CN111444615A - Photovoltaic array fault diagnosis method based on K nearest neighbor and IV curve - Google Patents

Photovoltaic array fault diagnosis method based on K nearest neighbor and IV curve Download PDF

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CN111444615A
CN111444615A CN202010228062.3A CN202010228062A CN111444615A CN 111444615 A CN111444615 A CN 111444615A CN 202010228062 A CN202010228062 A CN 202010228062A CN 111444615 A CN111444615 A CN 111444615A
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刘永杰
丁坤
张经炜
李元良
陈翔
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Abstract

The invention discloses a photovoltaic array fault diagnosis method based on K nearest neighbor and IV curves, which comprises the steps of extracting characteristic parameters in simulated IV characteristic curves of different fault types, different irradiances and different temperatures and carrying out standardization treatment to obtain characteristic vectors of the simulated IV characteristic curves of different fault types, different irradiances and different temperatures; carrying out dimensionality reduction on the feature vector of the simulation IV characteristic curve; and taking the feature vectors subjected to the dimension reduction processing of the simulation IV characteristic curves under all fault types as a training data set, taking the feature vectors subjected to the dimension reduction processing of the actual measurement IV characteristic curves of the current photovoltaic array as an actual measurement data set, and performing fault diagnosis on the current photovoltaic array by adopting a K neighbor classification algorithm. The method can accurately judge the faults of the photovoltaic array, can realize fault state detection and timely maintenance, reduces the fault risk and ensures the stable operation of the photovoltaic power station.

Description

Photovoltaic array fault diagnosis method based on K nearest neighbor and IV curve
Technical Field
The invention belongs to the technical field of photovoltaic array fault diagnosis, and particularly relates to a photovoltaic array fault diagnosis method based on K nearest neighbor and IV curves.
Background
The photovoltaic array is an important component of a photovoltaic system, and is susceptible to pollution and turbidity such as sunlight, snow, dust and the like due to long-term work outdoors with a severe environment, and may cause various concurrent faults of the direct current side of the photovoltaic array, such as a bypass diode short-circuit fault, a bypass diode open-circuit fault, a cable aging fault, local shadow shielding, and a bypass diode short-circuit and open-circuit accompanied by shadow shielding. This will reduce the power generation efficiency of the photovoltaic power plant, reduce its service life, even cause a fire to cause safety problems and increase the maintenance cost of the photovoltaic system, and therefore the state detection and fault diagnosis technology of the photovoltaic array is of particular interest to many scholars.
The related fault diagnosis methods proposed by the present scholars can be roughly divided into the following methods: the first detection based on the infrared thermal imager is usually used for detecting hot spots or some structural defects in the photovoltaic module by shooting the photovoltaic module through the infrared thermal imager because the photovoltaic module has obvious temperature difference characteristics in normal and fault states, but the infrared thermal imager needs to be additionally added and is expensive, so that the detection is not suitable for a small photovoltaic power station.
The second is based on measuring the voltage, current, power at the operating point of the photovoltaic array. The fault discrimination is mainly performed by calculating the deviation between the analog output and the measured output. For example, a student builds a Simulink simulation model and calculates the deviation between the power and the measured power to diagnose the fault. The method has low hardware technical requirements, but cannot judge the fault type more accurately.
A third current scholars also propose a fault detection method based on current-voltage (IV) curve measurement, and the commonly used methods include artificial neural networks, support vector machines, decision trees and the like, which have limited sample characteristic parameters, but the selection of the sample characteristic parameters directly affects the precision of the whole diagnostic model and cannot traitor multiple concurrent faults.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a photovoltaic array fault diagnosis method based on K nearest neighbor and IV curve, which can accurately judge various fault types such as array shadow occlusion, bypass diode short circuit, cable aging, bypass diode short circuit accompanying shadow occlusion, bypass diode open circuit and the like, can realize fault state detection and timely maintenance, reduce fault risk and ensure stable operation of a photovoltaic power station.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a photovoltaic array fault diagnosis method based on K nearest neighbor and an IV curve comprises the following steps:
acquiring simulation IV characteristic curves of different fault types, different irradiance and different temperatures;
extracting characteristic parameters in simulation IV characteristic curves of different fault types, different irradiances and different temperatures;
standardizing the extracted characteristic parameters to obtain standardized simulated IV characteristic curve characteristic vectors of different fault types, different irradiances and different temperatures;
carrying out dimensionality reduction on the feature vector of the simulation IV characteristic curve;
and taking the feature vectors of the simulation IV characteristic curve under all fault types after the dimension reduction processing as a training data set, taking the feature parameters of the current photovoltaic array actual measurement IV characteristic curve and the feature vectors after the dimension reduction processing as an actual measurement data set, and performing fault diagnosis on the current photovoltaic array by adopting a K neighbor classification algorithm based on the training data set and the actual measurement data set.
Further, the obtaining of the simulated IV characteristic curves of different fault types and different irradiance and temperatures includes:
acquiring environmental parameters of a previous month measured in a photovoltaic array monitoring system, wherein the environmental parameters comprise photovoltaic assembly coplanar irradiance G and assembly backboard temperature T;
uniformly sampling the actually measured environmental parameters of the previous month, wherein the number of the sampled samples is m;
the photovoltaic system fault modeling simulation method or the Simulink simulation model is utilized to simulate m IV characteristic curves under different irradiance and temperature corresponding to each fault type under n fault types, and m x n simulated IV characteristic curves under different fault types are obtained.
Further, the characteristic parameters in the simulated IV characteristic curve under different fault types, different irradiance and different temperatures are extracted, and the characteristic parameters comprise S1～S13：
S1＝G，
S2＝T，
Figure BDA0002428366480000021
S4＝V|I＝0＝Voc，
S5＝I|V＝0＝Isc，
S6＝max(V×I)＝Pm，
Figure BDA0002428366480000022
Figure BDA0002428366480000023
Figure BDA0002428366480000024
Figure BDA0002428366480000025
Figure BDA0002428366480000026
S12＝Pks_num，
S13＝Vall_num，
Wherein G represents the coplanar irradiance of the photovoltaic assembly, T represents the backboard temperature of the photovoltaic assembly, and IjRepresents the current value of the jth point on the simulated IV characteristic curve, f (I)j) The current value on the simulation IV characteristic curve is represented as IjVoltage value corresponding to time, V (I) represents voltage value corresponding to current I, VocRepresents the open circuit voltage, IscRepresenting short-circuit current, PmRepresents the maximum power point, I represents the current value on the simulated IV characteristic curve, V represents the voltage value on the simulated IV characteristic curve, VmRepresents the voltage at the maximum power point, ImShowing the current at the maximum power point, FF the fill factor, Isc_d1Represents a short-circuit current IscAt a first derivative value, Voc_d1Represents the open circuit voltage VocAnd a first derivative value is obtained, Pks _ num represents the number of peak points in the second derivative curve of the simulation IV characteristic curve after smoothing, and Vall _ num represents the number of valley points in the second derivative curve of the simulation IV characteristic curve after smoothing.
Further, the normalizing the extracted feature parameters includes:
Figure BDA0002428366480000031
wherein, s'ijRepresents the ith simulated IV characteristic curve, the jth normalized value of the characteristic parameter, sijRepresents the values of the ith simulated IV characteristic, the jth characteristic parameter,
Figure BDA0002428366480000039
denotes the mean value, σ, of the jth characteristic parameterjAnd d represents the number of characteristic parameters in the extracted simulated IV characteristic curve, and m represents the number of simulated IV characteristic curves corresponding to different irradiance and temperature for each fault type.
Further, the dimension reduction processing is carried out on the characteristic vector of the simulation IV characteristic curve by adopting a t-distribution neighborhood embedding algorithm, and the method comprises the following steps:
D1) and (3) reducing the dimension by taking the feature vector normalized by the feature parameter as raw data, wherein the raw data is expressed as:
Figure BDA0002428366480000032
D2) calculating the joint probability distribution of the low-dimensional mapping points:
Figure BDA0002428366480000033
wherein, s'iRepresents the ith row, s ' in the s ' matrix 'jRepresents the j-th row, σ, in the s' matrixiIs s'iGaussian variance of (1), Pj|iIs s'jTo s'iIs s'iAnd s'jSimilarity between high-dimensional data points;
D3) defining a joint probability distribution of high-dimensional data points:
Figure BDA0002428366480000034
with N (0, 10)；4I) Random initialization to obtain an initial solution:
Figure BDA0002428366480000035
D4) calculating low-dimensional similarity qij：
Figure BDA0002428366480000036
D5) Calculating K L divergence:
Figure BDA0002428366480000037
D6) calculate gradient minimization K L divergence:
Figure BDA0002428366480000038
D7) updating the initial solution to obtain the solution Y of the t iteration(t)：
Figure BDA0002428366480000041
Where α (t) represents momentum, and ρ represents a learning rate;
D8) and (5) circularly iterating the steps D4) -D7) until the iteration W times are finished, and finally obtaining low-dimensional data:
Y(W)＝{y1,y2,y3…ym}，
wherein, yiIs s'iThe vector after dimensionality reduction, i, is 1,2, … m.
Further, the fault diagnosis of the current photovoltaic array by using a K-nearest neighbor classification algorithm based on the training data set and the measured data set includes:
E1) calculating the distance between the measured data set and the training data concentration point:
Figure BDA0002428366480000042
wherein d is12Representing the distance, x, between the feature vectors in the measured data set and the feature vectors in the training data set1iThe i-th feature component, x, representing a feature vector in the measured data set2iRepresenting the ith characteristic component of the characteristic vector in the training data set, and dim representing the dimensionality of the characteristic vector after dimensionality reduction processing;
E2) sorting m × n distance values according to ascending order, wherein n represents the number of fault types;
E3) selecting the first K points with the minimum value in the sorting;
E4) counting the frequency of the first K points in each fault category in a circulating mode;
E5) and returning the fault category with the highest occurrence frequency of the previous K points, and taking the fault category as the fault type of the current photovoltaic array.
Further, K is an odd number.
The invention achieves the following beneficial effects:
the method can accurately judge the concurrent faults of shadow shielding of the photovoltaic array, short circuit of the bypass diode, cable aging, short circuit of the bypass diode accompanied by shadow shielding, open circuit of the bypass diode and the like, can realize fault state detection and timely maintenance, reduces fault risks and ensures stable operation of the photovoltaic power station.
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FIG. 1 is a flow chart of a fault diagnosis method of the present invention;
FIG. 2 is a schematic diagram of uniform sampling of environmental parameters according to the present invention;
FIG. 3 is a schematic diagram of the extraction of IV curve characteristic parameters according to the present invention;
FIG. 4 is a schematic diagram of data point distribution after dimensionality reduction by the t-distribution neighborhood embedding algorithm of the present invention.
Detailed Description
The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Referring to fig. 1, the invention provides a photovoltaic array fault diagnosis method based on K-nearest neighbor and IV curve, which specifically includes the following steps:
obtaining data of IV characteristic curves of different fault types, different irradiances and different temperatures through a photovoltaic system fault modeling simulation method or a Simulink simulation model, and specifically comprising the following steps:
A1) and acquiring the actually measured environmental parameters of the photovoltaic array monitoring system in the previous month, wherein the environmental parameters comprise the coplanar irradiance G of the photovoltaic assembly and the temperature T of the backboard of the assembly.
A2) The measured environmental parameters (G, T) of the previous month are uniformly sampled, referring to fig. 2, in order to improve the accuracy of the diagnostic model and make the simulated IV characteristic curve closer to the actual IV characteristic curve, so the measured environmental parameters (G, T) of the previous month are uniformly sampled, and the sampling result can better represent the distribution of the environmental parameters of the next month. The number of sampling samples in the invention is m.
A3) The photovoltaic system fault modeling simulation method or the Simulink simulation model is utilized to simulate m IV characteristic curves under different irradiance and temperature corresponding to each fault type under n fault types, and m x n IV characteristic curve data under different fault types are obtained.
B, extracting characteristic parameters in the IV characteristic curve under different fault types, different irradiance and different temperatures and actually measuring the characteristic parameters in the obtained IV characteristic curve, wherein the method specifically comprises the following steps:
B1) environmental parameters: coplanar irradiance: s1G, module back plate temperature S2＝T；
B2) Area of IV characteristic:
Figure BDA0002428366480000051
due to the IV characteristic voltage, the sequence of currents is discrete values with non-uniform spacing, and according to the trapezoidal rule, the approximate IV characteristic area integral is achieved using the i-th region values:
Figure BDA0002428366480000052
wherein, IjCurrent value f (I) at j-th point on IV characteristic curvej) Current value of I on the characteristic curve of IVjA corresponding voltage value;
B3) open circuit voltage: s4＝V|I＝0＝Voc；
B4) Short-circuit current: s5＝I|V＝0＝Isc；
B5) Maximum power point power: s6＝max(V×I)＝Pm；
B6) Voltage at maximum power point:
Figure BDA0002428366480000053
B7) current at maximum power point:
Figure BDA0002428366480000054
B8) filling-inAnd (3) charging factor:
Figure BDA0002428366480000055
B9) performing smoothing processing such as interpolation, filtering and the like on the m-n IV characteristic curves under different fault types;
and after the smoothing treatment, solving a first derivative of the IV characteristic curve:
Figure BDA0002428366480000056
taking short-circuit current IscTaking the value of the first derivative as a characteristic parameter:
Figure BDA0002428366480000061
taking open circuit voltage VocTaking the value of the first derivative as a characteristic parameter:
Figure BDA0002428366480000062
and (3) carrying out second-order derivation on the smoothed IV characteristic curve:
Figure BDA0002428366480000063
B10) searching the number of peak points in the second derivative curve, setting a threshold, considering the point as a peak point when the peak value is greater than the preset threshold, obtaining a valley point in the same way, and counting the number S of the peak points12Pks _ num, the number of valley points S13＝Vall_num。
Fig. 3 is a first derivative curve and a second derivative curve obtained by interpolating and filtering the IV characteristic curve and deriving the IV characteristic curve, and more information that can reflect the state of the photovoltaic array is obtained from the IV characteristic curve and the first derivative and the second derivative curve thereof.
Step C, extracting the obtained characteristic parameters (S)1—S13) The normalization process was performed as follows:
C1) under the same fault type, characteristic parameters of the IV characteristic curves under different irradiance and temperature form a characteristic vector matrix:
Figure BDA0002428366480000064
S∈Rm×dwherein d represents the number of characteristic parameters in the extracted IV characteristic curve, i.e. d is 13, and m represents the number of the IV characteristic curves; sijThe ith IV characteristic curve is shown, the j-th characteristic parameter value, i is 1,2, …, m, j is 1,2, …, d.
C2) And (3) carrying out standardization treatment on the extracted characteristic parameters:
Figure BDA0002428366480000065
wherein, s'ijRepresents the ith IV characteristic curve, the normalized value of the jth characteristic parameter,
Figure BDA0002428366480000066
represents the average value of the jth characteristic parameter; sigmajThe standard deviation of the jth characteristic parameter is shown.
D, adopting a t-distribution neighborhood embedding algorithm (t-SNE) to realize the dimensionality reduction of the feature vector and the extraction of sensitive features;
the t-SNE algorithm is used for expressing the similarity between data points in an original high-dimensional Euclidean space by using a conditional outline and effectively relieving the problem of crowding between mapping data points in a low-dimensional space;
the t-SNE algorithm measures the similarity between two distributions by using K-L divergence, so that the distance between points with lower similarity in a low-dimensional space of data is larger, the distance between points with higher similarity is smaller, and good conditions are provided for fault diagnosis by using a K Nearest Neighbor (KNN) classification algorithm subsequently.
The method adopts a t-distribution neighborhood embedding algorithm (t-SNE) to realize the dimensionality reduction of the feature vector and the extraction of sensitive features, and specifically comprises the following steps:
D1) and (3) reducing the dimension by taking the feature vector matrix standardized by the feature parameters as raw data, wherein the raw data is expressed as:
Figure BDA0002428366480000071
D2) the number of iterations W, the learning rate ρ, the momentum α (t), the complexity factor Perp, and the dimension dim are set.
D3) Calculating the joint probability distribution of the low-dimensional mapping points:
Figure BDA0002428366480000072
wherein, s'iRepresents the ith row, s ' in the s ' matrix 'jRepresents the j-th row, σ, in the s' matrixiIs data point s'iGaussian variance of (1), Pj|iIs s'iAnd s'jSimilarity between high-dimensional data points, Pj|iThe larger the value is, the more s'iAnd s'jThe higher the similarity between data points, Pj|iThe smaller the value is, the smaller is s'iAnd s'jThe lower the similarity between data points.
D4) Because of the conditional probability Pj|iIs symmetric, and P is defined for optimizing K L divergence by replacing conditional probabilities with joint probability distributionsijJoint probability distribution for high-dimensional data points:
Figure BDA0002428366480000073
Pj|iis s'jTo s'iProbability distribution of (P)i|jIs s'iTo s'jA probability distribution of (a);
with N (0, 10)-4I) Random initialization, initial solution
Figure BDA0002428366480000074
The elements in the initial solution represent the number of samples, i.e. the dimension dim.
D5) Calculating low-dimensional similarity qij：
Figure BDA0002428366480000075
D6) Calculating K L divergence:
Figure BDA0002428366480000076
D7) calculate gradient minimization K L divergence:
Figure BDA0002428366480000077
D8) updating the initial solution to obtain the solution Y of the t iteration(t)：
Figure BDA0002428366480000078
D9) Loop iteration steps D5) -D8) until the end of iteration W.
D10) Finally, low-dimensional data are obtained:
Y(W)＝{y1,y2,y3…ym}，
wherein, yiIs s'iReduced vector, i ═ 1,2, … m, yiDimension of (d) is dim.
And further, acquiring an actually measured IV characteristic curve of the current photovoltaic array, extracting characteristic parameters and performing dimension reduction treatment.
And E, adopting a K nearest neighbor classification algorithm (KNN) to realize the fault diagnosis of the photovoltaic array, which comprises the following steps:
E1) extracting characteristic parameters from simulation IV characteristic curves under all fault types and using the characteristic vectors subjected to dimensionality reduction as a training data set, extracting the characteristic parameters from actual measurement IV characteristic curves and using the characteristic vectors subjected to dimensionality reduction as an actual measurement data set, calculating the distance between the actual measurement data set and a training data concentration point, wherein the distance calculation formula adopts an Euclidean distance:
Figure BDA0002428366480000081
wherein x is1iThe i-th feature component, x, representing a feature vector in the measured data set2iAn ith feature component representing a feature vector in the training dataset;
E2) sequencing m × n distance values according to an increasing order, wherein m represents that m eigenvectors exist under each fault type, and n represents that n fault types exist;
E3) determining the selection value of K, wherein K selects odd numbers as much as possible; selecting the first K points with the minimum value in the sorting;
E4) counting the frequency of the first K points in each fault category in a circulating mode;
E5) and returning the fault category with the highest occurrence frequency of the previous K points, and taking the fault category as the fault type of the current photovoltaic array.
Fig. 4 is a data point distribution diagram after the dimension reduction by the t-distribution neighborhood embedding algorithm, and it can be seen that the distance of the point with lower similarity of the data in the low-dimensional space is larger, and the distance of the point with higher similarity is smaller, and from the diagram, three faults of fault 1, fault 2 and fault 3 can be seen, the distribution among the same fault types is denser, and the distance among the different fault types is larger.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Those of skill would further appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that such implementation decisions may be made by those skilled in the art using various means for implementing the functions described herein without departing from the scope of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. The industry has described the principles of the invention, and variations and modifications are possible without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (7)

1. A photovoltaic array fault diagnosis method based on K nearest neighbor and IV curves is characterized by comprising the following steps:
acquiring simulation IV characteristic curves of different fault types, different irradiance and different temperatures;
extracting characteristic parameters in simulation IV characteristic curves of different fault types, different irradiances and different temperatures;
standardizing the extracted characteristic parameters to obtain standardized simulated IV characteristic curve characteristic vectors of different fault types, different irradiances and different temperatures;
carrying out dimensionality reduction on the feature vector of the simulation IV characteristic curve;
and taking the feature vectors of the simulation IV characteristic curve under all fault types after the dimension reduction processing as a training data set, taking the feature parameters of the current photovoltaic array actual measurement IV characteristic curve and the feature vectors after the dimension reduction processing as an actual measurement data set, and performing fault diagnosis on the current photovoltaic array by adopting a K neighbor classification algorithm based on the training data set and the actual measurement data set.
2. The method for photovoltaic array fault diagnosis based on K nearest neighbor and IV curve of claim 1, wherein the obtaining of simulated IV characteristic curves under different fault types, irradiance and temperatures comprises:
acquiring environmental parameters of a previous month measured in a photovoltaic array monitoring system, wherein the environmental parameters comprise photovoltaic assembly coplanar irradiance G and assembly backboard temperature T;
uniformly sampling the actually measured environmental parameters of the previous month, wherein the number of the sampled samples is m;
the photovoltaic system fault modeling simulation method or the Simulink simulation model is utilized to simulate m IV characteristic curves under different irradiance and temperature corresponding to each fault type under n fault types, and m x n simulated IV characteristic curves under different fault types are obtained.
3. The method for photovoltaic array fault diagnosis based on K nearest neighbor and IV curve of claim 1, wherein the extracting of characteristic parameters in simulation IV characteristic curves of different fault types and different irradiance and temperature comprises S1～S13：
S1＝G，
S2＝T，
Figure FDA0002428366470000011
S4＝V|I＝0＝Voc，
S5＝I|V＝0＝Isc，
S6＝max(V×I)＝Pm，
Figure FDA0002428366470000012
Figure FDA0002428366470000013
Figure FDA0002428366470000014
Figure FDA0002428366470000015
Figure FDA0002428366470000021
S12＝Pks_num，
S13＝Vall_num，
Wherein G represents the coplanar irradiance of the photovoltaic assembly, T represents the backboard temperature of the photovoltaic assembly, and IjRepresents the current value of the jth point on the simulated IV characteristic curve, f (I)j) The current value on the simulation IV characteristic curve is represented as IjVoltage value corresponding to time, V (I) represents voltage value corresponding to current I, VocRepresents the open circuit voltage, IscRepresenting short-circuit current, PmRepresents the maximum power point, I represents the current value on the simulated IV characteristic curve, V represents the voltage value on the simulated IV characteristic curve, VmRepresents the voltage at the maximum power point, ImShowing the current at the maximum power point, FF the fill factor, Isc_d1Represents a short-circuit current IscAt a first derivative value, Voc_d1Represents the open circuit voltage VocAnd a first derivative value is obtained, Pks _ num represents the number of peak points in the second derivative curve of the simulation IV characteristic curve after smoothing, and Vall _ num represents the number of valley points in the second derivative curve of the simulation IV characteristic curve after smoothing.
4. The method for photovoltaic array fault diagnosis based on K-nearest neighbor and IV curve as claimed in claim 3, wherein the normalizing the extracted characteristic parameters comprises:
Figure FDA0002428366470000022
wherein, s'ijRepresents the ith simulated IV characteristic curve, the jth normalized value of the characteristic parameter, sijRepresents the values of the ith simulated IV characteristic, the jth characteristic parameter,
Figure FDA0002428366470000023
denotes the mean value, σ, of the jth characteristic parameterjAnd d represents the number of characteristic parameters in the extracted simulated IV characteristic curve, and m represents the number of simulated IV characteristic curves corresponding to different irradiance and temperature for each fault type.
5. The photovoltaic array fault diagnosis method based on K nearest neighbor and IV curve as claimed in claim 4, wherein the dimension reduction processing is performed on the simulation IV characteristic curve feature vector by using t-distribution neighborhood embedding algorithm, comprising:
D1) and (3) reducing the dimension by taking the feature vector normalized by the feature parameter as raw data, wherein the raw data is expressed as:
Figure FDA0002428366470000024
D2) calculating the joint probability distribution of the low-dimensional mapping points:
Figure FDA0002428366470000025
wherein, s'iRepresents the ith row, s ' in the s ' matrix 'jRepresents the j-th row, σ, in the s' matrixiIs s'iGaussian variance of (1), Pj|iIs s'jTo s'iIs s'iAnd s'jSimilarity between high-dimensional data points;
D3) defining a joint probability distribution of high-dimensional data points:
Figure FDA0002428366470000026
with N (0, 10)-4I) Random initialization to obtain an initial solution:
Figure FDA0002428366470000031
D4) calculating low-dimensional similarity qij：
Figure FDA0002428366470000032
D5) Calculating K L divergence:
Figure FDA0002428366470000033
D6) calculate gradient minimization K L divergence:
Figure FDA0002428366470000034
D7) updating the initial solution to obtain the solution Y of the t iteration(t)：
Figure FDA0002428366470000035
Where α (t) represents momentum, and ρ represents a learning rate;
D8) and (5) circularly iterating the steps D4) -D7) until the iteration W times are finished, and finally obtaining low-dimensional data:
Y(W)＝{y1,y2,y3…ym}，
wherein, yiIs s'iThe vector after dimensionality reduction, i, is 1,2, … m.
6. The method for photovoltaic array fault diagnosis based on K-nearest neighbor and IV curve of claim 5, wherein the fault diagnosis of the current photovoltaic array is performed by adopting a K-nearest neighbor classification algorithm based on the training data set and the measured data set, and the method comprises the following steps:
E1) calculating the distance between the measured data set and the training data concentration point:
Figure FDA0002428366470000036
wherein d is12Representing the distance, x, between the feature vectors in the measured data set and the feature vectors in the training data set1iThe i-th feature component, x, representing a feature vector in the measured data set2iRepresenting the ith characteristic component of the characteristic vector in the training data set, and dim representing the dimensionality of the characteristic vector after dimensionality reduction processing;
E2) sorting m × n distance values according to ascending order, wherein n represents the number of fault types;
E3) selecting the first K points with the minimum value in the sorting;
E4) counting the frequency of the first K points in each fault category in a circulating mode;
E5) and returning the fault category with the highest occurrence frequency of the previous K points, and taking the fault category as the fault type of the current photovoltaic array.
7. The photovoltaic array fault diagnosis method based on K neighbors and IV curves according to claim 6, wherein K is an odd number.
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