, by comparing last more row time and current time, not increased failure logging is deleted automatically for a long time by Reflector
It removes.
5. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 1, which is characterized in that described
History comparison algorithm includes:
With the environmental parameter for measuring the intensity of illumination at moment, temperature value is constituted, constituted with each branch current, each piece of panel voltage
Electric parameter, the input parameter as algorithm；
It is excluded with the association process of individual failure record sheet by failure logging recorded in retrieval individual failure record sheet
Abnormal electric current and voltage value in the electric parameter of input, to increase the accuracy of algorithm；
The current value of all branches is merged with the voltage value of all panels, uses electric current by electric current, voltage entirety average value processing
The whole result measured with the average value of voltage as this；
With the association process of history mean value record sheet, obtained whole result will be measured every time and is stored in history mean value record sheet
In；
It is obtained in standard value look-up table with the association process of standard value look-up table by light intensity and temperature value as search condition
To electric current and standard voltage value, it is compared with measured value；
Curve fitting process, using test serial number as abscissa, using the current differential of electric current and current standard value as ordinate
Current differential-time graph is fitted, then is made using testing serial number as abscissa, with the voltage difference of voltage and standard voltage value
Voltage difference-time graph is fitted for ordinate, the time graph fitted is to indicate whole aging or dust stratification trend.
6. by the fault detection algorithm of battery panel in large-sized photovoltaic power station described in claim 1 or 5, which is characterized in that institute
The history mean value record sheet stated includes:
Electric current ensemble average value measures the average value of obtained each branch current every time；
Voltage ensemble average value measures the average value of obtained each battery panel voltage every time；
Intensity of illumination, temperature value, i.e., the illumination intensity value and temperature value of measurement record can be in standards as environmental parameter every time
Corresponding voltage, current standard value are found in value look-up table；
Serial number is tested, for indicating the time sequencing of each test.
7. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 6, which is characterized in that described
Test serial number adds 1 to it automatically when test every time, the successive pass of each secondary test result is reflected with positive integer for indicating the time
System.
8. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 1, which is characterized in that described
Standard value more new algorithm is different according to power station local environment and used equipment, adaptively updates electricity during the test
The algorithm of stream, standard voltage value comprising:
The characteristic disposably updated, i.e. each group of standard value can only update once after algorithm operation, no longer change later；
With the association process of history mean value record sheet, adaptive updates process is opened when data reach certain scale in table；
One electric current set in advance updates threshold value, when the identical record number of light intensity value is greater than the threshold in history mean value record sheet
When value, the update of this group of standard value is carried out；
One voltage set in advance updates threshold value, when the identical record number of temperature value is greater than the threshold in history mean value record sheet
When value, the update of this group of standard value is carried out；
With the association process of standard value look-up table, all standard values are stored in standard value look-up table, standard value more new algorithm
Directly the data of the table will be updated.
9. by the fault detection algorithm of battery panel in large-sized photovoltaic power station described in claim 1 or 5 or 8, which is characterized in that
The standard value look-up table includes light intensity-current standard value look-up table and temperature-voltage standard value look-up table.
10. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 9, which is characterized in that described
Light intensity-current standard value look-up table include light intensity value, current standard value and more new logo, for the premise in known light intensity value
Under, a standard current value is obtained, the average current of each branch when as power station normal power generation, the measurement average value with electric current
It compares.
11. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 9, which is characterized in that described
Temperature-voltage standard value look-up table include temperature value, standard voltage value and more new logo, for the premise in known temperature value
Under, a standard voltage value is obtained, the average voltage of each piece of battery panel when as power station normal power generation, the measurement with voltage is put down
Mean value compares.
12. by the fault detection algorithm of battery panel in large-sized photovoltaic power station described in claim 10 or 11, which is characterized in that
The more new logo is for indicating that whether a certain group of data carried out update in standard value look-up table, were indicated with 0 or 1.
13. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 9, which is characterized in that described
Standard value look-up table, the empty table of primary standard value or one can be first set before algorithm starts.
14. the fault detection algorithm of battery panel in large-sized photovoltaic power station according to claim 1, which is characterized in that also wrap
An output is included as a result, the output result includes:
The position of the photovoltaic panel to break down, and its while breaking down corresponding electric current, voltage condition；
The aging of photovoltaic plant entirety or dust stratification state analysis result comprising recent work efficiency curve, overall electric current and voltage,
And corresponding environmental parameter situation；
And the output result is provided to output to display end.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610656228 | 2016-08-11 | ||
| CN2016106562285 | 2016-08-11 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107733357A CN107733357A (en) | 2018-02-23 |
| CN107733357B true CN107733357B (en) | 2019-06-25 |
Family
ID=61161603
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610945557.1A Active CN107733357B (en) | 2016-08-11 | 2016-11-02 | The fault detection algorithm of battery panel in a kind of large-sized photovoltaic power station |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107733357B (en) |
| WO (1) | WO2018028005A1 (en) |
Families Citing this family (39)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108879933A (en) * | 2018-08-06 | 2018-11-23 | 上海晶夏新能源科技有限公司 | A kind of novel modularized photovoltaic generating system |
| CN109379042B (en) * | 2018-09-30 | 2020-04-14 | 河北隆基泰和云能源科技有限公司 | Method and system for analyzing household photovoltaic shelter |
| CN110320874A (en) * | 2019-07-10 | 2019-10-11 | 上海建工材料工程有限公司 | The online fault detection method of the concrete production equipment of Intrusion Detection based on host electric current and system |
| CN110596600A (en) * | 2019-07-27 | 2019-12-20 | 广东毓秀科技有限公司 | Rail transit battery maintenance prediction method based on battery life calculation table |
| TWI718779B (en) * | 2019-11-22 | 2021-02-11 | 盈正豫順電子股份有限公司 | Adaptive low-duty-type power generation abnormality detection method and system for photovoltaic panels |
| CN111141662A (en) * | 2019-12-24 | 2020-05-12 | 中建材浚鑫科技有限公司 | Testing device and method of framed single-glass structure for photovoltaic module |
| CN111159650B (en) * | 2020-01-03 | 2023-09-15 | 上海枫昱能源科技有限公司 | Artificial intelligence electric circuit aging degree detection method and system |
| CN111178779A (en) * | 2020-01-03 | 2020-05-19 | 河北因能科技股份有限公司 | Household photovoltaic power station fault monitoring and early warning method |
| CN112596490B (en) * | 2020-02-28 | 2021-09-07 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Industrial robot fault detection method and device, computer equipment and storage medium |
| CN111555716B (en) * | 2020-03-13 | 2023-07-28 | 远景智能国际私人投资有限公司 | Method, device, equipment and storage medium for determining working state of photovoltaic array |
| CN111444606B (en) * | 2020-03-24 | 2024-02-13 | 阳光电源股份有限公司 | Component evaluation method and system for photovoltaic power station |
| CN111562037B (en) * | 2020-05-15 | 2021-09-28 | 云南电网有限责任公司电力科学研究院 | Thermometer fault detection method and device |
| CN112000068A (en) * | 2020-05-22 | 2020-11-27 | 上海飞鱼医疗科技有限公司 | Medical instrument performance quality management system based on block chain technology |
| CN112234941A (en) * | 2020-08-28 | 2021-01-15 | 南京瑞贻电子科技有限公司 | Method for detecting working condition of photovoltaic panel |
| CN113255203B (en) * | 2020-09-06 | 2022-08-12 | 诸暨市广亚电子科技有限公司 | Online electric line aging degree identification system and method based on ANFIS |
| CN112104326A (en) * | 2020-09-17 | 2020-12-18 | 江西三川新能源有限公司 | Power monitoring method and device for photovoltaic power generation |
| CN112098850B (en) * | 2020-09-21 | 2024-03-08 | 山东工商学院 | Lithium ion battery voltage fault diagnosis method and system based on SDO algorithm |
| CN112269110A (en) * | 2020-10-19 | 2021-01-26 | 合肥阳光新能源科技有限公司 | Arc fault judgment method |
| CN112367046B (en) * | 2020-11-11 | 2022-05-06 | 兰州理工大学 | Cloud edge cooperative remote operation and maintenance system suitable for distributed photovoltaic of remote areas |
| CN112668195A (en) * | 2020-12-31 | 2021-04-16 | 东软睿驰汽车技术（沈阳）有限公司 | Battery pack aging mechanism analysis method and device and related products |
| CN112785160B (en) * | 2021-01-25 | 2023-05-26 | 杭州易达光电有限公司 | Photovoltaic operation and maintenance management information display platform |
| CN113128153B (en) * | 2021-04-20 | 2022-08-30 | 合肥工业大学 | Active frequency conversion fault recording method triggered by composite threshold in photovoltaic power station |
| CN113541600B (en) * | 2021-05-25 | 2022-09-06 | 特变电工新疆新能源股份有限公司 | Method, system, equipment and storage medium for judging branch fault of photovoltaic power station |
| CN113792088A (en) * | 2021-09-10 | 2021-12-14 | 阳光电源股份有限公司 | Fault detection method and device for photovoltaic string and storage medium |
| CN113776591B (en) * | 2021-09-10 | 2024-03-12 | 中车大连机车研究所有限公司 | Locomotive auxiliary control unit data recording and fault analysis device and method |
| CN114781179B (en) * | 2022-05-12 | 2023-03-28 | 广东华矩检测技术有限公司 | Photovoltaic power station generated energy loss verification method based on optical fiber communication information acquisition |
| CN115002977B (en) * | 2022-07-18 | 2022-10-28 | 成都盛及航空科技发展有限公司 | Landing lamp fault detection platform and landing lamp fault detection method |
| WO2024065211A1 (en) * | 2022-09-27 | 2024-04-04 | 宁德时代新能源科技股份有限公司 | Photovoltaic array test method and system |
| CN116150666B (en) * | 2022-10-08 | 2023-10-31 | 深圳先进技术研究院 | Energy storage system fault detection method and device and intelligent terminal |
| CN116383914B (en) * | 2023-04-11 | 2023-10-03 | 国网浙江省电力有限公司 | Multi-dimensional information analysis-based power system fault judging device and method |
| CN116502112B (en) * | 2023-06-29 | 2023-10-24 | 深圳市联明电源有限公司 | New energy power supply test data management method and system |
| CN116707445B (en) * | 2023-08-04 | 2023-11-03 | 华能新能源股份有限公司山西分公司 | Photovoltaic module fault positioning method and system |
| CN117254596B (en) * | 2023-10-10 | 2024-04-09 | 雷玺智能科技(上海)有限公司 | Digital twinning-based energy storage power station full life cycle monitoring system and method |
| CN117081159A (en) * | 2023-10-16 | 2023-11-17 | 华电电力科学研究院有限公司 | Perovskite photovoltaic power generation system |
| CN117195134B (en) * | 2023-10-30 | 2024-01-30 | 苏州欣和智达能源科技有限公司 | Early warning method and device for hydrogen fuel base station power supply |
| CN117614385A (en) * | 2023-12-07 | 2024-02-27 | 天津市热电有限公司 | State detection method and system for photovoltaic panel |
| CN117498801B (en) * | 2023-12-29 | 2024-03-26 | 兰州理工大学 | Photovoltaic array shading fault diagnosis method based on KKPDC light transmittance detection |
| CN117705196B (en) * | 2024-01-31 | 2024-05-03 | 杭州高特电子设备股份有限公司 | Energy storage air conditioner temperature fault diagnosis method and energy storage equipment |
| CN117907845B (en) * | 2024-03-20 | 2024-05-17 | 山东泰开电力电子有限公司 | Electrochemical energy storage system insulation detection method based on electrical parameter analysis |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102129466A (en) * | 2011-03-22 | 2011-07-20 | 国网电力科学研究院 | Demonstration-based photovoltaic power station testing diagnosis and forecasting database establishment method |
| CN102244393A (en) * | 2010-05-12 | 2011-11-16 | 通用电气公司 | System and method for photovoltaic plant power curve measurement and health monitoring |
| CN104283512A (en) * | 2014-10-28 | 2015-01-14 | 上海许继电气有限公司 | Method for remotely monitoring and locating faults of set strings in photovoltaic power station system |
| CN104734632A (en) * | 2013-12-24 | 2015-06-24 | 珠海格力电器股份有限公司 | Method and device for diagnosing cleanliness of photovoltaic cell panel |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5607772B2 (en) * | 2013-02-08 | 2014-10-15 | 株式会社 日立産業制御ソリューションズ | Solar cell panel monitoring program, solar cell panel monitoring device, and solar cell panel monitoring method |
| CN104767482B (en) * | 2014-01-02 | 2017-05-31 | 上海岩芯电子科技有限公司 | A kind of photovoltaic module is aging and short trouble inline diagnosis method |
| CN104253586B (en) * | 2014-10-20 | 2016-09-14 | 武汉大学 | A kind of solar panel electric parameter on-line measurement evaluating apparatus and method |
| CN104601108B (en) * | 2015-02-10 | 2017-02-01 | 河海大学常州校区 | Small photovoltaic power station fault diagnosis method |
-
2016
- 2016-08-25 WO PCT/CN2016/096744 patent/WO2018028005A1/en active Application Filing
- 2016-11-02 CN CN201610945557.1A patent/CN107733357B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102244393A (en) * | 2010-05-12 | 2011-11-16 | 通用电气公司 | System and method for photovoltaic plant power curve measurement and health monitoring |
| CN102129466A (en) * | 2011-03-22 | 2011-07-20 | 国网电力科学研究院 | Demonstration-based photovoltaic power station testing diagnosis and forecasting database establishment method |
| CN104734632A (en) * | 2013-12-24 | 2015-06-24 | 珠海格力电器股份有限公司 | Method and device for diagnosing cleanliness of photovoltaic cell panel |
| CN104283512A (en) * | 2014-10-28 | 2015-01-14 | 上海许继电气有限公司 | Method for remotely monitoring and locating faults of set strings in photovoltaic power station system |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2018028005A1 (en) | 2018-02-15 |
| CN107733357A (en) | 2018-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107733357B (en) | The fault detection algorithm of battery panel in a kind of large-sized photovoltaic power station | |
| CN106154209B (en) | Electrical energy meter fault prediction technique based on decision Tree algorithms | |
| Catterson et al. | Online conditional anomaly detection in multivariate data for transformer monitoring | |
| CN108062571A (en) | Diagnosing failure of photovoltaic array method based on differential evolution random forest grader | |
| CN109416531A (en) | The different degree decision maker of abnormal data and the different degree determination method of abnormal data | |
| CN104391189A (en) | Three-stage-diagnosis-based large-scale photovoltaic array fault diagnosis and positioning method | |
| CN107069960B (en) | Online defect diagnosis method for secondary operation and maintenance management system | |
| CN109636110A (en) | A kind of method and device obtaining protective relaying device operating status | |
| CN107437113A (en) | A kind of distribution main equipment live detection criterion KBS and its implementation | |
| CN115061049B (en) | Method and system for rapidly detecting UPS battery fault of data center | |
| CN114528929B (en) | Multi-source data platform region measuring system and method | |
| CN116345699B (en) | Internet-based power transmission circuit information acquisition system and acquisition method | |
| CN115343623A (en) | Online detection method and device for electrochemical energy storage battery fault | |
| CN115480180A (en) | New energy battery health diagnosis and analysis method | |
| CN115236524A (en) | Insulation fault detection method and system for new energy automobile power battery | |
| CN109298227A (en) | A method of detection user's electricity is abnormal | |
| Frank et al. | Extracting operating modes from building electrical load data | |
| CN112015724A (en) | Method for analyzing metering abnormality of electric power operation data | |
| CN117411436B (en) | Photovoltaic module state detection method, system and storage medium | |
| CN116449235B (en) | Method and system for processing test data of energy storage battery | |
| CN112345972A (en) | Power failure event-based power distribution network line transformation relation abnormity diagnosis method, device and system | |
| CN116679151A (en) | Low-voltage transformer area line loss abnormity diagnosis method, device and storage medium | |
| CN108267709B (en) | Method and device for checking and classifying power failure | |
| CN115912359A (en) | Digitalized potential safety hazard identification, investigation and treatment method based on big data | |
| CN206832935U (en) | Electric car assembly line safety detecting system and electric car assembly line |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |