WO2020029327A1 - 一种基于改进随机森林算法的光伏阵列故障诊断方法 - Google Patents

一种基于改进随机森林算法的光伏阵列故障诊断方法 Download PDF

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WO2020029327A1
WO2020029327A1 PCT/CN2018/101743 CN2018101743W WO2020029327A1 WO 2020029327 A1 WO2020029327 A1 WO 2020029327A1 CN 2018101743 W CN2018101743 W CN 2018101743W WO 2020029327 A1 WO2020029327 A1 WO 2020029327A1
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photovoltaic array
fault diagnosis
fault
array
photovoltaic
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French (fr)
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陶洪峰
周超超
魏强
刘巍
周龙辉
王鹏
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Jiangnan University
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Jiangnan University
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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
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy

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  • the invention relates to the field of photovoltaic technology, in particular to a photovoltaic array fault diagnosis method based on an improved random forest algorithm.
  • CSP solar thermal power
  • photovoltaic power is similar to thermal power generation, but the thermal energy mainly comes from large-scale mirror collection. After heating the water, it uses steam to promote the operation of traditional generators to achieve power generation. Although this type of power generation avoids the photoelectric conversion of silicon crystals, Light intensity requirements are high and power generation costs are high.
  • Photovoltaic power generation is based on the photovoltaic effect, which directly converts solar energy into electricity. The construction period is short, and no waste residue, waste water and other pollutants will be generated during operation, especially for mountainous areas, islands and remote areas with less developed transportation. More important value.
  • photovoltaic power generation uses photovoltaic cell modules in series and parallel to form a photovoltaic array to operate, and photovoltaic arrays use more photovoltaic cell modules, the probability of failure of the photovoltaic cell module itself is higher, and the photovoltaic array runs for a long time Outdoors, the operating environment is harsh, and it is prone to aging and damage. As a result, the photovoltaic cell module's power generation efficiency is reduced or even stopped. When a photovoltaic cell module in a photovoltaic array fails, it will lead to a decrease in system efficiency and adversely affect the operation and scheduling of the power system. In severe cases, it may even cause property damage and casualties. Therefore, the photovoltaic array is faulty.
  • Diagnosis is of great significance.
  • the currently used fault diagnosis methods mainly include two types: direct method and indirect method. Indirect method is more typical with infrared heat detection method and power generation comparison method. Direct method is more typical with ground capacitance method and time. Domain reflection method, intelligent diagnosis algorithm, and electrical characteristic detection method. Among them, fault diagnosis by combining intelligent diagnosis algorithm and electrical characteristic detection method is currently a method with great potential.
  • the present inventor proposes a photovoltaic array fault diagnosis method based on an improved random forest algorithm. This method determines the operating condition of a photovoltaic cell module by collecting the voltage and current of the photovoltaic cell modules between the arrays, thereby realizing a fault. Positioning.
  • a photovoltaic array fault diagnosis method based on an improved random forest algorithm includes:
  • the photovoltaic array includes n branches, and each branch includes m photovoltaic cell modules.
  • the typical operating state of the photovoltaic array is used to indicate the status of each photovoltaic cell module in the photovoltaic array.
  • Running status, m and n are positive integers;
  • a data sample set of the photovoltaic array is constructed, and the data sample set is divided into a training sample set and a test sample set;
  • the photovoltaic array fault diagnosis model is trained by using the training sample set, and the photovoltaic array fault diagnosis model is tested by using the test sample set.
  • the photovoltaic array fault diagnosis model includes N decision trees, where N is a positive integer and N ⁇ 2 ;
  • the PV array fault diagnosis model completed by the test is used to diagnose the PV array to be diagnosed, and the voting results of N decision trees for each typical operating state are obtained;
  • the fault diagnosis result of the photovoltaic array to be diagnosed is obtained according to the voting results corresponding to each typical operating state, and the fault diagnosis result is used to indicate the operating state of each photovoltaic cell module in the photovoltaic array.
  • the array trunk parameters and each array branch parameter include the maximum power point voltage, the maximum power point current, the open circuit voltage and the short circuit current of the circuit, respectively.
  • the photovoltaic array fault diagnosis model includes N decision trees and the corresponding weights of each decision tree, and the photovoltaic array fault diagnosis model completed by the test is used to diagnose the PV array to be diagnosed, including:
  • N decision trees vote for each typical running state, and each decision tree casts its own weight as the number of votes;
  • the sub-training set includes For the p samples selected in this round of sampling, the out-of-package sample set includes the samples that were not sampled in this round of sampling, and p is a positive integer;
  • a decision tree is trained according to the sub-training sample set corresponding to each round of sampling, and the decision tree is tested by using the out-of-package sample set corresponding to the round of sampling to obtain the out-of-package accuracy rate.
  • the training N decision trees and the weights corresponding to each decision tree are summarized to obtain a photovoltaic array fault diagnosis model.
  • w (i) is the weight corresponding to the i-th decision tree
  • Hoob (i) is the out-of-package accuracy rate corresponding to the i-th decision tree
  • i and j are parameters, 1 ⁇ i ⁇ N and i is positive Integer.
  • the method further includes:
  • the N out-of-package sample sets are aggregated to obtain a total out-of-package sample set.
  • the samples are tested using the trained photovoltaic array fault diagnosis model to obtain the initial out-of-package accuracy rate;
  • the sample includes a total of K fault features. Noise is added to the k-th fault feature, and the sample is tested again using the photovoltaic array fault diagnosis model to obtain a new out-of-package accuracy rate. The initial out-of-package accuracy rate and the new out-of-package accuracy rate are calculated. The difference between the accuracy rates determines the importance metric of the kth feature, where K is a positive integer, k is a parameter, and the starting value of k is 1;
  • the processed training sample set is used to construct a photovoltaic array fault diagnosis model.
  • the typical operating state corresponding to the highest number of votes includes only one, the typical operating state corresponding to the highest number of votes is output as a fault diagnosis result;
  • the typical running state corresponding to the highest number of votes includes at least two, the L decision trees with the highest weights are selected to vote for each typical running state. If there is a typical running state to obtain the selection of the most decision trees, then The typical running state is output as the fault diagnosis result; otherwise, the typical running state selected by the decision tree with the highest weight is output as the fault diagnosis result, where L is a positive integer and 2 ⁇ L ⁇ N.
  • the present application discloses a photovoltaic array fault diagnosis method based on an improved random forest algorithm, which collects parameters of the main trunk of the photovoltaic array and each branch, and the voltage difference between the arrays between different branches.
  • the parameters of each branch can reflect the operating status of each branch in the photovoltaic array, thereby diagnosing which branch in the photovoltaic array has what kind of failure, and the voltage difference between the arrays between different branches can reflect the branch
  • the operating status of each photovoltaic cell module in the system so as to diagnose which photovoltaic cell module is faulty.
  • the combination of the two types of information effectively achieves the fault location of the photovoltaic array.
  • the random forest algorithm is used, which is suitable for practical applications.
  • the characteristics of photovoltaic arrays overcome the problems of large amount of data and long training time required by traditional neural network algorithms, and can complete diagnostic tasks simply and quickly.
  • the method disclosed in this application is based on a data-driven idea, and combines random forest algorithm, decision tree weighting, voting tie processing, and variable importance measurement to realize fault diagnosis of photovoltaic arrays and effectively improve diagnostic accuracy.
  • the method disclosed in this application uses an improved random forest fault diagnosis model structure. Through the integration of multiple decision trees, a strong classifier is constructed, which uses decision tree weighting, voting tie processing, and fault feature importance measures. A series of steps were improved, and finally an improved random forest algorithm was constructed. The diagnosis results also show that although the improved algorithm has slightly longer training time, it has higher accuracy and more reliable diagnosis.
  • FIG. 1 is a flowchart of a photovoltaic array fault diagnosis method disclosed in the present application.
  • Figure 2 is a schematic diagram of the construction of a 3 * 3 photovoltaic array.
  • Figure 3-a is the U-I output characteristic curve of a single solar cell module under different light intensities.
  • Figure 3-b is the U-P output characteristic curve of a single solar cell module under different light intensities.
  • Figure 3-c is the U-I output characteristic curve of a single solar cell module under different temperature conditions.
  • Figure 3-d is the U-P output characteristic curve of a single solar cell module under different temperature conditions.
  • FIG. 4 is an output characteristic curve of a single solar cell module at a light intensity of 600 W / m2, 800 W / m2, and 1000 W / m2.
  • Figure 5-a is a comparison of the output characteristic curves of a photovoltaic array when there is no fault and when a single-mode fault occurs.
  • Figure 5-b is a comparison diagram of the output characteristic curves of a photovoltaic array when there is no fault and when a multi-mode fault occurs.
  • Figure 6-a is the output characteristic curve of a photovoltaic array when a photovoltaic cell module is shorted in a branch.
  • Figure 6-b is the output characteristic curve of a photovoltaic array when one branch is open.
  • Figure 6-c is the output characteristic curve of a photovoltaic array when two photovoltaic cell modules in a branch have shadows.
  • Figure 6-d shows the output characteristic curve of a photovoltaic array when one photovoltaic cell module is short-circuited in one branch, and one photovoltaic cell module is shadowed in the other branch.
  • This application discloses a photovoltaic array fault diagnosis method based on an improved random forest algorithm. Please refer to FIG. 1.
  • the entire fault diagnosis process includes the following steps:
  • the first step is to build a photovoltaic array in the form of m * n.
  • the photovoltaic array includes n branches in parallel, and each branch includes m photovoltaic cell modules in series, where m and n are positive integers.
  • the photovoltaic cell modules in a photovoltaic array are usually solar cell modules.
  • the specifications of each photovoltaic cell module are the same to ensure the potential balance of the photovoltaic array.
  • Each photovoltaic cell module is connected with a bypass diode in parallel and each branch in series with an isolation diode. Protection, detection devices such as fuses, voltage and current sensors are correctly installed and working properly.
  • FIG. 2 For a schematic diagram of a 3 * 3 photovoltaic array, please refer to FIG. 2. It includes branch A, branch B, and branch C. Each branch includes 3 branches. For photovoltaic cells connected in series, Figure 2 does not show devices such as bypass diodes and isolation diodes.
  • the photovoltaic cell module in the photovoltaic array of this application uses SunTech STP 270-24 / Vd solar cell module of Suntech Solar Co., Ltd.
  • the parameter specification table of this type of solar cell module is shown in the following table:
  • the UI (voltage-current) output characteristic curve when the light intensity rises from 200 W / m 2 to 1000 W / m 2 (200, 400, 600, 800, and 1000 W / m 2 respectively ) is as follows
  • Figure 3-a shows the UP (voltage-power) output characteristic curve shown in Figure 3-b.
  • the UI output characteristic curve when the temperature drops from 35 ° C to 15 ° C (respectively 35 ° C, 30 ° C, 25 ° C, 20 ° C, and 15 ° C) is shown in Fig.
  • the UP (voltage-power) output characteristic curve is shown in Figure 3-d.
  • the product manual gives the output characteristic curve of this type of solar cell module under the light intensity of 600W / m 2 , 800W / m 2 and 1000W / m 2 as shown in FIG. 4.
  • the second step is to determine the typical operating state of the photovoltaic array during operation.
  • the typical operating state of the photovoltaic cell module in this application is used to indicate the operating state of each photovoltaic cell module in the photovoltaic cell module.
  • Figure 5-b shows the faulty branches of photovoltaic arrays when there are no faults and multi-mode faults occur.
  • Fault array, normal branch and normal array output characteristic curve where the normal branch refers to the branch in which each photovoltaic cell module is operating normally, the normal array refers to the photovoltaic array in which each branch is operating normally, the fault A branch is a branch in which a photovoltaic cell module fails, and a fault array refers to a photovoltaic array in which a branch fails.
  • the output characteristic curve has two regions: high voltage region and high current region. In the high voltage region, the condition of the branch can be determined by detecting the output current of each branch. In the high current region, the output voltage of the photovoltaic cell module can be detected. To achieve fault location.
  • the above four types of faults may occur during the operation of photovoltaic arrays.
  • the operating states of photovoltaic arrays mainly include five categories: normal operating states, short circuit fault states, open circuit fault states, shadow fault states, and mixed fault states :
  • the shadow fault state that is, any branch or multiple branches have shadows. Unlike the short-circuit fault state, there can be shadows of all photovoltaic cell modules in a branch, so the number of this state is large. .
  • two-level state classification can be adopted.
  • rough classification is performed to locate the branches in the photovoltaic array. This step does not involve fault location. You can choose some of the most common operations based on actual conditions and experience. status.
  • the major faults of the system have evolved on the basis of minor faults. The probability of direct major faults is very small, and the types and combinations of major faults are too many, so this application mainly Consider light and moderate faults, that is, consider that at least one branch in the photovoltaic array is in a normal operating state. For example, in the photovoltaic array shown in FIG. 2, assuming that the branch C in the photovoltaic array is always in a normal operating state, the following 20 operating states can be selected as the result of the first-level state classification, as shown in the following table:
  • the second-level status classification can be performed to locate the photovoltaic cell module in the branch, thereby achieving fault location.
  • the status label F2 indicates that a photovoltaic cell module in a branch is short-circuited.
  • the 3 * 3 photovoltaic array shown in FIG. 2 assuming that the branch C in the photovoltaic array is in a normal operating state, the The condition indicates that there is a short-circuit of a photovoltaic cell module on branch A or branch B.
  • the results of the second-level state classification based on this are shown in the following table:
  • results of the second-level status classification based on the first-level status classification corresponding to the status label F3 can be shown in the following table:
  • the third step is to collect the circuit parameters of the photovoltaic array.
  • the array trunk parameters of the photovoltaic array are parameters on the main trunk of the photovoltaic array.
  • the array trunk parameters in this application include the maximum power point voltage V m , the maximum power point current I m , and the open circuit voltage V oc of the main trunk. And short-circuit current I sc , which is because these four parameters can well characterize the UI output characteristic curve of the photovoltaic cell module.
  • the maximum power point voltage V m the maximum power point current I m , the open circuit voltage V oc, and the short-circuit current I sc of the branch are collected as the array branch parameters.
  • the circuit parameters of a photovoltaic array can be measured by configuring a voltage sensor and a current sensor in the photovoltaic array. For an m ⁇ n scale photovoltaic array, it is necessary to configure [n ⁇ (m-1) / 2] voltage sensors and n + 1 current sensors, where the symbol [] means rounding up n ⁇ (m-1) / 2.
  • the current sensor is set in each branch and the main trunk to detect the current of the branch and the current of the main trunk.
  • the voltage sensor is set between different branches to detect the voltage difference between the arrays between different branches. As shown in FIG. 2, three voltage sensors U a , U b and U c are provided .
  • the voltage sensor U a is disposed between the branch A and the branch B, and the voltage sensor U b is disposed between the branch B and the branch C.
  • the voltage sensor U c is arranged between the branch C and the branch A.
  • the detection of the branch current and the detection of the voltage difference between the branches are essentially to detect the voltage and current of each photovoltaic cell module.
  • the changes in the parameter values such as voltage and current reflect the operation of the photovoltaic cell module, thereby achieving line faults.
  • the principle of positioning is as follows:
  • the voltage matrix U pv array of the photovoltaic array is:
  • U 11 represents the output voltage of the first photovoltaic cell module of the first branch in the photovoltaic array, that is, the output voltage of PV1 in FIG. 2
  • U mn represents the m-th branch of the n-th branch in the photovoltaic array
  • the output voltage of the photovoltaic cell module that is, the output voltage of PV9 in FIG. 2, and so on. Then the photovoltaic array equation holds as follows:
  • U array is the output voltage of the photovoltaic array
  • the output voltage of each photovoltaic cell module should be 1 / m of the output voltage of the entire photovoltaic array.
  • PV matrix weight matrix A pv array that is, the potential of the voltage sensor placement point between the branches is:
  • u 11 U 21 -U 11
  • u 21 U 31 -U 21
  • u (m-1) 1 U m1 -U (m-1) 1 , and so on.
  • U is the output voltage of the photovoltaic cell module in normal operation
  • m is the total number of photovoltaic cell modules included in the branch
  • x is the number of photovoltaic cell modules that have failed in the faulty branch
  • U bad is the faulty branch.
  • the elements of the rows of the non-faulty branch in the weight matrix A pv array are equal, and the elements of the rows of the faulty branch and the non-faulty branch
  • the elements of each row of the road are not equal; and the elements of each column of the branch with a fault are not equal difference sequences.
  • the following table shows the characteristic values of the voltage difference between the arrays collected by each voltage sensor in the case of a partial short-circuit fault:
  • status label F4-14 For the meaning of the above status label, please refer to the content in the second step above. Those skilled in the art understand that the situation indicated by status label F4-14 is that PV1 on branch A and PV4 on branch B are short-circuited. By analogy, this application does not repeat them one by one.
  • the voltage difference between the arrays collected by the voltage sensor is proportional to the output voltage U array of the photovoltaic array, so it can be further simplified.
  • the array The voltage difference is divided by the output voltage U array of the photovoltaic array to obtain a voltage coefficient, and the voltage difference is used to represent the voltage difference between the arrays.
  • the fourth step is the construction of fault feature vectors and data sample sets. Construct fault feature vectors based on the collected array trunk parameters, array branch parameters, and the voltage difference between the arrays. After the fault feature vectors are obtained, they can be summarized and constructed into a data sample set of photovoltaic arrays. Data samples are collected from the fault feature vectors.
  • the collection method is a method known to those skilled in the art, which is not described in this application.
  • the data sample set is divided to obtain a training sample set and a test sample set, which can usually be divided into a training sample set and a test sample set according to a ratio of 2: 1.
  • the fifth step is the construction of a photovoltaic array fault diagnosis model based on an improved random forest algorithm.
  • the training sample set is X
  • the dimension of the fault feature vector is K, that is, the number of fault features of the sample is K, K is a positive integer
  • the number of decision trees to be established in fault diagnosis is N, N ⁇ 2, and N is Positive integer
  • the steps of constructing a photovoltaic array fault diagnosis model based on an improved random forest algorithm using training sample sets can be briefly described as:
  • a decision tree is obtained by training the sub-training sample set corresponding to each round of sampling, that is, the sub-training sample set X i is used to train the decision tree Q i .
  • q fault features are randomly selected from K fault features, where q is a positive integer and q ⁇ K, and the Gini coefficient corresponding to each fault feature in the q fault features is calculated.
  • the fault feature corresponding to the coefficient is used as the best split feature for node splitting.
  • the calculation method of the Gini coefficient can use the existing calculation formula, which is not described in detail in this application.
  • the weights of decision trees are equal by default. Therefore, some decision trees with excellent performance may not show their advantages and may even be overwhelmed by a majority vote.
  • the tree is weighted, and the out-of-package accuracy rate of the decision tree is used to evaluate the performance of the decision tree. After all the decision tree training is completed, the out-of-package accuracy rate of each decision tree is calculated, and the decision is obtained for the i-th sampling training.
  • Tree Q i using its corresponding out-of-package accuracy rate H oob (i) to calculate the weight of the decision tree Q i is as follows:
  • w (i) is the weight corresponding to the i-th decision tree
  • j is a parameter
  • the photovoltaic array fault diagnosis model is further optimized. As the size of a photovoltaic array expands, the number of branches and voltage sensors running at the same time will increase, and the dimension K of the fault feature vector will increase, which will increase the difficulty of model training. The fault characteristics of the sample are measured for importance to delete some fault characteristics.
  • the specific steps are as follows:
  • the PV array fault diagnosis model includes a total of N decision trees. Using each decision tree that is not trained with the sample to test the sample, you can determine the correct test and the wrong decision The number of trees to obtain the initial out-of-package accuracy e.
  • the sample includes a total of K fault characteristics. Noise is added to the k-th fault characteristic, and the sample is tested again using the photovoltaic array fault diagnosis model to obtain a new out-of-package accuracy rate e1, and the initial out-of-package accuracy rate is calculated. And the new out-of-package accuracy rate, e-e1, to determine the importance metric of the k-th feature, where K is a positive integer, k is a parameter, and the starting value of k is 1.
  • the importance metrics of the K fault features calculated using the samples in the outsourced sample set are generally consistent, but there are errors in actual operation. Therefore, the After the importance metric values of the K fault features are calculated for each sample, they are summed to determine the t fault features with the smallest importance metric value among the K fault features. Then for each sample in the training sample set, t fault features with the smallest importance metric among the K fault features of the sample are deleted to obtain a processed training sample set, 1 ⁇ t ⁇ K.
  • the processed training sample set is used to construct a photovoltaic array fault diagnosis model.
  • the seventh step is to test the fault diagnosis model of the photovoltaic array.
  • indicators such as diagnostic accuracy, average training time, and test time can be obtained.
  • the F1-F20 listed above the first-level state type of the photovoltaic array is diagnosed without fault feature deletion.
  • the improved random forest algorithm and ordinary random forest algorithm are used for fault diagnosis.
  • the training sample set and the test sample set. Both algorithms use the same decision tree model.
  • the number of decision trees is 40.
  • the experimental results are compared as follows:
  • the second-level status classification can be regarded as a sub-class of the first-level status classification.
  • the short-circuit fault status can be obtained as the second-level status.
  • the eighth step is to use the completed photovoltaic array fault diagnosis model to diagnose the PV array to be diagnosed, that is, to use N decision trees to vote for each typical operating state.
  • the voting method is used to vote, that is, each decision tree
  • the weight corresponding to itself is cast as the number of votes.
  • the voting results corresponding to each typical operating state are statistically obtained, and then the fault diagnosis results of the photovoltaic array to be diagnosed are obtained according to the voting results corresponding to each typical operating state.
  • the obtained fault diagnosis results are used to indicate each photovoltaic in the photovoltaic array.
  • step 3 If the typical running state corresponding to the highest number of votes includes only one, the typical running state corresponding to the highest number of votes is directly output as the fault diagnosis result, otherwise, the following step 3) is performed.

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Abstract

本发明公开了一种基于改进随机森林算法的光伏阵列故障诊断方法，涉及光伏技术领域，该方法通过光伏阵列总干路和各个支路的参数反映光伏阵列中各条支路的运行状态，通过不同支路之间的阵列间电压差反映支路中各个光伏电池组件的运行状态，从而实现对光伏阵列的故障定位，同时利用包外样本在决策树赋权投票、平局处理和故障特征的重要性度量三个部分进行优化改进，可以使得故障诊断的精度更高、可靠性更强。

Description

一种基于改进随机森林算法的光伏阵列故障诊断方法 技术领域
本发明涉及光伏技术领域，尤其是一种基于改进随机森林算法的光伏阵列故障诊断方法。
背景技术
随着能源需求的增长、化石能源的日益枯竭与成本上涨，以及全球气候变暖等诸多因素的影响，可再生能源技术得到了飞速发展，其中，太阳能具有易获取、无噪声、清洁、无穷无尽等优点，成为了可再生能源的重要部分。目前利用太阳能发电主要有两种形式：光热发电和光伏发电。光热发电与火力发电类似，但热能主要来自大规模的镜面收集，将水加热后，利用蒸汽推动传统发电机工作，从而实现发电，这种发电方式虽然规避了硅晶的光电转换，但对光照强度的要求高且发电成本偏高。而光伏发电基于光生伏特效应，直接将太阳能转化为电能，建设周期短，在运行时不会产生废渣、废水和其他污染物，尤其是对于交通不发达的山区、海岛和偏远地区，光伏发电具有更重要的价值。
一般情况下，光伏发电都是利用光伏电池组件串、并联组成光伏阵列进行运行的，而光伏阵列使用的光伏电池组件较多，光伏电池组件本身发生故障的概率就较高，并且光伏阵列长期运行在户外，运行环境恶劣，易出现老化和损坏等情况，从而造成光伏电池组件发电效率的降低甚至停止工作。当光伏阵列中某个光伏电池组件发生故障后，会导致系统效率的下降，并对电力系统的运行调度造成不利的影响，严重时，甚至造成财产损失和人员伤亡，因此，对光伏阵列进行故障诊断具有重要意义，目前所采用的故障诊断方法主要包括直接法和间接法两类，间接法比较典型的有红外热量检测法和发电功率对比法，直接法比较典型的有对地电容法、时域反射法、智能诊断算法和电特性检测法，其中，通过将智能诊断算法和电特性检测法进行结合进行故障诊断，是目前非常具有潜力的方法。
但目前的光伏阵列故障诊断方法都较少考虑故障定位的情况，主要原因是：目前的智能诊断算法多采用以BP(Back Propagation)为代表的神经网络，而光伏 阵列中的光伏电池组件的数目较多，当某一个或某多个光伏电池组件发生故障时，故障的排列组合形式较多，再加上不同的故障类型，有时甚至会出现上百种故障组合，则会需要较多的训练样本，并且训练时间也会随之增加，诊断精度也不能够得到保证，诊断难度较大。
发明内容
本发明人针对上述问题及技术需求，提出了一种基于改进随机森林算法的光伏阵列故障诊断方法，该方法通过采集阵列间光伏电池组件的电压和电流确定光伏电池组件的运行情况，从而实现故障定位。
本发明的技术方案如下：
一种基于改进随机森林算法的光伏阵列故障诊断方法，该方法包括：
确定光伏阵列在运行过程中的典型运行状态，光伏阵列包括n个支路，每个支路中包括m个光伏电池组件，光伏阵列的典型运行状态用于指示光伏阵列中的各个光伏电池组件的运行状态，m和n均为正整数；
在光伏阵列处于每个典型运行状态时，采集光伏阵列的阵列干路参数、各个支路对应的阵列支路参数以及不同支路之间的阵列间电压差；
根据阵列干路参数、阵列支路参数以及阵列间电压差构建故障特征向量，根据故障特征向量构建得到光伏阵列的数据样本集，将数据样本集划分为训练样本集和测试样本集；
基于随机森林算法利用训练样本集训练得到光伏阵列故障诊断模型，并利用测试样本集对光伏阵列故障诊断模型进行测试，光伏阵列故障诊断模型中包括N个决策树，N为正整数且N≥2；
利用测试完成的光伏阵列故障诊断模型对待诊断光伏阵列进行诊断，得到N个决策树对各个典型运行状态的投票结果；
根据各个典型运行状态对应的投票结果得到待诊断光伏阵列的故障诊断结果，故障诊断结果用于指示光伏阵列中的各个光伏电池组件的运行状态。
其进一步的技术方案为，阵列干路参数和每个阵列支路参数分别包括电路的最大功率点电压、最大功率点电流、开路电压和短路电流。
其进一步的技术方案为，光伏阵列故障诊断模型中包括N个决策树及每个决策树对应的权值，则利用测试完成的光伏阵列故障诊断模型对待诊断光伏阵列进行诊断，包括：
N个决策树对各个典型运行状态进行投票，每个决策树将自身对应的权值 作为票数投出；
统计得到各个典型运行状态对应的投票结果。
其进一步的技术方案为，基于随机森林算法利用训练样本集训练得到光伏阵列故障诊断模型，包括：
对包含p个样本的训练样本集进行N轮抽样，对于每一轮抽样，采用有放回的抽样方式抽样p次得到该轮抽样对应的子训练样本集和包外样本集，子训练集中包括该轮抽样中抽取到的p个样本，包外样本集中包括训练样本集中在该轮抽样中未被抽取的样本，p为正整数；
根据每一轮抽样对应的子训练样本集训练得到一个决策树，并利用该轮抽样对应的包外样本集对决策树进行测试得到包外准确率，根据包外准确率计算得到决策树对应的权值；
对训练得到的N个决策树和每个决策树对应的权值进行汇总得到光伏阵列故障诊断模型。
其进一步的技术方案为，根据包外准确率计算得到决策树对应的权值，包括计算：
Figure PCTCN2018101743-appb-000001
其中，w(i)为第i个决策树对应的权值，Hoob(i)为第i个决策树对应的包外准确率，i和j均为参数，1≤i≤N且i为正整数。
其进一步的技术方案为，该方法还包括：
将N个包外样本集进行汇总得到总包外样本集，对于总包外样本集中的每个样本，利用训练得到的光伏阵列故障诊断模型对样本进行测试得到初始包外准确率；
样本共包括K个故障特征，在其中第k个故障特征中加入噪声，并利用光伏阵列故障诊断模型重新对样本进行测试得到新的包外准确率，计算初始包外准确率和新的包外准确率之间的差值确定第k个特征的重要性度量值，K为正整数，k为参数且k的起始值为1；
在k＜K时，令k＝k+1，并再次执行在其中第k个故障特征中加入噪声的步骤；直至k＝K时得到K个故障特征的重要性度量值；
对于训练样本集中的每个样本，删除样本的K个故障特征中重要性度量值 最小的t个故障特征，得到处理后的训练样本集；
基于随机森林算法利用处理后的训练样本集构建得到光伏阵列故障诊断模型。
其进一步的技术方案为，根据各个典型运行状态对应的投票结果得到待诊断光伏阵列的故障诊断结果，包括：
若最高的投票数对应的典型运行状态仅包括一个，则将最高的投票数对应的典型运行状态作为故障诊断结果输出；
若最高的投票数对应的典型运行状态包括至少两个，则选取权值最高的L个决策树对各个典型运行状态重新进行投票，若存在一个典型运行状态获得最多个决策树的选择，则将典型运行状态作为故障诊断结果输出；否则将权值最高的决策树选择的典型运行状态作为故障诊断结果输出，L为正整数且2≤L＜N。
本发明的有益技术效果是：
1、本申请公开了一种基于改进随机森林算法的光伏阵列故障诊断方法，采集光伏阵列总干路和各个支路的参数以及不同支路之间的阵列间电压差，光伏阵列总干路和各个支路的参数可以反映光伏阵列中各条支路的运行状态，从而诊断出光伏阵列中哪条支路发生了何种故障，而不同支路之间的阵列间电压差可以反映出支路中各个光伏电池组件的运行状态，从而诊断出具体是哪个光伏电池组件发生了何种故障，两类信息的结合有效地实现了光伏阵列的故障定位；同时利用随机森林算法，该算法适用于实际光伏阵列的特点，克服了传统神经网络算法需要数据量大、训练时间长等问题，能够简单快速地完成诊断任务。
2、本申请公开的方法基于数据驱动的思想，将随机森林算法、决策树赋权、投票平局处理和变量重要性度量结合起来，实现光伏阵列的故障诊断并有效提升诊断精度。
3、本申请公开的方法采用改进随机森林的故障诊断模型结构，通过多个决策树的集成，构建了一个强分类器，在利用决策树赋权、投票平局处理和故障特征重要性度量等一系列步骤进行改进，最后构建改进随机森林算法，诊断结果也表明改进后的算法虽然训练时间稍有延长，但精度更高，诊断可靠性更强。
附图说明
图1是本申请公开的光伏阵列故障诊断方法的流程图。
图2是3*3的光伏阵列的搭建示意图。
图3-a是单个太阳能电池组件在不同光照强度下的U-I输出特性曲线。
图3-b是单个太阳能电池组件在不同光照强度下的U-P输出特性曲线。
图3-c是单个太阳能电池组件在不同温度情况下的U-I输出特性曲线。
图3-d是单个太阳能电池组件在不同温度情况下的U-P输出特性曲线。
图4是单个太阳能电池组件在光照强度600W/m2、800W/m2、1000W/m2情况下的输出特性曲线。
图5-a是光伏阵列在无故障和发生单模式故障时的输出特性曲线比较图。
图5-b是光伏阵列在无故障和发生多模式故障时的输出特性曲线比较图。
图6-a是光伏阵列在一个支路中有一个光伏电池组件短路时的输出特性曲线。
图6-b是光伏阵列在一个支路开路时的输出特性曲线。
图6-c是光伏阵列在一个支路中有两个光伏电池组件存在阴影时的输出特性曲线。
图6-d是光伏阵列在一个支路中有一个光伏电池组件短路，另一个支路中有一个光伏电池组件存在阴影时的输出特性曲线。
具体实施方式
下面结合附图对本发明的具体实施方式做进一步说明。
本申请公开了一种基于改进随机森林算法的光伏阵列故障诊断方法，请参考图1，整个故障诊断流程包括如下步骤：
第一步，搭建m*n形式的光伏阵列，该光伏阵列包括并联的n个支路，每个支路包括串联的m个光伏电池组件，m和n均为正整数。光伏阵列中的光伏电池组件通常是太阳能电池组件，各个光伏电池组件规格均相同，以保证光伏阵列的电位平衡，并且每个光伏电池组件并联旁路二极管、每个支路串联隔离二极管，同时快速熔断器、电压和电流传感器等保护、检测装置均安装正确并正常工作。
本申请以n＝3、m＝3为例，搭建的3*3的光伏阵列的示意图请参考图2，共包括支路A、支路B和支路C，每个支路中包括3个串联的光伏电池组件，图2未示出旁路二极管、隔离二极管等器件。本申请光伏阵列中的光伏电池组件采用无锡尚德太阳能有限公司的SunTech STP 270-24/Vd型太阳能电池组件， 该型号的太阳能电池组件的参数规格表如下表所示：
参数类型 参数值
开路电压(Voc) 44.5V
最大功率点电压(Vm) 35.0V
短路电流(Isc) 8.20A
最大功率点电流(Im) 7.71A
最大功率(Pmax) 270W
运行温度 -40℃～+85℃
系统最大承受电压 1000V DC
串联后保险丝的额定电流 20A
功率误差 ±3％
在保持温度不变的情况下，光照强度从200W/m 2上升到1000W/m 2(分别为200、400、600、800和1000W/m 2)时的U-I(电压-电流)输出特性曲线如图3-a所示、U-P(电压-功率)输出特性曲线如图3-b所示。在保持光照强度不变的情况下，温度从35℃下降到15℃(分别为35℃、30℃、25℃、20℃和15℃)时的U-I输出特性曲线如图3-c所示、U-P(电压-功率)输出特性曲线如图3-d所示。特别的，产品手册中给出了该型号的太阳能电池组件在光照强度为600W/m 2、800W/m 2和1000W/m 2情况下的输出特性曲线如图4所示。
第二步，确定光伏阵列在运行过程中的典型运行状态，本申请中的光伏电池组件的典型运行状态用于指示光伏电池组件中的各个光伏电池组件的运行状态。光伏阵列在实际运行时会出现较多类型的故障，主要有四类故障：短路故障、开路故障、阴影故障以及混合故障。当光伏阵列中的一个支路或多个支路中存在光伏电池组件发生故障时，该发生故障的支路以及整个光伏阵列的输出特性曲线都会发生变化，如图5-a示出了光伏阵列在无故障和发生单模式故障时的故障支路、故障阵列、正常支路和正常阵列的输出特性曲线，图5-b示出了光伏阵列在无故障和发生多模式故障时的故障支路、故障阵列、正常支路和正常阵列的输出特性曲线，其中正常支路是指支路中各个光伏电池组件均正常运行的支路，正常阵列是指各个支路均正常运行的光伏阵列，故障支路是支路中存在光伏电池组件发生故障的支路，故障阵列是指有支路发生故障的光伏阵列。由图中可以看出，正常支路的输出电流大，各个光伏电池组件的输出也相等，而故障支路的输出电流能力较弱，能看到明显的电流较低。输出特性曲线存在两个区域：高电压区域和大电流区域，在高电压区域可以通过检测各个支路的输出电流来判断支路的情况，而在大电流区域，可以检测光伏电池组件的输出电压来实现故障定位。
光伏阵列在运行过程中可能出现上述四类故障，再加上正常运行的情况， 光伏阵列的运行状态主要包括五大类：正常运行状态、短路故障状态、开路故障状态、阴影故障状态以及混合故障状态：
(1)、正常运行状态，即所有支路均正常运行。
(2)、短路故障状态，即任意一条支路或多条支路短路。需要说明的是，一条支路中不可能所有光伏电池组件都短路，若全部光伏电池组件都短路，则熔断装置熔断，该支路会开路。
(3)、开路故障状态，即任意一条支路或多条支路开路。
(4)、阴影故障状态，即任意一条支路或多条支路存在阴影，与短路故障状态不同，可以存在一条支路中所有光伏电池组件都存在阴影的情况，因此该状态的数量较多。
(5)、混合故障状态，主要包括光伏阵列中同时发生短路故障和阴影故障的情况。
对于这五大类运行状态，可以采用两级状态分类的方式，首先进行粗略分类从而定位到光伏阵列中的支路，这一步骤不涉及故障定位，可以根据实际情况和经验选择若干最为常见的运行状态。需要说明的是，在光伏系统中，系统的重度故障都是在轻度故障的基础上演变而来，直接发生重度故障的几率微乎其微，并且重度故障的故障类型排列组合过多，所以本申请主要考虑轻、中度故障，即考虑光伏阵列中至少存在一条支路处于正常运行状态的情况。比如在图2所示的光伏阵列中，假设光伏阵列中的支路C始终处于正常运行状态，则可以选择如下20种运行状态作为第一级状态分类的结果，如下表所示：
Figure PCTCN2018101743-appb-000002
Figure PCTCN2018101743-appb-000003
以状态标签F2、F7、F10和F18对应的运行状态为例，他们的输出特性曲线依次如图6-a、6-b、6-c和6-d所示。在第一级状态分类的基础上，可以进行第二级状态分类从而定位到支路中的光伏电池组件，从而实现故障定位。以状态标签F2为例，该情况表示一个支路中有一个光伏电池组件短路，在图2所示的3*3的光伏阵列中，假设光伏阵列中的支路C处于正常运行状态，则该情况表示支路A或支路B上存在一个光伏电池组件短路，共包括6种情况，则在此基础上的第二级状态分类的结果如下表所示：
Figure PCTCN2018101743-appb-000004
同样的，在状态标签F3对应的第一级状态分类的基础上进行的第二级状态分类的结果可以如下表所示：
Figure PCTCN2018101743-appb-000005
其余各种情况都可以以此类推。至此，通过两级状态分类就得到了光伏阵列的各个典型运行状态。
第三步，光伏阵列的电路参数的采集。在光伏阵列处于每个典型运行状态时，采集光伏阵列的阵列干路参数、各个支路对应的阵列支路参数以及不同支路之间的阵列间电压差。其中，光伏阵列的阵列干路参数是光伏阵列的总干路上的参数，本申请中的阵列干路参数包括总干路的最大功率点电压V m、最大功 率点电流I m、开路电压V oc和短路电流I sc，这是由于这四个参数能够很好的刻画光伏电池组件的U-I输出特性曲线。类似的，对于光伏阵列中的每个支路，采集支路的最大功率点电压V m、最大功率点电流I m、开路电压V oc和短路电流I sc作为阵列支路参数。如图2所示，在实际操作时，光伏阵列的电路参数可以通过在光伏阵列中配置电压传感器和电流传感器测得，对于m×n规模的光伏阵列，需要配置[n×(m-1)/2]个电压传感器和n+1个电流传感器，其中符号[]表示对n×(m-1)/2进行向上取整。电流传感器设置在各个支路中以及总干路中，用于检测支路的电流和总干路的电流，电压传感器设置在不同支路间，用于检测不同支路间的阵列间电压差，如图2中设置有3个电压传感器U a、U b和U c，电压传感器U a设置在支路A和支路B之间，电压传感器U b设置在支路B和支路C之间，电压传感器U c设置在支路C和支路A之间。支路电流的检测和支路间电压差的检测实质上都是为了检测每个光伏电池组件的电压和电流，通过电压和电流等参数数值的变化反映光伏电池组件的运行情况，从而实现行故障定位，其原理如下：
在光伏阵列中，光伏阵列的电压矩阵U pv  array为：
Figure PCTCN2018101743-appb-000006
其中，U 11表示光伏阵列中第1个支路的第1个光伏电池组件的输出电压，也即图2中的PV1的输出电压，U mn表示光伏阵列中第n个支路的第m个光伏电池组件的输出电压，也即图2中的PV9的输出电压，依次类推。则光伏阵列有等式成立如下：
Figure PCTCN2018101743-appb-000007
其中，U array是光伏阵列的输出电压，则
Figure PCTCN2018101743-appb-000008
也即，每个光伏电池组件的输出电压应该是整个光伏阵列的输出电压的1/m。
定义光伏阵列的权值矩阵A pv  array，即支路间的电压传感器放置点电位为：
Figure PCTCN2018101743-appb-000009
其中，u 11＝U 21-U 11，u 21＝U 31-U 21，u (m-1)1＝U m1-U (m-1)1，依次类推，则当光伏阵列无故障、处于正常运行状态时，权值矩阵A pv  array中各列元素的值是等差数列、各行元素的值是相等的。若某个支路中的光伏电池组件发生故障，其他光伏电 池组件的电压也会发生变化，利用如下公式计算变化后的光伏电池组件的输出电压为：
U×m＝U bad×(m-x)
其中，U表示正常运行的光伏电池组件的输出电压，m为支路中包含的光伏电池组件的总个数，x为故障支路中发生故障的光伏电池组件的个数，U bad为故障支路中正常运行的光伏电池组件的输出电压。比如在图2中，光伏电池组件PV1、PV2和PV3的输出电压分别为
Figure PCTCN2018101743-appb-000010
当光伏电池组件PV1发生故障时，它会被旁路二极管短路，则光伏电池组件PV1输出为0，上式中的x＝1，则有
Figure PCTCN2018101743-appb-000011
则光伏电池组件PV2和PV3两端的输出电压即为
Figure PCTCN2018101743-appb-000012
也即相应的光伏电池组件PV2和PV3的输出电压将会增加。则当光伏阵列处于故障状态(包括短路、开路、阴影和混合故障)时，权值矩阵A pv  array中无故障的支路的各行元素相等、存在故障的支路的各行元素与无故障的支路的各行元素不相等；且存在故障的支路的各列元素不是等差数列。下表示出了部分短路故障情况下，各个电压传感器采集到的阵列间电压差的特征值：
状态标签 Ua Ub Uc 状态标签 Ua Ub Uc
F1 Uarray/3 Uarray/3 Uarray/3 F3-46 2Uarray/3 2Uarray/3 Uarray/3
F2-1 Uarray/6 Uarray/3 2Uarray/3 F3-56 -Uarray/3 2Uarray/3 Uarray/3
F2-2 Uarray/6 Uarray/3 Uarray/6 F4-14 Uarray/2 Uarray/6 2Uarray/3
F2-3 2Uarray/3 Uarray/3 Uarray/6 F4-15 0 Uarray/6 2Uarray/3
F2-4 2Uarray/3 Uarray/6 Uarray/3 F4-16 0 2Uarray/3 2Uarray/3
F2-5 Uarray/6 Uarray/6 Uarray/3 F4-24 Uarray/2 Uarray/6 Uarray/6
F2-6 Uarray/6 2Uarray/3 Uarray/3 F4-25 0 Uarray/6 Uarray/6
F3-12 -Uarray/3 Uarray/3 2Uarray/3 F4-26 0 2Uarray/3 Uarray/6
F3-13 2Uarray/3 Uarray/3 2Uarray/3 F4-34 Uarray Uarray/6 Uarray/6
F3-23 2Uarray/3 Uarray/3 -Uarray/3 F4-35 Uarray/2 Uarray/6 Uarray/6
F3-45 2Uarray/3 -Uarray/3 Uarray/3 F4-36 Uarray/2 2Uarray/3 Uarray/6
上述状态标签的表示含义可以参考上述第二步中的内容，本领域技术人员理解的是，状态标签F4-14表示的情况是支路A上的PV1和支路B上的PV4短路，以此类推，本申请不一一赘述。
当光伏阵列存在阴影故障时，各个电压传感器采集到的阵列间电压差也会在一个范围内，部分阴影故障情况的特征值如下表所示：
状态标签 Ua Ub Uc
F9-1 Uarray/6<Ua<Uarray/3 Uarray/3 Uarray/3<Uc<2Uarray/3
F9-2 Uarray/6<Ua<Uarray/3 Uarray/3 Uarray/6<Uc<Uarray/3
F9-3 Uarray/3<Ua<2Uarray/3 Uarray/3 Uarray/6<Uc<Uarray/3
F9-4 Uarray/3<Ua<2Uarray/3 Uarray/6<Ub<Uarray/3 Uarray/3
F9-5 Uarray/6<Ua<Uarray/3 Uarray/6<Ub<Uarray/3 Uarray/3
F9-6 Uarray/6<Ua<Uarray/3 Uarray/3<Ub<2Uarray/3 Uarray/3
状态标签F9-1表示的情况是：支路A上的PV1存在阴影，同样的，本领域技术人员可以理解其余各个状态标签表示的含义，本申请不一一赘述。
由上述论述可知，光伏阵列发生故障时会在权值矩阵A pv  array中有所体现，进而会导致电压传感器采集到的阵列间电压差发生变化，因此本申请通过对采集阵列间电压差进行分析就能实现光伏阵列的故障定位。
另外，由上可知，电压传感器采集到的阵列间电压差都是与光伏阵列的输出电压U array成比例关系的，因此可以进一步简化，不直接利用采集到的阵列间电压差，而是将阵列间电压差除以光伏阵列的输出电压U array得到电压系数，以电压系数来表示阵列间电压差。
第四步，故障特征向量和数据样本集的构建。根据采集到的阵列干路参数、阵列支路参数以及阵列间电压差构建故障特征向量，得到故障特征向量后，就能汇总并构建成光伏阵列的数据样本集，由故障特征向量汇总得到数据样本集的方法是本领域技术人员都知道的方法，本申请对此不进行展开叙述。对数据样本集进行划分得到训练样本集和测试样本集，通常可以按照2:1的比例划分成为训练样本集和测试样本集。
第五步，基于改进随机森林算法的光伏阵列故障诊断模型的构建。假设训练样本集为X，故障特征向量的维度为K，也即样本的故障特征的数量为K，K为正整数；故障诊断中需要建立的决策树的数目为N，N≥2且N为正整数，则基于改进随机森林算法利用训练样本集构建得到光伏阵列故障诊断模型的步骤可以简述为：
1、对包含p个样本的训练样本集X进行N轮抽样，对于第i轮抽样，采用有放回的抽样方式抽样p次，由于是进行有放回地抽取，因此会出现部分样本被重复抽取而部分样本没有被抽到的情况，该轮抽样中抽取到的p个样本可以构成该轮抽样对应的子训练样本集X i，训练样本集中在该轮抽样中未被抽到的样本构成该轮抽样对应的包外样本集oob i(out-of-bag)，p为正整数，i为参数，1≤i≤N且i为正整数。
2、利用每一轮抽样对应的子训练样本集训练得到一个决策树，也即利用子训练样本集X i训练决策树Q i。在决策树的节点分裂时，从K个故障特征中随机选择q个故障特征，q为正整数且q≤K，计算q个故障特征中的各个故障特 征对应的基尼系数，将与最小的基尼系数对应的故障特征作为最佳分裂特征来进行节点分裂，基尼系数的计算方法可以采用现有的计算公式，本申请对此不作详细介绍。
3、对于第i轮抽样，其对应的包外样本集oob i中的样本由于没有参与决策树Q i的训练，因此可以将其当做测试集使用，利用包外样本集oob i对决策树Q i进行测试从而得到该决策树Q i的包外准确率H oob(i)。
在普通的随机森林算法中，决策树的权值默认是相等的，因此部分性能优秀的决策树可能无法表现自身优势，甚至会被淹没在多数投票中，所以本申请对此进行改进，对决策树进行了加权，利用决策树的包外准确率定义评价该决策树的性能优劣，在所有决策树训练完成后，计算得到各个决策树的包外准确率，对于第i轮抽样训练得到决策树Q i，利用其对应的包外准确率H oob(i)计算该决策树Q i的权值的计算公式如下：
Figure PCTCN2018101743-appb-000013
其中，w(i)即第i个决策树对应的权值，j为参数。
4、将所有决策树和每个决策树对应的权值追加到决策树集合中，从而汇总构成光伏阵列故障诊断模型。
第六步，光伏阵列故障诊断模型的进一步优化。随着光伏阵列规模的扩大，其同时运行的支路和电压传感器的数目也会随之增加，故障特征向量的维度K也会增加，这将增加模型训练的难度，而通过包外样本能够对样本的故障特征进行重要性度量从而删减部分故障特征，具体步骤如下：
1、将N个包外样本集oob i进行汇总得到总包外样本集，对于总包外样本集中的每个样本，利用训练得到的光伏阵列故障诊断模型对该样本进行测试得到该样本对应的初始包外准确率e，具体的：光伏阵列故障诊断模型中共包括N个决策树，利用其中未使用该样本进行训练的各个决策树对该样本进行测试，就能确定测试正确和测试错误的决策树的个数，从而得到初始包外准确率e。
2、该样本共包括K个故障特征，在其中第k个故障特征中加入噪声，并利用光伏阵列故障诊断模型重新对该样本进行测试得到新的包外准确率e1，计算初始包外准确率和新的包外准确率之间的差值e-e1，从而确定第k个特征的重要性度量值，K为正整数，k为参数且k的起始值为1。
3、在k＜K时，令k＝k+1，并再次执行上述步骤2，也即执行在其中第k个故障特征中加入噪声的步骤。直至k＝K时，得到该样本的全部K个故障特征的重要性度量值。
4、利用总包外样本集中的各个样本计算得到的K个故障特征的重要性度量值总的来说是趋于一致的，但实际操作时难免有误差，因此在利用总包外样本集中的各个样本分别计算得到K个故障特征的重要性度量值后，对其汇总从而确定K个故障特征中重要性度量值最小的t个故障特征。然后对于训练样本集中的每个样本，删除样本的K个故障特征中重要性度量值最小的t个故障特征，得到处理后的训练样本集，1≤t＜K。
5、基于随机森林算法利用处理后的训练样本集构建得到光伏阵列故障诊断模型。
第七步，光伏阵列故障诊断模型的测试。利用测试样本集对光伏阵列故障诊断模型进行测试，可以最终得到诊断精度、平均训练时间和测试时间等指标。按照上述列举的F1-F20，对于光伏阵列的第一级状态类型进行诊断，没有进行故障特征删减，采用改进随机森林算法和普通的随机森林算法进行故障诊断，其中按照2:1的比例划分训练样本集和测试样本集，两种算法使用相同的决策树模型，决策树数目为40，实验结果所得的指标对比如下：
诊断方法 训练时间/s 测试时间/s 诊断精度/％
普通随机森林 0.976120 0.38079 89
改进随机森林 1.472387 0.38711 91
由上表可以看出，本申请提供的基于改进随机森林的算法虽然在训练时间上稍有增加，但诊断精度更高。
二级状态分类可以看成是一级状态分类的一个子类，当光伏阵列规模较大时，其状态的排列组合较多，以短路故障状态为例，统计可得短路故障状态进行二级状态分类后的数目为49，实验结果所得的指标对比如下：同样设置决策树数目为40，决策树的平均诊断精度为64.13％，故障特征向量的维度K＝19，在删除重要性度量值最小的4维故障特征后，决策树的平均诊断精度上升至72.95％，整个光伏阵列故障诊断模型的诊断精度也随之提高。
诊断方法 训练时间/s 测试时间/s 诊断精度/％
普通随机森林 2.0646 0.49109 94.49
改进随机森林 2.1171 0.50935 96.12
第八步，利用测试完成的光伏阵列故障诊断模型对待诊断光伏阵列进行诊断，也即利用N个决策树对各个典型运行状态进行投票，投票时采用赋权投票的方式，也即每个决策树将自身对应的权值作为票数投出。
在投票完成后，统计得到各个典型运行状态对应的投票结果，然后根据各个典型运行状态对应的投票结果得到待诊断光伏阵列的故障诊断结果，得到的故障诊断结果用于指示光伏阵列中的各个光伏电池组件的运行状态，具体的：
1)、检测得到最高的投票数的典型运行状态的数量。
2)、若最高的投票数对应的典型运行状态仅包括一个，则直接将最高的投票数对应的典型运行状态作为故障诊断结果输出，否则执行如下步骤3)。
3)、在最高的投票数对应的典型运行状态包括至少两个时，本申请创造性的加入了平局处理策略，也即选取权值最高的L个决策树对各个典型运行状态重新进行投票，L为正整数且2≤L＜N，根据实际需要选择。若存在一个典型运行状态获得最多个决策树的选择，则将典型运行状态作为故障诊断结果输出；否则将权值最高的决策树选择的典型运行状态作为故障诊断结果输出。比如，以L＝3为例，在重新投票时，若存在两个决策树的投票结果相同，则输出该结果，否则输出权值最高的决策树的投票结果。
以上所述的仅是本申请的优选实施方式，本发明不限于以上实施例。可以理解，本领域技术人员在不脱离本发明的精神和构思的前提下直接导出或联想到的其他改进和变化，均应认为包含在本发明的保护范围之内。

Claims (7)

  1. 一种基于改进随机森林算法的光伏阵列故障诊断方法，其特征在于，所述方法包括：
    确定光伏阵列在运行过程中的典型运行状态，所述光伏阵列包括n个支路，每个支路中包括m个光伏电池组件，所述光伏阵列的典型运行状态用于指示所述光伏阵列中的各个光伏电池组件的运行状态，m和n均为正整数；
    在所述光伏阵列处于每个所述典型运行状态时，采集所述光伏阵列的阵列干路参数、各个支路对应的阵列支路参数以及不同支路之间的阵列间电压差；
    根据所述阵列干路参数、阵列支路参数以及阵列间电压差构建故障特征向量，根据所述故障特征向量构建得到所述光伏阵列的数据样本集，将所述数据样本集划分为训练样本集和测试样本集；
    基于随机森林算法利用所述训练样本集训练得到光伏阵列故障诊断模型，并利用所述测试样本集对所述光伏阵列故障诊断模型进行测试，所述光伏阵列故障诊断模型中包括N个决策树，N为正整数且N≥2；
    利用测试完成的所述光伏阵列故障诊断模型对待诊断光伏阵列进行诊断，得到所述N个决策树对各个所述典型运行状态的投票结果；
    根据各个所述典型运行状态对应的投票结果得到所述待诊断光伏阵列的故障诊断结果，所述故障诊断结果用于指示所述光伏阵列中的各个光伏电池组件的运行状态。
  2. 根据权利要求1所述的方法，其特征在于，
    所述阵列干路参数和每个所述阵列支路参数分别包括电路的最大功率点电压、最大功率点电流、开路电压和短路电流。
  3. 根据权利要求1或2所述的方法，其特征在于，所述光伏阵列故障诊断模型中包括N个决策树及每个所述决策树对应的权值，则所述利用测试完成的所述光伏阵列故障诊断模型对待诊断光伏阵列进行诊断，包括：
    所述N个决策树对各个所述典型运行状态进行投票，每个所述决策树将自身对应的权值作为票数投出；
    统计得到各个所述典型运行状态对应的投票结果。
  4. 根据权利要求3所述的方法，其特征在于，所述基于随机森林算法利用 所述训练样本集训练得到光伏阵列故障诊断模型，包括：
    对包含p个样本的训练样本集进行N轮抽样，对于每一轮抽样，采用有放回的抽样方式抽样p次得到该轮抽样对应的子训练样本集和包外样本集，所述子训练集中包括该轮抽样中抽取到的p个样本，所述包外样本集中包括所述训练样本集中在该轮抽样中未被抽取的样本，p为正整数；
    根据每一轮抽样对应的子训练样本集训练得到一个决策树，并利用该轮抽样对应的包外样本集对所述决策树进行测试得到包外准确率，根据所述包外准确率计算得到所述决策树对应的权值；
    对训练得到的N个决策树和每个决策树对应的权值进行汇总得到所述光伏阵列故障诊断模型。
  5. 根据权利要求4所述的方法，其特征在于，所述根据所述包外准确率计算得到所述决策树对应的权值，包括计算：
    Figure PCTCN2018101743-appb-100001
    其中，w(i)为第i个决策树对应的权值，Hoob(i)为第i个决策树对应的包外准确率，i和j均为参数，1≤i≤N且i为正整数。
  6. 根据权利要求4所述的方法，其特征在于，所述方法还包括：
    将N个所述包外样本集进行汇总得到总包外样本集，对于所述总包外样本集中的每个样本，利用训练得到的所述光伏阵列故障诊断模型对所述样本进行测试得到初始包外准确率；
    所述样本共包括K个故障特征，在其中第k个故障特征中加入噪声，并利用所述光伏阵列故障诊断模型重新对所述样本进行测试得到新的包外准确率，计算所述初始包外准确率和新的包外准确率之间的差值确定所述第k个特征的重要性度量值，K为正整数，k为参数且k的起始值为1；
    在k＜K时，令k＝k+1，并再次执行所述在其中第k个故障特征中加入噪声的步骤；直至k＝K时得到所述K个故障特征的重要性度量值；
    对于所述训练样本集中的每个样本，删除所述样本的K个故障特征中重要性度量值最小的t个故障特征，得到处理后的训练样本集；
    基于随机森林算法利用处理后的所述训练样本集构建得到光伏阵列故障诊断模型。
  7. 根据权利要求3所述的方法，其特征在于，所述根据各个所述典型运行状态对应的投票结果得到所述待诊断光伏阵列的故障诊断结果，包括：
    若最高的投票数对应的典型运行状态仅包括一个，则将最高的投票数对应的典型运行状态作为所述故障诊断结果输出；
    若最高的投票数对应的典型运行状态包括至少两个，则选取权值最高的L个决策树对各个所述典型运行状态重新进行投票，若存在一个典型运行状态获得最多个决策树的选择，则将所述典型运行状态作为所述故障诊断结果输出；否则将权值最高的决策树选择的典型运行状态作为所述故障诊断结果输出，L为正整数且2≤L＜N。
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