CN106650060B - Photovoltaic cell internal resistance attenuation coefficient prediction method - Google Patents

Photovoltaic cell internal resistance attenuation coefficient prediction method Download PDF

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CN106650060B
CN106650060B CN201611124348.7A CN201611124348A CN106650060B CN 106650060 B CN106650060 B CN 106650060B CN 201611124348 A CN201611124348 A CN 201611124348A CN 106650060 B CN106650060 B CN 106650060B
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internal resistance
attenuation coefficient
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CN106650060A (en
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孟可风
薛俊茹
李春来
张节潭
杨立滨
苏小玲
丛贵斌
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Shenyang University of Technology
State Grid Qinghai Electric Power Co Ltd
Electric Power Research Institute of State Grid Qinghai Electric Power Co Ltd
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State Grid Qinghai Electric Power Co Ltd
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Abstract

The invention discloses a method for predicting an attenuation coefficient of internal resistance of a photovoltaic cell, which comprises the following steps: according to the parameters obtained by real-time monitoring, establishing a time sequence of a photovoltaic cell internal resistance attenuation coefficient evolution system, and establishing a photovoltaic cell internal resistance attenuation coefficient equation; performing phase space reconstruction processing on the time sequence of the evolution system; processing a battery internal resistance attenuation coefficient equation according to a wavelet network method; and carrying out prediction calculation on the internal resistance attenuation coefficient of the photovoltaic cell on the time sequence after the phase space reconstruction. The technical problems that power grid and distributed photovoltaic power generation operation data resources cannot be effectively utilized, and the evaluation accuracy and the photovoltaic utilization efficiency are low are solved. The method has the advantages of improving the reliability of the model, improving the photovoltaic utilization rate, improving the accuracy of evaluation and improving the reliability and economy of the power distribution network power system after the photovoltaic system is connected. The method is applied to detecting the photovoltaic power generation system.

Description

Photovoltaic cell internal resistance attenuation coefficient prediction method
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a method for predicting an attenuation coefficient of internal resistance of a photovoltaic cell.
Background
Distributed photovoltaic power generation equipment and a power distribution network in a power system form a complex system, the output efficiency of a photovoltaic cell is influenced by the internal resistance of the photovoltaic cell, and the internal resistance of the photovoltaic cell is influenced by a plurality of influence factors. The photovoltaic cell internal resistance calculation method in the prior art has the technical problems that the influences of important factors such as the investment age and the number of the cell series-parallel connection assemblies are neglected, the power grid and the distributed photovoltaic power generation operation data resources cannot be effectively utilized, and the evaluation accuracy and the photovoltaic utilization efficiency are not high. The method considers multiple influence factors, monitors the power distribution network and the operating parameters and meteorological environment parameters of the photovoltaic system in the power distribution network in real time, carries out prediction calculation on the internal resistance attenuation coefficient of the distributed photovoltaic cell according to the monitoring parameters, controls the photovoltaic power generation system and the power distribution network in real time according to the calculation result, can effectively improve the reliability of the model, and thus greatly improves the photovoltaic utilization efficiency.
Disclosure of Invention
The invention provides a method for predicting the attenuation coefficient of the internal resistance of a photovoltaic cell, which solves the technical problems that the influence of important factors such as the investment age and the number of series-parallel components of the cell is neglected, the power grid and the distributed photovoltaic power generation operation data resources cannot be effectively utilized, and the evaluation accuracy and the photovoltaic utilization efficiency are not high.
The invention is realized by the following technical scheme, and the method comprises the following steps: step 1: according to the parameters obtained by real-time monitoring, establishing a time sequence of a photovoltaic cell internal resistance attenuation coefficient evolution system, and establishing a photovoltaic cell internal resistance attenuation coefficient equation; step 2: performing phase space reconstruction processing on the time sequence of the evolution system in the step 1; and step 3: processing a battery internal resistance attenuation coefficient equation according to a wavelet network method; and 4, step 4: and (3) carrying out prediction calculation on the internal resistance attenuation coefficient of the photovoltaic cell on the time sequence after the phase space reconstruction in the step (2).
Further, in order to better implement the method, the parameters obtained by real-time monitoring are the operation parameters of the photovoltaic system and the meteorological environment parameters in the power distribution network and the power distribution network.
Further, the evolution time sequence in step 1 is an evolution time sequence established at fixed time intervals.
Further, the evolution time sequence comprises the equivalent impedance measurement value of the photovoltaic power station access point, the access point voltage, the access point active value, the ambient temperature and the ambient illumination intensity.
Further, the evolving system time series in step 1 is at a series of time instants ts1,ts2,ts3,...tsnComprises the following steps:
Figure GDA0002268647330000021
the photovoltaic cell internal resistance attenuation coefficient equation is as follows:
Figure GDA0002268647330000022
wherein n is a natural number, n is 1,2, Tr is the measured battery input time, Tr is the external temperature, Sr is the external illumination, Cr is the number of series components, and Br is the number of parallel components.
Further, the step 2 comprises the following steps:
A. establishing an optimization objective model
Figure GDA0002268647330000023
Wherein i is 1,25n；
B. Constructing the time sequence { rx of the evolution system in the step 1iThe m-dimensional phase space rx ofi+1＝ψ (rxi,rxi- τ,...,rxi-(m-1) τ) Wherein i ═ 1,2.. k5nτ is the delay time and m is the embedding dimension.
Further, the step 3 comprises the following steps:
A. calculating an output layer of a wavelet network model of the attenuation coefficient of the internal resistance of the battery;
B. and (4) online correction of the wavelet network model of the attenuation coefficient of the internal resistance of the battery.
Further, step a in step 3 includes the following steps:
let the time sequence of the input signal be { rcxiTherein, by
Figure GDA0002268647330000025
Calculating the output value of the hidden layer, and calculating the output value of the output layer according to the output value of the hidden layer
Figure GDA0002268647330000026
Wherein i 1,25n，j＝1,2...l，ga(xzi)≥0，ga(xzi) Phi (j) is the output of the jth node of the hidden layer in the wavelet network, fjAs wavelet basis functions, αjIs fjOf the scaling factor, λjIs fjA translation factor of wijIs the size of the interconnection between the input layer and the hidden layer, l is the number of nodes in the hidden layer, wjRepresenting the connection weight between the hidden layer and the output layer.
Further, step B in step 3 includes the following steps:
according to er＝yr-yccWavelet network model for on-line correction of attenuation coefficient of internal resistance of battery in factrTo predict the output, yccIs the actual measurement.
Further, the step 4 comprises the following steps:
according to the corrected wavelet neural network in the step 3, the time sequence after phase space reconstruction in the step 2 is subjected to prediction calculation of internal resistance attenuation coefficient of the photovoltaic cell, and target model correction is introducedCondition, optimization of the objective function to ya＝minfar(rxi)+gar(rxi)；
Wherein i is 1,25n，gar(rxi)≥0,gar(rxi) Is a constraint term of the object model, yaThe attenuation coefficient of the internal resistance of the photovoltaic cell.
Further, the method is used for detecting the photovoltaic power generation system.
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Fig. 1 is a prediction flow chart.
Detailed Description
The present invention will be described in further detail with reference to examples, but the embodiments of the present invention are not limited thereto.
Example 1:
the method for predicting the attenuation coefficient of the internal resistance of the photovoltaic cell is adopted, the prediction process is shown in figure 1, and the method comprises the following steps:
step 1:
according to the parameters, the input time, the number of the battery series-parallel connection assemblies and the standard parameters obtained by real-time monitoring, at a series of moments ts1,ts2,ts3,...tsnThe time sequence for establishing the photovoltaic cell internal resistance attenuation coefficient evolution system is as follows:
Figure GDA0002268647330000031
establishing an attenuation coefficient equation of the internal resistance of the photovoltaic cell:
Figure GDA0002268647330000041
wherein n is a natural number, n is 1,2, Tr is the measured battery input time, Tr is the external temperature, Sr is the external illumination, Cr is the number of series components, and Br is the number of parallel components.
Step 2: and (3) performing phase space reconstruction processing on the time sequence of the evolution system in the step 1:
step 2.1: establishing optimizationsObject model
Figure GDA0002268647330000042
Wherein i is 1,25n；
Step 2.2: constructing the time sequence { rx of the evolution system in the step 1iThe m-dimensional phase space rx ofi+1＝ψ(rxi,rxi-τ,...,rxi-(m-1)τ) Wherein i ═ 1,2.. k5n，τ＝0.0152，m＝5。
And step 3: processing a battery internal resistance attenuation coefficient equation according to a wavelet network method:
step 3.1: calculating an output layer of the wavelet network model of the attenuation coefficient of the internal resistance of the battery:
let the time sequence of the input signal be { rcxiTherein, by
Figure GDA0002268647330000043
Calculating the output value of the hidden layer, and calculating the output value of the output layer according to the output value of the hidden layer
Figure GDA0002268647330000044
Wherein i 1,25n，j＝1,2...l，ga(xzi)≥0，ga(xzi) Phi (j) is the output of the jth node of the hidden layer in the wavelet network, fjAs wavelet basis functions, αjIs fjOf the scaling factor, λjIs fjA translation factor of wijIs the size of the interconnection between the input layer and the hidden layer, l is the number of nodes in the hidden layer, wjRepresenting the connection weight between the hidden layer and the output layer.
Step 3.2: on-line correction of a wavelet network model of the attenuation coefficient of the internal resistance of the battery:
according to er＝yr-yccWavelet network model for on-line correction of attenuation coefficient of internal resistance of battery in factrTo predict the output, yccFor actual measured values, to ensure model accuracy, correction values erThe best effect is achieved when the concentration is less than or equal to 0.0001.
And 4, step 4: and (3) carrying out prediction calculation on the internal resistance attenuation coefficient of the photovoltaic cell on the time sequence after the phase space reconstruction in the step (2):
according to the wavelet neural network corrected in the step 3, the time sequence after phase space reconstruction in the step 2 is subjected to prediction calculation of the internal resistance attenuation coefficient of the photovoltaic cell, target model correction conditions are introduced, and a target function is optimized to be ya＝minfar(rxi)+gar(rxi)。
Wherein i is 1,25n，gar(rxi)≥0,gar(rxi) For the constraint term of the object model,
Figure GDA0002268647330000051
yathe attenuation coefficient of the internal resistance of the photovoltaic cell.
The method is used for detecting the photovoltaic power generation system.
Compared with the prior art, the invention can obtain the following beneficial technical effects: (1) the reliability of the model is improved, (2) the photovoltaic utilization rate is improved, (3) the evaluation accuracy is improved, (4) the utilization rates of the power grid and distributed photovoltaic power generation operation data resources are improved, and (5) the reliability and the economy of the power system of the power distribution network after the photovoltaic system is connected are improved.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications and equivalent variations of the above embodiments according to the technical spirit of the present invention are included in the scope of the present invention.

Claims (1)

1. A method for predicting an attenuation coefficient of internal resistance of a photovoltaic cell is characterized by comprising the following steps: the method comprises the following steps:
(1) according to parameters, investment time, the number of the battery series-parallel connection assemblies and standard parameters obtained through real-time monitoring, a time sequence of a photovoltaic cell internal resistance attenuation coefficient evolution system is established, and a photovoltaic cell internal resistance attenuation coefficient equation is established;
(2) performing phase space reconstruction processing on the time sequence of the evolution system in the step (1);
(3) processing a battery internal resistance attenuation coefficient equation according to a wavelet network method;
(4) performing prediction calculation on the internal resistance attenuation coefficient of the photovoltaic cell on the time sequence after the phase space reconstruction in the step (2);
the parameters obtained by real-time monitoring are power distribution network and photovoltaic system operation parameters and meteorological environment parameters in the power distribution network;
the evolution time sequence in the step (1) is an evolution time sequence established at a fixed time interval; the evolution time sequence comprises an equivalent impedance measured value of the photovoltaic power station access point, access point voltage, an access point active value, ambient temperature and ambient illumination intensity;
the evolving system time sequence in step (1) is at a series of times ts1,ts2,ts3,...tsnComprises the following steps:
Figure FDA0002268647320000011
the photovoltaic cell internal resistance attenuation coefficient equation is as follows:
Figure FDA0002268647320000012
wherein n is a natural number, n is 1,2, Tr is the measured battery input time, Tr is the external temperature, Sr is the external illumination, Cr is the number of series components, and Br is the number of parallel components;
the step (2) comprises the following steps:
(A) establishing an optimized target model minfar(rx1,rx2...rxi..rxh5n) K, where i is 1,25n；
(B) Constructing the time sequence { rx of the evolution system in the step (1)iThe m-dimensional phase space rx ofi+1＝ψ(rxi,rxi-τ,...,rxi-(m-1)τ) Wherein i ═ 1,2.. k5nτ is the delay time, m is the embedding dimension;
the step (3) comprises the following steps:
(A) calculating an output layer of a wavelet network model of the attenuation coefficient of the internal resistance of the battery;
(B) on-line correction of a wavelet network model of the attenuation coefficient of the internal resistance of the battery;
the step (A) includes the steps of:
let the time sequence of the input signal be { rcxiTherein, by
Figure FDA0002268647320000021
Calculating the output value of the hidden layer, and calculating the output value of the output layer according to the output value of the hidden layer
Figure FDA0002268647320000022
Wherein i 1,25n，j＝1,2...l，ga(xzi)≥0，ga(xzi) Phi (j) is the output of the jth node of the hidden layer in the wavelet network, fjAs wavelet basis functions, αjIs fjOf the scaling factor, λjIs fjA translation factor of wijIs the size of the interconnection between the input layer and the hidden layer, l is the number of nodes in the hidden layer, wjRepresenting the connection weight between the hidden layer and the output layer;
wherein, yrIs a prediction output;
the step (B) comprises the steps of:
according to er＝yr-yccWavelet network model for on-line correction of attenuation coefficient of internal resistance of battery in factrTo predict the output, yccIs an actual measured value;
the step (4) comprises the following steps:
according to the wavelet neural network corrected in the step (3), the time sequence after the phase space reconstruction in the step (2) is subjected to photovoltaic cell internal resistance attenuation coefficient prediction calculation, target model correction conditions are introduced, and a target function is optimized to be ya＝minfar(rxi)+gar(rxi)；
Wherein i is 1,25n，gar(rxi)≥0,gar(rxi) Is a constraint term of the object model, yaThe attenuation coefficient of the internal resistance of the photovoltaic cell.
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