CN110544039A - Method and device for identifying shadow occlusion of photovoltaic string - Google Patents
Method and device for identifying shadow occlusion of photovoltaic string Download PDFInfo
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Abstract
The invention provides a method and a device for identifying shadow shielding of photovoltaic group strings, which are used for acquiring the direct current power of each photovoltaic group string corresponding to a target inverter in an effective acquisition period at each acquisition moment; carrying out data standardization processing on the direct current power of each photovoltaic group string at each acquisition moment to obtain the power generation efficiency of each photovoltaic group string at each acquisition moment; respectively calculating the power generation efficiency dispersion rate of all photovoltaic group strings at each acquisition moment; clustering the power generation efficiency of all photovoltaic group strings at each acquisition moment to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters; and at each acquisition moment, when the discrete rate of the power generation efficiency is greater than the discrete rate threshold value and the relative error is greater than the error threshold value, judging that the photovoltaic group strings in the cluster type cluster with the smaller central value have shadow shielding at the acquisition moment. The method realizes accurate identification of the shadow shielding of the photovoltaic string on the basis of no need of meteorological data.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a method and a device for identifying shadow occlusion of a photovoltaic string.
Background
photovoltaic power generation has been widely paid attention to and popularized in recent years due to its advantages of cleanness and high efficiency, and photovoltaic power generation environments are increasingly diversified, such as mountainous regions, deserts, gobi, roofs and the like. If the installation is improper, mountain, stand, foreign matter or growing nature vegetation will cause the shadow to shelter from photovoltaic module in photovoltaic module surrounding environment. Shadow shielding can cause a power generation curve of the photovoltaic module to be multimodal, so that a maximum power point cannot be tracked, and the photovoltaic power generation efficiency is influenced; the long-term shadow shielding of the fixed area can form a hot spot effect, increase the risk of aging and cracking of the photovoltaic module, and lead to the failure of the photovoltaic module in severe cases. Therefore, shadow occlusion identification is very necessary for improving the power generation capacity of the photovoltaic power station and reducing operation and maintenance risks.
Because the photovoltaic power generation capacity is mainly influenced by irradiation and temperature, the conventional shadow shielding identification method generally needs to use meteorological data such as irradiation and temperature to carry out cooperative judgment. However, in practical applications, in many photovoltaic power generation scenes, such as small and medium-sized power stations such as civil or industrial and commercial roofs, no environment sensor is configured, meteorological data cannot be acquired, and meteorological data acquired by photovoltaic power stations with large differences in installation environments of photovoltaic components such as mountain power stations through the environment sensor cannot represent component irradiation at different inclination angles and azimuth angles. Therefore, the existing method for identifying the shadow occlusion has the limitation of a photovoltaic power generation scene, and the shadow occlusion in all the photovoltaic power generation scenes cannot be accurately identified.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for identifying shadow occlusion of a photovoltaic string, which can accurately identify the shadow occlusion of the photovoltaic string without meteorological data.
in order to achieve the above purpose, the invention provides the following specific technical scheme:
A method for identifying shadow occlusion of a photovoltaic string comprises the following steps:
acquiring the direct current power of each photovoltaic group string corresponding to the target inverter at each acquisition moment in an effective acquisition period;
Carrying out data standardization processing on the direct current power of each photovoltaic group string at each acquisition moment to obtain the power generation efficiency of each photovoltaic group string at each acquisition moment;
respectively calculating the power generation efficiency discrete rate of all the photovoltaic string at each acquisition moment;
Clustering the power generation efficiency of all the photovoltaic group strings at each acquisition moment to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
At each acquisition moment, when the power generation efficiency discrete rate is greater than a discrete rate threshold value and the relative error is greater than an error threshold value, judging that shadow shielding exists in the photovoltaic group strings in the cluster type cluster with a smaller central value at the acquisition moment.
Optionally, the method further includes:
and determining the historical acquisition period which is not reported with the fault alarm information and is closest to the current time when the daily total power generation is greater than the power generation threshold as the effective acquisition period.
Optionally, the performing data standardization on the direct current power of each photovoltaic group string at each collection time to obtain the power generation efficiency of each photovoltaic group string at each collection time includes:
cleaning abnormal values of the direct current power of each photovoltaic group string at each acquisition time to obtain the effective direct current power of each photovoltaic group string at each acquisition time;
and calculating the power generation efficiency of each photovoltaic group string at each acquisition time according to the number of the photovoltaic components in each photovoltaic group string, the nominal power of the photovoltaic components, the actual power generation capacity coefficient of each photovoltaic group string and the effective direct current power of each photovoltaic group string at each acquisition time.
optionally, the method further includes:
acquiring the direct current power of each photovoltaic group string in a historical data set at each acquisition moment;
Calculating the power generation efficiency to be corrected of each photovoltaic group string at each acquisition time in the historical data set according to the direct current power of each photovoltaic group string at each acquisition time in the historical data set, the number of photovoltaic modules in each photovoltaic group string and the nominal power of the photovoltaic modules;
respectively normalizing the power generation efficiency to be corrected of each photovoltaic string in the historical data set at each acquisition time according to the maximum power generation efficiency to be corrected of each photovoltaic string in the historical data set, so as to obtain an actual power generation capacity coefficient of each photovoltaic string in the historical data set at each acquisition time;
And respectively calculating the average value of the actual generating capacity coefficient of each photovoltaic group string in the historical data set to obtain the actual generating capacity coefficient of each photovoltaic group string.
Optionally, the method further includes:
respectively calculating the power generation efficiency dispersion rate of all the photovoltaic string at each acquisition moment in the historical data set;
calculating the mean value and the standard deviation of the power generation efficiency dispersion rate of all the photovoltaic string in the historical data set;
and setting the dispersion rate threshold according to the Lauda rule and the mean value and standard deviation of the dispersion rate of the generating efficiency of all the photovoltaic string in the historical data set.
optionally, the method further includes:
Clustering the power generation efficiency of all the photovoltaic group strings at each acquisition time in the historical data set to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
calculating the mean value and the standard deviation of relative errors between the central values of the two clustering clusters in the historical data set;
And setting the error threshold according to the Lauda rule and the mean value and standard deviation of relative errors between the central values of the two clustering clusters in the historical data set.
optionally, the method further includes:
And taking the direct current power of the non-shielding photovoltaic group string at each acquisition moment in the effective acquisition period as a calculation sample, and adding the calculation sample into the historical data set.
a shadow occlusion recognition device for a photovoltaic string, comprising:
the direct current power acquisition unit is used for acquiring the direct current power of each photovoltaic group string corresponding to the target inverter at each acquisition moment in an effective acquisition period;
the standardization processing unit is used for carrying out data standardization processing on the direct current power of each photovoltaic group string at each acquisition time to obtain the power generation efficiency of each photovoltaic group string at each acquisition time;
the dispersion rate calculating unit is used for calculating the dispersion rate of the power generation efficiency of all the photovoltaic string at each acquisition moment respectively;
the cluster analysis unit is used for clustering the power generation efficiency of all the photovoltaic group strings at each acquisition moment to obtain two cluster clusters and calculating the relative error between the central values of the two cluster clusters;
and the shadow shielding judging unit is used for judging that the photovoltaic group strings in the cluster type clusters with smaller central values have shadow shielding at the acquisition time when the power generation efficiency discrete rate is greater than a discrete rate threshold value and the relative error is greater than an error threshold value at each acquisition time.
optionally, the apparatus further comprises:
and the effective acquisition period determining unit is used for determining a historical acquisition period which is not reported with the fault alarm information and is closest to the current time, wherein the daily total generated energy is greater than the generated energy threshold value, as the effective acquisition period.
optionally, the normalization processing unit is specifically configured to:
Cleaning abnormal values of the direct current power of each photovoltaic group string at each acquisition time to obtain the effective direct current power of each photovoltaic group string at each acquisition time;
And calculating the power generation efficiency of each photovoltaic group string at each acquisition time according to the number of the photovoltaic components in each photovoltaic group string, the nominal power of the photovoltaic components, the actual power generation capacity coefficient of each photovoltaic group string and the effective direct current power of each photovoltaic group string at each acquisition time.
optionally, the apparatus further comprises:
A power generation capability coefficient setting unit configured to:
acquiring the direct current power of each photovoltaic group string in a historical data set at each acquisition moment;
Calculating the power generation efficiency to be corrected of each photovoltaic group string at each acquisition time in the historical data set according to the direct current power of each photovoltaic group string at each acquisition time in the historical data set, the number of photovoltaic modules in each photovoltaic group string and the nominal power of the photovoltaic modules;
Respectively normalizing the power generation efficiency to be corrected of each photovoltaic string in the historical data set at each acquisition time according to the maximum power generation efficiency to be corrected of each photovoltaic string in the historical data set, so as to obtain an actual power generation capacity coefficient of each photovoltaic string in the historical data set at each acquisition time;
and respectively calculating the average value of the actual generating capacity coefficient of each photovoltaic group string in the historical data set to obtain the actual generating capacity coefficient of each photovoltaic group string.
optionally, the apparatus further comprises:
a dispersion rate threshold setting unit configured to:
Respectively calculating the power generation efficiency dispersion rate of all the photovoltaic string at each acquisition moment in the historical data set;
calculating the mean value and the standard deviation of the power generation efficiency dispersion rate of all the photovoltaic string in the historical data set;
And setting the dispersion rate threshold according to the Lauda rule and the mean value and standard deviation of the dispersion rate of the generating efficiency of all the photovoltaic string in the historical data set.
optionally, the apparatus further comprises:
an error threshold setting unit configured to:
Clustering the power generation efficiency of all the photovoltaic group strings at each acquisition time in the historical data set to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
calculating the mean value and the standard deviation of relative errors between the central values of the two clustering clusters in the historical data set;
and setting the error threshold according to the Lauda rule and the mean value and standard deviation of relative errors between the central values of the two clustering clusters in the historical data set.
Optionally, the apparatus further comprises:
and the historical data set updating unit is used for adding the direct current power of the non-shielding photovoltaic group string at each acquisition moment in the effective acquisition period into the historical data set as a calculation sample.
compared with the prior art, the invention has the following beneficial effects:
the invention discloses a shadow occlusion identification method of a photovoltaic group string, which judges whether the photovoltaic group string corresponding to a target inverter has shadow occlusion at each acquisition time by respectively judging whether the power generation efficiency discrete rate of all the photovoltaic group strings corresponding to the target inverter is greater than the discrete rate threshold value under the condition of no occlusion and whether the relative error between the central values of two clustering clusters of all the photovoltaic group strings corresponding to the target inverter is greater than the error threshold value under the condition of no occlusion. The method and the device only carry out shadow occlusion identification according to the direct current power of the photovoltaic string, do not depend on meteorological data such as irradiation, temperature and the like, are suitable for shadow occlusion identification under all photovoltaic power generation scenes, and improve the universality of the method and the device under various photovoltaic power generation scenes.
further, due to the fact that the direct current power of the photovoltaic string is affected by the individualized differences of different photovoltaic string access components, installation inclination angles and azimuth angles are inconsistent, attenuation degrees are different, string adaptation and the like, the obtained direct current power of each photovoltaic string at each acquisition time is subjected to standardized processing, the individual differences of each photovoltaic string are eliminated, the power generation efficiency of each photovoltaic string at each acquisition time under the same order of magnitude is obtained, then subsequent processing is carried out, and the accuracy of judging whether the photovoltaic string is shaded or not is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of the relationship between shielding/non-shielding group power and irradiation;
FIG. 2 is a schematic diagram of the overall power generation efficiency of a multi-string system;
FIG. 3 is a schematic flow chart of a method for identifying shadow occlusion of a photovoltaic string according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a method for setting an actual power generation capacity coefficient of a photovoltaic string according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the discrete ratio of the overall power generation efficiency of each group of strings according to the embodiment of the present invention;
FIG. 6 is a schematic diagram of scattered clustering of cluster power generation efficiency at a certain shielding moment, disclosed by the embodiment of the invention;
FIG. 7 is a schematic diagram of the power generation efficiency and the dispersion rate of each group of strings in 5 continuous effective acquisition periods according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a shadow occlusion recognition device for a photovoltaic string according to an embodiment of the present invention.
Detailed Description
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
referring to fig. 1, the direct current power curve and the irradiation curve of the non-occlusion group string are substantially coincident, which shows that the linear correlation between the direct current power curve and the irradiation curve is very high, but the occlusion group string obviously shows that the power is reduced in the occlusion period and is no longer coincident with the irradiation curve, the irradiation of the photovoltaic group strings P _16#1 and P _16#2 is the same at 16: 08-17: 44, but the direct current power of the photovoltaic group strings P _16#1 and P _16#2 is obviously different, and thus, the power generation efficiency of the photovoltaic group strings is obviously different between the case of the existence of the shadow occlusion and the case of the absence of the shadow occlusion. Therefore, the photovoltaic power generation device is popularized to the situation of multiple groups of strings, and the power generation power of different photovoltaic strings is different from 16:08 to 17: 44. On the basis, the method calculates the power generation efficiency dispersion rate of all the photovoltaic group strings at each acquisition time, aggregates the power generation efficiency of all the photovoltaic group strings, and judges whether the power generation efficiency dispersion rate of all the photovoltaic group strings corresponding to the target inverter is larger than the dispersion rate threshold value under the condition of no shielding or not and whether the relative error between the central values of two clustering clusters of all the photovoltaic group strings corresponding to the target inverter is larger than the error threshold value under the condition of no shielding or not, so as to judge whether the photovoltaic group strings corresponding to the target inverter have shadow shielding at each acquisition time or not.
Specifically, referring to fig. 3, the method for identifying shadow occlusion of a photovoltaic string disclosed in this embodiment includes the following steps:
S101: acquiring the direct current power of each photovoltaic group string corresponding to the target inverter at each acquisition moment in an effective acquisition period;
Compared with a non-shielding photovoltaic string, the shielded photovoltaic string has lower direct current power, and when a photovoltaic module in the photovoltaic string has a fault, the direct current power of the photovoltaic string is also lower, so that the interference of equipment faults needs to be eliminated.
because under low irradiation weather such as overcast and rainy, there is not the shadow to shelter from by photovoltaic group cluster nearly, only can effectively discern the shadow and shelter from under high irradiation weather, because this example does not possess meteorological data again, can't evaluate weather characteristics, consequently, this embodiment needs come indirect evaluation weather according to daily generated energy. The method comprises the steps of setting the lowest value of daily generated energy in high-irradiation weather as a generated energy threshold value, counting the daily total generated energy, judging the high-irradiation weather when the daily total generated energy is larger than the generated energy threshold value, otherwise, judging the high-irradiation weather is low-irradiation weather, and filtering direct-current power in the low-irradiation weather in order to eliminate the interference of the low-irradiation weather on shadow shielding identification.
on this basis, the embodiment determines the historical acquisition period which is closest to the current time and has no fault alarm information reported and has the total daily power generation amount larger than the power generation amount threshold as the effective acquisition period.
the effective collection period is 1 day, and the time when irradiation exists in 1 day is taken as the collection time, for example, 8: and taking the integral point time between 00 and 17:00 as the acquisition time.
further, in order to improve the reliability of the shadow occlusion identification, the shadow occlusion identification may be performed by using the dc power data of a plurality of consecutive effective acquisition periods, for example, 5 consecutive effective acquisition periods, that is, 5 days.
s102: carrying out data standardization processing on the direct current power of each photovoltaic group string at each acquisition moment to obtain the power generation efficiency of each photovoltaic group string at each acquisition moment;
due to the fact that the direct current power of the photovoltaic string is affected by the individuation differences of different photovoltaic string access components, the installation inclination angle and the azimuth angle are inconsistent, the attenuation degree is different, string adaptation and the like, in order to eliminate the individual differences of the photovoltaic strings, data standardization processing is conducted on the direct current power of each photovoltaic string at each acquisition moment, and the power generation efficiency of each photovoltaic string at each acquisition moment under the same order of magnitude is obtained.
Specifically, firstly, abnormal value cleaning is performed on the direct current power of each photovoltaic group string at each acquisition time to obtain the effective direct current power of each photovoltaic group string at each acquisition time, wherein the abnormal value can be an extreme point.
and then, calculating the power generation efficiency of each photovoltaic group string at each acquisition time according to the number of the photovoltaic components in each photovoltaic group string, the nominal power of the photovoltaic components, the actual power generation capacity coefficient of each photovoltaic group string and the effective direct current power of each photovoltaic group string at each acquisition time.
Specifically, the calculation formula of the power generation efficiency is as follows:
eta represents the power generation efficiency of the photovoltaic string. Ppv represents the nominal power of the photovoltaic modules, N represents the number of photovoltaic modules connected into each string of groups, i.e. N × Ppv represents the rated output power of the string of photovoltaic groups. k represents the actual generating capacity coefficient of each photovoltaic string.
The data standardization processing is to eliminate the influence of capacity difference caused by inconsistent access photovoltaic module number of each photovoltaic group string and corresponds to the introduction of N × Ppv; and secondly, the influence of individual differences of the power generation capacity of the photovoltaic string due to different installation inclination angles and direction angles, different attenuation degrees, photovoltaic string adaptation and other factors of each string is eliminated, namely the introduction of a corresponding k value.
specifically, referring to fig. 4, the actual generating capacity coefficient k of each photovoltaic string is set as follows:
s201: acquiring the direct current power of each photovoltaic group string corresponding to the target inverter in the historical data set at each acquisition moment;
the historical data set may be dc power data of the non-occluded photovoltaic module 30 days prior to the active acquisition cycle.
s202: calculating the power generation efficiency to be corrected of each photovoltaic group string at each acquisition time in the historical data set according to the direct current power of each photovoltaic group string at each acquisition time in the historical data set, the number of photovoltaic assemblies in each photovoltaic group string and the nominal power of the photovoltaic assemblies;
specifically, the calculation formula of the power generation efficiency to be corrected is as follows:
s203: respectively carrying out normalization processing on the power generation efficiency to be corrected of each photovoltaic group string in the historical data set at each acquisition moment according to the maximum power generation efficiency to be corrected of each photovoltaic group string in the historical data set, so as to obtain the actual power generation capacity coefficient of each photovoltaic group string in the historical data set at each acquisition moment;
Wherein, the normalization processing formula is as follows:
and ki is an actual power generation capacity coefficient of the photovoltaic string after normalization processing at the acquisition time, eta 'is the power generation efficiency to be corrected of the photovoltaic string at the acquisition time, and Max (eta') is the maximum power generation efficiency to be corrected of the photovoltaic string in the historical data set.
the power generation efficiency of all the photovoltaic string at a certain acquisition moment is compared with the highest power generation efficiency by normalization processing, namely the power generation capacity of the photovoltaic string corresponding to the highest power generation efficiency is determined to be 1, other photovoltaic string is less than 1, the power generation capacity of each photovoltaic string is normalized to be in the range of [0,1] under the influence of the elimination of the whole capacity difference, and the difference of the value of each photovoltaic string represents the difference of the direct current power actually generated by the photovoltaic string under the same meteorological condition, namely the individual difference.
s204: and respectively calculating the average value of the actual generating capacity coefficient of each photovoltaic group string in the historical data set to obtain the actual generating capacity coefficient of each photovoltaic group string.
in order to improve the accuracy of evaluating the generating capacity of the photovoltaic string, the value calculated at one collecting moment obviously has no representativeness, so the normalized generating capacity coefficient of the photovoltaic string at a plurality of collecting moments is averaged to obtain an average generating capacity coefficient, and the value k represents the actual generating capacity coefficient of the photovoltaic string, and the formula is as follows:
s103: respectively calculating the power generation efficiency dispersion rate of all photovoltaic group strings at each acquisition moment;
calculating the mean value mu and the standard deviation sigma of the power generation efficiency eta i of all photovoltaic strings at the same acquisition time, and solving the dispersion rate at the time, wherein the calculation formula is as follows:
wherein C.V is the power generation efficiency dispersion rate of all photovoltaic strings corresponding to the target inverter.
s104: clustering the power generation efficiency of all photovoltaic group strings at each acquisition moment to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
for the same collection time, performing one-dimensional clustering on the power generation efficiency eta i of all the photovoltaic group strings by applying a K-means algorithm, wherein the number of clustering clusters is 2, obtaining the values S1 and S2 (arranged according to descending order of size) of the centers of the two clustering clusters, and calculating the relative error between the two values, wherein the formula is as follows:
s105: and at each acquisition moment, when the discrete rate of the power generation efficiency is greater than the discrete rate threshold value and the relative error is greater than the error threshold value, judging that the photovoltaic group strings in the cluster type cluster with the smaller central value have shadow shielding at the acquisition moment.
referring to fig. 5, the discrete rate of the string power generation efficiency η is small and varies smoothly during the non-shadow-shielded period, and the discrete rate increases abruptly and is distinguished significantly during the shadow-shielded period.
Referring to fig. 6, an example of clustering analysis of the power generation efficiency η of each sub-string at a certain acquisition time with occlusion is selected, and it can be seen that: the center difference of the 2 clusters is obvious, the relative error of the center values of the two clusters is large, and the power generation efficiency of each point at the center of S2 is much lower than that of the center of S1.
In order to improve the reliability, please refer to fig. 7, the direct current power data of 5 effective acquisition cycles, i.e., 5 days, are selected for shadow occlusion recognition, in 5 consecutive effective days of 3 months, the occlusion groups are all distributed in the same time period, and the power generation efficiency dispersion rate is obviously suddenly changed only in the occlusion period.
Therefore, the method can accurately judge whether the photovoltaic group strings corresponding to the target inverter have shadow shielding at each acquisition time by respectively judging whether the power generation efficiency dispersion rate of all the photovoltaic group strings corresponding to the target inverter is greater than the dispersion rate threshold value under the condition of no shielding and whether the relative error between the central values of two clustering clusters of all the photovoltaic group strings corresponding to the target inverter is greater than the error threshold value under the condition of no shielding.
further, the discrete rate threshold is set as follows:
Respectively calculating the power generation efficiency discrete rate C.Vi of all photovoltaic group strings at each acquisition moment in the historical data set;
Calculating the mean value [ mu ] cv and the standard deviation [ sigma ] cv of the power generation efficiency dispersion rate of all photovoltaic group strings in the historical data set;
the boundary is determined according to the abnormal value of μ ± 3 σ indicated by the law of raydeda (3 σ law), and the threshold value δ 1 of the dispersion rate is set to μ cv +3 σ cv, and exceeding the value indicates that the dispersion rate is higher than the normal value and exceeds the range of the non-occlusion case, so that there is a possibility that the shadow occludes the photovoltaic string.
The error threshold is set as follows:
clustering the power generation efficiency of all photovoltaic strings at each acquisition time in the historical data set to obtain two cluster clusters, and calculating the relative error delta S between the central values of the two cluster clusters;
calculating the mean value mus and the standard deviation sigma s of the relative error between the central values of two cluster classes in the historical data set;
according to the Lauda rule and the mean value mu s and the standard deviation sigma s of relative errors between the central values of two cluster types in the historical data set, setting an error threshold value delta 2 to be mu s +3 sigma s, and indicating that obvious power generation differences exist among all photovoltaic group strings when the error threshold value is exceeded, the power generation efficiency is obviously distinguished, and the photovoltaic group strings under the condition of low power have the possibility of shadow shielding.
if the situation that the photovoltaic group strings are shielded at a certain collecting moment is judged, the photovoltaic group strings in the clustering clusters with smaller central values are judged to have shadow shielding at the collecting moment, the recognition results are fed back to a monitoring cloud platform end to alarm so as to prompt and guide operation and maintenance personnel to carry out next adjustment work, and the results of the day can be used as new calculation samples to be added into a historical data set so as to update the dispersion rate threshold value and the error threshold value in a rolling manner, so that the accuracy and the effectiveness of the judgment of the invention are improved.
when the method is initially applied, the initial guidance of human experience is needed, the acquisition of the historical data set is judged by human, then the historical data set is added along with the results of data rolling and judgment of the existing algorithm, and the human selection can be gradually separated, so that the automatic selection is realized. Of course, the accuracy of judgment of the existing algorithm can be evaluated by regular human intervention, and feedback regulation is guided.
referring to fig. 8, the method for identifying shadow occlusion of a photovoltaic string disclosed in the embodiment includes:
a dc power obtaining unit 801, configured to obtain a dc power of each photovoltaic group string corresponding to the target inverter at each collection time in an effective collection period;
the standardization processing unit 802 is configured to perform data standardization processing on the direct current power of each photovoltaic group string at each acquisition time to obtain the power generation efficiency of each photovoltaic group string at each acquisition time;
a dispersion ratio calculation unit 803, configured to calculate a dispersion ratio of the power generation efficiency of all the photovoltaic string at each acquisition time respectively;
the cluster analysis unit 804 is used for clustering the power generation efficiency of all the photovoltaic group strings at each acquisition time to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
a shadow occlusion determining unit 805, configured to determine, at each acquisition time, that there is shadow occlusion in the photovoltaic string in the cluster class with the smaller central value at the acquisition time when the power generation efficiency discrete rate is greater than the discrete rate threshold and the relative error is greater than the error threshold.
Optionally, the apparatus further comprises:
And the effective acquisition period determining unit is used for determining a historical acquisition period which is not reported with the fault alarm information and is closest to the current time, wherein the daily total generated energy is greater than the generated energy threshold value, as the effective acquisition period.
Optionally, the normalization processing unit 802 is specifically configured to:
cleaning abnormal values of the direct current power of each photovoltaic group string at each acquisition time to obtain the effective direct current power of each photovoltaic group string at each acquisition time;
And calculating the power generation efficiency of each photovoltaic group string at each acquisition time according to the number of the photovoltaic components in each photovoltaic group string, the nominal power of the photovoltaic components, the actual power generation capacity coefficient of each photovoltaic group string and the effective direct current power of each photovoltaic group string at each acquisition time.
optionally, the apparatus further comprises:
a power generation capability coefficient setting unit configured to:
acquiring the direct current power of each photovoltaic group string in a historical data set at each acquisition moment;
calculating the power generation efficiency to be corrected of each photovoltaic group string at each acquisition time in the historical data set according to the direct current power of each photovoltaic group string at each acquisition time in the historical data set, the number of photovoltaic modules in each photovoltaic group string and the nominal power of the photovoltaic modules;
Respectively normalizing the power generation efficiency to be corrected of each photovoltaic string in the historical data set at each acquisition time according to the maximum power generation efficiency to be corrected of each photovoltaic string in the historical data set, so as to obtain an actual power generation capacity coefficient of each photovoltaic string in the historical data set at each acquisition time;
And respectively calculating the average value of the actual generating capacity coefficient of each photovoltaic group string in the historical data set to obtain the actual generating capacity coefficient of each photovoltaic group string.
Optionally, the apparatus further comprises:
A dispersion rate threshold setting unit configured to:
Respectively calculating the power generation efficiency dispersion rate of all the photovoltaic string at each acquisition moment in the historical data set;
Calculating the mean value and the standard deviation of the power generation efficiency dispersion rate of all the photovoltaic string in the historical data set;
and setting the dispersion rate threshold according to the Lauda rule and the mean value and standard deviation of the dispersion rate of the generating efficiency of all the photovoltaic string in the historical data set.
optionally, the apparatus further comprises:
an error threshold setting unit configured to:
clustering the power generation efficiency of all the photovoltaic group strings at each acquisition time in the historical data set to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
Calculating the mean value and the standard deviation of relative errors between the central values of the two clustering clusters in the historical data set;
And setting the error threshold according to the Lauda rule and the mean value and standard deviation of relative errors between the central values of the two clustering clusters in the historical data set.
optionally, the apparatus further comprises:
and the historical data set updating unit is used for adding the direct current power of the non-shielding photovoltaic group string at each acquisition moment in the effective acquisition period into the historical data set as a calculation sample.
in the shadow occlusion recognition device for photovoltaic string disclosed in this embodiment, it is determined whether the photovoltaic string corresponding to the target inverter has shadow occlusion at each acquisition time by respectively determining whether the discrete rate of the power generation efficiency of all the photovoltaic strings corresponding to the target inverter is greater than the discrete rate threshold value under the non-occlusion condition and whether the relative error between the central values of two clusters of all the photovoltaic strings corresponding to the target inverter is greater than the error threshold value under the non-occlusion condition. The method and the device only carry out shadow occlusion identification according to the direct current power of the photovoltaic string, do not depend on meteorological data such as irradiation, temperature and the like, are suitable for shadow occlusion identification under all photovoltaic power generation scenes, and improve the universality of the method and the device under various photovoltaic power generation scenes.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
it is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 identical elements in a process, method, article, or apparatus that comprises the element.
the steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
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.
Claims (10)
1. A method for identifying shadow occlusion of a photovoltaic string is characterized by comprising the following steps:
Acquiring the direct current power of each photovoltaic group string corresponding to the target inverter at each acquisition moment in an effective acquisition period;
Carrying out data standardization processing on the direct current power of each photovoltaic group string at each acquisition moment to obtain the power generation efficiency of each photovoltaic group string at each acquisition moment;
Respectively calculating the power generation efficiency discrete rate of all the photovoltaic string at each acquisition moment;
clustering the power generation efficiency of all the photovoltaic group strings at each acquisition moment to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
at each acquisition moment, when the power generation efficiency discrete rate is greater than a discrete rate threshold value and the relative error is greater than an error threshold value, judging that shadow shielding exists in the photovoltaic group strings in the cluster type cluster with a smaller central value at the acquisition moment.
2. the method of claim 1, further comprising:
And determining the historical acquisition period which is not reported with the fault alarm information and is closest to the current time when the daily total power generation is greater than the power generation threshold as the effective acquisition period.
3. The method according to claim 1, wherein the step of performing data normalization on the dc power of each pv string at each collection time to obtain the power generation efficiency of each pv string at each collection time comprises:
cleaning abnormal values of the direct current power of each photovoltaic group string at each acquisition time to obtain the effective direct current power of each photovoltaic group string at each acquisition time;
And calculating the power generation efficiency of each photovoltaic group string at each acquisition time according to the number of the photovoltaic components in each photovoltaic group string, the nominal power of the photovoltaic components, the actual power generation capacity coefficient of each photovoltaic group string and the effective direct current power of each photovoltaic group string at each acquisition time.
4. the method of claim 3, further comprising:
Acquiring the direct current power of each photovoltaic group string in a historical data set at each acquisition moment;
Calculating the power generation efficiency to be corrected of each photovoltaic group string at each acquisition time in the historical data set according to the direct current power of each photovoltaic group string at each acquisition time in the historical data set, the number of photovoltaic modules in each photovoltaic group string and the nominal power of the photovoltaic modules;
Respectively normalizing the power generation efficiency to be corrected of each photovoltaic string in the historical data set at each acquisition time according to the maximum power generation efficiency to be corrected of each photovoltaic string in the historical data set, so as to obtain an actual power generation capacity coefficient of each photovoltaic string in the historical data set at each acquisition time;
and respectively calculating the average value of the actual generating capacity coefficient of each photovoltaic group string in the historical data set to obtain the actual generating capacity coefficient of each photovoltaic group string.
5. the method of claim 4, further comprising:
Respectively calculating the power generation efficiency dispersion rate of all the photovoltaic string at each acquisition moment in the historical data set;
calculating the mean value and the standard deviation of the power generation efficiency dispersion rate of all the photovoltaic string in the historical data set;
and setting the dispersion rate threshold according to the Lauda rule and the mean value and standard deviation of the dispersion rate of the generating efficiency of all the photovoltaic string in the historical data set.
6. the method of claim 4, further comprising:
Clustering the power generation efficiency of all the photovoltaic group strings at each acquisition time in the historical data set to obtain two cluster clusters, and calculating the relative error between the central values of the two cluster clusters;
Calculating the mean value and the standard deviation of relative errors between the central values of the two clustering clusters in the historical data set;
And setting the error threshold according to the Lauda rule and the mean value and standard deviation of relative errors between the central values of the two clustering clusters in the historical data set.
7. the method of claim 4, further comprising:
And taking the direct current power of the non-shielding photovoltaic group string at each acquisition moment in the effective acquisition period as a calculation sample, and adding the calculation sample into the historical data set.
8. a shadow occlusion recognition device for a photovoltaic string, comprising:
the direct current power acquisition unit is used for acquiring the direct current power of each photovoltaic group string corresponding to the target inverter at each acquisition moment in an effective acquisition period;
the standardization processing unit is used for carrying out data standardization processing on the direct current power of each photovoltaic group string at each acquisition time to obtain the power generation efficiency of each photovoltaic group string at each acquisition time;
the dispersion rate calculating unit is used for calculating the dispersion rate of the power generation efficiency of all the photovoltaic string at each acquisition moment respectively;
the cluster analysis unit is used for clustering the power generation efficiency of all the photovoltaic group strings at each acquisition moment to obtain two cluster clusters and calculating the relative error between the central values of the two cluster clusters;
and the shadow shielding judging unit is used for judging that the photovoltaic group strings in the cluster type clusters with smaller central values have shadow shielding at the acquisition time when the power generation efficiency discrete rate is greater than a discrete rate threshold value and the relative error is greater than an error threshold value at each acquisition time.
9. The apparatus of claim 8, further comprising:
And the effective acquisition period determining unit is used for determining a historical acquisition period which is not reported with the fault alarm information and is closest to the current time, wherein the daily total generated energy is greater than the generated energy threshold value, as the effective acquisition period.
10. the apparatus according to claim 8, wherein the normalization processing unit is specifically configured to:
Cleaning abnormal values of the direct current power of each photovoltaic group string at each acquisition time to obtain the effective direct current power of each photovoltaic group string at each acquisition time;
And calculating the power generation efficiency of each photovoltaic group string at each acquisition time according to the number of the photovoltaic components in each photovoltaic group string, the nominal power of the photovoltaic components, the actual power generation capacity coefficient of each photovoltaic group string and the effective direct current power of each photovoltaic group string at each acquisition time.
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