CN114168657A - Method, system, equipment and medium for detecting wind power abnormal data in real time - Google Patents
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Abstract
The embodiment of the disclosure provides a method, a system, equipment and a medium for detecting wind power abnormal data in real time, which belong to the technical field of data processing, and specifically comprise the following steps: selecting target data in normal operation from historical wind power data corresponding to the wind turbine generator; obtaining a completely random felling tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree; acquiring data to be detected and updating the normal data structure model to obtain a target detection model; and inputting the data to be detected into the target detection model to obtain a detection result. According to the scheme, the stable normal data structure model is established by selecting the data in normal operation, the structure of the normal data is learned to update the model, and then the obtained target detection model is used for detecting the data to be detected, so that the detection efficiency, the operation convenience and the detection precision are improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of data processing, in particular to a method, a system, equipment and a medium for detecting wind power abnormal data in real time.
Background
Currently, data acquisition and monitoring control systems collect data from different sensors of Wind turbines (WT for short) in real time. Data collection and monitoring control data may be used for power curve modeling, wind speed and power prediction, WT control, and wind farm performance assessment. Whether the data is reliable directly affects the analysis and assessment of WT status. However, in the actual operation process, due to the influence of factors such as measurement, transmission, control and wind curtailment, a large amount of abnormal data is always collected by the data collection and monitoring control system. Such anomalous data, if not handled in a timely manner, can not only affect the real-time analysis of WT status, but can also accumulate over time in historical data. Wind power data is more random in time and anomalous data is more difficult to detect than other data. Therefore, the research on related methods is urgent.
However, the current research on wind power data anomaly detection mainly focuses on historical data, the historical data comprises scattered data and time sequence data, the quality requirement of the method on the data set is high, and the data cost or the detection time of the method for detecting the anomaly data in real time is greatly increased. In the case of insufficient research on real-time detection of wind power abnormal data, other fields have conducted some research on real-time detection of abnormal data. The mainstream method is to judge the deviation of abnormal data according to the trend of the data stream, but when the data fluctuation is large in a certain period, the data distribution in the window becomes dispersed, the detection performance is degraded, and when the randomness of the data becomes large, it may be difficult to judge whether the high-dimensional data is abnormal.
Therefore, a real-time detection method for wind power abnormal data, which is efficient, convenient and high in detection precision, is urgently needed.
Disclosure of Invention
In view of this, embodiments of the present disclosure provide a method, a system, a device, and a medium for detecting wind power abnormal data in real time, which at least partially solve the problems of complex operation and poor detection efficiency and accuracy in the prior art.
In a first aspect, an embodiment of the present disclosure provides a method for detecting wind power abnormal data in real time, including:
selecting target data in normal operation from historical wind power data corresponding to the wind turbine generator;
obtaining a completely random felling tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree;
acquiring data to be detected and updating the normal data structure model to obtain a target detection model;
and inputting the data to be detected into the target detection model to obtain a detection result.
According to a specific implementation manner of the embodiment of the present disclosure, the step of selecting target data in normal operation from historical wind power data corresponding to a wind turbine generator includes:
and selecting characteristic values of a plurality of characteristics in the historical wind power data in corresponding moments to form the target data.
According to a specific implementation manner of the embodiment of the present disclosure, the step of obtaining a completely random felled tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felled tree includes:
completing segmentation on all the target data to obtain the completely random cut-down trees;
setting a left node in the completely random cut-down tree as 0 and setting a right node as 1 to obtain position vectors of all nodes in the completely random cut-down tree;
calculating the depth corresponding to each layer of nodes in the completely random cut-down tree;
calculating the index of each node according to the position vector and the depth corresponding to each node;
and establishing and initializing the normal data structure model according to all the indexes.
According to a specific implementation manner of the embodiment of the present disclosure, the step of acquiring the data to be detected and updating the normal data structure model to obtain the target detection model includes:
selecting brother nodes corresponding to the data to be detected from the normal data structure model;
calculating the characteristic difference between the data to be detected and the brother node;
and updating the normal data structure model according to the characteristic difference to obtain the target detection model.
According to a specific implementation manner of the embodiment of the present disclosure, the step of inputting the data to be detected into the target detection model to obtain a detection result includes:
sampling the data to be detected, inserting the sampled data into the normal data structure model and calculating the complexity change of the target detection model;
and judging whether all the data to be detected are abnormal according to the complexity change and the change threshold value, and outputting the detection result.
According to a specific implementation manner of the embodiment of the present disclosure, after the step of inputting the data to be detected into the target detection model to obtain the detection result, the method further includes:
rejecting abnormal data in the data to be detected according to the detection result;
and calculating the characteristic difference of the data which is most similar to the rest data in the normal data structure model, and determining whether to keep the rest data in the normal data structure model or not according to the characteristic difference and a characteristic threshold value.
In a second aspect, an embodiment of the present disclosure provides a wind power abnormal data real-time detection system, including:
the selection module is used for selecting target data in normal operation from historical wind power data corresponding to the wind turbine generator;
the establishing module is used for obtaining a completely random felling tree according to the target data and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree;
the acquisition module is used for acquiring data to be detected and updating the normal data structure model to obtain a target detection model;
and the detection module is used for inputting the data to be detected into the target detection model to obtain a detection result.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the wind power anomaly data real-time detection method in the first aspect or any implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, where the computer instructions are configured to cause the computer to execute the method for detecting wind power anomaly data in real time in any implementation manner of the foregoing first aspect or first aspect.
In a fifth aspect, the present disclosure also provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is caused to execute the wind power anomaly data real-time detection method in the foregoing first aspect or any implementation manner of the first aspect.
The wind power abnormal data real-time detection scheme in the embodiment of the disclosure includes: selecting target data in normal operation from historical wind power data corresponding to the wind turbine generator; obtaining a completely random felling tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree; acquiring data to be detected and updating the normal data structure model to obtain a target detection model; and inputting the data to be detected into the target detection model to obtain a detection result.
The beneficial effects of the embodiment of the disclosure are: according to the scheme, the stable normal data structure model is established by selecting the data in normal operation, the structure of the normal data is learned to update the model, and then the obtained target detection model is used for detecting the data to be detected, so that the detection efficiency, the operation convenience and the detection precision are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for detecting abnormal wind power data in real time according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a real-time detection framework of a real-time detection method for wind power abnormal data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a real-time wind power anomaly data detection system according to an embodiment of the present disclosure;
fig. 4 is a schematic view of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a real-time detection method for wind power abnormal data, which can be applied to the real-time detection process of the wind power abnormal data in a wind power generation scene.
Referring to fig. 1, a schematic flow chart of a method for detecting abnormal wind power data in real time according to an embodiment of the present disclosure is shown. As shown in fig. 1, the method mainly comprises the following steps:
s101, selecting target data in normal operation from historical wind power data corresponding to a wind turbine generator;
optionally, in step S101, selecting target data in normal operation from historical wind power data corresponding to the wind turbine generator includes:
and selecting characteristic values of a plurality of characteristics in the historical wind power data in corresponding moments to form the target data.
In specific implementation, the generated data is mainly concentrated in a stable region in consideration of the normal operation period of the wind turbine generator. For the wind power data, the wind power data are mainly distributed near the power curve, so that stable wind power data are collected to establish a normal data structure model, abnormal data can be identified according to the distribution characteristics of the normal data, and the influence of real-time data fluctuation on detection can be reduced. Target data in normal operation can be selected from historical wind power data corresponding to the wind turbine generator.
For example, a historical data set D is selected from a data acquisition and monitoring control system, where D ═ Di}，i＝1，…，t,diAnd the sampling data of the wind turbine generator at the moment i. di＝(di1,…,din)，dijIs the characteristic value of the jth characteristic of the WT at time i. Then, the normal data set X is selected according to equations (1) to (3).
D1＝{di|0.95pr＜dip＜1.05pr} (2)
D2＝{di|vcurve-Δv＜div＜vcurve+Δv} (3)
Where w is the number of normal data in X. dpAnd dvPower and wind speed, respectively. dipAnd divRespectively power and wind speed at time i. p is a radical ofrAnd vrRated power and wind speed, respectively. v. ofcurveIs the corresponding wind speed on the power curve. Δ v is the range of normal data wind speeds.
S102, obtaining a completely random felling tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree;
in specific implementation, after the target data are obtained and the target data are stable normal data, a completely random cut-down tree can be obtained according to the target data, and a normal data structure model of the wind turbine generator is established and initialized according to the completely random cut-down tree.
S103, collecting data to be detected and updating the normal data structure model to obtain a target detection model;
in specific implementation, considering that the normal data structure model is established according to historical data, the model has strong dependence on initial data, and the expansibility in a data stream is not enough, the normal data structure model can be updated by collecting data to be detected, a target detection model is obtained, the structure of the wind power normal data in the data stream is learned, the dependence on an initial normal data set is reduced, and the expansibility of the model in the data stream is enhanced.
And S104, inputting the data to be detected into the target detection model to obtain a detection result.
After the target detection model is obtained, the data to be detected may be input into the target detection model, and the target detection model identifies whether the state of each data in the data to be detected is normal, so as to obtain the detection result, where a specific frame diagram of the real-time detection method for wind power abnormal data is shown in fig. 2.
According to the real-time detection method for the wind power abnormal data, the stable normal data structure model is established by selecting the data in normal operation, the structure of the normal data is learned to update the model, and then the obtained target detection model is used for detecting the data to be detected, so that the detection efficiency, the operation convenience and the detection precision are improved.
On the basis of the above embodiment, the step of obtaining a completely random felling tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree includes:
completing segmentation on all the target data to obtain the completely random cut-down trees;
setting a left node in the completely random cut-down tree as 0 and setting a right node as 1 to obtain position vectors of all nodes in the completely random cut-down tree;
calculating the depth corresponding to each layer of nodes in the completely random cut-down tree;
calculating the index of each node according to the position vector and the depth corresponding to each node;
and establishing and initializing the normal data structure model according to all the indexes.
For example, the steps of building and initializing the normal data structure model may be as follows:
step 1, according to
Randomly selects a feature in the target data X, where li＝maxx∈SXi-minx∈SXiIs the difference between the ith feature maximum and minimum feature values.
Step 2, selecting Xi,ave＝(minXi+maxXi) And/2 as the boundary of the split data.
Step 3, Xleft＝{X|Xi≤Xi,ave},Xright＝X\Xleft。
Step 4, at XleftAnd XrightAnd (3) performing the steps 1-3 in an upward recursion mode until all data are separated to obtain a Complete Random Cut Tree (CRCT).
Step 5, setting a bit 0 for the left child node and a bit 1 for the right child node of each node in the completely random cut-down tree to obtain position vectors f (x) of all nodesi)。
Step 6, setting depth for each layer of nodes in the completely random cut-down treeiAnd adding 1 to the depth of each layer of node added to the completely random cut-down tree.
Step 7, calculating the index of each node according to the position vector of each node and the depth of each node in the completely random cut-down tree
And 8, obtaining the Normal Data Structure Model (NDSM) as the complete random cut-down tree with the node index.
Wherein step 1 selects different features by non-uniform probability taking into account the influence of different feature orders of magnitude. In order to avoid the branches in the binary tree from being biased to one side due to uneven distribution of data, the mean value of the selected features is used as the boundary for dividing the data in step 2. In step 4, in order to avoid mutual occlusion between data caused by data accumulation of the same node, steps 1-3 are repeated until all data are completely divided. In step 7, each data point in the normal data structure model is located using a node index.
On the basis of the above embodiment, the step of acquiring the data to be detected and updating the normal data structure model to obtain the target detection model includes:
selecting brother nodes corresponding to the data to be detected from the normal data structure model;
calculating the characteristic difference between the data to be detected and the brother node;
and updating the normal data structure model according to the characteristic difference to obtain the target detection model.
In particular, considering that the quality of the model depends on the selection of normal data, selecting more comprehensive data in establishing the model inevitably increases the data cost. A model update strategy can be set that enables the normal data structure model to learn new normal data structures over time and reduce reliance on initial data selection.
The specific steps are as follows:
step 1, if xtIf the data is abnormal, the index of the data is returned and deleted from the NDSM according to the index.
Step 2, if xtIs normal data, then x is found in the NDSMtThe sibling nodes of (1).
Step 3, traversing xtAnd xqAll features of (1), and calculating Δ Di＝|xt，i-xq，iL where xt，iAnd xq，iAre each xtAnd xqThe ith characteristic value of (1).
Step 4, if
X is thentIs reserved, diIs the minimum resolution of the ith feature of the WT and the maximum amount of data that can be extended does not exceed
Step 5, when the data volume in the NDSM reaches the maximum value and the newly acquired data xtAnd (4) deleting the data with the maximum depth in the NDSM if the condition in the step (4) is met.
Step 6, if xtDeletion x without satisfying the conditions in step 4t。
And 7, returning the updated normal data structure model. Wherein, the key is to evaluate the similarity of the data (step 2-4). In a binary tree, if two data xtAnd xqIf the difference between the two data is less than a certain degree, the two data have a high probability of being under the same parent node. So that one is found under the same parent node according to whether two data are in the same parent nodeThe two most similar data in the dataset. In terms of data distribution, the two data have substantially the same effect on the other data, and one node may replace the other. When the difference between the ith characteristics of two sibling nodes is less than a certain value, x is addedtAnd xqIs judged as redundant data.
Further, the step of inputting the data to be detected into the target detection model to obtain a detection result includes:
sampling the data to be detected, inserting the sampled data into the normal data structure model and calculating the complexity change of the target detection model;
and judging whether all the data to be detected are abnormal according to the complexity change and the change threshold value, and outputting the detection result.
In implementation, the data in the target detection model may change over time in consideration of model update. In order to better adapt to the dynamic change, a dynamic abnormal data real-time detection process is provided.
Firstly, for the data x at the current timetSamples are taken and inserted into the normal data structure model.
Then, insert xtThe position vector of the node in the target detection model is changed. To compute the change in position vector, we define the model complexity DISP at time ttAnd the sum of the position vectors of all the nodes in the target detection model is quantized, and is calculated as follows:
wherein, WtIs the amount of data in the target detection model at time t.
According to DISPt-1And DISPtInsert xtInduced position vector change, i.e. model complexity change Δ DISPtThe following can be obtained:
ΔDISPt＝DISP-DISPt-1 (5)
finally, judging x according to the distribution condition of normal data in the target detection model at the t-1 momenttWhether it is abnormal. The judgment conditions are given in (6).
Qt＝Qt-1,mean+λQt-1,std (7)
Wherein Q istAnd obtaining a threshold value of the normal data structure model at the time t-1. λ is a weighting factor, typically between 0-2. Δ DISPt-1，iThe model complexity change caused by deleting i nodes at the t-1 th moment in the target detection model. Qt-1，meanAnd Qt-1，stdIs Δ DISPt-1，iMean and standard deviation of (d).
It should be noted that the choice of λ has an influence on the model update. As λ becomes larger, more data will be retained in the normal data structure model, and the detection time will also become larger.
Optionally, after the step of inputting the data to be detected into the target detection model to obtain the detection result, the method further includes:
rejecting abnormal data in the data to be detected according to the detection result;
and calculating the characteristic difference of the data which is most similar to the rest data in the normal data structure model, and determining whether to keep the rest data in the normal data structure model or not according to the characteristic difference and a characteristic threshold value.
In specific implementation, after the detection result is obtained, the abnormal data in the data to be detected can be removed according to the detection result, then the characteristic difference of the data which is most similar to the rest data in the normal data structure model is calculated, and whether the rest data is kept in the normal data structure model or not is determined according to the characteristic difference and the characteristic threshold.
The overall process of the embodiment of the disclosure specifically comprises: firstly, a normal data set X capable of reflecting the whole data structure of the wind turbine generator is obtained in a data acquisition and monitoring control system, and a normal data structure model is initialized by the X.
Secondly, sample data xtInsert normal data structure model, calculate insert xtModel complexity DISP of front and back normal data structure modelt-1And DISPtObtained bytInduced complexity change Δ DISPt. Then based on Δ DISP and threshold QtJudgment of xtWhether it is abnormal.
Finally, deleting the abnormal data from the normal data structure model; for normal data, calculate xtAnd x in the Normal data Structure modeltDifference D between ith features of most similar datanearest. Then according to DnearestAnd diJudgment of xtWhether or not to remain in the normal data structure model.
To further illustrate the wind power abnormal data real-time detection method of the embodiment of the present disclosure, the detection performance may be evaluated, and precision, recycle, and F may be selected1As an evaluation index. These criteria are defined as follows:
precision＝TP/(TP+FP) (10)
recall＝TP/(TP+FN) (11)
F1＝(2×precision×recall)/(precision+recall) (12)
where TP, FP, FN and TN are the number of detected true positive, false negative and true negative data, respectively.
In addition, in order to evaluate the degree of dispersion between the power curve and the data in the normal data structure model, an additional evaluation index, Root Mean Square Error (RMSE), may be used, and the calculation formula is:
wherein, RMSEiRMSE for the ith wind speed interval (wind speed interval was selected according to the standard power curve provided by the manufacturer). N is a radical ofiIs the data amount of the ith interval. PiIs the power on the power curve of the i-th interval. Pi，jIs the jth power data of the ith interval.
It should be noted that the smaller the RMSE, the lower the data dispersion, and the more stable the data structure.
Corresponding to the above method embodiment, referring to fig. 3, an embodiment of the present disclosure further provides a wind power abnormal data real-time detection system 30, including:
the selection module 301 is configured to select target data in normal operation from historical wind power data corresponding to a wind turbine generator;
the establishing module 302 is used for obtaining a completely random felling tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree;
the acquisition module 303 is configured to acquire data to be detected and update the normal data structure model to obtain a target detection model;
the detection module 304 is configured to input the data to be detected into the target detection model to obtain a detection result.
The system shown in fig. 3 may correspondingly execute the content in the above method embodiment, and details of the part not described in detail in this embodiment refer to the content described in the above method embodiment, which is not described again here.
Referring to fig. 4, an embodiment of the present disclosure also provides an electronic device 40, including: at least one processor and a memory communicatively coupled to the at least one processor. The storage stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the wind power abnormal data real-time detection method in the foregoing method embodiment.
The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions for causing the computer to execute the wind power abnormal data real-time detection method in the foregoing method embodiment.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the wind power anomaly data real-time detection method in the aforementioned method embodiments.
Referring now to FIG. 4, a block diagram of an electronic device 40 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device 40 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 40 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 40 to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device 40 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the steps associated with the method embodiments.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, enable the electronic device to perform the steps associated with the method embodiments.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (9)
1. A real-time detection method for wind power abnormal data is characterized by comprising the following steps:
selecting target data in normal operation from historical wind power data corresponding to the wind turbine generator;
obtaining a completely random felling tree according to the target data, and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree;
acquiring data to be detected and updating the normal data structure model to obtain a target detection model;
and inputting the data to be detected into the target detection model to obtain a detection result.
2. The method according to claim 1, wherein the step of selecting the target data in normal operation from the historical wind power data corresponding to the wind turbine generator comprises:
and selecting characteristic values of a plurality of characteristics in the historical wind power data in corresponding moments to form the target data.
3. The method according to claim 1, wherein the step of obtaining a completely random felled tree from the target data and establishing and initializing a normal data structure model of the wind turbine generator from the completely random felled tree comprises:
completing segmentation on all the target data to obtain the completely random cut-down trees;
setting a left node in the completely random cut-down tree as 0 and setting a right node as 1 to obtain position vectors of all nodes in the completely random cut-down tree;
calculating the depth corresponding to each layer of nodes in the completely random cut-down tree;
calculating the index of each node according to the position vector and the depth corresponding to each node;
and establishing and initializing the normal data structure model according to all the indexes.
4. The method according to claim 1, wherein the step of collecting the data to be detected and updating the normal data structure model to obtain the target detection model comprises:
selecting brother nodes corresponding to the data to be detected from the normal data structure model;
calculating the characteristic difference between the data to be detected and the brother node;
and updating the normal data structure model according to the characteristic difference to obtain the target detection model.
5. The method according to claim 4, wherein the step of inputting the data to be detected into the target detection model to obtain the detection result comprises:
sampling the data to be detected, inserting the sampled data into the normal data structure model and calculating the complexity change of the target detection model;
and judging whether all the data to be detected are abnormal according to the complexity change and the change threshold value, and outputting the detection result.
6. The method according to claim 1, wherein after the step of inputting the data to be detected into the target detection model to obtain the detection result, the method further comprises:
rejecting abnormal data in the data to be detected according to the detection result;
and calculating the characteristic difference of the data which is most similar to the rest data in the normal data structure model, and determining whether to keep the rest data in the normal data structure model or not according to the characteristic difference and a characteristic threshold value.
7. The utility model provides a wind-powered electricity generation power abnormal data real-time detection system which characterized in that includes:
the selection module is used for selecting target data in normal operation from historical wind power data corresponding to the wind turbine generator;
the establishing module is used for obtaining a completely random felling tree according to the target data and establishing and initializing a normal data structure model of the wind turbine generator according to the completely random felling tree;
the acquisition module is used for acquiring data to be detected and updating the normal data structure model to obtain a target detection model;
and the detection module is used for inputting the data to be detected into the target detection model to obtain a detection result.
8. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the wind power anomaly data real-time detection method of any one of the preceding claims 1-6.
9. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the wind power anomaly data real-time detection method of any one of the preceding claims 1-6.
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| CN117408537A (en) * | 2023-12-15 | 2024-01-16 | 安徽科派自动化技术有限公司 | Electric energy quality monitoring system capable of realizing real-time risk prediction |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117408537A (en) * | 2023-12-15 | 2024-01-16 | 安徽科派自动化技术有限公司 | Electric energy quality monitoring system capable of realizing real-time risk prediction |
| CN117408537B (en) * | 2023-12-15 | 2024-05-07 | 安徽科派自动化技术有限公司 | Electric energy quality monitoring system capable of realizing real-time risk prediction |
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