CN107133713A - A kind of photovoltaic plant intelligently cleans the method for building up of decision system - Google Patents
A kind of photovoltaic plant intelligently cleans the method for building up of decision system Download PDFInfo
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- CN107133713A CN107133713A CN201710145368.0A CN201710145368A CN107133713A CN 107133713 A CN107133713 A CN 107133713A CN 201710145368 A CN201710145368 A CN 201710145368A CN 107133713 A CN107133713 A CN 107133713A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The method for building up of decision system is intelligently cleaned the present invention relates to a kind of photovoltaic plant, successively including setting up dust eclipsing loss computing subsystem, setting up weather prognosis subsystem, set up generated energy predicting subsystem and set up intelligence cleaning decision-making subsystem.One aspect of the present invention, from the actual demand of photovoltaic plant O＆M, by monitoring dynamic photovoltaic module array dust eclipsing loss rate on the spot there is provided more accurate, more representative photovoltaic module dust eclipsing loss rate, is used as the data basis of decision-making.Photovoltaic plant can be avoided to only rely on the blindness of operation maintenance personnel intuitive judgment so that cleaning decision-making possesses the calculating basis of quantization.On the other hand, using the algorithm computation schema of automation, make the formulation of cleaning decision-making with more ageing, for photovoltaic plant provide dynamically, cleaning frequency of maximum revenue.
Description
Technical field
The method for building up of decision system is intelligently cleaned the present invention relates to a kind of photovoltaic plant.
Background technology
At present, difficult array cleaning, cleaning cost height, scavenging period hardly possible are there is during solar photovoltaic power plant O＆M
The problems such as to hold, photovoltaic plant is also resulted in because array is blocked and lower power production by dust, gene-ration revenue is unsatisfactory for being expected.
For photovoltaic plant carry out rationally, effectively clean decision-making, find dynamic optimal clear on the premise of cleaning cost is substantially stationary
The cycle is washed, is the intelligent O＆M of following photovoltaic plant, the inevitable requirement of efficient O＆M.
The cleaning decision-making of photovoltaic array, need to be according to power station power generation situation, and considers regional climate situation, dust accumulation
Situation etc. and formulate.Due to photovoltaic array, dust stratification situation is dynamic change in natural environment, it is difficult to the warp of a period of time
Value is tested to determine the cleaning frequency fixed for a long time.In addition, the meteorological condition such as solar radiation situation, sleet, is also that influence cleaning is determined
The key factor of plan.At this stage, cleaning of the photovoltaic plant operator to array in power station often relies only on generated energy data, very
Judge to the observation to field arrays surface dirt is only relied on.Simultaneously as photovoltaic array cleaning cost is higher, it is difficult to high frequency time
Cleaning, causes many photovoltaic plants to be cleaned for a long time without component, or is just cleaned when generated energy is abnormal.Dust is blocked
When serious, not only the generated energy of photovoltaic plant is had a major impact, the also safe operation on photovoltaic module is significantly affected, because
This needs is a set of can be provided intelligence for photovoltaic plant, reasonably clean decision system, under fixed cleaning cost input, most
Obtain to limits generated energy income.
Decision system is intelligently cleaned using photovoltaic plant of the present invention, can provide real based on dynamic for photovoltaic plant
Survey the place component dust eclipsing loss rate of data, the situation that reflection component directly perceived is blocked by dust.On this basis, by calculating
Method analysis calculate, provides the dynamic cleaning cycle decision-making with changes in environmental conditions for photovoltaic plant O＆M, realize gene-ration revenue and
Clean the optimization of cost trade-offs, protection power station benefit.
Chinese patent 201510785343.8 disclose it is a kind of realize photovoltaic plant solar panel intelligently cleaning be
System and method, including environment harvester, to gather intensity of solar radiation and solar radiation quantity data；Generating information is adopted
Acquisition means, to gather actual power generation；The communication server, in environment harvester, generating information collecting device sum
Communicated according between processing server；Data processing server, to the theoretical generated energy for calculating photovoltaic plant, theoretical generating
Income and loss gene-ration revenue simultaneously judge whether to need cleaning；Display terminal, to advise to user's displaying cleaning.The device pair
The effect analysis of dust stratification is not complete, still there is certain gap apart from optimization collocation.
The content of the invention
It is an object of the invention to overcome above shortcomings in the prior art, and provide a kind of step simply, intelligence
Efficient photovoltaic plant intelligently cleans the method for building up of decision system.The photovoltaic plant that this method is set up intelligently cleans decision system
The representative dust eclipsing loss rate accurately and efficiently obtained in the certain time period of power station is ensure that, and combines meteorological data
Predict that the comprehensive maximum revenue cleaning decision-making for providing power station photovoltaic array provides for photovoltaic plant O＆M with power station generated energy
Intelligence, reliable support.
The present invention the used technical scheme that solves the above problems is：A kind of photovoltaic plant intelligently cleans building for decision system
Cube method, it is characterised in that：In turn include the following steps：
1）Set up dust eclipsing loss computing subsystem：Using monitoring mode on the spot, one or more groups of tools in photovoltaic plant are chosen
Representational photovoltaic array group as a comparison, chooses one or more groups of photovoltaic arrays as reference group around contrast groups, right
Contrast groups and reference group carry out real-time generated energy monitoring respectively, and contrast groups are timed in real-time generated energy monitoring process
Cleaning, the real-time generated energy monitoring result to contrast groups is monitored with the real-time generated energy of the reference group of natural dust stratification under identical environment
As a result contrasted, calculate the dust eclipsing loss rate for obtaining contrast groups；
2）Set up weather prognosis subsystem：By the way of monitoring on the spot and external meteorological data are complementary, power station area is obtained in real time
Domain weather condition and solar radiation situation, support is provided for generated energy prediction；
3）Step 1）With step 2）After end, generated energy predicting subsystem is set up：According to the generated energy situation of all photovoltaic arrays,
With reference to dust eclipsing loss rate, the generated energy situation of change of contrast groups and the generated energy situation of change of reference group are calculated respectively, in advance
Survey generating capacity of the whole photovoltaic plant within a cleaning frequency；
4）Step 3）After end, intelligence cleaning decision-making subsystem is set up：Collection step 1）, step 2）With step 3）In all processing
As a result, photovoltaic plant gross generation revenue function in one cleaning frequency of calculating and the maximum for the difference for cleaning cost function are passed through
Value, the optimal cleaning frequency is fed back to power station personnel, so as to reach the purpose of automatic decision and intelligence cleaning.
Dust eclipsing loss computing subsystem, weather prognosis subsystem, generated energy predicting subsystem and intelligence cleaning decision-making
Subsystem collectively forms photovoltaic plant intelligently cleaning decision system.Dust eclipsing loss computing subsystem is blocked for calculating dust
Loss, weather prognosis subsystem is used for weather prognosis, and generated energy predicting subsystem is predicted for generated energy, intelligence cleaning decision-making
System is used to feed back the optimal cleaning frequency, so as to reach the purpose of automatic decision and intelligence cleaning.
On the spot monitoring of the dust eclipsing loss computing subsystem based on photovoltaic array generated energy, its operation reserve is：Choose
One group or several groups representational arrays of photovoltaic plant on-site, are timed, high frequency time is cleaned, exist so as to obtain photovoltaic array
Real-time generated energy data under free from dust circumstance of occlusion.Generated electricity again by the generic array of natural dust stratification near monitoring standard array
Amount, carries out conversion contrast, so that the typical dust eclipsing loss rate of photovoltaic plant can be represented by obtaining.Weather prognosis subsystem with
Generated energy predicting subsystem cooperation.Weather prognosis subsystem is the basis of built photovoltaic power station power generation amount prediction, using on the spot too
The positive Radiation monitoring form complementary with external weather forecast, data basis is provided for generated energy forecasting system.Generated energy prediction
The typical dust eclipsing loss rate of photovoltaic plant that system is then provided according to dust eclipsing loss computing subsystem, calculates photovoltaic array
The generated energy situation of change under the conditions of timely cleaning with natural product ash, predicts reason of the whole audience area within a cleaning frequency respectively
By generated energy.Intelligence cleaning decision-making subsystem integrated novel intelligent cleaning decision making algorithm, be by photovoltaic plant running is abstract
Cleaning point and natural dust stratification cycle（It is referred to as the cleaning frequency）Alternate time shaft.Within a cleaning frequency, by function algorithm
Theoretical generated energy of the whole station array within the natural dust stratification cycle after cleaning in time is calculated, with reference to Spot Price, generated energy is calculated and receives
Benefit prediction.Then, it is considered to clean cost function, calculate the difference of generated energy earnings forecast value and cleaning cost, ask for two functions it
The maximum of difference, and the cleaning frequency number of days n of maximum condition will be met as the optimal clean cycle of decision-making.
The external meteorological data source of the present invention includes meteorogical phenomena database and representative weather station, and meteorogical phenomena database is external in state
The on-site in photovoltaic plant is set up in meteorological department of family, representative weather station, and representative weather station includes total solar radiation instrument, ring
Environmental temperature and humidity sensor and anemobiagraph.
Implementation steps 1 of the present invention）Before, the dust eclipsing loss rate to photovoltaic array carries out the monitoring of a period of time.
Step 1 of the present invention）The monitoring current of middle contrast groups is A1, the monitoring current of reference group is A2, current value deviation is
Δ, then Δ=（A1- A2）/A2。
Step 3 of the present invention）It is middle to carry out theoretical generated energy and actual power generation calculating, wherein, theoretical generated energy is within first day
Q1, theoretical generated energy is Q within n-th dayn, Qn= n×Qn-1/（N-1）, n-th day dust eclipsing loss rate is Δn, first day actual
Generated energy predicted value is q1, n-th day actual power generation predicted value is qn, q1= Q1, qn= Qn ×（1- Δsn）, n >=2.
Step 4 of the present invention）The difference of middle photovoltaic plant gross generation revenue function and cleaning cost function is f（T, Δ）,
Wherein f（T, Δ）=（qn- C）/ T, C are fixed cleaning cost, and T is the cleaning frequency, to f（T, Δ）During maximizing, n=
When 1, q1Using actual monitoring value, the actual power value of prediction second day to n days, and calculating target function value, second day
Rise, using algorithm predicted value iteration day by day, update anticipation trend, find the scope of function maxima point, so that it is determined that optimal clear
Wash the value of cycle T.
In the prior art, due to photovoltaic power station component substantial amounts, place occupation of land is wide, and power station many executions at this stage
The O＆M mode of " few people safeguards ", causes power station assembly array cleaning difficulty, power station generated energy is difficult to ensure that, is especially embodied in me
The photovoltaic plant in the more serious region of state's northwest sand and dust.By artificial differentiation more than the cleaning decision-making of current photovoltaic plant, means are generally
Manual observation is fed back, with reference to power station generated energy Monitoring Data.Compared with prior art, the present invention can pass through by weather monitoring
Detection timing on the spot, the generated energy situation of the standard array of high frequency time cleaning, it is automatic to calculate photovoltaic on-site dust eclipsing loss
Rate, reasonable analysis full field array cleaning in time within the cleaning frequency changes with the generated energy under the conditions of natural product ash, final autonomous
Ground provides the intelligence cleaning decision-making based on actual conditions for photovoltaic plant personnel, realizes the income of " generated energy-cleaning cost " most
Bigization.The present invention improves the automation, rationalization, intelligent and optimization degree of photovoltaic power station component cleaning.
One aspect of the present invention, from the actual demand of photovoltaic plant O＆M, by monitoring dynamic photovoltaic module on the spot
Array dust eclipsing loss rate is used as decision-making there is provided more accurate, more representative photovoltaic module dust eclipsing loss rate
Data basis.Photovoltaic plant can be avoided to only rely on the blindness of operation maintenance personnel intuitive judgment so that cleaning decision-making possesses quantization
Calculating basis.On the other hand, using the algorithm computation schema of automation, make the formulation of cleaning decision-making with more ageing, be
Photovoltaic plant provides the dynamic, cleaning frequency of maximum revenue.
Brief description of the drawings
Fig. 1 is the operational process schematic diagram of the embodiment of the present invention.
Fig. 2 is the schematic diagram of the dust eclipsing loss rate variation tendency of the embodiment of the present invention, and wherein abscissa is that electricity is inclined
Difference, ordinate is dust eclipsing loss rate, and unit is %.
Embodiment
Below in conjunction with the accompanying drawings and the present invention is described in further detail by embodiment, following examples are to this hair
Bright explanation and the invention is not limited in following examples.
Embodiment.
Referring to Fig. 1 to Fig. 2.
Intelligently cleaning decision system is sub by dust eclipsing loss computing subsystem, weather prognosis for photovoltaic plant in the present embodiment
System, generated energy predicting subsystem and intelligence cleaning decision-making subsystem etc. are constituted.
Wherein, intelligently the method for building up of cleaning decision system includes setting up dust eclipsing loss calculating successively photovoltaic plant
System, set up weather prognosis subsystem, set up generated energy predicting subsystem and set up intelligence cleaning decision-making subsystem.
（1）Dust eclipsing loss computing subsystem：In photovoltaic plant on-site, one group or several groups of standard arrays are selected, are made
For the contrast groups of fixed time cleaning.By the way of timing, high frequency time cleaning, it is ensured that the clean level of standard array, it is allowed to basic
Run under conditions of not blocked by dust, and it monitors generated energy situation in real time.Meanwhile, also to multigroup near standard array
Regular array does generated energy data monitoring, and regular array is run under conditions of natural dust stratification, and over time, dust stratification degree adds
Weight, its generated energy situation will produce gap with standard array.By correction data, the representative number of on-site can be calculated
According to, including the theoretical generated energy of photovoltaic array after dynamic dust eclipsing loss rate, cleaning, when not cleaning photovoltaic array hair
Electricity reduction trend etc..These data will be used as the basis of subsequent arithmetic.
The system hardware mainly includes data acquisition unit, the array automatic flushing device for being integrated in array header box.Software
In terms of algorithm, the predominantly calculating of dust eclipsing loss rate.
Install that to clean the array put automatically be standard array for a string additional for example, choosing, and nearly 7 days of neighbouring a string of generic arrays
Current data is contrasted, every night 0:00 calculates the current deviation average value on the once same day（It should be noted that being picked in data statistics
Except solar irradiance is less than 200W/m2, and current value is less than 0.2A and the situation more than 10A）.To the electric current at a certain moment
It is worth deviation delta, with standard array electric current A1With generic array electric current A2Monitor value is calculated：
Δ=（A1- A2） / A2
Average drift gage is calculated within nearly 7 days：
Δ7 =Σ（Δn×A2）/ΣA2
, it is necessary to carry out the monitoring of a period of time to dust eclipsing loss situation before system formally puts into operation, to obtain follow-up week
The baseline values of phase prediction, can be selected 5 days, 7 days or other make number of days by oneself as the prediction accumulation cycle.It is 1 in the present embodiment
Individual month and more than.Typically, the influence of the special circumstances such as sleet, strong wind is rejected, dust eclipsing loss rate, which should be, fluctuates incremental near line
Property trend, as shown in Figure 2.When starting the dust occlusion prediction cycle, it can set first 3 days in the cycle and be averaged using a upper cycle
Dust eclipsing loss slope, obtains the cycle data of dust eclipsing loss rate.
（2）Weather prognosis subsystem：It should be made up of two parts.One is being external in the meteorological data of national weather department
Storehouse, meteorological history, the forecast in photovoltaic plant region can be obtained in real time, be that basis is done in generated energy prediction.Secondly being built upon light
The representative weather station of overhead utility on-site, including the inclination angle total solar radiation instrument consistent with place array, ambient temperature and humidity are passed
Sensor, anemobiagraph etc..By history with being follow-up with reference to the solar radiation data of actual monitoring based on forecast meteorological data
Generated energy forecasting system provides data.
（3）Generated energy predicting subsystem：The dust that the system is responsible for receiving the offer of dust eclipsing loss computing system is blocked
Loss late parameter and standard array generated energy parameter, the solar radiation data provided with reference to weather prognosis system and weather prognosis number
According to the development generated energy prediction of photovoltaic plant whole station.On the one hand, calculate after the cleaning immediately of whole station component, the cleaning week of n days by a definite date
Theoretical generated energy in phase, now needs the growth by the dynamic dust eclipsing loss rate during this to take into account in calculating, therefore
Within n days cleaning frequency, theoretical generated energy is on a declining curve.On the other hand, calculate in the case of not cleaning, with component natural product
It is grey constantly to increase, the estimated generated energy situation in power station.By both sides generated energy prediction overlap compare, to user reflection cleaning with
No generated energy contrast situation.
The system generated energy predictor formula is as follows：
1）Theoretical generated energy prediction：The main influence for considering weather conditions to power station generated energy, is carried with reference to weather prognosis subsystem
The actual power generation data in a upper cycle before the weather forecasting data of confession start with prediction, using average substitution method, day by day
The theoretical generated energy in power station is predicted, prediction algorithm is as follows：
If first day theoretical generated energy Q1, it is known that then
Q2= 2×Q1/ 1
Q3= 3×Q2/ 2
Q4= 4×Q3/ 3……
Qn= n×Qn-1/（N-1）
2）Actual power generation is predicted：It is main to consider dust eclipsing loss rate ΔnInfluence to power station generated energy, using theory generating
Predicted value is measured to calculate basis, if prediction starts, component has been cleaned or array dust stratification is negligible, then daily actual power generation is pre-
Survey as follows：
q1= Q1
q2= Q2×（1- Δs2）
q3= Q3×（1- Δs3）
q4= Q4 ×（1- Δs4）……
qn= Qn ×（1- Δsn）
（4）Intelligence cleaning decision-making subsystem：As the kernel subsystems of cleaning decision-making, decision-making subsystem is responsible for collecting preamble whole
The result of system, with reference to the estimated cleaning cost of setting, is calculated in the cleaning frequency, generated energy after cleaning by function algorithm
The maximum that income makes the difference with cleaning cost, so that it is determined that cleaning frequency number of days T, instructs power station to carry out component washing and cleaning operation.
Cleaning decision making algorithm is to obtain cleaning revenue function maximum as target, i.e. MAX f（T, Δ）.
f（T, Δ）=（qn- C）/ T
In formula, qnFor actual power generation predicted value, C is fixed cleaning cost, and T is the cleaning frequency.
To f（T, Δ）During maximizing, initial day（n=1）When, using actual monitoring value, prediction second day to n days
Actual power value, and calculating target function value.Second day rises, using algorithm predicted value iteration day by day, updates anticipation trend, seeks
The scope of function maxima point is looked for, so that it is determined that the value of optimal clean cycle T.
Furthermore, it is necessary to explanation, the specific embodiment described in this specification, is named the shape of its parts and components
Title etc. can be different, and the above content described in this specification is only to structure example explanation of the present invention.It is all according to
The equivalence changes or simple change done according to the construction described in inventional idea of the present invention, feature and principle, are included in this hair
In the protection domain of bright patent.Those skilled in the art can do various to described specific embodiment
The modification of various kinds or supplement or using similar mode substitute, without departing from the present invention structure or surmount present claims
Scope defined in book, all should belong to protection scope of the present invention.
Claims (6)
1. a kind of photovoltaic plant intelligently cleans the method for building up of decision system, it is characterised in that：In turn include the following steps：
1）Calculate dust eclipsing loss：Using monitoring mode on the spot, choose one or more groups of representative in photovoltaic plant
Photovoltaic array group as a comparison, chooses one or more groups of photovoltaic arrays as reference group, to contrast groups and ginseng around contrast groups
Real-time generated energy monitoring is carried out respectively according to group, and contrast groups are timed with cleaning in real-time generated energy monitoring process, to right
Real-time generated energy monitoring result than group is carried out with the real-time generated energy monitoring result of the reference group of natural dust stratification under identical environment
Contrast, calculates the dust eclipsing loss rate for obtaining contrast groups；
2）Weather prognosis：By the way of monitoring on the spot and external meteorological data are complementary, power station region weather condition is obtained in real time
With solar radiation situation, support is provided for generated energy prediction；
3）Step 1）With step 2）After end, generated energy prediction is carried out：According to the generated energy situation of all photovoltaic arrays, with reference to ash
Dirt eclipsing loss rate, calculates the generated energy situation of change of contrast groups and the generated energy situation of change of reference group respectively, and prediction is whole
Generating capacity of the photovoltaic plant within a cleaning frequency；
4）Step 3）After end, the optimal cleaning frequency is fed back：Collection step 1）, step 2）With step 3）In it is all processing knot
Really, by calculating the maximum of the difference of photovoltaic plant gross generation revenue function and cleaning cost function in a cleaning frequency,
The optimal cleaning frequency is fed back to power station personnel, so as to reach the purpose of automatic decision and intelligence cleaning.
2. photovoltaic plant according to claim 1 intelligently cleans the method for building up of decision system, it is characterised in that：Step 2）
In external meteorological data source include meteorogical phenomena database and representativeness weather station, meteorogical phenomena database is external in national weather department,
The on-site in photovoltaic plant is set up in representative weather station, and representative weather station includes total solar radiation instrument, ambient temperature and humidity and passed
Sensor and anemobiagraph.
3. photovoltaic plant according to claim 1 or 2 intelligently cleans the method for building up of decision system, it is characterised in that：It is real
Apply step 1）Before, the dust eclipsing loss rate to photovoltaic array carries out the monitoring of a period of time.
4. photovoltaic plant according to claim 3 intelligently cleans the method for building up of decision system, it is characterised in that：Step 1）
The monitoring current of middle contrast groups is A1, the monitoring current of reference group is A2, current value deviation be Δ, then Δ=（A1- A2）/A2。
5. photovoltaic plant according to claim 4 intelligently cleans the method for building up of decision system, it is characterised in that：Step 3）
It is middle to carry out theoretical generated energy and actual power generation calculating, wherein, theoretical generated energy is Q within first day1, theoretical generated energy is within n-th day
Qn, Qn= n×Qn-1/（N-1）, n-th day dust eclipsing loss rate is Δn, first day actual power generation predicted value is q1, n-th day
Actual power generation predicted value is qn, q1= Q1, qn= Qn ×（1- Δsn）, n >=2.
6. photovoltaic plant according to claim 5 intelligently cleans the method for building up of decision system, it is characterised in that：Step 4）
The difference of middle photovoltaic plant gross generation revenue function and cleaning cost function is f（T, Δ）, wherein f（T, Δ）=（qn- C）/
T, C are fixed cleaning cost, and T is the cleaning frequency, during n=1, q1Predict second day to the reality of n days using actual monitoring value
Generating value, and calculating target function value, second day rise, using algorithm predicted value iteration day by day, update anticipation trend, find letter
The scope of number maximum of points, so that it is determined that the value of optimal clean cycle T.
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| CN108960453A (en) * | 2018-07-31 | 2018-12-07 | 江苏林洋新能源科技有限公司 | Photovoltaic plant dust stratification economy cleans calculation method |
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| CN110649883A (en) * | 2019-09-29 | 2020-01-03 | 合肥阳光新能源科技有限公司 | Cleaning method and device and computer equipment |
| CN111222763A (en) * | 2019-12-27 | 2020-06-02 | 中节能万年太阳能科技有限公司 | Photovoltaic module washs decision-making instrument |
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| CN112561190A (en) * | 2020-12-23 | 2021-03-26 | 宁夏中科嘉业新能源研究院（有限公司） | Photovoltaic power station cleaning model prediction method based on discrete particle swarm algorithm |
| CN112650976A (en) * | 2020-12-23 | 2021-04-13 | 宁夏中科嘉业新能源研究院（有限公司） | Photovoltaic power station dust accumulation calculation method |
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| CN112737496A (en) * | 2020-12-23 | 2021-04-30 | 合肥阳光新能源科技有限公司 | Method and system for detecting surface dust deposition of photovoltaic module and photovoltaic power generation system |
| CN112884200A (en) * | 2021-01-15 | 2021-06-01 | 国网电子商务有限公司 | Photovoltaic power station assembly cleaning operation and maintenance time prediction method and device |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203149765U (en) * | 2013-02-17 | 2013-08-21 | 英利集团有限公司 | Photovoltaic system alarm equipment |
| CN103390199A (en) * | 2013-07-18 | 2013-11-13 | 国家电网公司 | Photovoltaic power generation capacity/power prediction device |
| CN104915548A (en) * | 2015-05-25 | 2015-09-16 | 东南大学 | Photovoltaic module dust removal strategy optimization method |
| WO2015140363A1 (en) * | 2014-03-20 | 2015-09-24 | GARCÍA GALLARDO JOSÉ Mª | Device for cleaning rows of solar panels in photovoltaic power stations, solar farms and installations on roofs of more than thousand square metres |
| CN105215034A (en) * | 2015-11-16 | 2016-01-06 | 上海许继电气有限公司 | Realize the system and method for photovoltaic plant solar panel intelligence cleaning |
| CN106130464A (en) * | 2016-08-05 | 2016-11-16 | 江阴海润太阳能电力有限公司 | The decision method that a kind of photovoltaic power station component cleans |
-
2017
- 2017-03-13 CN CN201710145368.0A patent/CN107133713A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203149765U (en) * | 2013-02-17 | 2013-08-21 | 英利集团有限公司 | Photovoltaic system alarm equipment |
| CN103390199A (en) * | 2013-07-18 | 2013-11-13 | 国家电网公司 | Photovoltaic power generation capacity/power prediction device |
| WO2015140363A1 (en) * | 2014-03-20 | 2015-09-24 | GARCÍA GALLARDO JOSÉ Mª | Device for cleaning rows of solar panels in photovoltaic power stations, solar farms and installations on roofs of more than thousand square metres |
| CN104915548A (en) * | 2015-05-25 | 2015-09-16 | 东南大学 | Photovoltaic module dust removal strategy optimization method |
| CN105215034A (en) * | 2015-11-16 | 2016-01-06 | 上海许继电气有限公司 | Realize the system and method for photovoltaic plant solar panel intelligence cleaning |
| CN106130464A (en) * | 2016-08-05 | 2016-11-16 | 江阴海润太阳能电力有限公司 | The decision method that a kind of photovoltaic power station component cleans |
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