CN106204332B - A kind of photovoltaic plant efficiency decaying prediction technique - Google Patents
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Abstract
The invention discloses a kind of photovoltaic plant efficiency decaying prediction techniques, choose actual measurement power station running environment parameter and output power first as actual measurement sample；Power station simulation platform is established, actual measurement sample environment parameter is input in emulation platform, the simulation sample under respective environment parameter is obtained；Regard two groups of sample powers as signal source, measured power signal and simulated power signal are denoised respectively respectively, the power station delivery efficiency EFF value at corresponding moment is calculated with the data after denoising；Gained EFF value sequence is decomposed with EMD method, all EFF values after the decomposition of EMD method are stored as historical series；The sample drawn point from historical series is entered into efficiency decaying prediction model, obtains predicted value.The prediction deficiency for compensating for existing model and decaying to photovoltaic plant efficiency under different open-air conditions of the invention, is conducive to the Market Operation of photovoltaic plant, is conducive to the prognostic and health management technology for further realizing photovoltaic plant.
Description
Technical field
The present invention relates to a kind of photovoltaic plant efficiency decaying prediction techniques, belong to technical field of photovoltaic power generation.
Background technique
With the rapid development of photovoltaic power generation technology, the whole nation it is built or in the photovoltaic plant quantity built increasing, scale
Increasingly huge, but the problems in operation process also constantly highlights.
Firstly, photovoltaic industry downstream race is fierce, power station operation is undesirable.As photovoltaic products price continues to fall, enterprise
Eye is invested industry downstream one after another by industry, seizes photovoltaic plant market.However, part is transported during fierce market competition
Enterprise, battalion is eliminated unavoidably, thus causes photovoltaic plant transfer problem.Photovoltaic plant in the process of running, component, electricity
Efficiency decline problem, i.e. efficiency attenuation problem can occur with the time in cable, inverter and other related spare and accessory parts.Especially component
Decaying can further result in entire array and generate the problems such as secondary efficiency decline because of component mismatch, make the residue of component
Economic value reduces.Due to lacking effective photovoltaic plant efficiency decaying appraisal procedure, during operation enterprise substitutes, light
There are many difficulties for the practical transfer of overhead utility.
Secondly, the shadow of photovoltaic plant attenuation factor is not considered in the assessment of photovoltaic system operating status and fault diagnosis model
It rings.Existing photovoltaic system evaluation mainly has physical method, statistical method and artificial intelligence approach with prediction model.Physics
The photovoltaic effect of photovoltaic cell is equivalent to diode circuit model by method.Description photovoltaic electric is modeled by the circuit model
The working condition in pond, and then photovoltaic system model is established for system evaluation.Statistical method passes through the statistics to historical data
And analysis, establish the prediction model of relationship between description meteorologic factor variable and photovoltaic power generation system output power.Artificial intelligence
Method is according to historical events sequence, by the training of certain learning rules, establishes prediction meteorologic factor variable and photovoltaic power exports
Between relationship.Above-mentioned three kinds of methods do not consider system physical efficiency attenuation factor in assessment system operational efficiency, with actual measurement
The health data of data comparison is the data assumed when photovoltaic system efficiency is 100%.Since photovoltaic plant effect can not be grasped
Rate downward trend and time, it is difficult to it is further proposed that accurately and reliably photovoltaic plant performance estimating method, causes photovoltaic plant to transport
After row a period of time, optimum state can not be continued to operate in, reduces investor's income.Also it is unfavorable for further realizing photovoltaic electric
The prognostic and health management stood.Therefore, it is necessary to propose a kind of prediction technique of efficiency of plant decaying, efficiency of plant is predicted
Decline, and then realize photovoltaic plant prognostic and health management, guarantee that photovoltaic system is safe and reliable, creates greatest benefit.
Summary of the invention
In view of the shortcomings of the prior art, the purpose of the present invention is the efficiency decaying to photovoltaic system to predict,
To assess the photovoltaic system surplus value and system is made to obtain maximum return, a kind of photovoltaic plant residue EFFICIENCY PREDICTION method is disclosed.
To achieve the goals above, the present invention is to realize by the following technical solutions:
A kind of photovoltaic plant efficiency decaying prediction technique, which comprises the following steps:
A. at interval of the one group of power station running environment ginseng of actual measurement in one minute in good weather day 8:00AM to 4:00PM segment
Several and output power is as actual measurement sample.Power station simulation platform is established, actual measurement sample environment parameter is input to power station simulation and is put down
In platform, simulation sample is obtained.Regard two kinds of sample datas as signal source, respectively to measured power signal and simulated power signal into
Row denoising, with the EFF value (power station delivery efficiency) at the actual measurement sample power moment corresponding to simulation sample power calculation after denoising.
It b. is chronomere by EFF value sequence EMD (Empirical Mode Decomposition, experience using week
Mode decomposition) method is pre-processed, and is found out it and declined percentage DEFFAs ring than correction factor, and it is used for next
The EFF value in week calculates.
C. gained EFF value in step b is stored as historical series.The time is reasonably selected according to actual prediction time limit length
Interval, chooses EFF sample point at timed intervals from historical series, is calculated, is obtained final by efficiency decaying prediction model
Predicted value.
Good weather pattern includes: fine day, clear to cloudy, low irradiation weather in above-mentioned steps a.
The denoising method of measured power signal and simulated power signal uses wavelet method, correlation ginseng in above-mentioned steps a
Number is chosen as follows:
Since station output is larger by such environmental effects, actual measurement sample fluctuation per minute is larger, therefore takes ruler
Spend function are as follows:
Wherein, ψ (τ) is scaling function, and τ is the time, and unit is s (second).
Then the translation function of ψ (τ) is { ψ (τ-k) }k∈Z, then
Enable uk=(- 1)1-kv1-k, then
(3) formula is converted into frequency-domain expression, i.e.,It will be secondly can be obtained into flexible translation:
Wherein, vj,k(τ) is the translation space constituted based on ψ (τ), and k is integer, j2=-1 is unit imaginary number, formula
It (4) is wavelet basis function, coefficient uniquely determines in formula.
Power station simulation platform is the analogue system built based on Matlab software Simulink module in above-mentioned steps a,
Topological structure and model parameter are consistent with practical Power station structure and component parameter.
The calculation method of EFF in above-mentioned steps a are as follows:
Wherein, P is measured power, PsFor simulated power
Above-mentioned steps b middle ring is than coefficient DEFFRegeneration period be limited to per week, calculation method are as follows:
Wherein, EFFmax,wFor the maximum value of EFF sequence in one week, EFFmin,wFor the minimum value of EFF sequence in one week.
Correction factor D is recycled in above-mentioned steps bEFFRegeneration period be limited to per week, modification method are as follows:
Wherein, EFFnFor the EFF value in next week.
EFF sequence is handled using EMD method in above-mentioned steps b.
Efficiency attenuation model calculation method in above-mentioned steps c are as follows:
Defined function X (t) indicates power station in the performance number of t moment.X (t) meets:
In formula: t is time variable, and unit is the moon；γ is Brownian movement coefficient；θ is degeneration factor；X (0) indicates that power station is pre-
Survey the efficiency of initial time；λ(t；θ) indicate the linear main attenuation of efficiency of plant；τ(t；γ) for about the continuous non-of time t
Subtraction function, B (τ (t；It γ)) is non-linear Brownian movement, the uncertainty of description amount of degradation on a timeline；A is coefficient of deviation,
Characterize power station component quality otherness；σ is diffusion coefficient, indicates the degree of fluctuation of uncertain decaying.
Long according to efficiency of plant degradation period, the year stable characteristic of attenuation, takes X (0)=0 in lifetime,Then (8) formula simplifies are as follows:
X (t)=atθ+σB(tγ) (10)
The failure threshold in power station is set as D, even X (t) then determines product failure more than D.Corresponding efficiency die-away time
It may be expressed as:
T=inf { t, X (t) >=D } (11)
Wherein, inf indicates the infimum of power station attenuation sequence X (t), and T is the power station out-of-service time；
Then, under the premise of given a, the efficiency of plant attenuation probability density function of (9) formula are as follows:
Wherein
Wherein, gTIt (t) is T moment power station failure condition probability density, A'TThe full probability expression for being T for power station remaining life
Formula.σ0For the standard deviation of coefficient of deviation a, μ0The mean value of coefficient of deviation a；
Enable Dh=D-X (th), thFor a certain moment point, k is integer, DhFor thMoment remaining life, then photovoltaic plant when
Carve thRemaining efficiency phase (Lh) it is regarded as X (th) reach DhTime span, i.e.,
Wherein
B=Δ Λ (th+l；θ)2+σ2Δτ(th+l；γ) (17)
Fixed θ, γ, σ0, it is 0 that σ, which seeks likelihood equation to above formula and enables it, solves μ0=9.683, by its band likelihood equation
B=1, γ=1, σ can further be obtained0=2.667, σ=0.139, so far, known to the coefficient whole of equation.
The present invention has the advantages that compared with prior art:
Algorithm of the invention is concisely reasonable, and it is accurate that calculating process is stablized, and the invention proposes the decaying of the efficiency in power station and declines
The prediction model for subtracting the phase compensates for the considerations of existing model decays to photovoltaic plant efficiency under open-air conditions deficiency, effectively more
The vacancy for having mended the field is more advantageous to the marketization of photovoltaic plant.Meanwhile it also compensating for existing photovoltaic system operating status and commenting
Estimate model accuracy problem to be improved, is conducive to the health control technology for further realizing photovoltaic system.
Detailed description of the invention
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments；
Fig. 1 is prediction technique flow chart of the invention；
Fig. 2 is actual measurement sample power signal curve figure；
Fig. 3 is the denoising procedure chart of power curve；
Fig. 4 is measured power signal denoising result curve figure；
Fig. 5 is EFF sequence EMD exploded view；
Fig. 6 is that EFF sequence EMD extracts result figure；
Fig. 7 is the decaying of photovoltaic plant efficiency by inputoutput test and model prediction comparative result figure.
Specific embodiment
To be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, below with reference to
Specific embodiment, the present invention is further explained.
As shown in Figure 1, i indicates the number of days in a week, a kind of photovoltaic plant efficiency decaying prediction side of the invention in Fig. 1
Method, comprising the following steps:
A. at interval of the one group of power station running environment ginseng of actual measurement in one minute in good weather day 8:00AM to 4:00PM segment
Several and output power is as actual measurement sample.Power station simulation platform is established, actual measurement sample environment parameter is input to power station simulation and is put down
In platform, simulation sample is obtained.Regard two kinds of sample datas as signal source, respectively to measured power signal and simulated power signal into
Row denoising, with the power station delivery efficiency EFF value at the actual measurement sample power moment corresponding to simulation sample power calculation after denoising.
B. EFF value sequence is pre-processed using week as chronomere with EMD method, and finds out it and declines percentage
DEFFAs ring than correction factor, and the EFF value for being used for next week calculates.
C. gained EFF value in step b is stored as historical series.The time is reasonably selected according to actual prediction time limit length
Interval, chooses EFF sample point at timed intervals from historical series, is calculated, is obtained final by efficiency decaying prediction model
Predicted value.
Preferably, good weather pattern includes: fine day, clear to cloudy, low irradiation weather in step a.
Further, the denoising method of measured power signal and simulated power signal uses wavelet method, phase in step a
It is as follows to close parameter selection:
Since station output is larger by such environmental effects, actual measurement sample fluctuation per minute is larger, therefore takes ruler
Spend function are as follows:
Wherein, ψ (τ) is scaling function, and τ is the time, and unit is s (second).
Then the translation function of ψ (τ) is { ψ (τ-k) }k∈Z, then
Enable uk=(- 1)1-kv1-k, then
(3) formula is converted into frequency-domain expression, i.e.,It will be secondly can be obtained into flexible translation:
Wherein, vj,k(τ) is the translation space constituted based on ψ (τ), and k is integer, j2=-1 is unit imaginary number, formula
It (4) is wavelet basis function, coefficient uniquely determines in formula.
Further, power station simulation platform is the emulation system built based on Matlab software Simulink module in step a
System, topological structure and model parameter are consistent with practical Power station structure and component parameter.
Still further, in step a EFF calculation method are as follows:
Wherein, P is measured power, PsFor simulated power
Further, step b middle ring is than coefficient DEFFRegeneration period be limited to per week, calculation method are as follows:
Wherein, EFFmax,wFor the maximum value of EFF sequence in one week, EFFmin,wFor the minimum value of EFF sequence in one week.
Further, correction factor D is recycled in step bEFFRegeneration period be limited to per week, modification method are as follows:
Wherein, EFFnFor the EFF value in next week.
Still further, EFF sequence is handled using EMD method in step b.
Further, efficiency attenuation model calculation method in step c are as follows:
Defined function X (t) indicates power station in the performance number of t moment.X (t) meets:
In formula: t is time variable, and unit is the moon；γ is Brownian movement coefficient；θ is degeneration factor；X (0) indicates that power station is pre-
Survey the efficiency of initial time；λ(t；θ) indicate the linear main attenuation of efficiency of plant；τ(t；γ) for about the continuous non-of time t
Subtraction function, B (τ (t；It γ)) is non-linear Brownian movement, the uncertainty of description amount of degradation on a timeline；A is coefficient of deviation,
Characterize power station component quality otherness；σ is diffusion coefficient, indicates the degree of fluctuation of uncertain decaying.
Long according to efficiency of plant degradation period, the year stable characteristic of attenuation, takes X (0)=0 in lifetime,Then 8 formulas simplify are as follows:
X (t)=atθ+σB(tγ) (28)
The failure threshold in power station is set as D, even X (t) then determines product failure more than D.Corresponding efficiency die-away time
It may be expressed as:
T=inf { t, X (t) >=D } (29)
Wherein, inf indicates the infimum of power station attenuation sequence X (t), and T is the power station out-of-service time.
Then, under the premise of given a, the efficiency of plant attenuation probability density function of (9) formula are as follows:
Wherein
Wherein, gTIt (t) is T moment power station failure condition probability density, A'TThe full probability expression for being T for power station remaining life
Formula.σ0For the standard deviation of coefficient of deviation a, μ0The mean value of coefficient of deviation a；
Enable Dh=D-X (th), thFor a certain moment point, k is integer, DhFor thMoment remaining life, then photovoltaic plant when
Carve thRemaining efficiency phase (Lh) it is regarded as X (th) reach DhTime span, i.e.,
Wherein
B=Δ Λ (th+l；θ)2+σ2Δτ(th+l；γ) (35)
Fixed θ, γ, σ0, it is 0 that σ, which seeks likelihood equation to above formula and enables it, solves μ0=9.683, by its band likelihood equation
B=1, γ=1, σ can further be obtained0=2.667, σ=0.139, so far, known to the coefficient whole of equation.
Embodiment 1:
1. the acquisition of data used in the present embodiment surveys platform from Hohai University open air 10kWp.It has chosen first 2014
November 3 raw sample data, at interval of the 1 group of data of extraction in 1 minute in 8:00AM to 4:00PM period, totally 480 groups of works
To survey sample, its power signal is denoised.Original power signal curve as shown in Fig. 2, treatment process as shown in figure 3,
S is original power signal in Fig. 3, and a4 is main signal, and d1, d2, d3, d4 are 4 kinds of Gausses for being entrained in different frequency in main signal
White noise, 5 signal overlaps constitute original power signal, s=a together4+d1+d2+d3+d4, power signal curve after denoising
As shown in Figure 4.As can be seen that the sample curve after denoising eliminates the interference of outside noise signal, whole smoothness increases,
Burr disappears.Sample environment parameter is input in power station simulation platform, the lower 480 groups of simulation samples of same environmental parameter is obtained, adopts
Its power signal is denoised with same method.When corresponding to the calculation of simulated power signal with the measured power signal after denoising
The power station delivery efficiency EFF value at quarter.
2. data in 8:00AM to 4:00PM period on November 9,3 days to 2014 November in 2014 are chosen, by step a
In method data are pre-processed, then by EFF value sequence with EMD method carry out decompose and smoothing processing.EMD treatment process
As shown in figure 5, signal is original EFF sequence in Fig. 5, i.e., without the EFF sequence of EMD algorithm process, imf1 to imf8 is
The component that EMD algorithm decomposites, after gradually rejecting these components, remaining res is a monotonic function, as at EMD algorithm
The new EFF sequence obtained after reason.It is as shown in Figure 6 that EMD extracts result.As can be seen that EMD carries out spy to the characteristic parameter of EFF
Sign is extracted, and by the EFF sequence stationaryization of non-stationary and dullnessization, more accurately grasps the characteristic signal of original signal.According to
EMD is extracted as a result, calculating DEFF, D can be obtainedEFF=(0.99945-0.99932) 0.99945 × 100%=0.013% of ÷, i.e., originally
All efficiencies of plant have decayed 0.013%, and as ring than correction factor, and the EFF value for being used for next week calculates.
3. choose in November, 2014 in December, 2014 be total to bimestrial actual measurement sample, by the method for step a and step b into
Row processing, obtains final EFF sequence.Using day as interval, one group of data is being chosen in the same time daily, it is defeated as forecast sample
Enter into efficiency decaying prediction model.Obtain in January, 2015 to November actual measurement platform delivery efficiency decaying predicted value, and by its
It is compared with measured value, as a result as shown in Figure 7.It can be seen from the figure that prediction technique described in this patent is effective.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.Industry description
Merely illustrate the principles of the invention, without departing from the spirit and scope of the present invention, the present invention also have various change and
It improves, these changes and improvements all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended power
Sharp claim and its equivalent thereof.
Claims (5)
- The prediction technique 1. a kind of photovoltaic plant efficiency decays, which comprises the following steps:A. within good weather day 8:00AM to 4:00PM segment, at interval of the one group of power station running environment parameter of actual measurement in 1 minute With output power as actual measurement sample；Power station simulation platform is established, actual measurement sample environment parameter is input to power station simulation platform In, obtain simulation sample；Regard actual measurement sample and simulation sample data as signal source, respectively to measured power signal and emulation function Rate signal is denoised, with the power station delivery efficiency at the actual measurement sample power moment corresponding to simulation sample power calculation after denoising EFF value；B. EFF value sequence is handled using week as chronomere with EMD method, and finds out EFF value decline percentage DEFFMake It is ring than correction factor, and by DEFFEFF value for next week calculates；C. gained EFF value in step b is stored as historical series, according to actual prediction time limit length, between the reasonable selection time Every choosing EFF sample point at timed intervals from historical series, calculated, obtained final pre- by efficiency prediction model of decaying Measured value；Efficiency decaying prediction model calculation method in the step c are as follows:Defined function X (t) indicates power station in the performance number of t moment, and X (t) meets:In formula: t is time variable, and unit is the moon；γ is Brownian movement coefficient；θ is degeneration factor；X (0) indicates power station prediction just The efficiency at moment beginning；λ(t；θ) indicate the linear main attenuation of efficiency of plant；τ(t；γ) non-to subtract letter about the continuous of time t Number, B (τ (t；It γ)) is non-linear Brownian movement, the uncertainty of description amount of degradation on a timeline；A is coefficient of deviation, characterization Power station component quality otherness；σ is diffusion coefficient, indicates the degree of fluctuation of uncertain decaying；Long according to efficiency of plant degradation period, the year stable characteristic of attenuation, takes X (0)=0 in lifetime,τ(t；γ)=tγ, then formula (8) simplifies are as follows:X (t)=atθ+σB(tγ) (9)The failure threshold in power station is set as D, even X (t) then determines product failure, corresponding efficiency die-away time can table more than D It is shown as:T=inf { t, X (t) >=D } (10)Wherein, inf indicates the infimum of power station attenuation sequence X (t), and T is the power station out-of-service time；Then, under the premise of given a, the efficiency of plant attenuation probability density function of formula (9) are as follows:WhereinWherein, gTIt (t) is T moment power station failure condition probability density, A'TThe full probability expression formula for being T for power station remaining life, σ0For the standard deviation of coefficient of deviation a, μ0The mean value of coefficient of deviation a；Enable Dh=D-X (th), thFor a certain moment point, k is integer, DhFor thMoment remaining life, then photovoltaic plant is in moment th Remaining efficiency phase LhIt is considered X (th) reach DhTime span, i.e.,WhereinB=Δ Λ (th+l；θ)2+σ2Δτ(th+l；γ) (16)Fixed θ, γ, σ0, it is 0 that σ, which seeks likelihood equation to above formula and enables it, solves μ0=9.683, by its band likelihood equation into one Step can obtain b=1, γ=1, σ0=2.667, σ=0.139, so far, known to the coefficient whole of equation；The calculation method of EFF value in the step a are as follows:Wherein, P is measured power, PsFor simulated power；The step b middle ring is than correction factor DEFFRegeneration period be limited to per week, DEFFCalculation method are as follows:Wherein, EFFmax,wFor the maximum value of EFF sequence in one week, EFFmin,wFor the minimum value of EFF sequence in one week；The modification method of the EFF value in next week are as follows:Wherein, EFFnFor the EFF value in next week.
- The prediction technique 2. a kind of photovoltaic plant efficiency according to claim 1 decays, it is characterised in that: in the step a Good weather pattern includes: fine day, clear to cloudy, low irradiation weather.
- The prediction technique 3. a kind of photovoltaic plant efficiency according to claim 1 decays, it is characterised in that: in the step a The denoising method of measured power signal and simulated power signal uses wavelet method, and scaling function is chosen as follows:Wherein, ψ (τ) is scaling function, and τ is the time, and unit is the second.
- The prediction technique 4. a kind of photovoltaic plant efficiency according to claim 3 decays, it is characterised in that: the wavelet method Wavelet basis function calculated result it is as follows:Wherein, vj,k(τ) is the translation space constituted based on ψ (τ), and k is integer, j2=-1 is unit imaginary number, and formula (4) is small Wave basic function, coefficient uniquely determines in formula.
- The prediction technique 5. a kind of photovoltaic plant efficiency according to claim 1 decays, it is characterised in that: in the step a Power station simulation platform is the analogue system built based on Matlab software Simulink module, the topology of the power station simulation platform Structure and model parameter are consistent with practical Power station structure and component parameter.
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| CN106533361B (en) * | 2016-12-14 | 2018-08-03 | 河海大学常州校区 | A kind of photovoltaic module method for diagnosing faults based on wavelet analysis |
| CN107169794A (en) * | 2017-05-09 | 2017-09-15 | 中国农业大学 | A kind of meter and the photovoltaic plant cost Prices Calculation of component power decay |
| CN109670652A (en) * | 2018-12-29 | 2019-04-23 | 石家庄科林电气股份有限公司 | A kind of failure prediction method of photovoltaic module |
| CN110458343B (en) * | 2019-07-26 | 2023-04-07 | 国网山东省电力公司泰安供电公司 | Method for predicting regional photovoltaic power generation capacity attenuation |
| CN112288285B (en) * | 2020-10-30 | 2023-04-07 | 西安热工研究院有限公司 | Photovoltaic power station string attenuation rate evaluation method based on generalized extreme value distribution |
| CN113642895B (en) * | 2021-08-16 | 2023-12-12 | 中国科学院电工研究所 | Residual performance evaluation method of off-grid photovoltaic power station |
| CN114692064B (en) * | 2022-04-07 | 2022-11-29 | 合肥工业大学 | Photovoltaic module service life calculation method based on environmental parameters |
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