CN109298228A - A kind of Intelligence Diagnosis method and system based on photovoltaic group string current anomaly - Google Patents
A kind of Intelligence Diagnosis method and system based on photovoltaic group string current anomaly Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16528—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values using digital techniques or performing arithmetic operations
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- 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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses a kind of Intelligence Diagnosis methods based on photovoltaic group string current anomaly: S1, the rated power X and realtime power P for obtaining target device, and calculates deviation MAD based on rated power X and realtime power P；S2, respectively acquisition group string electric current I1, I2, I3, and, group string current maxima Iupper；S3, judge whether group string electric current I1, I2, I3 are abnormal according to the range and group string electric current I1, I2, I3 of deviation MAD and the comparison result of group string current maxima Iupper；S4, the judging result formulation group string current diagnostic result based on step S3.Collection process of the present invention is simple to operation, realizes the monitoring of the parameter to the photovoltaic modulies of substantial amounts in the process of running, provides feasibility basis for its fault detection and positioning；The different monitoring results of target device are reported step by step finally, not only ensure that the simple and reliable property of data transmission, but also improve data transmission efficiency, have spent rate of false alarm and rate of failing to report again significantly.
Description
Technical field
The present invention relates to photovoltaic power generation software technology field more particularly to a kind of intelligence based on photovoltaic group string current anomaly
Change diagnostic method and system.
Background technique
By the fast development in more than ten years, China's photovoltaic industry has entered the large-scale development stage.Cut photovoltaic generating system
Operation level be influence system benefit key element, it will directly affect photovoltaic generating system operation expense, hair
Electrical efficiency and generating reliability, how the operation of safeguards system high level is each side's questions of common interest.However it is so huge
The photovoltaic generating system of scale, number of devices is huge, when some device fails, all checks one by one all devices, work
It measures very huge.By taking one 50,000 kilowatts of photovoltaic plant as an example, inverter has more than 100, more than 800 of header box, battery pack
Part is more than 190,000 pieces, also large number of direct current confluence branch, in addition current photovoltaic generating system distribution or geographical location
It is remote, have inconvenient traffic or build in building roof, equipment routing inspection and manage extremely difficult.
Also have at present for the problem that the data acquisition of photovoltaic generating system and monitoring system, but it exists and has: 1) monitoring
Data can not adopt, is insincere.It does not organize string monitoring or there was only simple group string data acquisition, monitoring measurement accuracy is not high, measures
Data inaccuracy；2) monitoring data reports difficulty.Monitoring data is uploaded by RS-485 bus, and transmission rate is low, communication failure
It is more, accidentally alert and to fail to report situation serious；3) fault location is difficult.Photovoltaic module and number of nodes are huge, lack effective failure
Positioning means, fault detection are compared mainly by manual inspection, by multimeter hand dipping, and the troubleshooting period is long, influences hair
Electric output, maintenance efficiency is low, investment manpower is big；4) system administration lacks digitlization means.Monitoring information is simply acquired and is presented,
Mass data report is handled by hand by Excel, and aggregation of data analysis ability is poor, and generate electricity performance analysis and improvement shortage quantization hand
Section cannot achieve multisystem unified management.As it can be seen that it is necessary to existing photovoltaic generating system group string current anomaly diagnostic method
It makes improvements.
Summary of the invention
Technical problems based on background technology, the invention proposes a kind of intelligence based on photovoltaic group string current anomaly
Change diagnostic method and system.
Intelligence Diagnosis method proposed by the present invention based on photovoltaic group string current anomaly, comprising the following steps:
S1, the rated power X and realtime power P for obtaining target device, and calculated based on rated power X and realtime power P
The value that deviates MAD；
S2, respectively acquisition group string electric current I1, I2, I3, and, group string current maxima Iupper；
S3, the ratio of current maxima Iupper of being gone here and there according to the range and group string electric current I1, I2, I3 and group of deviation MAD
Relatively result judges whether group string electric current I1, I2, I3 are abnormal；
S4, the judging result formulation group string current diagnostic result based on step S3.
Preferably, step S1 is specifically included:
Obtain the rated power X and realtime power P of target device；
Deviation MAD, the formula are calculated according to following formula are as follows:
MAD=P/X.
Preferably, step S3 is specifically included:
Deviation MAD is obtained, and deviation MAD is compared with predetermined deviation M1, M2, M3, M4, M5:
As M4≤MAD < M5, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤aIupper, decision set string electric current In is abnormal, if In > aIupper, decision set string electric current In is normal；
As M3≤MAD < M4, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤bIupper, decision set string electric current In is abnormal, if In > bIupper, decision set string electric current In is normal；
As M2≤MAD < M3, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤cIupper, decision set string electric current In is abnormal, if In > cIupper, decision set string electric current In is normal；
As M1≤MAD < M2, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤dIupper, decision set string electric current In is abnormal, if In > dIupper, decision set string electric current In is normal；
As MAD < M1, judge without next step；
Wherein, M1=0.3, M2=0.4, M3=0.6, M4=0.8, M5=1, a, b, c, d are preset value, a=0.9, b
=0.8, c=0.7, d=0.6, n={ 1,2,3 }.
Preferably, step S4 is specifically included:
If group string electric current In is abnormal, the discrete value of calculating group string electric current In, and judges whether the discrete value is less than threshold value,
If so, a group string electric current In is determined as normal value, if it is not, a group string electric current In is determined as exceptional value, and the group string electric current is positioned
Position and report；
If group string electric current In is normal, a group string electric current In is determined as normal value.
Intelligence Diagnosis system proposed by the present invention based on photovoltaic group string current anomaly, comprising:
Deviation computing module, for obtaining the rated power X and realtime power P of target device, and based on rated power X and
Realtime power P calculates deviation MAD；
Data obtaining module, for acquisition group string electric current I1, I2, I3 respectively, and, group string current maxima Iupper；
Abnormal judgment module, for going here and there electric current most according to the range and group string electric current I1, I2, I3 and group of deviation MAD
The comparison result of big value Iupper judges whether group string electric current I1, I2, I3 are abnormal；
Current diagnostic module, for the judging result formulation group string current diagnostic result based on abnormal judgment module.
Preferably, the deviation computing module is specifically used for:
Obtain the rated power X and realtime power P of target device；
Deviation MAD, the formula are calculated according to following formula are as follows:
MAD=P/X.
Preferably, the abnormal judgment module is specifically used for:
Deviation MAD is obtained, and deviation MAD is compared with predetermined deviation M1, M2, M3, M4, M5:
As M4≤MAD < M5, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤aIupper, decision set string electric current In is abnormal, if In > aIupper, decision set string electric current In is normal；
As M3≤MAD < M4, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤bIupper, decision set string electric current In is abnormal, if In > bIupper, decision set string electric current In is normal；
As M2≤MAD < M3, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤cIupper, decision set string electric current In is abnormal, if In > cIupper, decision set string electric current In is normal；
As M1≤MAD < M2, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤dIupper, decision set string electric current In is abnormal, if In > dIupper, decision set string electric current In is normal；
As MAD < M1, judge without next step；
Wherein, M1=0.3, M2=0.4, M3=0.6, M4=0.8, M5=1, a, b, c, d are preset value, a=0.9, b
=0.8, c=0.7, d=0.6, n={ 1,2,3 }.
Preferably, the current diagnostic module is specifically used for:
If group string electric current In is abnormal, the discrete value of calculating group string electric current In, and judges whether the discrete value is less than threshold value,
If so, a group string electric current In is determined as normal value, if it is not, a group string electric current In is determined as exceptional value, and the group string electric current is positioned
Position and report；
If group string electric current In is normal, a group string electric current In is determined as normal value.
Intelligence Diagnosis method proposed by the present invention based on photovoltaic group string current anomaly, is first running target device
Different parameters in the process are acquired, this collection process is simple to operation, are realized and are being transported to the photovoltaic module of substantial amounts
The monitoring of parameter during row provides feasibility basis for its fault detection and positioning；Then according to target device in light
Different type in photovoltaic generating system to carry out targetedly classified Monitoring and positioning to target device, improves data and acquired
The validity of journey and monitoring result realizes the positioning fast and accurately to target device malfunction monitoring；Finally target is set
Standby different monitoring results report step by step, not only ensure that the simple and reliable property of data transmission, but also improve data transmission effect
Rate has spent rate of false alarm and rate of failing to report again significantly.
Detailed description of the invention
Fig. 1 is a kind of step schematic diagram of Intelligence Diagnosis method based on photovoltaic group string current anomaly；
Fig. 2 is a kind of structural schematic diagram of Intelligence Diagnosis system based on photovoltaic group string current anomaly；
Fig. 3 is a kind of step process of the embodiment of Intelligence Diagnosis method and system based on photovoltaic group string current anomaly
Figure.
Specific embodiment
As shown in Figure 1-3, Fig. 1-3 is a kind of Intelligence Diagnosis side based on photovoltaic group string current anomaly proposed by the present invention
Method and system.
Referring to Fig.1, the Intelligence Diagnosis method proposed by the present invention based on photovoltaic group string current anomaly, including following step
It is rapid:
S1, the rated power X and realtime power P for obtaining target device, and calculated based on rated power X and realtime power P
The value that deviates MAD；
Wherein, deviation MAD, the formula are calculated according to following formula are as follows:
MAD=P/X.
S2, respectively acquisition group string electric current I1, I2, I3, and, group string current maxima Iupper；
S3, the ratio of current maxima Iupper of being gone here and there according to the range and group string electric current I1, I2, I3 and group of deviation MAD
Relatively result judges whether group string electric current I1, I2, I3 are abnormal；
In present embodiment, step S3 is specifically included:
Deviation MAD is obtained, and deviation MAD is compared with predetermined deviation M1, M2, M3, M4, M5:
As M4≤MAD < M5, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤aIupper, decision set string electric current In is abnormal, if In > aIupper, decision set string electric current In is normal；
As M3≤MAD < M4, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤bIupper, decision set string electric current In is abnormal, if In > bIupper, decision set string electric current In is normal；
As M2≤MAD < M3, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤cIupper, decision set string electric current In is abnormal, if In > cIupper, decision set string electric current In is normal；
As M1≤MAD < M2, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤dIupper, decision set string electric current In is abnormal, if In > dIupper, decision set string electric current In is normal；
As MAD < M1, judge without next step；
Wherein, M1=0.3, M2=0.4, M3=0.6, M4=0.8, M5=1, a, b, c, d are preset value, a=0.9, b
=0.8, c=0.7, d=0.6, n={ 1,2,3 }.
S4, the judging result formulation group string current diagnostic result based on step S3.
In present embodiment, step S4 is specifically included:
If group string electric current In is abnormal, the discrete value of calculating group string electric current In, and judges whether the discrete value is less than threshold value,
If so, a group string electric current In is determined as normal value, if it is not, a group string electric current In is determined as exceptional value, and the group string electric current is positioned
Position and report；
If group string electric current In is normal, a group string electric current In is determined as normal value.
It is the Intelligence Diagnosis system proposed by the present invention based on photovoltaic group string current anomaly referring to Fig. 2, Fig. 2, comprising:
Deviation computing module, for obtaining the rated power X and realtime power P of target device, and based on rated power X and
Realtime power P calculates deviation MAD；
Wherein, deviation MAD, the formula are calculated according to following formula are as follows:
MAD=P/X.
Data obtaining module, for acquisition group string electric current I1, I2, I3 respectively, and, group string current maxima Iupper；
Abnormal judgment module, for going here and there electric current most according to the range and group string electric current I1, I2, I3 and group of deviation MAD
The comparison result of big value Iupper judges whether group string electric current I1, I2, I3 are abnormal；
In present embodiment, the exception judgment module is specifically used for:
Deviation MAD is obtained, and deviation MAD is compared with predetermined deviation M1, M2, M3, M4, M5:
As M4≤MAD < M5, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤aIupper, decision set string electric current In is abnormal, if In > aIupper, decision set string electric current In is normal；
As M3≤MAD < M4, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤bIupper, decision set string electric current In is abnormal, if In > bIupper, decision set string electric current In is normal；
As M2≤MAD < M3, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤cIupper, decision set string electric current In is abnormal, if In > cIupper, decision set string electric current In is normal；
As M1≤MAD < M2, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper
Relationship:
If In≤dIupper, decision set string electric current In is abnormal, if In > dIupper, decision set string electric current In is normal；
As MAD < M1, judge without next step；
Wherein, M1=0.3, M2=0.4, M3=0.6, M4=0.8, M5=1, a, b, c, d are preset value, a=0.9, b
=0.8, c=0.7, d=0.6, n={ 1,2,3 }.
Current diagnostic module, for the judging result formulation group string current diagnostic result based on abnormal judgment module.
In present embodiment, the current diagnostic module is specifically used for:
If group string electric current In is abnormal, the discrete value of calculating group string electric current In, and judges whether the discrete value is less than threshold value,
If so, a group string electric current In is determined as normal value, if it is not, a group string electric current In is determined as exceptional value, and the group string electric current is positioned
Position and report；
If group string electric current In is normal, a group string electric current In is determined as normal value.
For the workflow for more clearly illustrating present embodiment, it is described further below with reference to Fig. 3:
One assemblies monitor module is set on every piece of photovoltaic module, a header box is set on each header box and is monitored
Module detects voltage, electric current and the power of each photovoltaic module, every road photovoltaic group string, and by multiple monitoring modular groups
It is woven to network, and cooperates environment monitor, photovoltaic monitoring terminal, makes the number of monitoring with each equipment of collected photovoltaic generating system
It can gradually be uploaded to host computer along network node according to other reference datas, thus the operating status of photovoltaic generating system is carried out
Comprehensive analysis realizes the function of abnormal current diagnosis.
The Intelligence Diagnosis method based on photovoltaic group string current anomaly that present embodiment proposes, first exists to target device
Different parameters in operational process are acquired, this collection process is simple to operation, realize the photovoltaic module to substantial amounts
The monitoring of parameter in the process of running provides feasibility basis for its fault detection and positioning；Then according to target device
Different type in photovoltaic generating system to carry out targetedly classified Monitoring and positioning to target device, improves data and adopts
The validity of collection process and monitoring result realizes the positioning fast and accurately to target device malfunction monitoring；Finally by mesh
The different monitoring results of marking device report step by step, not only ensure that the simple and reliable property of data transmission, but also improve data biography
Defeated efficiency has spent rate of false alarm and rate of failing to report again significantly.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (8)
1. a kind of Intelligence Diagnosis method based on photovoltaic group string current anomaly, which comprises the following steps:
S1, the rated power X and realtime power P for obtaining target device, and calculated partially based on rated power X and realtime power P
Difference MAD；
S2, respectively acquisition group string electric current I1, I2, I3, and, group string current maxima Iupper；
S3, range and group string electric current I1, I2, I3 knot compared with organizing and going here and there current maxima Iupper according to deviation MAD
Fruit judges whether group string electric current I1, I2, I3 are abnormal；
S4, the judging result formulation group string current diagnostic result based on step S3.
2. the Intelligence Diagnosis method according to claim 1 based on photovoltaic group string current anomaly, which is characterized in that step
S1 is specifically included:
Obtain the rated power X and realtime power P of target device；
Deviation MAD, the formula are calculated according to following formula are as follows:
MAD=P/X.
3. the Intelligence Diagnosis method according to claim 1 based on photovoltaic group string current anomaly, which is characterized in that step
S3 is specifically included:
Deviation MAD is obtained, and deviation MAD is compared with predetermined deviation M1, M2, M3, M4, M5:
As M4≤MAD < M5, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤aIupper, decision set string electric current In is abnormal, if In > aIupper, decision set string electric current In is normal；
As M3≤MAD < M4, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤bIupper, decision set string electric current In is abnormal, if In > bIupper, decision set string electric current In is normal；
As M2≤MAD < M3, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤cIupper, decision set string electric current In is abnormal, if In > cIupper, decision set string electric current In is normal；
As M1≤MAD < M2, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤dIupper, decision set string electric current In is abnormal, if In > dIupper, decision set string electric current In is normal；
As MAD < M1, judge without next step；
Wherein, M1=0.3, M2=0.4, M3=0.6, M4=0.8, M5=1, a, b, c, d are preset value, a=0.9, b=
0.8, c=0.7, d=0.6, n={ 1,2,3 }.
4. the Intelligence Diagnosis method according to claim 3 based on photovoltaic group string current anomaly, which is characterized in that step
S4 is specifically included:
If group string electric current In is abnormal, the discrete value of calculating group string electric current In, and judges whether the discrete value is less than threshold value, if
It is that a group string electric current In is determined as normal value, if it is not, a group string electric current In is determined as exceptional value, and positions the group string electric current
It position and reports；
If group string electric current In is normal, a group string electric current In is determined as normal value.
5. a kind of Intelligence Diagnosis system based on photovoltaic group string current anomaly characterized by comprising
Deviation computing module, for obtaining the rated power X and realtime power P of target device, and based on rated power X and in real time
Power P calculates deviation MAD；
Data obtaining module, for acquisition group string electric current I1, I2, I3 respectively, and, group string current maxima Iupper；
Abnormal judgment module, for the range and group string electric current I1, I2, I3 and group string current maxima according to deviation MAD
The comparison result of Iupper judges whether group string electric current I1, I2, I3 are abnormal；
Current diagnostic module, for the judging result formulation group string current diagnostic result based on abnormal judgment module.
6. the Intelligence Diagnosis system according to claim 1 based on photovoltaic group string current anomaly, which is characterized in that described
Deviation computing module is specifically used for:
Obtain the rated power X and realtime power P of target device；
Deviation MAD, the formula are calculated according to following formula are as follows:
MAD=P/X.
7. the Intelligence Diagnosis system according to claim 1 based on photovoltaic group string current anomaly, which is characterized in that described
Abnormal judgment module is specifically used for:
Deviation MAD is obtained, and deviation MAD is compared with predetermined deviation M1, M2, M3, M4, M5:
As M4≤MAD < M5, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤aIupper, decision set string electric current In is abnormal, if In > aIupper, decision set string electric current In is normal；
As M3≤MAD < M4, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤bIupper, decision set string electric current In is abnormal, if In > bIupper, decision set string electric current In is normal；
As M2≤MAD < M3, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤cIupper, decision set string electric current In is abnormal, if In > cIupper, decision set string electric current In is normal；
As M1≤MAD < M2, the size of further judgement group string electric current I1, I2, I3 and group string current maxima Iupper are closed
System:
If In≤dIupper, decision set string electric current In is abnormal, if In > dIupper, decision set string electric current In is normal；
As MAD < M1, judge without next step；
Wherein, M1=0.3, M2=0.4, M3=0.6, M4=0.8, M5=1, a, b, c, d are preset value, a=0.9, b=
0.8, c=0.7, d=0.6, n={ 1,2,3 }.
8. the Intelligence Diagnosis system according to claim 7 based on photovoltaic group string current anomaly, which is characterized in that described
Current diagnostic module is specifically used for:
If group string electric current In is abnormal, the discrete value of calculating group string electric current In, and judges whether the discrete value is less than threshold value, if
It is that a group string electric current In is determined as normal value, if it is not, a group string electric current In is determined as exceptional value, and positions the group string electric current
It position and reports；
If group string electric current In is normal, a group string electric current In is determined as normal value.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110855241A (en) * | 2019-12-04 | 2020-02-28 | 合肥阳光新能源科技有限公司 | Photovoltaic system fault diagnosis method and device |
| JP2021035098A (en) * | 2019-08-20 | 2021-03-01 | 株式会社ミライト | Deterioration detection method for solar cell string, deterioration detection system, and deterioration detection device |
| JP2021035323A (en) * | 2020-02-17 | 2021-03-01 | 株式会社ミライト | Deterioration detection method for solar cell string, deterioration detection system, and deterioration detection device |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2317329A2 (en) * | 2009-10-08 | 2011-05-04 | Adensis GmbH | Direct current metering point for detecting defective PV modules in a PV assembly |
| CN103687254A (en) * | 2013-11-21 | 2014-03-26 | 上海申通地铁集团有限公司 | Troubleshooting method and troubleshooting system for energy-saving lamps |
| CN104796082A (en) * | 2015-04-22 | 2015-07-22 | 中国科学院广州能源研究所 | System and method for diagnosing faults of photovoltaic power generation systems in online manner |
| CN105337575A (en) * | 2015-11-17 | 2016-02-17 | 广州健新自动化科技有限公司 | Method and system for state prediction and fault diagnosis of photovoltaic power station |
| CN105554494A (en) * | 2015-12-09 | 2016-05-04 | 浙江省公众信息产业有限公司 | Snow point image detection method and device and video quality detection device and system |
| 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 |
| CN106803742A (en) * | 2015-11-26 | 2017-06-06 | 中国电力科学研究院 | A kind of detection method for photovoltaic plant scene photovoltaic group string uniformity and efficiency |
| CN106961249A (en) * | 2017-03-17 | 2017-07-18 | 广西大学 | A kind of diagnosing failure of photovoltaic array and method for early warning |
| CN107949088A (en) * | 2016-10-12 | 2018-04-20 | 佛山市顺德区美的电热电器制造有限公司 | The Poewr control method and power control device and electromagnetic oven of electromagnetic heating system |
| CN108055002A (en) * | 2017-12-14 | 2018-05-18 | 艾思玛新能源技术（上海）有限公司苏州高新区分公司 | A kind of photovoltaic string formation monitoring method and system |
-
2018
- 2018-09-13 CN CN201811067017.3A patent/CN109298228A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2317329A2 (en) * | 2009-10-08 | 2011-05-04 | Adensis GmbH | Direct current metering point for detecting defective PV modules in a PV assembly |
| CN103687254A (en) * | 2013-11-21 | 2014-03-26 | 上海申通地铁集团有限公司 | Troubleshooting method and troubleshooting system for energy-saving lamps |
| CN104796082A (en) * | 2015-04-22 | 2015-07-22 | 中国科学院广州能源研究所 | System and method for diagnosing faults of photovoltaic power generation systems in online manner |
| CN105337575A (en) * | 2015-11-17 | 2016-02-17 | 广州健新自动化科技有限公司 | Method and system for state prediction and fault diagnosis of photovoltaic power station |
| CN106803742A (en) * | 2015-11-26 | 2017-06-06 | 中国电力科学研究院 | A kind of detection method for photovoltaic plant scene photovoltaic group string uniformity and efficiency |
| CN105554494A (en) * | 2015-12-09 | 2016-05-04 | 浙江省公众信息产业有限公司 | Snow point image detection method and device and video quality detection device and system |
| 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 |
| CN107949088A (en) * | 2016-10-12 | 2018-04-20 | 佛山市顺德区美的电热电器制造有限公司 | The Poewr control method and power control device and electromagnetic oven of electromagnetic heating system |
| CN106961249A (en) * | 2017-03-17 | 2017-07-18 | 广西大学 | A kind of diagnosing failure of photovoltaic array and method for early warning |
| CN108055002A (en) * | 2017-12-14 | 2018-05-18 | 艾思玛新能源技术（上海）有限公司苏州高新区分公司 | A kind of photovoltaic string formation monitoring method and system |
Non-Patent Citations (1)
| Title |
|---|
| 李永军，等: "分布式并网光伏发电站系统的应用", 《集成电路应用》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2021035098A (en) * | 2019-08-20 | 2021-03-01 | 株式会社ミライト | Deterioration detection method for solar cell string, deterioration detection system, and deterioration detection device |
| CN110855241A (en) * | 2019-12-04 | 2020-02-28 | 合肥阳光新能源科技有限公司 | Photovoltaic system fault diagnosis method and device |
| JP2021035323A (en) * | 2020-02-17 | 2021-03-01 | 株式会社ミライト | Deterioration detection method for solar cell string, deterioration detection system, and deterioration detection device |
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