CN109271736B - Fault type determination method and device for photovoltaic module - Google Patents
Fault type determination method and device for photovoltaic module Download PDFInfo
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Abstract
The application provides a method and a device for judging the fault type of a photovoltaic assembly, which are used for judging the linear correlation degree between the corresponding maximum power and the effective irradiation of the photovoltaic assembly to be detected at the same time; if the linear correlation degree is good, the normal state or the soft shadow exists, and the normal state and the soft shadow are further distinguished according to the effective irradiation change rate. If the linear correlation is poor, indicating that a hard shadow exists or the power limiting state exists, further distinguishing the hard shadow from the power limiting state according to the power change rate. According to the scheme, the fault type judgment can be completed in a normal grid-connected maximum power tracking state only by collecting the maximum power of the photovoltaic module, the module temperature and the short-circuit current of the reference module under the grid-connected condition without introducing other monitoring equipment or scanning equipment or changing the normal working mode of the string.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a method and a device for judging a fault type of a photovoltaic module.
Background
The photovoltaic power generation has the advantages of cleanness and high efficiency, the rapid development of the field of new energy power generation is promoted, and the evaluation, maintenance and fault detection of power stations and photovoltaic modules are challenged by the continuous improvement of installed capacity, the running environments of different regions, different grid-connected structures and other factors.
From external factors such as weather conditions, installation environment, and arrangement between the photovoltaic module and the inverter, the main factors affecting the generated power of the photovoltaic module and the inverter include: hard shadows, soft shadows, and limited power. Wherein the hard shadow mainly means that the photovoltaic module is shielded by the shadow for a long time; the soft shadow refers to short-time blocking of the photovoltaic module; limiting power means that the inverter output power is limited by its own capacity or a power limit policy, and is kept at the rated maximum output power or a certain limited power.
At present, no effective judgment method is available for accurately judging whether the photovoltaic module has hard shadow, soft shadow and limited power.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for determining a fault type of a photovoltaic module, which can effectively distinguish whether a hard shadow, a soft shadow and a power limiting state exist in the photovoltaic module. The specific technical scheme is as follows:
in a first aspect, the present application provides a method for determining a fault type of a photovoltaic module, including:
judging whether the linear correlation degree between the maximum power and the effective irradiation corresponding to the photovoltaic module to be detected at the same moment meets a preset condition or not;
if the linear correlation does not meet the preset condition, determining the power change rate corresponding to the photovoltaic module to be tested at the moment;
if the power change rate is larger than a preset power threshold value, determining that the photovoltaic module to be tested has a hard shadow at the moment; if the power change rate is smaller than or equal to the preset power threshold, determining that the photovoltaic module to be tested is in a power limiting state at the moment;
if the linearity accords with the preset condition, determining the effective irradiation change rate corresponding to the photovoltaic module to be tested at the moment;
if the effective irradiation change rate is larger than a preset irradiation threshold value, determining that a soft shadow exists in the photovoltaic module to be tested; and if the effective irradiation change rate is less than or equal to the preset irradiation threshold value, determining that the photovoltaic module to be tested normally works at the moment.
Optionally, the determining whether the linear correlation between the maximum power and the effective irradiation corresponding to the photovoltaic module to be measured at the same time meets a preset condition includes:
calculating the fitting power corresponding to the effective irradiation at each moment in the judging day;
calculating the power relative error between the corresponding fitting power and the actual maximum power of the photovoltaic module to be tested at the same moment;
if the absolute value of the relative error of the power at the moment is less than or equal to a preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment meets a preset condition;
and if the absolute value of the relative power error is larger than the preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment does not accord with the preset condition.
Optionally, the determining a power change rate corresponding to the photovoltaic module to be tested at the time includes:
and calculating the absolute value of the difference value between the maximum power of the photovoltaic module to be tested at the moment and the maximum power corresponding to the last moment at the moment to obtain the power change rate.
Optionally, the determining the effective irradiation change rate corresponding to the photovoltaic module to be tested at the time includes:
calculating the difference value between the effective irradiation of the photovoltaic module to be measured at the moment and the effective irradiation corresponding to the last moment at the moment to obtain the first-order irradiation difference corresponding to the moment, and calculating the first-order irradiation difference corresponding to the photovoltaic module to be measured at the last moment at the moment;
and calculating the absolute value of the difference between the irradiation first-order difference corresponding to the moment and the irradiation first-order difference corresponding to the last moment of the moment to obtain the irradiation change rate of the photovoltaic module to be measured at the moment.
Optionally, the method further comprises:
calculating to obtain the fitting power corresponding to each first type of sample data;
calculating the power sample relative error between the actually measured power and the fitting power corresponding to the same moment in each first type of sample data;
calculating the average value and the standard deviation of the relative errors of the power samples of each first type of sample data;
determining the preset error threshold to be three times the standard deviation according to a Lauda criterion.
Optionally, the method further comprises:
obtaining an irradiation first-order difference corresponding to the specified time by using a difference value between the effective irradiation of the same photovoltaic module at the specified time and the effective irradiation corresponding to the last time of the specified time in the second type of sample data, and calculating the irradiation first-order difference corresponding to the photovoltaic module at the last time of the specified time;
calculating the difference between the irradiation first-order difference corresponding to the same photovoltaic module at the appointed time and the irradiation first-order difference corresponding to the last time of the appointed time to obtain the irradiation change rate corresponding to the photovoltaic module at the appointed time;
calculating the irradiation change rate corresponding to each second type of sample data, and calculating the average value and the standard deviation of each irradiation change rate;
according to the Lauda criterion, determining that the preset irradiation threshold is three times of the standard deviation corresponding to the irradiation change rate;
and the second type of sample data is data of the photovoltaic module in a normal state obtained in a preset time period before the judgment day.
Optionally, the method further comprises:
calculating the power change rate of the same photovoltaic module in the third type of sample data at a specified moment;
calculating the average value and the standard deviation of the power change rate corresponding to each third type of sample data;
and determining that the preset power threshold is three times of the standard deviation corresponding to the power change rate according to the Lauda criterion.
Optionally, the method further comprises:
after the fault type of the photovoltaic module to be tested is judged, recording a fault time period corresponding to the fault type;
and uploading the fault type of the photovoltaic module to be tested and the fault time period to a monitoring platform.
In a second aspect, the present application further provides a device for determining a fault type of a photovoltaic module, including:
the linear correlation degree judging module is used for judging whether the linear correlation degree between the corresponding maximum power and the effective irradiation of the photovoltaic module to be detected at the same moment meets a preset condition or not;
the power change rate judging module is used for determining the power change rate corresponding to the photovoltaic module to be tested at the moment when the linear correlation degree does not accord with the preset condition; when the power change rate is larger than a preset power threshold value, determining that the photovoltaic module to be tested has a hard shadow at the moment; when the power change rate is smaller than or equal to the preset power threshold, determining that the photovoltaic module to be tested is in a power limiting state at the moment;
the irradiation change rate judging module is used for determining the effective irradiation change rate corresponding to the photovoltaic module to be tested at the moment when the linearity accords with the preset condition; when the effective irradiation change rate is larger than a preset irradiation threshold value, determining that a soft shadow exists in the photovoltaic module to be tested; and when the effective irradiation change rate is smaller than or equal to the preset irradiation threshold value, determining that the photovoltaic module to be tested normally works at the moment.
Optionally, the linear correlation degree determining module is specifically configured to:
calculating the fitting power corresponding to the effective irradiation at each moment in the judging day;
calculating the power relative error between the corresponding fitting power and the actual maximum power of the photovoltaic module to be tested at the same moment;
if the absolute value of the relative error of the power at the moment is less than or equal to a preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment meets a preset condition;
and if the absolute value of the relative power error is larger than the preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment does not accord with the preset condition.
According to the method for judging the fault type of the photovoltaic module, the linear correlation degree between the maximum power and the effective irradiation of the photovoltaic module to be detected at the same moment is obtained; if the linear correlation degree is good, the photovoltaic module is in a normal state or has a soft shadow, the normal state and the soft shadow are further distinguished according to the effective irradiation change rate, and if the effective irradiation change rate is larger than a preset irradiation threshold value, the soft shadow exists; and if the effective irradiation change rate is less than or equal to the preset irradiation threshold value, the device is in a normal state. If the linear correlation degree is poor, indicating that the photovoltaic module has a hard shadow or is in a power limiting state, and further distinguishing the hard shadow from the power limiting state according to the power change rate; if the power change rate is larger than the preset power value, a hard shadow exists, and if the power change rate is smaller than or equal to the preset power value, the power limiting state is achieved. According to the method, the maximum power of the photovoltaic module, the module temperature and the short-circuit current of the reference module under the grid-connected condition are only required to be collected, other monitoring equipment or scanning equipment is not required to be introduced, the normal working mode of the string is not required to be changed, and the fault type judgment can be completed under the normal grid-connected maximum power tracking state.
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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for determining a fault type of a photovoltaic module according to an embodiment of the present disclosure;
FIG. 2 is a graph illustrating a relationship between power and irradiance in the presence of hard shadowing and power limiting conditions according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a power variation curve and an irradiation variation curve in the presence of a hard shadow and a power-limited state according to an embodiment of the present disclosure;
FIG. 4 is a graph of power versus irradiance for soft shadows according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a power variation curve and an irradiation variation curve with soft shadow according to an embodiment of the present disclosure;
FIG. 6 is a diagram illustrating a linear relationship between maximum power and effective irradiation provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of a process for determining a linear correlation between maximum power and effective irradiance according to an embodiment of the present disclosure;
FIG. 8 is a schematic diagram illustrating a threshold determination according to an embodiment of the present disclosure;
fig. 9 is a block diagram of a device for determining a fault type of a photovoltaic module according to an embodiment of the present application.
Detailed Description
The hard shadow mainly refers to that the photovoltaic module is shielded by the shadow for a long time due to unreasonable arrangement of the front and back intervals of the module, mountain shielding, foreign matters or growing plants and the like; the hard shadow generally has the characteristics of seasonality, time regularity, blocking area fixity and the like. The hard shadow can cause the mismatch of the components to reduce the power generation efficiency, and the long-term blocking of the hard shadow can bring hot spots and hidden crack hidden dangers, so that the method has important significance in identifying the hard shadow.
The soft shadow is mainly influenced by weather moving cloud layers and flowing personnel, the photovoltaic module is shielded for a short time, and the soft shadow is generally represented as irregular distribution and intermittence. The soft shadow identification is used for distinguishing from the hard shadow, meanwhile, the long-term illumination quality of the power station can be effectively evaluated through the soft shadow identification, data reference is provided for photovoltaic power prediction, and the photovoltaic power prediction precision is further improved.
The power limitation is shown in the way that the output power of the inverter is limited by the capacity of the inverter or a power limitation policy, so that the output power of the inverter is kept at the rated maximum output power or a certain limited power, and the judgment of the power limitation is beneficial to evaluating whether the installed capacity configuration of the power station is reasonable and is beneficial to optimizing the design of the optimal capacity allocation ratio of the power station.
Therefore, the three fault types can be effectively distinguished, and the method has important significance on power station operation and power grid dispatching; however, the current way of identifying the limited power state relies mainly on empirical observations by maintenance personnel; the hard and soft shadow distinguishing method mainly includes: one way is to regard the date when the actual power generation amount is lower than the measurement criterion and the duration reaches the set threshold as the presence of the shadow, using the history average power generation amount in recent years as the measurement criterion. The other mode is that an I-V and P-V data curve is collected in an I-V scanning mode of the photovoltaic module, and whether the photovoltaic module has a shadow condition or not is distinguished and the type of the shadow is distinguished by combining an intelligent algorithm according to the change trend of the curve or the change characteristics of equivalent parameters. However, this method is suitable for an intelligent component or requires an external scanning device to acquire data, and the scanning itself needs to be performed in a non-maximum power tracking mode, which results in a loss of output power.
In order to solve the technical problem, the application provides a method for judging the fault type of a photovoltaic module, wherein a planar photovoltaic module without shadow shielding is arranged in the environment where the photovoltaic module to be detected is located as a reference module; deducing the effective radiation actually absorbed by the photovoltaic module according to the collected short-circuit current of the reference module; then, obtaining the linear correlation degree between the maximum power and the effective irradiation; if the linear correlation degree is good, the photovoltaic module is in a normal state or has a soft shadow, the normal state and the soft shadow are further distinguished according to the effective irradiation change rate, and if the effective irradiation change rate is larger than a preset irradiation threshold value, the soft shadow exists; and if the effective irradiation change rate is less than or equal to the preset irradiation threshold value, the device is in a normal state. If the linear correlation degree is poor, indicating that the photovoltaic assembly has a hard shadow or is in a power limiting state, and further distinguishing the hard shadow from the power limiting state according to the power change rate; if the power change rate is larger than the preset power value, a hard shadow exists, and if the power change rate is smaller than or equal to the preset power value, the power limiting state is achieved.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all embodiments of the present invention. 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, a flowchart of a method for determining a fault type of a photovoltaic module according to an embodiment of the present application is shown, where the method is applied to a photovoltaic power generation system, and is mainly used to distinguish the fault type of the photovoltaic module, and mainly includes a hard shadow, a soft shadow, and a limited power.
As shown in fig. 1, the method may include the steps of:
and S110, collecting the short-circuit current of the reference assembly, the maximum power corresponding to the photovoltaic assembly to be tested and the assembly temperature at different moments of the judging day.
Independently setting a plane photovoltaic assembly without shadow shielding as a reference assembly in the environment of the photovoltaic assembly to be detected, and collecting short-circuit current I of the reference assembly at different moments of a judging day sc 。
Meanwhile, the maximum power P of the photovoltaic module to be tested is collected at different moments of the judging day m Temperature T of the component c 。
Preferably, the data collected on this decision day is recorded in a database for subsequent screening of the database for suitable sample data.
And S120, calculating to obtain the corresponding effective irradiation of the photovoltaic module to be measured at the same moment by using the short-circuit current and the module temperature corresponding to the same moment.
The acquisition mode of irradiation is generally divided into a direct mode and an indirect mode, wherein the direct mode adopts irradiation acquisition equipment for acquisition; indirect means is the calculation of the irradiation by derivation of readily available and accurate electrical parameters. Due to the influences of the solar time angle, the weather condition and the type of the irradiator, the irradiation quantity acquired in a direct mode is difficult to accurately represent the irradiation actually absorbed by the photovoltaic module; therefore, the application adopts an indirect mode to obtain effective irradiation.
Experiments prove that the short-circuit current of the photovoltaic module has a direct corresponding relation with the irradiation and the module temperature, so that the effective irradiation of the photovoltaic module to be measured at the moment can be calculated by using the short-circuit current of the reference module and the module temperature of the photovoltaic module to be measured which correspond at the same moment.
The expression for calculating the effective irradiation is shown in equation 1:
in formula 1, G e The plane effective irradiation of the photovoltaic module is realized; g 0 Irradiation under Standard Test Condition (STC), G 0 Is a fixed value G 0 ＝1000W/m 2 (ii) a The standard test environment refers to a photovoltaic standard test environment with the temperature of 25 ℃ and the Atmospheric Mass (AM) of 1.5.
T c Is the measured component temperature; t is c.0 Is the test temperature, T, at STC c.0 ＝25℃；I sc.0 Is the short circuit current of the component at STC,
is the short-circuit current temperature coefficient of the photovoltaic module; wherein, I sc.0 And
the value can be inquired from a parameter nameplate of the photovoltaic module, and is usually a fixed value.
Considering that the azimuth angle and the inclination angle of the photovoltaic module of the actual power station are different due to environment or arrangement, the plane effective irradiation G needs to be combined with the actual environment condition according to the mathematical relationship between the plane irradiation and the inclined plane irradiation e Converted into effective irradiation G of inclined plane et 。
In order to normalize the influence of the assembly temperature on the maximum power into the irradiation, a comprehensive parameter can be adopted to represent the influence brought by two factors of the irradiation and the assembly temperature, and the influence factor of the maximum power is normalized from a binary problem into a unitary problem; the effect of the component temperature on the maximum power can be normalized to the irradiance using equation 2, equation 2 being as follows:
in formula 2, G corr Is the corrected irradiation;
the coefficient is the maximum power temperature coefficient of the photovoltaic module under STC, the percentage of the maximum power reduction amplitude of the photovoltaic module is represented when the temperature of the photovoltaic module rises by 1 ℃, and the parameter is a fixed value and can be obtained from a parameter nameplate of the photovoltaic module.
The irradiation obtaining mode of the embodiment considers factors such as attenuation of the assembly, change of the sun angle, errors caused by temperature and the like, and therefore the accuracy is higher than that of a direct mode.
In other application scenarios of the present application, if the requirement on the precision of irradiation is not high, the bevel effective irradiation obtained by conversion may be directly used as the final effective irradiation, or other effective irradiation obtaining manners are adopted to obtain the effective irradiation of the photovoltaic module, which is not limited in the present application.
S130, judging whether the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment corresponds to a preset condition or not by utilizing the maximum power and the effective irradiation of the photovoltaic module to be tested at the same moment; if not, executing S140; if yes, executing S170;
the maximum power P of the photovoltaic module is verified through experiments m And corrected irradiation G corr Has linear correlation. Moreover, P can be obtained using sample data m And G corr A linear fit relationship between them.
If P m And G corr The linear correlation degree meets a preset condition (the preset condition can be any one condition for representing the linear correlation degree, P) m And G corr The condition that the linear correlation degree is good is met), the photovoltaic module to be tested is indicated to be possibly in a normal state, or a soft shadow may exist; if P m And G corr And if the linear correlation degree is poor, the photovoltaic module to be tested is indicated to be in a hard shadow or in a power limiting state.
In the first of this applicationIn an embodiment, the preset condition may be to determine the actual maximum power P of the photovoltaic module to be tested at a certain moment m Calculating the fitting power P corresponding to the moment by using a fitting formula m.cal Then, P corresponding to the same time is calculated m And P m.cal If the absolute value of the relative error is less than or equal to the preset error threshold, it indicates P m And G corr The linear correlation degree is good; if the absolute value of the relative error is larger than the preset error threshold, P is indicated m And G corr The linear correlation is poor.
S140, judging the size relation between the power change rate corresponding to the photovoltaic module to be tested at the moment and a preset power threshold value; if the power is greater than the preset power threshold, executing S150; if the power is less than or equal to the preset power threshold, executing S160;
if the photovoltaic module to be tested is at P of a certain moment m And G corr The linear correlation does not meet the preset condition, and hard shadow and power limiting state need to be distinguished continuously according to the power change rate.
In an embodiment of the present application, the power change rate is an absolute value | Δ P | of a first-order power difference of the photovoltaic module to be measured, where the first-order power difference Δ P is calculated by formula 3:
ΔP＝P m (k)-P m (k-1) (formula 3)
In formula 3, P m (k) Represents the maximum power, P, corresponding to the photovoltaic module at the moment k m And (k-1) represents the maximum power corresponding to the k-1 moment, and the value of k can be a positive integer.
Presetting a power threshold delta p The method can be calculated according to sample data in a normal state or in a soft shadow, preferably, the sample data is dynamically changed and is always calculated according to the sample data in a preset time period before the judgment day, that is, the preset power threshold value is not a fixed and unchangeable numerical value but can be adaptively adjusted.
When a hard shadow occurs, P m And G corr The linear relationship between changes, and, P m The relative shadow-free condition is reduced; when in a power limited stateWhen is, P m And G corr Change in a linear relationship therebetween, P m Substantially unchanged.
S150, the photovoltaic module to be tested has a hard shadow at the moment.
If the corresponding power change rate of the photovoltaic module to be tested at a certain moment is greater than a preset power threshold value, namely | delta P | is greater than delta p It shows that the maximum power has obvious change, and the photovoltaic module is influenced by hard shadow.
P in region A of the Power vs. irradiance graph shown in FIG. 2 m And G corr Do not conform to a linear relationship; in the power and irradiance variation diagram shown in FIG. 3, curve I represents P m Curve II represents G corr The variation curve of (d); p in region A in FIG. 3 m And G corr Clearly not in line with the relationship, and, P m And therefore, a hard shadow exists in a time period corresponding to the area a.
And S160, determining that the photovoltaic module to be tested is in a power limiting state at the moment.
If the power change rate corresponding to the photovoltaic module to be tested at a certain moment is less than or equal to the preset power threshold, namely | delta P | ≦ delta p The maximum power change range is small, and the photovoltaic module is in a power limiting state.
As shown in fig. 2, P in the region B m And G corr The linear relationship between them changes, however, in FIG. 3, P is in region B m Almost unchanged, and therefore, the period corresponding to the region B is in the power limited state.
S170, judging the size relation between the effective irradiation change rate corresponding to the photovoltaic module to be tested at the moment and a preset irradiation threshold value; if the radiation intensity is larger than the preset irradiation threshold value, S180 is executed; if less than or equal to the preset irradiation threshold value, S190 is performed.
If the photovoltaic module to be tested is at P of a certain moment m And G corr The linear correlation degree meets the preset condition, and the soft shadow and the normal state need to be continuously distinguished according to the corresponding effective irradiation change rate at the moment.
In one embodiment of the present application, the effective irradiation change rate is an absolute value | Δ G |, of a second order difference of effective irradiation, where the second order difference Δ G of effective irradiation can be calculated by using the following equation 4:
ΔG＝(G corr (k)-G corr (k-1))-(G corr (k-1)-G corr (k-2)) (equation 4)
In formula 4, G corr (k) Representing the effective irradiation, G, corresponding to the photovoltaic module at time k corr (k-1) represents the effective irradiation corresponding to the photovoltaic module at the moment k-1, G corr And (k-2) represents the effective irradiation of the photovoltaic module corresponding to the moment k-2, and the value of k can be a positive integer.
The preset irradiation threshold value delta G The method can be calculated according to the sample data in the normal state, preferably, the sample data in this case is dynamically changed and is always calculated according to the sample data in the preset time period before the decision day, that is, the preset power threshold is not a fixed and unchangeable value but can be adaptively adjusted.
And S180, determining that the photovoltaic module to be tested has soft shadow.
When the photovoltaic module has soft shadow, P m And G corr The linear relationship between the radiation and the radiation is good, but the effective radiation and the maximum power fluctuate severely.
If the effective irradiation of the photovoltaic module to be measured at a certain moment is larger than the preset irradiation threshold value, namely | delta G | is larger than delta G It shows that the irradiation fluctuation is severe, and the photovoltaic module to be measured is influenced by the soft shadow.
As shown in fig. 4, P in the region C m And G corr The linear relation between the two is good; as shown in FIG. 5, curve III represents P m Curve IV shows G corr Of region C in FIG. 5 m And G corr The change of (C) is severe, and therefore, a soft shadow exists in a time period corresponding to the region C.
And S190, determining that the photovoltaic module to be tested normally works at the moment.
If the effective irradiation of the photovoltaic module to be measured at a certain moment is less than or equal to the preset irradiation threshold value, namely | delta G | < delta | G And the irradiation change is smooth, namely the photovoltaic module to be tested is not obviously abnormally interfered.
Preferably, after the fault type of the photovoltaic module to be tested is determined by the process, the fault time period corresponding to each fault type is recorded; the fault type and the fault time period of the photovoltaic module to be tested can be uploaded to the monitoring platform through the communication module. The monitoring platform can send alarm prompt information to a user, so that the user can know the fault type and the fault time period of the photovoltaic module in time.
According to the method for judging the fault type of the photovoltaic module, the maximum power and the module temperature of the photovoltaic module to be detected are collected, the short-circuit current of the reference module is collected, and the effective irradiation of the photovoltaic module to be detected is deduced according to the short-circuit current; according to the linear correlation degree between the effective irradiation and the maximum power, the maximum power change rate and the irradiation change rate, soft shadow, hard shadow and limited power can be distinguished. According to the method, the maximum power of the photovoltaic module, the module temperature and the short-circuit current of the reference module under the grid-connected condition are only required to be collected, other monitoring equipment or scanning equipment is not required to be introduced, the normal working mode of the string is not required to be changed, and the fault type judgment can be completed under the normal grid-connected maximum power tracking state.
The process of determining whether the linear correlation between the maximum power and the effective irradiation meets the preset condition will be described in detail below:
collecting P of at least one photovoltaic module which is in a clean state and has no fault within a preset time period before the judgment date m 、T c And of the reference component sc Training as sample data; wherein the preset time period can be set according to actual requirements, for example, 30 days.
Calculating to obtain the plane effective irradiation G of the photovoltaic module according to the formula 1 e Then G is added e Converted into effective irradiation G et (ii) a And the influence of the component temperature on the maximum power is normalized into an irradiation factor by using a formula 2 to obtain the final effective irradiation G corr 。
Proved by experiments, the maximum power P m And effective irradiation G corr Has linear correlation, as shown in FIG. 6, the horizontal axis in FIG. 6 is G corr The longitudinal axis is P m 。
P can be obtained according to sufficient sample data by utilizing a fitting algorithm m And G corr The linear expression between is as follows:
P m ＝f(G corr ) = kx + b (equation 5)
After obtaining a linear expression (i.e., a first linear fitting formula) between the maximum power and the effective irradiation according to the sample data, as shown in fig. 7, the process of determining whether the linear correlation between the maximum power and the effective irradiation meets the preset condition may include the steps of:
and S210, calculating fitting power corresponding to effective irradiation at each moment in the judging day.
The step can be used for effectively irradiating the photovoltaic module to be measured at each moment in the judging day by G corr Substituting the obtained value into a formula 5 to calculate the fitting power P corresponding to the photovoltaic module to be tested at each moment m.cal 。
And S220, calculating the power relative error between the corresponding fitting power and the actual maximum power of the photovoltaic module to be tested at the same moment.
The step is to calculate the corresponding P of the photovoltaic module to be measured at the same time m.cal Maximum power P actually collected m Absolute value | P of relative error therebetween error L, wherein the relative error P error Can be calculated using equation 6:
and S230, if the absolute value of the power relative error at the moment is greater than a preset error threshold, determining that the linear correlation degree between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment does not accord with a preset condition.
The preset error threshold value delta is calculated according to the sample data with better linear correlation and the Laplace criterion. Preferably, the sample data is dynamically changed and is always calculated by judging the sample data with better linear correlation in a preset time period before the day, that is, the preset error threshold is not a fixed numerical value but can be adaptively adjusted.
If the absolute value of the calculated relative error of the power is larger than a preset error threshold value, namely | P error If is > delta, indicates P m And G corr The linear correlation of the photovoltaic module to be tested is low, and the photovoltaic module to be tested is not in accordance with the preset condition, and at the moment, a hard shadow may exist in the photovoltaic module to be tested or the photovoltaic module to be tested is in a power limiting state.
The hard shadow and the limited power state need to be further distinguished according to the maximum power change characteristic of the photovoltaic module to be tested.
S240, if the absolute value of the power relative error is smaller than or equal to a preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment meets a preset condition.
If the absolute value of the relative error of the power is less than or equal to the preset error threshold, i.e. | P error | < delta > indicates P m And G corr The linear correlation of the photovoltaic module to be tested is high, and the photovoltaic module to be tested is in accordance with the preset condition, and at the moment, the photovoltaic module to be tested may have soft shadow or be in a normal state.
The soft shadow and the normal state need to be further distinguished according to the effective irradiation change characteristics of the photovoltaic module to be detected.
In this embodiment, the fitting power is calculated by using a linear fitting formula obtained by training according to sample data, and a relative error between the fitting power and the actual power is calculated. And judging whether the linear correlation of the maximum power and the effective irradiation meets the preset condition or not according to the relative error, wherein the process is simple and accurate.
The preset error threshold δ and the preset power threshold δ according to the above embodiments will be described in detail p And a preset irradiation threshold value delta G The determination process of (2);
δ、δ p 、δ G the calculation methods are similar, sample data in a preset time period before the decision day are used, the standard deviation between the sample data is calculated, and the standard deviation is calculated according to LaviadaThe exact (3 σ criterion) is calculated.
1) Calculation process of delta
First, the sample data used to calculate δ is P m And G corr Data with better linear correlation, namely first-class sample data; for example, P of at least one photovoltaic module collected in a clean state without failure within 30 days before the day is determined m And G corr . Wherein, P m And G corr The better linear correlation refers to data in a normal state and data with soft shadow;
utilizing P corresponding to each time in first type sample data m And G corr Fitting to obtain a linear fitting formula P m.cal ＝f(G corr ) And calculating each G using the linear fitting formula corr Corresponding fitting power P m.cal (ii) a Calculating each power relative error P in the first sample data by using formula 4 error Then calculate each P error The mean value μ and the standard deviation σ of, the formula is as follows:
in equations 7 and 8, x i Is to calculate a sample point, in which x i ＝P error 。
The determination of delta is based on the Laplace criterion (3 sigma criterion), and the 3 sigma criterion indicates that the detected data are reasonably distributed in the range of [ mu-3 sigma, mu +3 sigma ]]Data beyond this interval is not subject to random errors and should be considered as outliers. Thus, δ sets the sample outlier boundary to μ ± 3 σ; however, consider sample x in this scenario i Obeying normal distribution mu → 0 and having only a single boundary, as shown in FIG. 8, the data with poor linear correlation are all smaller than the fitting power value, i.e., P m <P m.cal Therefore, δ =3 σ is set.
In the same way, delta p And delta G The calculation is the same as above, and is briefly described below:
2)δ G is calculated by
Calculating delta G Sample data in a normal state, that is, second type of sample data, needs to be used; using each G in the second kind of sample data corr And calculating according to formula 4 to obtain each G corr Corresponding irradiation second-order difference delta G, and then calculating the average value mu and the standard deviation sigma of each delta G according to a formula 7 and a formula 8; according to the 3 σ criterion, δ G ＝3σ。
2)δ p Is calculated by
Calculating delta p Sample data for the limited power state, i.e., a third type of sample data, is to be used; using each P in the third kind of sample data m Calculating to obtain corresponding delta P, and then calculating to obtain the average value mu and the standard deviation sigma of each delta P according to a formula 7 and a formula 8; according to the 3 σ criterion, δ p ＝3σ。
In one embodiment of the present application, δ p 、δ G The self-adaptive adjustment can be carried out for the following reasons:
the first type of sample data, the second type of sample data and the third type of sample data are dynamically updated data, for example, data in a preset time period before the judgment day are all used as sample data, the judgment day is changed along with actual use, and the preset time period is a fixed value, for example, 30 days or shorter time; for example, the determination date is 5/month/1/day, and data obtained 30 days before 5/month/1/day (i.e., 4/month/1/4/month/30) is used as sample data; if the day is 5 months and 2 days, taking data obtained 30 days before 5 months and 2 days (namely 4 months and 2 days to 5 months and 1 day) as sample data; it can be seen that, as time goes on, new data of each day is added into the data in the sample database continuously, and the data with the most advanced date is abandoned, so that the delta and the delta of the day are judged p 、δ G Calculated from data collected 30 days before the decision date to delta, delta p 、δ G Adaptive adjustment of (2).
Corresponding to the embodiment of the method for judging the fault type of the photovoltaic module, the application also provides an embodiment of a device for judging the fault type of the photovoltaic module.
Referring to fig. 9, a block diagram of a failure type determination apparatus for a photovoltaic module according to an embodiment of the present application is shown, where the apparatus is applied to a photovoltaic power generation system. As shown in fig. 9, the apparatus may include: the system comprises an acquisition module 110, an effective irradiation acquisition module 120, a linear correlation degree judgment module 130, a power change rate judgment module 140 and an irradiation change rate judgment module 150.
And the acquisition module 110 is used for acquiring the short-circuit current of the reference component at different moments of the determination day, and acquiring the maximum power and component temperature corresponding to the photovoltaic component to be detected.
The reference assembly is a planar photovoltaic assembly which is arranged in the environment where the photovoltaic assembly to be detected is located and is not blocked by shadow.
And the effective irradiation obtaining module 120 is configured to calculate, by using the short-circuit current and the component temperature corresponding to the same time, an effective irradiation corresponding to the photovoltaic component to be measured at the time.
In an embodiment of the application, the effective irradiation of the photovoltaic module to be measured can be calculated by adopting formula 1 to obtain plane effective irradiation, and then the plane effective irradiation is converted into inclined plane effective irradiation; and then normalizing the influence of the temperature of the component on the maximum power into the irradiation by using a formula 2 to obtain the final effective irradiation.
The linear correlation degree judging module 130 is configured to judge whether the linear correlation degree between the maximum power and the effective irradiation corresponding to the photovoltaic module to be detected at the same moment meets a preset condition by using the maximum power and the effective irradiation of the photovoltaic module to be detected at the same moment;
in one embodiment of the present application, P obtained by pre-training may be utilized m And G corr Linear fitting formula (for example, formula 5) between the two, and calculating each G of the photovoltaic module to be measured in the determined days corr Corresponding fitting power P m.cal Then, P corresponding to the same time is calculated using equation 6 m And P m.cal Relative error P of error Absolute value | P of the relative error error Characterization of P m And G corr Linear betweenAnd (4) correlation degree.
If | P error If is > delta, indicates P m And G corr The linear correlation of the photovoltaic module to be tested is low, and the photovoltaic module to be tested is not in accordance with the preset condition, and at the moment, a hard shadow may exist in the photovoltaic module to be tested or the photovoltaic module to be tested is in a power limiting state; if | P error | < delta > indicates P m And G corr The linear correlation of the photovoltaic module to be tested is high, and the photovoltaic module to be tested is in accordance with the preset condition, and at the moment, the photovoltaic module to be tested may have soft shadow or be in a normal state.
The power change rate judging module 140 is configured to, when the linear correlation does not meet a preset condition, judge a magnitude relationship between a power change rate corresponding to the photovoltaic module to be tested at the moment and a preset power threshold; when the power change rate is larger than a preset power threshold value, determining that a hard shadow exists; and when the power change rate is smaller than or equal to a preset power threshold value, determining that the power limiting state is achieved.
If the photovoltaic module to be tested is at P of a certain moment m And G corr The linear correlation does not meet the preset condition, and the hard shadow and the power limiting state need to be continuously distinguished according to the power change rate.
In one embodiment of the present application, the power change rate may be characterized by an absolute value | Δ P | of the power first order difference, wherein the power first order difference Δ P may be calculated by equation 3.
If | Δ P | is > δ p The maximum power is obviously changed, and the photovoltaic assembly is influenced by hard shadow; if | Delta P | ≦ delta p The maximum power change amplitude is small, and the photovoltaic module is in a power limiting state.
The irradiation change rate judging module 150 is configured to, when the linearity between the maximum power and the effective irradiation meets a preset condition, judge a magnitude relationship between an effective irradiation change rate corresponding to the photovoltaic module to be measured at the moment and a preset irradiation threshold; when the effective irradiation change rate is larger than a preset irradiation threshold value, determining that a soft shadow exists; and when the effective irradiation change rate is less than or equal to the preset irradiation threshold value, determining that the moment normally works.
If the photovoltaic module to be tested is at P of a certain moment m And G corr The linear correlation degree meets the preset condition, and the soft shadow and the normal state need to be continuously distinguished according to the corresponding effective irradiation change rate at the moment.
In one embodiment of the present application, the effective irradiation change rate may be an absolute value | Δ G | of a second-order difference of effective irradiation, where the second-order difference Δ G of effective irradiation may be calculated by using formula 4.
If | Δ G | is > δ G The irradiation fluctuation is severe, and the photovoltaic module to be tested is influenced by the soft shadow; if | Δ G | ≦ δ G And the irradiation change is smooth, namely the photovoltaic module to be tested is not obviously abnormally interfered and is in a normal state.
In the preferred embodiment of the present application, δ p 、δ G For the calculation method, reference may be made to corresponding contents in the above method embodiments, and details are not repeated here.
According to the fault type judgment device of the photovoltaic module, the maximum power and the module temperature of the photovoltaic module to be tested are collected, the short-circuit current of the reference module is collected, and the effective irradiation of the photovoltaic module to be tested is deduced according to the short-circuit current; according to the linear correlation degree between the effective irradiation and the maximum power, the maximum power change rate and the irradiation change rate, soft shadow, hard shadow and limited power can be distinguished. According to the method, the maximum power of the photovoltaic module, the module temperature and the short-circuit current of the reference module under the grid-connected condition are only acquired, other monitoring equipment or scanning equipment is not required to be introduced, the normal working mode of the photovoltaic module string is not required to be changed, and the fault type judgment can be completed under the normal grid-connected maximum power tracking state.
While, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present invention is not limited by the illustrated ordering of acts, as some steps may occur in other orders or concurrently with other steps in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the device-like embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The steps in the method of the embodiments of the present application may be sequentially adjusted, combined, and deleted according to actual needs.
The device and the modules and sub-modules in the terminal in the embodiments of the present application can be combined, divided and deleted according to actual needs.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal, apparatus and method may be implemented in other manners. For example, the above-described terminal embodiments are merely illustrative, and for example, the division of a module or a sub-module is only one logical division, and there may be other divisions when the terminal is actually implemented, for example, a plurality of sub-modules or modules may be combined or integrated into another module, or some features may be omitted or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or modules, and may be in an electrical, mechanical or other form.
The modules or sub-modules described as separate components may or may not be physically separate, and the components described as modules or sub-modules may or may not be physical modules or sub-modules, may be located in one place, or may be distributed on a plurality of network modules or sub-modules. Some or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, each functional module or sub-module in the embodiments of the present application may be integrated into one processing module, or each module or sub-module may exist alone physically, or two or more modules or sub-modules may be integrated into one module. The integrated modules or sub-modules may be implemented in the form of hardware, or may be implemented in the form of software functional modules or sub-modules.
Finally, it should also be noted that, in this document, relational terms such as first and second, and the like are 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 phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
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 is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A method for judging the fault type of a photovoltaic module is characterized by comprising the following steps:
judging whether the linear correlation degree between the maximum power and the effective irradiation corresponding to the photovoltaic module to be detected at the same moment meets a preset condition or not;
if the linear correlation does not meet the preset condition, calculating the absolute value of the difference value between the maximum power of the photovoltaic module to be tested at the moment and the maximum power corresponding to the last moment at the moment to obtain the power change rate corresponding to the photovoltaic module to be tested at the moment;
if the power change rate is larger than a preset power threshold value, determining that the photovoltaic module to be tested has a hard shadow at the moment; if the power change rate is smaller than or equal to the preset power threshold, determining that the photovoltaic module to be tested is in a power limiting state at the moment;
if the linear correlation degree meets the preset condition, calculating a difference value between the effective irradiation of the photovoltaic module to be measured at the moment and the effective irradiation corresponding to the last moment at the moment to obtain an irradiation first-order difference corresponding to the moment, and calculating an irradiation first-order difference corresponding to the photovoltaic module to be measured at the last moment at the moment; calculating the absolute value of the difference between the irradiation first-order difference corresponding to the moment and the irradiation first-order difference corresponding to the last moment of the moment to obtain the effective irradiation change rate of the photovoltaic module to be measured at the moment;
if the effective irradiation change rate is larger than a preset irradiation threshold value, determining that a soft shadow exists in the photovoltaic module to be tested; and if the effective irradiation change rate is less than or equal to the preset irradiation threshold value, determining that the photovoltaic module to be tested normally works at the moment.
2. The method according to claim 1, wherein the judging whether the linear correlation between the maximum power and the effective irradiation corresponding to the photovoltaic module to be tested at the same time meets a preset condition comprises:
calculating fitting power corresponding to effective irradiation at each moment in a judgment day;
calculating the power relative error between the corresponding fitting power and the actual maximum power of the photovoltaic module to be tested at the same moment;
if the absolute value of the relative power error at the moment is less than or equal to a preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment meets a preset condition;
and if the absolute value of the relative power error is larger than the preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment does not accord with the preset condition.
3. The method of claim 2, further comprising:
calculating to obtain fitting power corresponding to each first type of sample data, wherein the first type of sample data is data of the photovoltaic module which is in a normal state and has a soft shadow and is obtained in a preset time period before the judgment day;
calculating the power sample relative error between the actually measured power and the fitting power corresponding to the same moment in each first type of sample data;
calculating the average value and the standard deviation of the relative errors of the power samples of each first type of sample data;
determining the preset error threshold to be three times the standard deviation according to a Lauda criterion.
4. The method of claim 1, further comprising:
utilizing the irradiation change rate corresponding to the same photovoltaic module in the second type of sample data at the appointed time;
calculating the irradiation change rate corresponding to each second type of sample data, and calculating the average value and the standard deviation of each irradiation change rate;
according to the Lauda criterion, determining that the preset irradiation threshold is three times of the standard deviation corresponding to the irradiation change rate;
and the second type of sample data is data of the photovoltaic module in a normal state obtained in a preset time period before the judgment day.
5. The method of claim 1, further comprising:
calculating the power change rate of the same photovoltaic module in the third type of sample data at a specified moment;
calculating the average value and the standard deviation of the power change rate corresponding to each third type of sample data;
according to the Lauda criterion, determining that the preset power threshold is three times of the standard deviation corresponding to the power change rate;
and the third type of sample data is data of the photovoltaic module in the power limiting state obtained in a preset time period before the judgment day.
6. The method of claim 1, further comprising:
after the fault type of the photovoltaic module to be tested is judged, recording a fault time period corresponding to the fault type;
and uploading the fault type and the fault time period of the photovoltaic module to be tested to a monitoring platform.
7. A failure type determination device for a photovoltaic module, comprising:
the linear correlation degree judging module is used for judging whether the linear correlation degree between the corresponding maximum power and the effective irradiation of the photovoltaic module to be detected at the same moment meets a preset condition or not;
the power change rate judging module is used for calculating the absolute value of the difference value between the maximum power of the photovoltaic component to be tested at the moment and the maximum power corresponding to the last moment at the moment when the linear correlation degree does not accord with the preset condition, so as to obtain the power change rate corresponding to the photovoltaic component to be tested at the moment; when the power change rate is larger than a preset power threshold value, determining that the photovoltaic module to be tested has a hard shadow at the moment; when the power change rate is smaller than or equal to the preset power threshold, determining that the photovoltaic module to be tested is in a power limiting state at the moment;
the irradiation change rate judging module is used for calculating the difference value between the effective irradiation of the photovoltaic module to be measured at the moment and the effective irradiation corresponding to the last moment of the moment when the linear correlation degree meets the preset condition, obtaining the irradiation first-order difference corresponding to the moment, and calculating the irradiation first-order difference corresponding to the photovoltaic module to be measured at the last moment of the moment; calculating the absolute value of the difference between the irradiation first-order difference corresponding to the moment and the irradiation first-order difference corresponding to the last moment of the moment to obtain the effective irradiation change rate of the photovoltaic module to be measured at the moment; when the effective irradiation change rate is larger than a preset irradiation threshold value, determining that a soft shadow exists in the photovoltaic module to be tested; and when the effective irradiation change rate is smaller than or equal to the preset irradiation threshold value, determining that the photovoltaic module to be tested normally works at the moment.
8. The apparatus of claim 7, wherein the linear correlation determining module is specifically configured to:
calculating fitting power corresponding to effective irradiation at each moment in a judgment day;
calculating the power relative error between the corresponding fitting power and the actual maximum power of the photovoltaic module to be tested at the same moment;
if the absolute value of the relative error of the power at the moment is less than or equal to a preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment meets a preset condition;
and if the absolute value of the relative power error is larger than the preset error threshold, determining that the linear correlation between the maximum power and the effective irradiation of the photovoltaic module to be tested at the moment does not accord with the preset condition.
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Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110071692B (en) * | 2019-05-27 | 2020-08-28 | 阳光电源股份有限公司 | Photovoltaic module fault determination method and device and controller |
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| CN115687933A (en) * | 2022-12-30 | 2023-02-03 | 北京志翔科技股份有限公司 | Photovoltaic string abnormal classification data enhancement method, device, equipment and storage medium |
| CN116111728B (en) * | 2023-04-13 | 2023-06-23 | 深圳戴普森新能源技术有限公司 | Interruption control method for photovoltaic inverter system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103840742A (en) * | 2014-03-04 | 2014-06-04 | 常州恒大光伏科技有限公司 | Intelligent management method and system for photovoltaic module |
| CN104391189A (en) * | 2014-10-13 | 2015-03-04 | 合肥工业大学 | Three-stage-diagnosis-based large-scale photovoltaic array fault diagnosis and positioning method |
| CN104601108A (en) * | 2015-02-10 | 2015-05-06 | 河海大学常州校区 | Small photovoltaic power station fault diagnosis method |
| CN104767481A (en) * | 2015-04-28 | 2015-07-08 | 北京汉能光伏投资有限公司 | Method and system for monitoring working state of solar photovoltaic power station |
| CN104779913A (en) * | 2015-04-28 | 2015-07-15 | 北京汉能光伏投资有限公司 | Method and system for monitoring working state of solar photovoltaic power station |
| CN104779911A (en) * | 2015-04-28 | 2015-07-15 | 北京汉能光伏投资有限公司 | Method and system for monitoring working state of solar photovoltaic power station |
| CN105337575A (en) * | 2015-11-17 | 2016-02-17 | 广州健新自动化科技有限公司 | Method and system for state prediction and fault diagnosis of photovoltaic power station |
| CN106059492A (en) * | 2016-05-05 | 2016-10-26 | 江苏方天电力技术有限公司 | Photovoltaic assembly shadow fault type determination method based on power prediction |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6479645B2 (en) * | 2015-12-15 | 2019-03-06 | 株式会社日立製作所 | Diagnostic system and diagnostic method for photovoltaic power generation system |
-
2018
- 2018-10-12 CN CN201811188503.0A patent/CN109271736B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103840742A (en) * | 2014-03-04 | 2014-06-04 | 常州恒大光伏科技有限公司 | Intelligent management method and system for photovoltaic module |
| CN104391189A (en) * | 2014-10-13 | 2015-03-04 | 合肥工业大学 | Three-stage-diagnosis-based large-scale photovoltaic array fault diagnosis and positioning method |
| CN104601108A (en) * | 2015-02-10 | 2015-05-06 | 河海大学常州校区 | Small photovoltaic power station fault diagnosis method |
| CN104767481A (en) * | 2015-04-28 | 2015-07-08 | 北京汉能光伏投资有限公司 | Method and system for monitoring working state of solar photovoltaic power station |
| CN104779913A (en) * | 2015-04-28 | 2015-07-15 | 北京汉能光伏投资有限公司 | Method and system for monitoring working state of solar photovoltaic power station |
| CN104779911A (en) * | 2015-04-28 | 2015-07-15 | 北京汉能光伏投资有限公司 | Method and system for monitoring working state of solar photovoltaic power station |
| CN105337575A (en) * | 2015-11-17 | 2016-02-17 | 广州健新自动化科技有限公司 | Method and system for state prediction and fault diagnosis of photovoltaic power station |
| CN106059492A (en) * | 2016-05-05 | 2016-10-26 | 江苏方天电力技术有限公司 | Photovoltaic assembly shadow fault type determination method based on power prediction |
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