A method, device and system for detecting abnormality of a tracker in a photovoltaic power generation system

Abstract

The present invention provides a method, device and system for detecting abnormality of a tracker in a photovoltaic power generation system. The method comprises: analyzing the operation data of each tracker in a detection time period to determine whether the tracker generates abnormal fluctuations and the time point at which the abnormal fluctuations occur; dividing the detection time period into a plurality of time sub-segments, traversing each time sub-segment, and comparing and analyzing the operation data of each tracker in each tracker group in the same time sub-segment to determine whether there are heterogeneous trackers in each time sub-segment tracker group and the number of the heterogeneous trackers; traversing each tracker, if the tracker generates abnormal fluctuations in the detection time period, and the tracker group to which the tracker belongs has a heterogeneous tracker in the time sub-segment to which the time point at which the abnormal fluctuations occur, and the heterogeneous tracker includes the tracker, then the tracker is an abnormal tracker. The present invention finds abnormal trackers more accurately and timely.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to an abnormality detection method, device and system for a tracker in a photovoltaic power generation system. Background Art

[0002] At present, the photovoltaic industry generally uses photovoltaic power station trackers to rotate with the sun to obtain more radiation and thus obtain more power generation gains. The corresponding control system structure is generally as follows: 1. Several trackers share a control box, and each control box is connected to the communication box of the photovoltaic power station sub-array through wired or wireless.

[0003] The communication box collects information such as wind speed and wind direction through sensors, and sends this information to the control box. The control box defines the operating posture based on the received information and transmits its own operating data to the communication box. The communication box only plays the role of information collection, information sending and receiving. The software system of the control box performs logical judgment and fault alarm based on its own pre-defined maximum protection current parameters or maximum bracket angle parameters. The current here refers to the driving current of the motor, and the angle refers to the rotation angle of the photovoltaic bracket. Since the actual operation of the tracker is multi-conditional, setting a smaller upper limit threshold will cause the system to falsely alarm in weather such as strong winds and heavy snow. If the upper limit threshold is set to a larger value, it will lead to untimely abnormal detection, and the tracker will be damaged by the time the problem is discovered. Specifically, as shown in the following scenarios:

[0004] Scenario 1: The tracker uses a motor to drive multiple reduction mechanisms to rotate the entire tracker that is tens of meters or even hundreds of meters long. In actual use, due to a component failure of a driven tracker, the driven rotation cannot rotate, and the active rotation is still rotating according to the previously set instructions. As long as the current or angle limit does not reach the upper limit threshold, the active rotation will continue to rotate while the driven rotation at the other end has completely stopped. This driving force is a very large force that can directly bend the main shaft of the tracker. There is no one on-site real-time duty within a few kilometers of the photovoltaic power station. By the time the on-site personnel discover it, the main shaft may have been twisted into a knot and the components have been damaged.

[0005] Scenario 2: A motor is damaged or a reducer leaks oil, causing increased friction in the system. These cannot be determined by the naked eye to indicate that the tracker has a problem. At this time, the current value may not have reached the upper threshold of the system, so the inspection personnel cannot find the problem tracker and repair it in time.

[0006] Scenario 3: The bolts connecting a reducer to the main shaft are loose and cannot drive the main shaft, but the drive system is still operating normally. The rotation process causes partial damage to the main shaft. Summary of the invention

[0007] One of the purposes of the present invention is to provide a method, device and system for detecting anomalies of a tracker in a photovoltaic power generation system, so as to solve the problems of low accuracy or untimely detection in existing methods for detecting anomalies of a tracker.

[0008] The technical solution provided by the present invention is as follows:

[0009] A method for detecting anomalies of a tracker in a photovoltaic power generation system, comprising:

[0010] Grouping trackers in a photovoltaic power generation system into groups with the same design and installed in close geographical locations;

[0011] Obtain the operating data of each tracker during the detection period;

[0012] Analyze the operation data of each tracker during the detection time period to determine whether the tracker has abnormal fluctuations and the time point of occurrence of the abnormal fluctuations when the abnormal fluctuations occur;

[0013] Divide the detection time period into a number of time sub-segments, traverse the time sub-segments, compare and analyze the operation data of each tracker in each tracker group in the same time sub-segment, and determine whether there is a heterogeneous tracker in the tracker group in each time sub-segment and the number of the heterogeneous tracker if there is a heterogeneous tracker; the heterogeneous tracker is a tracker that operates significantly differently from most trackers in the tracker group;

[0014] Traverse each tracker. If the tracker generates data with abnormal fluctuations during the detection time period, and the tracker group to which the tracker belongs has a heterogeneous tracker in the time subsegment to which the abnormal fluctuation occurs, and the heterogeneous tracker contains the tracker, then the tracker is an abnormal tracker.

[0015] In some embodiments, obtaining operation data of a tracker during a detection period includes:

[0016] Acquiring current data and bracket angle data of a tracker during a detection period;

[0017] According to the current data and the bracket angle data of the tracker at any two adjacent time points within the detection time period, the current change data required for the unit angle change of the tracker at the corresponding time point is obtained;

[0018] The current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period.

[0019] In some embodiments, the current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period, including:

[0020] The current change data required for the unit angle change of the tracker at each time point in the detection time period are amplified according to preset rules, and the amplified data constitute the operation data of the tracker in the detection time period.

[0021] In some embodiments, a Smoothed Z-score algorithm is used to analyze the operating data of each tracker during the detection time period to determine whether the tracker has abnormal fluctuations and the time point of occurrence of the abnormal fluctuations when the abnormal fluctuations occur.

[0022] In some embodiments, comparative analysis is performed on the operation data of each tracker in a tracker group in the same time sub-segment to determine whether there is a heterogeneous tracker in the tracker group in each time sub-segment and the number of the heterogeneous tracker when there is a heterogeneous tracker, including:

[0023] Acquire the operation data of each tracker in the tracker group in the time subsegment;

[0024] Calculate the mean and standard deviation of the operating data according to the operating data of each tracker in the tracker group in the time sub-segment;

[0025] Based on the mean and standard deviation, a reasonable interval is constructed according to the normal distribution rule;

[0026] The operation data of each tracker in the tracker group in the time subsegment is traversed. If the operation data of the tracker in the time subsegment falls outside the reasonable interval, then there is a heterogeneous tracker in the tracker group in the time subsegment, and the tracker is the heterogeneous tracker.

[0027] In some embodiments, comparative analysis is performed on the operation data of each tracker in a tracker group in the same time sub-segment to determine whether there is a heterogeneous tracker in the tracker group in each time sub-segment and the number of the heterogeneous tracker when there is a heterogeneous tracker, including:

[0028] Acquire the operation data of each tracker in the tracker group in the time subsegment;

[0029] Sorting the operation data of each tracker in the tracker group in the time subsegment to obtain a first quartile and a third quartile;

[0030] Calculate the difference between the third quartile and the first quartile to obtain the interquartile range IQR;

[0031] According to the first quartile and the interquartile range IQR, the lower limit of the reasonable interval is obtained;

[0032] According to the third quartile and the interquartile range IQR, the upper limit of the reasonable interval is obtained;

[0033] The operation data of each tracker in the tracker group in the time subsegment is traversed. If the operation data of the tracker in the time subsegment falls outside the reasonable interval, then there is a heterogeneous tracker in the tracker group in the time subsegment, and the tracker is the heterogeneous tracker.

[0034] The present invention also provides a method for detecting anomalies of a tracker in a photovoltaic power generation system, comprising:

[0035] Grouping trackers in a photovoltaic power generation system into groups with the same design and installed in close geographical locations;

[0036] Obtain the operating data of each tracker during the detection period;

[0037] Analyze the operation data of each tracker during the detection time period to determine whether the tracker has abnormal fluctuations and the time point of occurrence of the abnormal fluctuations when the abnormal fluctuations occur;

[0038] Selecting a tracker that has generated abnormal fluctuations within the detection time period, and determining a second detection time subsegment according to a time point at which the abnormal fluctuations of the tracker occurred;

[0039] Comparatively analyzing the operation data of each tracker in the tracker group to which the tracker belongs in the second detection time subsegment, determining whether there is a heterogeneous tracker in the tracker group in the second detection time subsegment and the number of the heterogeneous tracker if there is a heterogeneous tracker;

[0040] If there is a heterogeneous tracker in the tracker group during the second detection time subsegment, and the heterogeneous tracker includes the tracker, then the tracker is an abnormal tracker.

[0041] In some embodiments, obtaining operation data of a tracker during a detection period includes:

[0042] Acquiring current data and bracket angle data of a tracker during a detection period;

[0043] According to the current data and the bracket angle data of the tracker at any two adjacent time points within the detection time period, the current change data required for the unit angle change of the tracker at the corresponding time point is obtained;

[0044] The current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period.

[0045] The present invention also provides an abnormality detection device for a tracker in a photovoltaic power generation system, comprising:

[0046] Memory for storing computer programs;

[0047] The processor is used to implement the steps of the aforementioned method for detecting anomalies of a tracker in a photovoltaic power generation system when running the computer program.

[0048] The present invention also provides a photovoltaic power generation system, comprising a plurality of trackers, a plurality of control boxes and a communication box;

[0049] The control box is electrically connected to the tracker and is used to collect current data and bracket angle data corresponding to the tracker;

[0050] The communication box is connected to the control box by wire or wirelessly, and is used to execute the aforementioned method for detecting abnormality of a tracker in a photovoltaic power generation system.

[0051] The method, device and system for detecting abnormality of a tracker in a photovoltaic power generation system provided by the present invention can at least bring about the following beneficial effects: the present invention monitors whether the operation data of the tracker itself has sudden changes, and compares it with the operation data of other similar trackers in the tracker group to which it belongs. When it is found that the operation data of a certain tracker is abnormal in both detections, the tracker is judged to be abnormal; this abnormality detection method has high accuracy and is more timely than the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The following will explain the preferred implementation mode in a clear and understandable manner with reference to the accompanying drawings, and further explain the above characteristics, technical features, advantages and implementation methods of a method for detecting anomalies of a tracker in a photovoltaic power generation system.

[0053] Figure 1 is a flow chart of an embodiment of a method for detecting an abnormality of a tracker in a photovoltaic power generation system of the present invention;

[0054] Figure 2 is a flow chart of another embodiment of a method for detecting an abnormality of a tracker in a photovoltaic power generation system of the present invention;

[0055] Figure 3 It is a structural schematic diagram of an embodiment of an abnormality detection device for a tracker in a photovoltaic power generation system of the present invention;

[0056] Figure 4is a structural schematic diagram of an embodiment of a photovoltaic power generation system of the present invention;

[0057] Figure 5 It is a schematic diagram of the tracker's working current change curve, angle change curve, and current change curve required for unit angle change. DETAILED DESCRIPTION

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0059] In order to simplify the drawings, only the parts related to the present invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0060] In one embodiment of the present invention, Figure 1 As shown, a method for detecting anomalies of a tracker in a photovoltaic power generation system includes:

[0061] Step S100 groups the trackers in the photovoltaic power generation system, and groups the trackers with the same design and installed in close geographical locations into one group.

[0062] Different trackers may control different photovoltaic modules and use different drive motors, which will result in incomparability of their operating data. For this reason, trackers with the same design and installed in a preset geographical location are generally grouped together. The basic principle of grouping is to ensure that the operating rules of each tracker in the group are the same and the operating data are similar, which can be used for mutual comparison in engineering to identify whether the operating data of certain trackers is abnormal.

[0063] Step S200 obtains the operating data of each tracker during the detection period.

[0064] The operation data of the tracker may be the driving current data of the motor and / or the bracket angle data, or may be data derived from the driving current data of the motor and the bracket angle data, or other data that can reflect the operation status of the tracker.

[0065] In the photovoltaic power generation system, the control box collects the current data of the tracker, the bracket angle data or other data that can reflect the operating status of the tracker. The communication box collects this data from the control box regularly, or the control box actively reports this data to the communication box. These data or their derivative data are used as the operating data of the tracker. The derivative data can be generated by the control box or the communication box.

[0066] Step S300 analyzes the operation data of each tracker during the detection period to determine whether the tracker has abnormal fluctuations and the time point when the abnormal fluctuations occur.

[0067] Under normal circumstances, the running data of the tracker fluctuates within a certain range. However, when the tracker fails or is disturbed by external factors (for example, a sudden strong wind), the running data will fluctuate abnormally. Abnormal fluctuation means that the data fluctuation exceeds the normal fluctuation range. The normal fluctuation range can be obtained based on the historical running data under normal circumstances. Perform anomaly detection on the running data of the tracker over a period of time. If there are abnormal fluctuation points in the running data, it means that the tracker has abnormal fluctuations.

[0068] Step S400 divides the detection time period into several time sub-segments, traverses each time sub-segment, and compares and analyzes the operation data of each tracker in each tracker group in the same time sub-segment to determine whether there are heterogeneous trackers in the tracker groups of each time sub-segment and the numbers of the heterogeneous trackers; heterogeneous trackers are trackers that operate significantly differently from most trackers in the tracker group.

[0069] Specifically, although the operation paces of the trackers belonging to the same tracker group are similar, they are not completely consistent. Therefore, instead of directly comparing and analyzing the operation data of each tracker at the same time point, the operation data within a certain time tolerance (such as 3 minutes) is used as the analysis data. This time tolerance is set based on experience.

[0070] Divide the detection time period into several time sub-segments according to the time tolerance. Analyze the operation data of each tracker in each time sub-segment and find outliers. For example, if the detection time period is 30 minutes and the time tolerance is 3 minutes, there are 10 time sub-segments. Compare the tracker data within the group for each of the 10 time sub-segments.

[0071] If there is an outlier, it means there is a heterogeneous tracker. The number of the tracker to which the outlier belongs is the number of the heterogeneous tracker.

[0072] Step S500 traverses each tracker. If the tracker generates abnormal fluctuations during the detection time period, and the tracker group to which the tracker belongs has a heterogeneous tracker in the time sub-segment to which the abnormal fluctuation occurs, and the heterogeneous tracker includes the tracker, then the tracker is an abnormal tracker.

[0073] Specifically, there are two types of abnormal data detection, the first type is the context time series abnormality detection of a single tracker, which is completed by step S300, and the second type is the data abnormality detection within the tracker group, which is completed by step S400.

[0074] Step S300 is to complete data anomaly detection of a single tracker with the tracker as a unit. The detection result of the tracker includes whether there is an abnormal fluctuation. If there is an abnormal fluctuation, it also includes the time point when the abnormal fluctuation occurs.

[0075] Step S400 is to complete the detection of data anomalies within each time sub-segment in a tracker group. The detection result of the tracker group in each time sub-segment includes whether there is a heterogeneous tracker. The heterogeneous tracker is not limited to 1, and may be 2 or more. If there is a heterogeneous tracker, the number of the heterogeneous tracker is also included.

[0076] A tracker that detects an abnormality in only one type of abnormal data detection does not necessarily malfunction. For example, in windy weather, the tracker detects an abnormality in the first type of abnormal data detection, but it is not abnormal in the second type of abnormal data detection, that is, the tracker is normal. After the strong wind disappears, the operating data returns to normal.

[0077] If a tracker is detected as abnormal in both abnormal data detections, the tracker is an abnormal tracker. The abnormal tracker is determined by combining the detection results of the tracker and the detection results of the tracker group in each time sub-segment, which improves the judgment accuracy of the abnormal tracker.

[0078] In one embodiment, obtaining the operation data of a tracker in the detection time period in step S200 includes:

[0079] Step S210: obtaining current data and bracket angle data of a tracker during a detection period;

[0080] Step S220 obtains the current change data required for the unit angle change of the tracker at the corresponding time point according to the current data and the bracket angle data of any two adjacent time points of the tracker within the detection time period;

[0081] In step S230, the current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period.

[0082] Specifically, based on the current data I of the tracker and the bracket angle data α, the formula The current change data β required for unit angle change is obtained. Figure 5As shown in the figure, β can significantly represent the change in the amplitude of the current when the system resistance changes, which is helpful to find abnormal points in abnormal data detection. Therefore, using β as operating data is helpful to improve the accuracy of abnormal data detection.

[0083] In one embodiment, step S230 includes:

[0084] In step S231, the current change data required for the unit angle change of the tracker at each time point in the detection time period are amplified according to a preset rule, and the amplified data constitute the operation data of the tracker in the detection time period.

[0085] In order to amplify the difference more obviously, the current change data β required for the unit angle change is further amplified, for example, according to γ ​​= a*β δ + b, calculate γ, and use γ as the operating data. Among them, a, b, and δ are preset parameters.

[0086] In one embodiment, step S300 includes:

[0087] Step S310 uses the Smoothed Z-score algorithm to analyze the operating data of each tracker during the detection period to determine whether the tracker has abnormal fluctuations. If there are abnormal fluctuations, the time point of the abnormal fluctuation is also included.

[0088] The Smoothed Z-score algorithm uses the most recent historical data to make predictions. If the actual value exceeds the predicted data range, the actual value is an outlier.

[0089] In one embodiment, step S400 compares and analyzes the operation data of each tracker in a tracker group in the same time sub-segment to determine whether there are heterogeneous trackers in the tracker group of each time sub-segment and the numbers of the heterogeneous trackers, including:

[0090] Step S410: obtaining the operation data of each tracker in the tracker group in the time sub-segment;

[0091] Step S420: Calculate the mean and standard deviation of the operating data according to the operating data of each tracker in the tracker group in the time sub-segment;

[0092] Step S430 constructs a reasonable interval according to the mean and standard deviation and the normal distribution rule;

[0093] Step S440 traverses the operating data of each tracker in the tracker group in the time sub-segment. If the operating data of the tracker in the time sub-segment falls outside the reasonable range, there is a heterogeneous tracker in the tracker group in the time sub-segment, and the tracker is a heterogeneous tracker.

[0094] For example, the reasonable interval is set to [u-3σ, u+3σ], where u is the mean and σ is the standard deviation. Data outside the reasonable interval are outliers or outliers.

[0095] In one embodiment, step S400 compares and analyzes the operation data of each tracker in a tracker group in the same time sub-segment to determine whether there are heterogeneous trackers in the tracker group of each time sub-segment and the numbers of the heterogeneous trackers, including:

[0096] Step S410: obtaining the operation data of each tracker in the tracker group in the time sub-segment;

[0097] Step S421 sorts the operation data of each tracker in the tracker group in the time sub-segment to obtain the first quartile Q1 and the third quartile Q3;

[0098] Step S422 calculates the difference between the third quartile Q3 and the first quartile Q1 to obtain the interquartile range IQR;

[0099] Step S431 obtains the lower limit of the reasonable interval according to the first quartile Q1 and the interquartile range IQR;

[0100] Step S432 obtains the upper limit of the reasonable interval according to the third quartile Q3 and the interquartile range IQR;

[0101] Step S441 traverses the operation data of each tracker in the tracker group in the time sub-segment. If the operation data of the tracker in the time sub-segment falls outside the reasonable range, there is a heterogeneous tracker in the tracker group in the time sub-segment, and the tracker is a heterogeneous tracker.

[0102] Specifically, the quartile data calculation method is used. The interquartile range IQR = Q3-Q1, the lower limit of the reasonable interval (for example = Q1-1.5*IQR), and the upper limit of the reasonable interval (for example = Q3+1.5*IQR) are calculated, where 1.5 is a preset coefficient that can be adjusted as needed. Outliers are defined as data that is lower than the lower limit of the reasonable interval or higher than the upper limit of the reasonable interval.

[0103] In one embodiment of the present invention, Figure 2 As shown, a method for detecting anomalies of a tracker in a photovoltaic power generation system includes:

[0104] Step S100 groups the trackers in the photovoltaic power generation system, and groups the trackers with the same design and installed in close geographical locations into one group;

[0105] Step S200 obtains the operation data of each tracker during the detection period;

[0106] Step S300 analyzes the operation data of each tracker during the detection period to determine whether the tracker has abnormal fluctuations and the time point when the abnormal fluctuations occur;

[0107] Step S600: selecting a tracker that has generated abnormal fluctuations within a detection time period, and determining a second detection time subsegment according to the time point of occurrence of the abnormal fluctuations of the tracker;

[0108] Step S700 compares and analyzes the operation data of each tracker in the tracker group to which the tracker belongs in the second detection time subsegment to determine whether there is a heterogeneous tracker in the tracker group in the second detection time subsegment and the number of the heterogeneous tracker;

[0109] In step S800 , if there is a heterogeneous tracker in the tracker group in the second detection time subsegment, and the heterogeneous tracker includes the tracker, then the tracker is an abnormal tracker.

[0110] In one embodiment, obtaining operation data of a tracker during a detection period includes:

[0111] Step S210: obtaining current data and bracket angle data of a tracker during a detection period;

[0112] Step S220 obtains the current change data required for the unit angle change of the tracker at the corresponding time point according to the current data and the bracket angle data of any two adjacent time points of the tracker within the detection time period;

[0113] In step S230, the current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period.

[0114] In one embodiment, step S230 includes:

[0115] In step S231, the current change data required for the unit angle change of the tracker at each time point in the detection time period are amplified according to a preset rule, and the amplified data constitute the operation data of the tracker in the detection time period.

[0116] In one embodiment, step S300 includes:

[0117] Step S310 uses the Smoothed Z-score algorithm to analyze the operating data of each tracker during the detection period to determine whether the tracker has abnormal fluctuations and the time point when the abnormal fluctuations occur.

[0118] In one embodiment, step S700 compares and analyzes the operation data of each tracker in the tracker group in the second detection time subsegment to determine whether there is a heterogeneous tracker in the tracker group in the second detection time subsegment and the number of the heterogeneous tracker, including:

[0119] Step S710: obtaining the operation data of each tracker in the tracker group in the second detection time subsegment;

[0120] Step S720: Calculate the mean and standard deviation of the operating data according to the operating data of each tracker in the tracker group in the second detection time subsegment;

[0121] Step S730 constructs a reasonable interval according to the mean and standard deviation and the normal distribution rule;

[0122] Step S740 traverses the operating data of each tracker in the tracker group in the second detection time subsegment. If the operating data of a tracker in the second detection time subsegment falls outside a reasonable range, there is a heterogeneous tracker in the tracker group in the second detection time subsegment, and the tracker is a heterogeneous tracker.

[0123] In one embodiment, step S700 compares and analyzes the operation data of each tracker in the tracker group in the second detection time subsegment to determine whether there is a heterogeneous tracker in the tracker group in the second detection time subsegment and the number of the heterogeneous tracker, including:

[0124] Step S710: obtaining the operation data of each tracker in the tracker group in the second detection time subsegment;

[0125] Step S721 sorts the operation data of each tracker in the tracker group in the second detection time subsegment to obtain the first quartile and the third quartile;

[0126] Step S722 calculates the difference between the third quartile and the first quartile to obtain the interquartile range IQR;

[0127] Step S731 obtains the lower limit of the reasonable interval according to the first quartile and the interquartile range IQR;

[0128] Step S732: obtaining the upper limit of the reasonable interval according to the third quartile and the interquartile range IQR;

[0129] Step S741 traverses the operating data of each tracker in the tracker group in the second detection time subsegment. If the operating data of a tracker in the second detection time subsegment falls outside a reasonable range, there is a heterogeneous tracker in the tracker group in the second detection time subsegment, and the tracker is a heterogeneous tracker.

[0130] In one embodiment of the present invention, Figure 3As shown, a device 20 for detecting an abnormality of a tracker in a photovoltaic power generation system includes a memory 21 and a processor 22 .

[0131] The memory 21 is used to store a computer program 23 .

[0132] When the processor 22 runs the computer program 23 , the steps of the abnormality detection method for a tracker in a photovoltaic power generation system as described in the above-mentioned embodiment are implemented.

[0133] In one embodiment of the present invention, Figure 4 As shown, a photovoltaic power generation system comprises:

[0134] A number of trackers 100, a number of control boxes 200 and a communication box 300;

[0135] The control box 200 is electrically connected to the tracker 100 and is used to collect current data and bracket angle data of the corresponding tracker;

[0136] The communication box 300 is connected to the control box 200 by wire or wirelessly, and is used to execute the abnormality detection method of the tracker in the photovoltaic power generation system described in the above embodiment.

[0137] Specifically, the communication box regularly (for example, every 5 minutes) inspects and collects the current and angle data of the tracker collected by each control box, and summarizes and monitors the current and bracket angle data of the tracker in the photovoltaic power generation system. It not only monitors the sudden changes in the tracker's own operating data, but also compares the data with other similar trackers in the tracker group. When it is found that the operating parameters of a tracker are abnormal in both abnormal detection states, the communication box immediately activates an alarm or issues a command to stop the operation of the tracker.

[0138] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for detecting abnormality of a tracker in a photovoltaic power generation system, characterized in that: include: Grouping trackers in a photovoltaic power generation system into groups with the same design and installed in close geographical locations; Obtain the operating data of each tracker during the detection period; Analyze the operation data of each tracker during the detection time period to determine whether the tracker has abnormal fluctuations and the time point of occurrence of the abnormal fluctuations when the abnormal fluctuations occur; Divide the detection time period into a number of time sub-segments, traverse each time sub-segment, compare and analyze the operation data of each tracker in each tracker group in the same time sub-segment, determine whether there is a heterogeneous tracker in the tracker group in each time sub-segment and the number of the heterogeneous tracker if there is a heterogeneous tracker; the heterogeneous tracker is a tracker that operates significantly differently from most trackers in the tracker group; Traversing each tracker, if the tracker generates data with abnormal fluctuations within the detection time period, and the tracker group to which the tracker belongs has a heterogeneous tracker in the time subsegment to which the abnormal fluctuation occurs, and the heterogeneous tracker includes the tracker, then the tracker is an abnormal tracker; Wherein, obtaining the operation data of a tracker during the detection time period includes: Acquiring current data and bracket angle data of a tracker during a detection period; According to the current data and the bracket angle data of the tracker at any two adjacent time points within the detection time period, the current change data required for the unit angle change of the tracker at the corresponding time point is obtained; The current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period.

2. The method for detecting abnormality of a tracker in a photovoltaic power generation system according to claim 1, characterized in that: The current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period, including: The current change data required for the unit angle change of the tracker at each time point in the detection time period are amplified according to preset rules, and the amplified data constitute the operation data of the tracker in the detection time period.

3. A method for detecting abnormality of a tracker in a photovoltaic power generation system according to claim 1 or 2, characterized in that: The Smoothed Z-score algorithm is used to analyze the operation data of each tracker in the detection time period to determine whether the tracker has abnormal fluctuations and the time point of the abnormal fluctuations when the abnormal fluctuations occur.

4. The method for detecting abnormality of a tracker in a photovoltaic power generation system according to claim 1, characterized in that: Comparative analysis is performed on the operation data of each tracker in a tracker group in the same time sub-segment to determine whether there is a heterogeneous tracker in the tracker group in each time sub-segment and the number of the heterogeneous tracker when there is a heterogeneous tracker, including: Acquire the operation data of each tracker in the tracker group in the time subsegment; Calculate the mean and standard deviation of the operating data according to the operating data of each tracker in the tracker group in the time sub-segment; Based on the mean and standard deviation, a reasonable interval is constructed according to the normal distribution rule; The operation data of each tracker in the tracker group in the time subsegment is traversed. If the operation data of the tracker in the time subsegment falls outside the reasonable interval, then there is a heterogeneous tracker in the tracker group in the time subsegment, and the tracker is the heterogeneous tracker.

5. The method for detecting abnormality of a tracker in a photovoltaic power generation system according to claim 1, characterized in that: Comparative analysis is performed on the operation data of each tracker in a tracker group in the same time sub-segment to determine whether there is a heterogeneous tracker in the tracker group in each time sub-segment and the number of the heterogeneous tracker when there is a heterogeneous tracker, including: Acquire the operation data of each tracker in the tracker group in the time subsegment; Sorting the operation data of each tracker in the tracker group in the time subsegment to obtain a first quartile and a third quartile; Calculate the difference between the third quartile and the first quartile to obtain the interquartile range IQR; According to the first quartile and the interquartile range IQR, the lower limit of the reasonable interval is obtained; According to the third quartile and the interquartile range IQR, the upper limit of the reasonable interval is obtained; The operation data of each tracker in the tracker group in the time subsegment is traversed. If the operation data of the tracker in the time subsegment falls outside the reasonable interval, then there is a heterogeneous tracker in the tracker group in the time subsegment, and the tracker is the heterogeneous tracker.

6. A method for detecting abnormality of a tracker in a photovoltaic power generation system, characterized in that: include: Grouping trackers in a photovoltaic power generation system into groups with the same design and installed in close geographical locations; Obtain the operating data of each tracker during the detection period; Analyze the operation data of each tracker during the detection time period to determine whether the tracker has abnormal fluctuations and the time point of occurrence of the abnormal fluctuations when the abnormal fluctuations occur; Selecting a tracker that has generated abnormal fluctuations within the detection time period, and determining a second detection time subsegment according to a time point at which the abnormal fluctuations of the tracker occurred; Comparatively analyzing the operation data of each tracker in the tracker group to which the tracker belongs in the second detection time subsegment, determining whether there is a heterogeneous tracker in the tracker group in the second detection time subsegment and the number of the heterogeneous tracker if there is a heterogeneous tracker; If there is a heterogeneous tracker in the tracker group during the second detection time subsegment, and the heterogeneous tracker includes the tracker, then the tracker is an abnormal tracker; Wherein, obtaining the operation data of a tracker during the detection time period includes: Acquiring current data and bracket angle data of a tracker during a detection period; According to the current data and the bracket angle data of the tracker at any two adjacent time points within the detection time period, the current change data required for the unit angle change of the tracker at the corresponding time point is obtained; The current change data required by the tracker for the unit angle change at each time point in the detection time period constitutes the operation data of the tracker in the detection time period.

7. The method for detecting abnormality of a tracker in a photovoltaic power generation system according to claim 6, characterized in that: Comparing and analyzing the operation data of each tracker in the tracker group to which the tracker belongs in the second detection time subsegment, determining whether there is a heterogeneous tracker in the tracker group in the second detection time subsegment and the number of the heterogeneous tracker when there is a heterogeneous tracker, including: Acquire operation data of each tracker in the tracker group in the second detection time subsegment; Calculate the mean and standard deviation of the operating data according to the operating data of each tracker in the tracker group in the second detection time subsegment; Based on the mean and standard deviation, a reasonable interval is constructed according to the normal distribution rule; The operation data of each tracker in the tracker group in the second detection time subsegment is traversed. If the operation data of a tracker in the second detection time subsegment falls outside the reasonable interval, there is a heterogeneous tracker in the tracker group in the second detection time subsegment, and the tracker is the heterogeneous tracker.

8. The method for detecting abnormality of a tracker in a photovoltaic power generation system according to claim 6, characterized in that: Comparing and analyzing the operation data of each tracker in the tracker group to which the tracker belongs in the second detection time subsegment, determining whether there is a heterogeneous tracker in the tracker group in the second detection time subsegment and the number of the heterogeneous tracker when there is a heterogeneous tracker, including: Acquire operation data of each tracker in the tracker group in the second detection time subsegment; Sorting the operation data of each tracker in the tracker group in the second detection time subsegment to obtain a first quartile and a third quartile; Calculate the difference between the third quartile and the first quartile to get the interquartile range IQR; According to the first quartile and the interquartile range IQR, the lower limit of the reasonable interval is obtained; According to the third quartile and the interquartile range IQR, the upper limit of the reasonable interval is obtained; The operation data of each tracker in the tracker group in the second detection time subsegment is traversed. If the operation data of a tracker in the second detection time subsegment falls outside the reasonable interval, then there is a heterogeneous tracker in the tracker group in the second detection time subsegment, and the tracker is a heterogeneous tracker.

9. An abnormality detection device for a tracker in a photovoltaic power generation system, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of a method for detecting anomalies of a tracker in a photovoltaic power generation system according to any one of claims 1 to 8 when running the computer program.

10. A photovoltaic power generation system, characterized in that: Including several trackers, several control boxes and communication boxes; The control box is electrically connected to the tracker and is used to collect current data and bracket angle data corresponding to the tracker; The communication box is connected to the control box by wire or wirelessly, and is used to execute the abnormality detection method of a tracker in a photovoltaic power generation system as described in any one of claims 1-8.

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