CN106650060A - Prediction method of internal resistance attenuation coefficient for photovoltaic cells - Google Patents
Prediction method of internal resistance attenuation coefficient for photovoltaic cells Download PDFInfo
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Abstract
The invention discloses a prediction Method of Internal Resistance attenuation coefficient for Photovoltaic Cells. The method comprises the following steps: establishing a time series of an evolutionary system of an internal resistance attenuation coefficient of a photovoltaic cell according to a parameter obtained by real-time monitoring, establishing an equation of the internal resistance attenuation coefficient of the photovoltaic cell; performing a phase space reconstruction processing on the time series of the evolution system; processing a battery internal resistance attenuation coefficient equation according to a wavelet network method; predicting and calculating the internal resistance attenuation coefficient of the photovoltaic cells on the time series after the phase space reconstruction. The invention has the advantage of solving the technical problems that a power grid and distributed photovoltaic power generation operation data resources cannot be effectively utilized, and evaluation accuracy and the efficiency of PV are low; the reliability of the model is improved; the utilization rate of the PV is improved; the accuracy of the evaluation is improved; and the technical effect of the reliability and economy of the distribution system after the PV system is improved. The invention is applied to detect a photovoltaic power generation system.
Description
Technical field
The invention belongs to technical field of photovoltaic power generation, more particularly to a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology.
Background technology
The system that distributed photovoltaic power generation equipment and power distribution network constitute a complexity in power system, photovoltaic cell internal resistance
Size affect the delivery efficiency of photovoltaic cell, photovoltaic cell internal resistance size is affected by multiple influence factors.In prior art
Photovoltaic cell internal resistance computational methods exist have ignored input the key factor such as the time limit and battery series-parallel connection component count impact,
Effectively utilizes electrical network and distributed photovoltaic power generation service data resource are unable to, the not high skill of the degree of accuracy and photovoltaic utilization ratio is assessed
Art problem.The present invention considers multiple influence factor, and power distribution network and its interior photovoltaic system operational factor and environment parament are entered
Row real-time monitoring, and calculating is predicted to distributed photovoltaic internal resistance of cell attenuation coefficient according to monitoring parameter, tie according to calculating
Fruit is controlled in real time to photovoltaic generating system and power distribution network, the reliability of model can be effectively improved, so as to greatly improve
Photovoltaic utilization ratio.
The content of the invention
The present invention provides a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology, solves and have ignored the input time limit and battery
The impact of the key factors such as connection in series-parallel component count, it is impossible to effectively utilizes electrical network and distributed photovoltaic power generation service data resource,
The not high technical problem of the assessment degree of accuracy and photovoltaic utilization ratio.
The present invention is achieved through the following technical solutions, and methods described includes including methods described：Step 1：According to prison in real time
The parameter for obtaining is surveyed, the time series of photovoltaic cell internal resistance attenuation coefficient Evolution System is set up, photovoltaic cell internal resistance decay is set up
Coefficient equation；Step 2：To the step:The time series of Evolution System carries out phase space reconfiguration process in 1；Step 3：According to
Wavelet network method treatment of battery internal resistance attenuation coefficient equation；Step 4：To the time sequence after phase space reconfiguration in the step 2
Row carry out the prediction of photovoltaic cell internal resistance attenuation coefficient and calculate.
Further, to better implement the present invention, the parameter that the real-time monitoring is obtained is in power distribution network and power distribution network
Photovoltaic system operational factor and environment parament.
Further, evolution time series is the evolution time series set up under Fixed Time Interval in the step 1.
Further, the evolution time series includes the photovoltaic plant access point equiva lent impedance measured value, access point
Voltage, access point has work value, environment temperature, environment illumination intensity.
Further, Evolution System time series described in step 1 is in a series of moment ts1,ts2,ts3,...tsnFor：
The photovoltaic cell internal resistance attenuation coefficient equation is：
Wherein, n is natural number, n=1, and 2 ..., tr are measurement battery making time, and Tr is ambient temperature, Sr ambient lights
According to Cr is series component quantity, and Br is parallel component quantity.
Further, the step 2 is comprised the following steps：
A, set up optimization aim model minfar(rx1,rx2...rxi..rxh5n), wherein i=1,2 ... k5n；
Time series { the rx of B, the Evolution System built in the step 1iM dimension phase space rxi+1=ψ (rxi,
rxi-τ,...,rxi-(m-1)τ), wherein, i=1,2 ... k5n, τ is that m is Embedded dimensions between time delay.
Further, the step 3 is comprised the following steps：
The calculating of A, internal resistance of cell attenuation coefficient Wavelet-network model output layer；
B, internal resistance of cell attenuation coefficient Wavelet-network model on-line amending.
Further, step A is comprised the following steps in the step 3：
If input signal time series is { rcxi, wherein, byCalculate hidden layer
Output valve, calculate output layer output valve according to hidden layer output valve
Wherein i=1,2...k5n, j=1,2...l, ga(xzi) >=0, ga(xzi) for SVMs object module amendment
, φ (j) be wavelet network in j-th node of hidden layer output, fjFor wavelet basis function, αjFor fjContraction-expansion factor, λjFor
fjShift factor, wijIt is the size connected each other between input layer and hidden layer, l is node in hidden layer, wjRepresent implicit
Connection weight between layer and output layer.
Further, step B is comprised the following steps in the step 3：
According to er=yr-yccTrue on-line amending internal resistance of cell attenuation coefficient Wavelet-network model, yrFor prediction output, ycc
For actual measured value.
Further, the step 4 is comprised the following steps：
According to the time series in step 3 in the wavelet neural network corrected is to the step 2 after phase space reconfiguration
Carry out the prediction of photovoltaic cell internal resistance attenuation coefficient to calculate, introduce object module correction conditions, objective function optimization is ya=min
far(rxi)+gar(rxi)；
Wherein, i=1,2...k5n, gar(rxi)≥0,gar(rxi) for object module bound term, yaFor in photovoltaic cell
Resistance attenuation coefficient.
Further, methods described is used to detect photovoltaic generating system.
Description of the drawings
Fig. 1 is prediction flow chart.
Specific embodiment
The present invention is described in further detail with reference to embodiment, but the embodiment of this reality invention is not limited to
This.
Embodiment 1：
Using a kind of above-mentioned photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology, pre- flow gauge such as Fig. 1 comprises the steps：
Step 1：
Parameter, making time and battery series-parallel connection component count and the canonical parameter obtained according to real-time monitoring, one
Serial moment ts1,ts2,ts3,...tsnThe time series for setting up photovoltaic cell internal resistance attenuation coefficient Evolution System is：
Set up photovoltaic cell internal resistance attenuation coefficient equation：
Wherein, n is natural number, n=1, and 2 ..., tr are measurement battery making time, and Tr is ambient temperature, Sr ambient lights
According to Cr is series component quantity, and Br is parallel component quantity.
Step 2：Phase space reconfiguration process is carried out to the time series of Evolution System in the step 1：
Step 2.1：Set up optimization aim modelWherein i=1,2 ... k5n；
Step 2.2：Build the time series { rx of the Evolution System in the step 1iM dimension phase space rxi+1=ψ
(rxi,rxi-τ,...,rxi-(m-1)τ), wherein, i=1,2 ... k5n, τ=0.0152, m=5.
Step 3：According to wavelet network method treatment of battery internal resistance attenuation coefficient equation：
Step 3.1：The calculating of internal resistance of cell attenuation coefficient Wavelet-network model output layer：
If input signal time series is { rcxi, wherein, byCalculate hidden layer
Output valve, calculate output layer output valve according to hidden layer output valve
Wherein i=1,2...k5n, j=1,2...l, ga(xzi) >=0, ga(xzi) for SVMs object module amendment
, φ (j) be wavelet network in j-th node of hidden layer output, fjFor wavelet basis function, αjFor fjContraction-expansion factor, λjFor
fjShift factor, wijIt is the size connected each other between input layer and hidden layer, l is node in hidden layer, wjRepresent implicit
Connection weight between layer and output layer.
Step 3.2：Internal resistance of cell attenuation coefficient Wavelet-network model on-line amending：
According to er=yr-yccTrue on-line amending internal resistance of cell attenuation coefficient Wavelet-network model, yrFor prediction output, ycc
For actual measured value, in order to ensure model accuracy, correction value erReach when≤0.0001 optimal.
Step 4：The prediction of photovoltaic cell internal resistance attenuation coefficient is carried out to the time series after phase space reconfiguration in the step 2
Calculate：
According to the time in the step 3 in the wavelet neural network corrected is to the step 2 after phase space reconfiguration
Sequence carries out the prediction of photovoltaic cell internal resistance attenuation coefficient and calculates, and introduces object module correction conditions, and objective function optimization is ya=
min far(rxi)+gar(rxi)。
Wherein, i=1,2...k5n, gar(rxi)≥0,gar(rxi) for object module bound term,yaFor photovoltaic cell internal resistance attenuation coefficient.
Methods described is used to detect photovoltaic generating system.
The present invention can obtain following Advantageous Effects relative to prior art：(1) reliability of model, (2) are improved
Photovoltaic utilization rate is improve, (3) improve the accuracy of assessment, (4) improve electrical network and distributed photovoltaic power generation service data resource
Utilization rate, (5) improve power distribution network power system photovoltaic system access after reliability and economy.
The above, is only presently preferred embodiments of the present invention, not does any pro forma restriction to the present invention, it is every according to
According to any simple modification, equivalent variations that the technical spirit of the present invention is made to above example, the protection of the present invention is each fallen within
Scope.
Claims (10)
1. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology, it is characterised in that：Methods described includes：
(1) parameter, making time and battery series-parallel connection component count and the canonical parameter obtained according to real-time monitoring, sets up light
The time series of volt internal resistance of cell attenuation coefficient Evolution System, sets up photovoltaic cell internal resistance attenuation coefficient equation；
(2) phase space reconfiguration process is carried out to the time series of Evolution System in the step (1)；
(3) according to wavelet network method treatment of battery internal resistance attenuation coefficient equation；
(4) carry out the prediction of photovoltaic cell internal resistance attenuation coefficient to the time series after phase space reconfiguration in the step (2) to calculate.
2. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 1, is characterized in that：The real-time prison
It is photovoltaic system operational factor and environment parament in power distribution network and power distribution network to survey the parameter for obtaining.
3. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 1, is characterized in that：The step
(1) evolution time series is the evolution time series set up under Fixed Time Interval in.
4. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 3, is characterized in that：During the evolution
Between sequence include the photovoltaic plant access point equiva lent impedance measured value, access point voltage, access point has a work value, environment temperature,
Environment illumination intensity.
5. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 4, is characterized in that：In step (1)
The Evolution System time series is in a series of moment ts1,ts2,ts3,...tsnFor：
The photovoltaic cell internal resistance attenuation coefficient equation is：
Wherein, n is natural number, n=1, and 2 ..., tr are measurement battery making time, and Tr is ambient temperature, and Sr ambient lights are shone, Cr
For series component quantity, Br is parallel component quantity.
6. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 1, is characterized in that：The step
(2) comprise the following steps：
(A) optimization aim model is set upWherein i=1,2 ... k5n；
(B) time series { rx of the Evolution System in the step (1) is builtiM dimension phase space rxi+1=ψ (rxi,
rxi-τ,...,rxi-(m-1)τ), wherein, i=1,2 ... k5n, τ is that m is Embedded dimensions between time delay.
7. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 1, is characterized in that：The step
(3) comprise the following steps：
(A) calculating of internal resistance of cell attenuation coefficient Wavelet-network model output layer；
(B) internal resistance of cell attenuation coefficient Wavelet-network model on-line amending.
8. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 7, is characterized in that：The step
(A) comprise the following steps：
If input signal time series is { rcxi, wherein, byCalculate the defeated of hidden layer
Go out value, according to hidden layer output valve output layer output valve is calculated
Wherein i=1,2...k5n, j=1,2...l, ga(xzi) >=0, ga(xzi) for SVMs object module correction term,
φ (j) be wavelet network in j-th node of hidden layer output, fjFor wavelet basis function, αjFor fjContraction-expansion factor, λjFor fj's
Shift factor, wijIt is the size connected each other between input layer and hidden layer, l is node in hidden layer, wjRepresent hidden layer with
Connection weight between output layer.
9. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 7, is characterized in that：The step
(B) comprise the following steps：
According to er=yr-yccTrue on-line amending internal resistance of cell attenuation coefficient Wavelet-network model, yrFor prediction output, yccFor reality
Border measured value.
10. a kind of photovoltaic cell internal resistance attenuation coefficient Forecasting Methodology according to claim 1, is characterized in that：The step
(4) comprise the following steps：
According to the time series in step (3) in the wavelet neural network corrected is to the step (2) after phase space reconfiguration
Carry out the prediction of photovoltaic cell internal resistance attenuation coefficient to calculate, introduce object module correction conditions, objective function optimization is ya=min
far(rxi)+gar(rxi)；
Wherein, i=1,2...k5n, gar(rxi)≥0,gar(rxi) for object module bound term, yaDecline for photovoltaic cell internal resistance
Subtract coefficient.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107301266A (en) * | 2017-05-15 | 2017-10-27 | 中山职业技术学院 | A kind of ferric phosphate lithium cell LOC evaluation methods and system |
| CN107359860A (en) * | 2017-06-28 | 2017-11-17 | 河海大学常州校区 | A kind of perovskite solar cell electron lifetime method of testing based on EIS analyses |
| CN110458343A (en) * | 2019-07-26 | 2019-11-15 | 国网山东省电力公司泰安供电公司 | A kind of method of region photovoltaic power generation capacity attenuation prediction |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107301266A (en) * | 2017-05-15 | 2017-10-27 | 中山职业技术学院 | A kind of ferric phosphate lithium cell LOC evaluation methods and system |
| CN107359860A (en) * | 2017-06-28 | 2017-11-17 | 河海大学常州校区 | A kind of perovskite solar cell electron lifetime method of testing based on EIS analyses |
| CN110458343A (en) * | 2019-07-26 | 2019-11-15 | 国网山东省电力公司泰安供电公司 | A kind of method of region photovoltaic power generation capacity attenuation prediction |
| 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 |
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