CN104410360A - Safe operation method of photovoltaic power generation system, training method for artificial neural network and real-time detection method in safe operation method, and real-time detection device - Google Patents
Safe operation method of photovoltaic power generation system, training method for artificial neural network and real-time detection method in safe operation method, and real-time detection device Download PDFInfo
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- CN104410360A CN104410360A CN201410558392.3A CN201410558392A CN104410360A CN 104410360 A CN104410360 A CN 104410360A CN 201410558392 A CN201410558392 A CN 201410558392A CN 104410360 A CN104410360 A CN 104410360A
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- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000011897 real-time detection Methods 0.000 title claims abstract description 23
- 238000010248 power generation Methods 0.000 title abstract description 7
- 238000013528 artificial neural network Methods 0.000 title abstract description 4
- 238000005070 sampling Methods 0.000 claims abstract description 21
- 238000010891 electric arc Methods 0.000 claims description 43
- 230000001537 neural effect Effects 0.000 claims description 30
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 238000003909 pattern recognition Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 abstract description 4
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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
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- 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
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Abstract
The invention relates to a safe operation method of a photovoltaic power generation system. The safe operation method comprises a training method for an artificial neural network for detecting a direct current failure arc of the photovoltaic power generation system, and a real-time detection method of the direct current failure arc of the photovoltaic power generation system, wherein the steps in the real-time detection method can be completed by establishing a functional module frame and using a computer program instruction to control a computer system. In order to accurately, simply and conveniently test the direct current failure arc in the photovoltaic power generation system, direct current obtained by sampling is converted into a frequency domain, the harmonic energy of the frequency domain within a set frequency band is computed, and then the artificial neural network judges whether the direct current failure arc happens according to the harmonic energy.
Description
Technical field
The present invention relates to photovoltaic generating system method for safe operation, it comprises for detecting photovoltaic generating system DC Line Fault electric arc and training method to artificial neural net, also comprise photovoltaic generating system DC Line Fault electric arc real-time detection method, each step in real-time detection method wherein by setting up functional module construction, can have been come by computer program instructions computer for controlling system.
Background technology
In recent years, photovoltaic generating system extensive use, the high-voltage dc power supply of what the installation of most of photovoltaic array utilized is all long string, which increases the safety problem relevant with electric arc.Electric arc is a kind of gas discharge phenomenon, and arcing events often contains huge energy, constitutes a threat to the safety of surrounding devices and staff.Electric arc is divided into direct-current arc and alternating current arc by current properties, due to the application starting comparatively morning of alternating current, detection method at present for AC fault electric arc is quite ripe, relevant alternating current arc failure protecting device also comes into the market, but the direct-current arc in photovoltaic generating system and the character of alternating current arc have a great difference, direct-current arc is a kind of random not stationary signal, there is no alternating current periodically " flat shoulder " portion's characteristic, tradition is no longer applicable based on the detection method of waveform or time domain specification, so the detection relative difficulty of photovoltaic generating system direct-current arc.
Early stage DC Line Fault arc method for measuring identifies according to characteristics such as arc glow, heating and generation electromagnetic radiation, and these methods generally will use multiple transducer to collect electric arc generation information, and cost is high, and recall rate is low, and its application is restricted.The electrical characteristics such as current many applied voltages, electric current detect DC Line Fault electric arc, passing threshold compares to determine the generation of DC Line Fault electric arc, very flexible, and along with the change of system complexity, considering that the threshold value determined after many factors may cause testing result inaccurate because not considering other factors, being difficult to determine to draw an appropriate threshold value.
Summary of the invention
How accurately, easily the technical problem to be solved in the present invention tests DC Line Fault electric arc in photovoltaic generating system.
Thinking of the present invention extracts the parameter that can reflect DC Line Fault arc characteristic rightly, transfers to artificial neural net to judge whether DC Line Fault electric arc occurs according to this parameter.
The present invention provides photovoltaic generating system method for safe operation, and comprise the training method to artificial neural net and photovoltaic generating system DC Line Fault electric arc real-time detection method, details are as follows.
First the training method to artificial neural net is provided:
P. respectively at generation DC Line Fault electric arc with when there is not DC Line Fault electric arc, the following sample acquisition step of multiple exercise, obtain and organize learning sample more, each sample acquisition step comprises following A, B, C:
---the predeterminated position sampling direct current of A. in photovoltaic generating system;
---B. is converted to frequency domain the direct current that obtains of sampling, and calculates its harmonic energy in setting frequency range;
---whether C., to calculate the harmonic energy value of gained as input signal, there is DC Line Fault electric arc as output signal during sampling, form the one group of learning sample carrying out pattern recognition training for artificial neural net;
Q. above-mentioned many group learning samples are adopted to carry out pattern recognition training to artificial neural net, until this artificial neural net possesses the recognition capability judging whether to occur DC Line Fault electric arc according to the harmonic energy value of direct current.
Artificial neural net has possessed such recognition capability, and we just can detect DC Line Fault electric arc in real time in this photovoltaic generating system, and it is as follows that the present invention provides real-time detection method:
A. the described predeterminated position real-time sampling direct current in photovoltaic generating system;
B. with the identical mode in above-mentioned B, sampling, the direct current that obtains is converted to frequency domain, calculates its harmonic energy in described setting frequency range;
Q. the harmonic energy value calculating gained is input to described artificial neural net, is judged whether accordingly to there occurs DC Line Fault electric arc by this artificial neural net.
Each step in real-time detection method by setting up functional module construction, can have been come by computer program instructions computer for controlling system, and these computer program instructions store in a computer-readable storage medium.
Beneficial effect of the present invention and principle thereof:
(1) inventor notices: can produce a large amount of energy suddenly when direct-current arc occurs, therefore the noise of current signal within the scope of wider frequency territory must have obvious increase, therefore, the present invention is converted to direct current the harmonic energy value that frequency-domain calculations draws can reflect DC Line Fault arc characteristic rightly, and testing result is accurate;
(2) although noise occurrence and the several factors (operating state etc. of the quantity of such as photovoltaic group string and connected mode, inverter) that increase are relevant, there is uncertainty, but the present invention is by many group learning sample training of human artificial neural networks, make after artificial neural net possesses recognition capability, judge DC Line Fault electric arc by artificial neural net, solve the problem because the polytropy of photovoltaic generating system load, the complexity of DC power supply connection and threshold value are difficult to determine and bring;
(3) only need predeterminated position sampling direct current in photovoltaic generating system, without the need to detecting voltage, easy to detect and cost is low.
Accompanying drawing explanation
Fig. 1 is photovoltaic power generation system structure figure.
Embodiment
Certain photovoltaic power generation system structure is as Fig. 1, and photovoltaic array comprises multiple photovoltaic group string, its produce direct current after header box confluxes, by inverter inversion to AC network.
The present embodiment preferably, be preset as at the main line up-sampling direct current that confluxes, no matter be then, on each branch road of photovoltaic array inside, electric arc occurs, or electric arc occurs the main line after many branch roads conflux, the current signal collected can embody the harmonic energy characteristic of DC Line Fault electric arc.
Possessing the recognition capability judging whether DC Line Fault electric arc occurs in order to allow artificial neural net, needing to adopt many group learning samples to train artificial neural net.Often organize learning sample obtaining step as follows:
---the predeterminated position in photovoltaic generating system is with frequency f
ssampling direct current.
---to sampling, the direct current obtained carries out FFT conversion, is converted to frequency domain, obtains current spectrum.In order to identify the harmonic energy characteristic of direct current more accurately, we are frequency range 0 ~ f
s/ 2 are divided into multiple frequency range F1, F2 by low frequency to high frequency, Calculate each frequency range F1, F2, interior harmonic energy, wherein the harmonic energy of each frequency range represents, as the input signal in this group learning sample with (its value is larger represents that harmonic energy is larger) by the squared magnitude of each harmonic of direct current in this frequency range.
---if during sampling, DC Line Fault electric arc does not occur, then the output signal in this group learning sample is " non-electric arc "; If there occurs DC Line Fault electric arc during sampling, then the output signal in this group learning sample is " being electric arc ".
In Fig. 1, inverter can be set as different operating states.With regard to the multiple operating state (easily extensible is to the multiple operating state of photovoltaic generating system) of inverter, we are respectively at generation DC Line Fault electric arc with when there is not DC Line Fault electric arc, the above-mentioned sample acquisition step of multiple exercise obtains many group learning samples and carries out pattern recognition training to artificial neural net, until this artificial neural net possesses the recognition capability judging whether to occur DC Line Fault electric arc according to the harmonic energy value of direct current, we just can detect DC Line Fault electric arc in real time in this photovoltaic generating system, and detecting step is as follows:
A. on the main line after header box, with frequency f
sreal-time sampling direct current;
B. FFT conversion is carried out to the direct current obtained of sampling, be converted to frequency domain, obtain current spectrum; Calculate each frequency range F1, F2 ... in harmonic energy, wherein the harmonic energy of each frequency range is by the squared magnitude of each harmonic of direct current in this frequency range with represent;
Q. the harmonic energy value calculating gained is input to described artificial neural net, is judged whether accordingly to there occurs DC Line Fault electric arc by this artificial neural net.
Photovoltaic generating system method for safe operation performs as above, and taking this can DC Line Fault electric arc in Timeliness coverage photovoltaic generating system.
Frequency range 0 ~ f
s/ 2 are divided into multiple frequency range F1, F2 ... can not be divide equally; As for being divided into how many frequency ranges, can decide according to specific needs, frequency range can capture meticulous arc characteristic at most, and frequency range at least neural net can judge as early as possible, Timeliness coverage DC Line Fault electric arc.
Claims (18)
1., for detecting photovoltaic generating system DC Line Fault electric arc and training method to artificial neural net, it is characterized in that comprising the steps:
P. respectively at generation DC Line Fault electric arc with when there is not DC Line Fault electric arc, the following sample acquisition step of multiple exercise, obtain and organize learning sample more, each sample acquisition step comprises following A, B, C:
---the predeterminated position sampling direct current of A. in photovoltaic generating system;
---B. is converted to frequency domain the direct current that obtains of sampling, and calculates its harmonic energy in setting frequency range;
---whether C., to calculate the harmonic energy value of gained as input signal, there is DC Line Fault electric arc as output signal during sampling, form the one group of learning sample carrying out pattern recognition training for artificial neural net;
Q. above-mentioned many group learning samples are adopted to carry out pattern recognition training to artificial neural net, until this artificial neural net possesses the recognition capability judging whether to occur DC Line Fault electric arc according to the harmonic energy value of direct current.
2. training method according to claim 1, the predeterminated position in A is confluxing on main line.
3. training method according to claim 1, the conversion in B adopts FFT conversion.
4. training method according to claim 1, the setting frequency range in B has multiple.
5. training method according to claim 1, the harmonic energy in B is by the squared magnitude of each harmonic and represent.
6. training method according to claim 1, step P performs respectively under the multiple operating state of photovoltaic generating system.
7. training method according to claim 6, the multiple operating state of described photovoltaic generating system comprises the multiple operating state of inverter.
8. photovoltaic generating system DC Line Fault electric arc real-time detection method, is characterized in that comprising the steps:
A. the predeterminated position real-time sampling direct current in photovoltaic generating system;
B. the direct current that obtains of sampling is converted to frequency domain, calculates its harmonic energy in setting frequency range;
Q. the harmonic energy value calculating gained is input to the artificial neural net trained, is judged whether accordingly to there occurs DC Line Fault electric arc by this artificial neural net.
9. real-time detection method according to claim 8, the predeterminated position in a is confluxing on main line.
10. real-time detection method according to claim 8, the conversion in b adopts FFT conversion.
11. real-time detection methods according to claim 8, the setting frequency range in b has multiple.
12. real-time detection methods according to claim 8, the harmonic energy in b is by the squared magnitude of each harmonic and represent.
13. photovoltaic generating system DC Line Fault electric arc real-time detection apparatus, is characterized in that comprising as lower device:
A. the predeterminated position real-time sampling direct current in photovoltaic generating system;
B. the direct current that obtains of sampling is converted to frequency domain, calculates its harmonic energy in setting frequency range;
Q. the harmonic energy value calculating gained is input to the artificial neural net trained, is judged whether accordingly to there occurs DC Line Fault electric arc by this artificial neural net.
14. real-time detection apparatus according to claim 13, the predeterminated position in a is confluxing on main line.
15. real-time detection apparatus according to claim 13, the conversion in b adopts FFT conversion.
16. real-time detection apparatus according to claim 13, the setting frequency range in b has multiple.
17. real-time detection apparatus according to claim 13, the harmonic energy in b is by the squared magnitude of each harmonic and represent.
18. photovoltaic generating system method for safe operation, it is characterized in that comprising the training method described in any one of claim 1 ~ 7 and real-time detection method according to claim 8, this training method is identical with this real-time detection method predeterminated position, conversion regime direct current being converted to frequency domain is identical, setting frequency range is identical, identical to the account form of harmonic energy, the artificial neural net in this real-time detection method is the artificial neural net obtained after performing this training method.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105067945A (en) * | 2015-09-07 | 2015-11-18 | 苏州长风自动化科技有限公司 | Intelligent detection unit with direct-current arcing detection function and junction box |
| CN105403816A (en) * | 2015-10-30 | 2016-03-16 | 国家电网公司 | Identification method of DC fault electric arc of photovoltaic system |
| CN105490641A (en) * | 2015-12-31 | 2016-04-13 | 西安交通大学 | Photovoltaic system fault electric arc detection method based on comprehensive multiple characteristic quantities |
| CN106961248A (en) * | 2017-04-25 | 2017-07-18 | 西安交通大学 | Mix the photovoltaic system fault arc detection method of quadratic form time-frequency distributions feature and the analysis of self adaptation multiplicative function |
| CN107219447A (en) * | 2017-06-19 | 2017-09-29 | 安徽江淮汽车集团股份有限公司 | A kind of direct-current arc detection method and system based on impedance characteristic |
| CN107340459A (en) * | 2016-11-24 | 2017-11-10 | 安徽江淮汽车集团股份有限公司 | A kind of DC Line Fault arc method for measuring and system |
| CN108107329A (en) * | 2016-11-24 | 2018-06-01 | 上海航空电器有限公司 | A kind of alternating current arc frequency domain detection method |
| CN108155874A (en) * | 2018-01-17 | 2018-06-12 | 江阴市余润光伏发电有限公司 | A kind of photovoltaic power station monitoring system |
| CN109239517A (en) * | 2018-09-12 | 2019-01-18 | 国网青海省电力公司电力科学研究院 | A kind of discrimination method of new photovoltaic system direct current arc fault and type |
| CN110618366A (en) * | 2019-11-05 | 2019-12-27 | 阳光电源股份有限公司 | Direct current arc detection method and device |
| CN113076691A (en) * | 2019-09-23 | 2021-07-06 | 华为技术有限公司 | Direct current arc detection method, device, equipment, system and storage medium |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105067945A (en) * | 2015-09-07 | 2015-11-18 | 苏州长风自动化科技有限公司 | Intelligent detection unit with direct-current arcing detection function and junction box |
| CN105403816A (en) * | 2015-10-30 | 2016-03-16 | 国家电网公司 | Identification method of DC fault electric arc of photovoltaic system |
| CN105490641A (en) * | 2015-12-31 | 2016-04-13 | 西安交通大学 | Photovoltaic system fault electric arc detection method based on comprehensive multiple characteristic quantities |
| CN107340459B (en) * | 2016-11-24 | 2019-06-04 | 安徽江淮汽车集团股份有限公司 | A kind of DC Line Fault arc method for measuring and system |
| CN107340459A (en) * | 2016-11-24 | 2017-11-10 | 安徽江淮汽车集团股份有限公司 | A kind of DC Line Fault arc method for measuring and system |
| CN108107329A (en) * | 2016-11-24 | 2018-06-01 | 上海航空电器有限公司 | A kind of alternating current arc frequency domain detection method |
| CN106961248A (en) * | 2017-04-25 | 2017-07-18 | 西安交通大学 | Mix the photovoltaic system fault arc detection method of quadratic form time-frequency distributions feature and the analysis of self adaptation multiplicative function |
| CN107219447A (en) * | 2017-06-19 | 2017-09-29 | 安徽江淮汽车集团股份有限公司 | A kind of direct-current arc detection method and system based on impedance characteristic |
| CN107219447B (en) * | 2017-06-19 | 2019-12-03 | 安徽江淮汽车集团股份有限公司 | A kind of direct-current arc detection method and system based on impedance characteristic |
| CN108155874A (en) * | 2018-01-17 | 2018-06-12 | 江阴市余润光伏发电有限公司 | A kind of photovoltaic power station monitoring system |
| CN109239517A (en) * | 2018-09-12 | 2019-01-18 | 国网青海省电力公司电力科学研究院 | A kind of discrimination method of new photovoltaic system direct current arc fault and type |
| CN113076691A (en) * | 2019-09-23 | 2021-07-06 | 华为技术有限公司 | Direct current arc detection method, device, equipment, system and storage medium |
| CN110618366A (en) * | 2019-11-05 | 2019-12-27 | 阳光电源股份有限公司 | Direct current arc detection method and device |
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