CN107395121B - Based on Grubbs test method and outlier detection photovoltaic array fault detection method - Google Patents
Based on Grubbs test method and outlier detection photovoltaic array fault detection method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
- H02S50/10—Testing of PV devices, e.g. of PV modules or single PV cells
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
本发明公开了基于格拉布斯准则及离群点检测光伏阵列故障检测方法，其特征在于，包括以下步骤：步骤A：每5秒获取光伏阵列各组串的实时电流、电压和光伏阵列的辐照、温度；步骤B：建立光伏阵列仿真模型，将采集的辐照、温度带入到模型获得参考电流、电压；步骤C：由实际电流与参考电流做差，并将光伏阵列各组串的差值组合成一个阵列，应用格拉布斯准则检测出异常数据点，记录异常数据的故障特征值为1，否则为0；步骤D：将电流差值每隔20秒按照顺序组合一次形成一个一维数组，应用离群点算法获得各个电流差值的LOF，最后将LOF因子按时间分配给各个组串；步骤E：最后根据步骤C与D的结果综合判断是否出现故障。本发明能够实时检测光伏组件的故障，尤其是早期故障。
The invention discloses a photovoltaic array fault detection method based on the Grubbs criterion and outlier detection, which is characterized in that it comprises the following steps: Step A: acquiring the real-time current, voltage and radiation of each string of the photovoltaic array every 5 seconds irradiance and temperature; step B: build a photovoltaic array simulation model, bring the collected irradiance and temperature into the model to obtain reference current and voltage; step C: make the difference between the actual current and the reference current, and compare the The difference values are combined into an array, and the abnormal data points are detected by applying the Grubbs criterion, and the fault characteristic value of the recorded abnormal data is 1, otherwise it is 0; Step D: Combine the current difference values in order every 20 seconds to form a one Dimensional array, apply the outlier algorithm to obtain the LOF of each current difference, and finally assign the LOF factor to each string according to time; Step E: Finally, comprehensively judge whether there is a fault according to the results of steps C and D. The invention can detect the failure of the photovoltaic module in real time, especially the early failure.
Description
Technical field
The present invention relates to Grubbs test method and outlier detection photovoltaic array fault detection method is based on, belong to photovoltaic hair
Electro-technical field.
Background technique
In recent years, China's theCourse of PV Industry is swift and violent, and by the end of 2015, accumulative photovoltaic installed capacity reached 43GW, jumps
Photovoltaic installed capacity No. 1 in the world is occupied, and photovoltaic products have to miniaturization, the trend development of household recently.Photovoltaic hair
The power generation performance and irradiation level, temperature of electric system have very big relevance, since outdoor photovoltaic products are often in high temperature
Exposure, rain erosion, running environment is severe, relatively common so as to cause the appearance operation troubles of photovoltaic products.Therefore to light
The intelligent measurement of overhead utility problem real compared with maintenance increasingly becomes one, the O&M for raising photovoltaic products are convenient
Property, the method for the intelligent trouble diagnosis of all kinds of photovoltaic products is come into being.
The common operation troubles of photovoltaic module has shadow occlusion, component aging, component bypass, short circuit, hot spot, system event
Barrier also includes crack, degumming etc..Since photovoltaic products are influenced very big, event of the general method to early stage by irradiation level, temperature
Barrier is difficult to detect, from foreign literature it is found that at present frequently with the knowledge discriminating fault types such as neural network, fuzzy algorithmic approach,
However for neural network, need to be trained to faulty characteristic, and when breaks down to photovoltaic products
Definition be not quite similar, and be difficult to detect initial failure, therefore the method for neural network has uncertainty, is only able to detect
More serious failure.How real-time detection to the failure of photovoltaic products, especially initial failure seems important.
Summary of the invention
It is an object of the invention to using based on Grubbs test method and outlier detection photovoltaic array fault detection method
Come the failure of real-time detection photovoltaic module, especially initial failure, with solve that the artificial Judging fault in China at this stage occurs when
Between the inaccuracy put, the problem of randomness, diseconomy.
In order to solve the above technical problem, the present invention provides based on Grubbs test method and the event of outlier detection photovoltaic array
Hinder detection method, comprising the following steps:
Step A: the meteorology of output characteristic parameter (electric current, the voltage) and photovoltaic array of photovoltaic array each group string is obtained in real time
Parameter (irradiation, temperature), every five seconds acquisition are primary；
Step B: establishing photovoltaic array simulation model, and the irradiation acquired in the step A, temperature are brought into photovoltaic array
Simulation model obtains reference current, voltage；
Step C: being made the difference by actual current and reference current, and the difference of photovoltaic array each group string is combined into a battle array
Column detect exceptional data point using Grubbs test method, and the fault eigenvalue of recording exceptional data is 1, is otherwise 0；
Step D: by above-mentioned current differential, every 20 seconds, combination once formed an one-dimension array in sequence, using peeling off
Point algorithm obtains the factor values LOF that peels off of each current differential, and the LOF factor is temporally finally distributed to each group of string.
Step E: finally whether broken down according to the result comprehensive descision of the step C and D.
Above-mentioned steps B specifically includes the following steps:
B1 5 parameter model of photovoltaic cell) is established.
B2 the simulation model) based on building photovoltaic array in the tool box simulink in MATLAB.
Exceptional data point, and the fault signature of recording exceptional data are detected using Grubbs test method in above-mentioned steps C
Value is 1, is otherwise 0；To current differential one-dimension array, average first, in accordance with formula (1) to each current differential, according still further to formula
(2) standard deviation of electric current one-dimension array is acquired；Finally Grubbs value G is acquired according to formula (3)i, and by GiWith GlimValue compares,
If Gi>Glim, then otherwise it is 0 that fault eigenvalue, which is 1,；
Grubbs test method reference table under 1 95% confidence level of table
Wherein, xiIndicate the current differential of i-th of element in current differential one-dimension array,Indicate one dimension of current differential
Average current difference in group, S indicate the standard deviation of electric current one-dimension array, GiIndicate Grubbs value, GlimIndicate 95% confidence
Grubbs value under degree, n indicate an element number in one-dimensional group of number of electric current.
For example, electric current one-dimension array has 6 elements if the photovoltaic array is there are six group string, then corresponding n is 6,
Under 0.95 fiducial probability, the G for 1 acquisition of tabling look-uplimIt is 1.822.
Outlier detection in above-mentioned steps D method particularly includes:
Preceding 45 seconds current differential data are taken out every 20 seconds first, are ranked up according to the time, form one one
Then dimension group obtains final LOF value using following algorithm to this one-dimension array.
If data set I ∈ Rn×m, wherein n is an element number in one-dimensional group of number of electric current, and m is variable number；
Defining k distance is each its nearest the distance between observation object of observation object distance, observes the k distance of object p
dk(p):
dk(p)=d (p, o) (4)
Wherein, o is a nearest point of observation of k observation object neighbouring with p in data set I；
The k of p observation object is defined apart from field Nk(p):
Nk(p)=Q ∈ X/ { p } | d (p, Q) <=dk(p)} (5)
Wherein Q is the observation object in data set I；
Local reach distance of the definition observation object p relative to observation object o:
reach-distk(p, o)=max { dk(p),d(p,o)} (6)
The local reachability density lrd of definition observation object pk(p):
The local outlier factor LOF of definition observation objectk(p)。
The specific method is as follows in above-mentioned steps E table:
The final LOF value table of table 2
If fault eigenvalue is that 0, LOF value is greater than 5, then LOF value takes 5, if LOF value is constant less than or equal to 5, LOF value；If
Fault eigenvalue is 1, and no matter LOF value is constant above or below 5, LOF value.
The 5 parameter physical models of above-mentioned steps B1 are as follows:
In formula: UPVFor component output voltage, IPVElectric current, I are exported for componentphFor photogenerated current, IoTo be reversely saturated electricity
Stream, q are electron charge (1.602 × 10-19C), and n' is ideal factor, and K is Boltzmann constant (1.38 × 10-23J/K), T
(KShi temperature) is photovoltaic module temperature, RsFor equivalent series resistance, RshFor equivalent parallel resistance.
The invention has the benefit that
(1), the present invention using based on Grubbs test method and outlier detection photovoltaic array fault detection method come real-time
The failure of photovoltaic module, especially initial failure are detected, with the solution time point that the artificial Judging fault in China occurs at this stage
The problem of inaccuracy, randomness, diseconomy；
(2), the present invention gets rid of the method with sensor detection failure, with Grubbs test method and outlier detection side
Method combines the real time monitoring and fault detection implemented to photovoltaic array, efficiently solves the failure inspection under complicated weather condition
It surveys, false detection rate, timeliness with higher and preferable economy can be reduced as far as possible.
(3), further, it is also possible to solve the collection of historical data required for neural network, the difficulty of selection simultaneously.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the real-time current of 4 string components；
Fig. 3 is Grubbs test method testing result；
Fig. 4 is outlier detection result；
Fig. 5 is comprehensive detection result.
Specific embodiment
To be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, below with reference to
Specific embodiment, the present invention is further explained.
Flow chart of the invention as shown in Figure 1, photovoltaic module method for diagnosing faults of the invention, comprising the following steps:
Step A: the meteorology of output characteristic parameter (electric current, the voltage) and photovoltaic array of photovoltaic array each group string is obtained in real time
Parameter (irradiation, temperature), every five seconds acquisition are primary；
Step B: establishing photovoltaic array simulation model, and irradiation collected in step A, temperature are brought into photovoltaic array and are imitated
True mode obtains reference current, voltage；Specifically:
B1 5 parameter model of photovoltaic cell) is established.
B2 the simulation model) based on building photovoltaic array in the tool box simulink in MATLAB.
Step C: being made the difference by actual current and reference current, and the difference of photovoltaic array each group string is combined into a battle array
Column detect exceptional data point using Grubbs test method, and the fault eigenvalue of recording exceptional data is 1, is otherwise 0；It is right
Current differential one-dimension array averages to each current value first, in accordance with formula (1), acquires electric current one-dimension array according still further to formula (2)
Standard deviation；Finally Grubbs value G is acquired according to formula (3)i, and by GiWith the corresponding G in tablelimValue compares, if Gi>
Glim, then otherwise it is 0 that fault eigenvalue, which is 1,；
Grubbs test method reference table under 1 95% confidence level of table
Wherein, xiIndicate the current differential of i-th of element in current differential one-dimension array,Indicate one dimension of current differential
Average current difference in group, S indicate the standard deviation of electric current one-dimension array, GiIndicate Grubbs value, GlimIndicate 95% confidence
Grubbs value under degree, n indicate an element number in one-dimensional group of number of electric current.
For example, electric current one-dimension array has 6 elements if the photovoltaic array is there are six group string, then corresponding n is 6,
Under 0.95 fiducial probability, the G for 1 acquisition of tabling look-uplimIt is 1.822.
Step D: by above-mentioned current differential, every 20 seconds, combination once formed an one-dimension array in sequence, using peeling off
Point algorithm obtains the factor values that peel off (LOF) of each current differential, and the LOF factor is temporally finally distributed to each group of string.Such as
The real-time current of 4 string component shown in Fig. 2, first took out preceding 45 seconds current differential data every 20 seconds, according to the time
It is ranked up, forms an one-dimension array, such as preceding 45 seconds data are I1={ 0.01,0.1,0.02,0.01 }, I2=
{ 0.01,0.01,0.02,0.01 }, I3={ 0.01,0.01,0.01,0.01 }, I4={ 0.01,0.6,0.02,0.01 }.It is then new
Composition I=0.01,0.1,0.02,0.01,0.01,0.01,0.02,0.01,0.01,0.01,0.01,0. 01,0.01,
0.6,0.02,0.01}.Final LOF value is obtained using following algorithm to the one-dimension array.
If data set I ∈ Rn×m, wherein n is an element number in one-dimensional group of number of electric current, and m is variable number.
Define 1.k distance --- each observation its nearest the distance between observation object of object distance.Observe the k of object p
Distance dk(p):
dk(p)=d (p, o) (4)
Wherein o is a nearest point of observation of k observation object neighbouring with p in data set I.
The k of 2.p observation object is defined apart from field Nk(p)
Nk(p)=Q ∈ X/ { p } | d (p, Q) <=dk(p)} (5)
Wherein Q is the observation object in data set I.
Define local reach distance of the 3. observation object p relative to observation object o.
reach-distk(p, o)=max { dk(p),d(p,o)} (6)
Define the local reachability density lrd of 4. observation object pk(p)
Define the local outlier factor LOF of 5. observation objectsk(p)。
Step E: finally whether broken down according to the result comprehensive descision of step C and D.Specific steps are shown in Table 2: if failure
Characteristic value is that 0, LOF value is greater than 5, then LOF value takes 5, if LOF value is constant less than or equal to 5, LOF value；If fault eigenvalue is 1,
No matter LOF value is constant above or below 5, LOF value.
The final LOF value table of table 2
Specific visible Fig. 3-Fig. 5 is malfunction test on March 14th, 2017 as a result, abscissa indicates time, ordinate in Fig. 2
Indicate real-time current；Abscissa indicates the time in Fig. 3, and ordinate indicates Grubbs value, and signal-fault indicates failure letter
Number；Abscissa indicates the time in Fig. 4, and ordinate indicates the factor values LOF that peels off；Abscissa indicates the time in Fig. 5, and ordinate indicates
The factor values that peel off LOF, threshold are critical value.
5 parameter physical models in step B1 are as follows:
In formula: UPVFor component output voltage, IPVElectric current, I are exported for componentphFor photogenerated current, IoTo be reversely saturated electricity
Stream, q are electron charge (1.602 × 10-19C), and n' is ideal factor, and K is Boltzmann constant (1.38 × 10-23J/K), T
(KShi temperature) is photovoltaic module temperature, RsFor equivalent series resistance, RshFor equivalent parallel resistance.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.Industry description
Merely illustrate the principles of the invention, without departing from the spirit and scope of the present invention, the present invention also have various change and
It improves, these changes and improvements all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended power
Sharp claim and its equivalent thereof.
Claims (5)
1. being based on Grubbs test method and outlier detection photovoltaic array fault detection method, which is characterized in that including following step
It is rapid:
Step A: the meteorologic parameter of the output characteristic parameter and photovoltaic array of photovoltaic array each group string, every five seconds acquisition are obtained in real time
Once；
Step B: establishing photovoltaic array simulation model, and the irradiation acquired in the step A, temperature are brought into photovoltaic array emulation
Model obtains reference current, voltage；
Step C: being made the difference by actual current and reference current, and the difference of photovoltaic array each group string is combined into an array, is answered
Exceptional data point is detected with Grubbs test method, and the fault eigenvalue of recording exceptional data is 1, is otherwise 0；
Step D: by above-mentioned current differential, every 20 seconds, combination once formed an one-dimension array in sequence, counted using peeling off
Method obtains the factor values LOF that peels off of each current differential, and the LOF factor is temporally finally distributed to each group of string；
Step E: finally whether broken down according to the result comprehensive descision of the step C and D；
Exceptional data point is detected using Grubbs test method in the step C, and the fault eigenvalue of recording exceptional data is
1, it is otherwise 0；It to current differential one-dimension array, averages first, in accordance with formula (1) to each current differential, is asked according still further to formula (2)
Obtain the standard deviation of electric current one-dimension array；Finally Grubbs value G is acquired according to formula (3)i, and by GiWith GlimValue compares, if Gi>
Glim, then otherwise it is 0 that fault eigenvalue, which is 1,；
Wherein, xiIndicate the current differential of i-th of element in current differential one-dimension array,It indicates in current differential one-dimension array
Average current difference, S indicate electric current one-dimension array standard deviation, GiIndicate Grubbs value, GlimIt indicates under 95% confidence level
Grubbs value, n indicates element number in one-dimensional group of number of electric current.
2. according to claim 1 be based on Grubbs test method and outlier detection photovoltaic array fault detection method,
It is characterized in that, the step B is specifically includes the following steps: B1) establish 5 parameter model of photovoltaic cell；
B2 the simulation model) based on building photovoltaic array in the tool box simulink in MATLAB.
3. according to claim 1 be based on Grubbs test method and outlier detection photovoltaic array fault detection method,
It is characterized in that, outlier detection in the step D method particularly includes:
Preceding 45 seconds current differential data are taken out every 20 seconds first, are ranked up according to the time, a dimension is formed
Then group obtains final LOF value using following algorithm to this one-dimension array；
If data set I ∈ Rn×m, wherein n is an element number in one-dimensional group of number of electric current, and m is variable number；
Defining k distance is each its nearest the distance between observation object of observation object distance, observes the k distance d of object pk(p):
dk(p)=d (p, o) (4)
Wherein, o is a nearest point of observation of k observation object neighbouring with p in data set I；
The k of p observation object is defined apart from field Nk(p):
Nk(p)=Q ∈ X/ { p } | d (p, Q) <=dk(p)} (5)
Wherein Q is the observation object in data set I；
Local reach distance of the definition observation object p relative to observation object o:
reach-distk(p, o)=max { dk(p),d(p,o)} (6)
The local reachability density lrd of definition observation object pk(p):
The local outlier factor LOF of definition observation objectk(p)。
4. according to claim 1 be based on Grubbs test method and outlier detection photovoltaic array fault detection method,
Be characterized in that, the specific method is as follows in step E: if fault eigenvalue is that 0, LOF value is greater than 5, then LOF value takes 5, if LOF value is small
In being equal to 5, then LOF value is constant；If fault eigenvalue is 1, no matter LOF value is constant above or below 5, LOF value.
5. according to claim 2 be based on Grubbs test method and outlier detection photovoltaic array fault detection method,
It is characterized in that: the 5 parameter physical models of the step B1 are as follows:
In formula: UPVFor component output voltage, IPVElectric current, I are exported for componentphFor photogenerated current, IoFor reverse saturation current, q is
Electron charge 1.602 × 10-19C, n' are ideal factor, and K is Boltzmann constant 1.38 × 10-23J/K, T are photovoltaic module KShi
Temperature, RsFor equivalent series resistance, RshFor equivalent parallel resistance.
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| CN108880465A (en) * | 2018-06-26 | 2018-11-23 | 广东石油化工学院 | Photovoltaic plant fault early warning method and system |
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