CN114997256A - Method and device for detecting abnormal power of wind power plant and storage medium - Google Patents

Method and device for detecting abnormal power of wind power plant and storage medium Download PDF

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CN114997256A
CN114997256A CN202210185544.4A CN202210185544A CN114997256A CN 114997256 A CN114997256 A CN 114997256A CN 202210185544 A CN202210185544 A CN 202210185544A CN 114997256 A CN114997256 A CN 114997256A
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戴云泽
李建国
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Shanghai Dianji University
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Abstract

The invention relates to a method, equipment and a storage medium for detecting abnormal power of a wind power plant, wherein the method comprises the following steps: s1, acquiring a power and wind speed scattered point data set D of the wind power plant; s2, identifying discrete abnormal data in the power and wind speed scattered data set by adopting a local abnormal factor LOF algorithm to obtain an intermediate data set D'; and step S3, identifying the stacking abnormal data in the intermediate data set D' by adopting an isolated forest algorithm IF. Compared with the prior art, the method has the advantages of high abnormal data detection accuracy and high detection efficiency.

Description

Method and device for detecting abnormal power of wind power plant and storage medium
Technical Field
The invention relates to the field of wind power plant control, in particular to a method, equipment and a storage medium for detecting abnormal power of a wind power plant.
Background
In the actual operation of the wind power plant, abnormal data can occur under the influence of factors such as poor operation environment, abnormal load shedding of units, measurement and faults of communication equipment, meanwhile, accurately obtaining data such as wind speed and power of actual operation of the wind power plant is a basis for predicting the output of the wind power plant, and a wind power plant equivalent output curve based on historical data is also commonly used for evaluating the operation state of the wind power plant. Therefore, it is important to effectively identify the wind power abnormal data.
Currently, there are several methods for detecting abnormal data: 1) based on the abnormal detection of the probability statistical model, the method judges whether the data is abnormal or not by calculating the deviation of the standard data and the actual data, but the standard data set has certain blindness and uncertainty; 2) based on abnormal data detection of clustering, the method divides a sample of unknown marking information into a plurality of subsets according to certain rules and requirements, and takes a sample point which is subordinate to a certain subset and is lower than a threshold value as abnormal data; 3) based on the abnormal data detection of density and distance, the method calculates the number of sample points in a certain unit space in a multidimensional space, and the method does not need a large amount of training data.
The density-based anomaly detection algorithm is mainly used for intuitively and simply displaying outlier samples in different density data sets by calculating the anomaly level of each sample point in the data sets. However, for a given data set, the euclidean distance between each data object and all other points and the local reachable distance of the data points need to be obtained, the calculation amount is large, and when the size of the data set is large, the algorithm is inefficient to operate.
In view of the above situation, it is necessary to design a wind farm abnormal data detection method with high accuracy and high efficiency.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method, equipment and a storage medium for detecting abnormal power of a wind power plant, which have high accuracy and high detection efficiency.
The purpose of the invention can be realized by the following technical scheme:
the invention provides a method for detecting abnormal power of a wind power plant, which comprises the following steps:
s1, acquiring a power and wind speed scattered point data set D of the wind power plant;
step S2, identifying discrete abnormal data in the power and wind speed scattered point data set by adopting a local abnormal factor LOF algorithm to obtain an intermediate data set D';
and step S3, identifying the stacking abnormal data in the intermediate data set D' by adopting an isolated forest algorithm IF.
Preferably, the data of the power and wind speed scatter data set of the wind farm in the step S1 includes scatter data and stack data.
Preferably, the step S2 is specifically: and calculating a local abnormal factor LOF corresponding to each data point in the data set, regarding the data points with the local abnormal factor LOF values higher than the LOF threshold value as abnormal data, and removing the abnormal data.
Preferably, the local anomaly factor LOF is an average value of ratios of local reachable densities of data points in the k-distance neighborhood to local reachable densities of data points.
Preferably, the step S3 is specifically:
1) randomly sampling the intermediate data set D' to construct an isolated binary tree and construct an isolated forest model;
2) for any sample point in each isolated binary tree, traversing the isolated binary tree from a root node until reaching a leaf node, calculating the average path length c (p) of the sample point, and carrying out abnormal scoring on the sample based on an abnormal index function;
3) and (4) regarding the measured points with the abnormal scoring values higher than the scoring threshold values as abnormal points and removing the abnormal points.
Preferably, the expression of the anomaly indicator function is:
Figure BDA0003523211290000021
in the formula, h x For the path length of sample point x from the leaf node to the root node, E (h) x ) The path length mean value of the sample point x in all isolated binary trees iTree; c (p) the average path length of the isolated binary tree constructed for the p sample data.
Preferably, the path length h of the sample point from the leaf node to the root node x The expression of (a) is:
Figure BDA0003523211290000022
preferably, the average path length c (p) is expressed by:
Figure BDA0003523211290000023
in the formula, H p-1 Where ═ ln (p-1) + ξ, ξ is the euler constant and p is the number of samples.
According to a second aspect of the invention, there is provided an electronic device comprising a memory having stored thereon a computer program and a processor implementing any of the methods when the program is executed.
According to a third aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the preceding claims.
Compared with the prior art, the invention has the following advantages:
1) the method overcomes the defects that discrete abnormal data points and local abnormal factor LOF (Long term evolution) algorithms in the existing isolated forest algorithm are difficult to identify, the identification effect of the stacked abnormal point clusters aiming at large data volume is poor, the detection algorithm combining the LOF algorithm and the IF algorithm is adopted to classify and detect the abnormal data with different characteristics of the power and wind speed scatter points of the wind power plant, and the detection accuracy and the detection efficiency of the abnormal data are improved;
2) the isolated forest algorithm adopted by the invention realizes multiple segmentation of the data set through the isolated binary tree, thereby accurately identifying the stacked abnormal data with high distribution density.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of the achievable distance in the LOF algorithm;
FIG. 3 is a wind farm abnormal data distribution diagram;
FIG. 4 is a graph of abnormal data detection results based on the LOF algorithm;
fig. 5 is a diagram showing the detection result of abnormal data based on the IF algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
As shown in fig. 1, this embodiment provides a method for detecting abnormal power of a wind farm, where the method includes the following steps:
s1, acquiring a power and wind speed scattered point data set D of the wind power plant, wherein the scattered data set D comprises scattered data and stacked data;
step S2, identifying discrete abnormal data in the power and wind speed scattered data set by adopting a local abnormal factor LOF algorithm to obtain an intermediate data set D', which is specifically as follows:
calculating a local abnormal factor LOF corresponding to each data point in the data set, regarding the data point with the local abnormal factor LOF value higher than the LOF threshold value as abnormal data, and removing the abnormal data; the local anomaly factor LOF is an average value of the ratio of the local reachable density of the data points in the k-distance neighborhood to the local reachable density of the data points.
Step S3, identifying stacking abnormal data in the intermediate data set D' by adopting an isolated forest algorithm IF, and comprising the following substeps:
1) randomly sampling the intermediate data set D' to construct an isolated binary tree and construct an isolated forest model;
2) for any sample point in each isolated binary tree, traversing the isolated binary tree from a root node until reaching a leaf node, calculating the average path length c (p) of the sample point, and carrying out abnormal scoring on the sample based on an abnormal index function;
the expression of the abnormal index function is as follows:
Figure BDA0003523211290000041
in the formula, h x For the path length of sample point x from the leaf node to the root node, E (h) x ) The path length mean value of the sample point x in all isolated binary trees iTree; c (p) the average path length of the isolated binary tree constructed for p sample data.
The path length h of the sample point from the leaf node to the root node x The expression of (a) is:
Figure BDA0003523211290000042
the average path length c (p) is expressed as:
Figure BDA0003523211290000043
in the formula, H p-1 Where ═ ln (p-1) + ξ, ξ is the euler constant and p is the number of samples.
3) And (4) regarding the measured points with the abnormal scoring values higher than the scoring threshold values as abnormal points and removing the abnormal points.
The local outlier factor algorithm LOF and the isolated forest algorithm IF used in the present invention are explained in detail below.
1. An abnormality detection method based on a Local Outlier Factor (LOF) is a typical density-based Outlier detection method; wherein, the local abnormal factor is used for characterizing the abnormal degree of the data.
Definition 1: k distance, the distance between the kth closest point and the data point, among the points closest to the data point;
definition 2: a k-distance neighborhood, which is a data set with a distance from a data point less than or equal to the k-distance of the data point, i.e., a set of points in a region with the data point as the center and the k-distance as the radius;
definition 3: the reachable distance is k-distance of o if the data p is closer to o, and when the data p is farther away from o, the reachable distance is regarded as actual distance d (p, o) of o, as shown in fig. 2;
definition 4: local accessibility density (local accessibility density), which is the reciprocal of the average accessibility density value of a data point p relative to objects within its k-distance neighborhood;
definition 5: a Local Outlier Factor (Local Outlier Factor), which is defined as the average of the ratio of the Local reachable density of a data point within a k-distance neighborhood to the Local reachable density of p, for a given k and data point p. The local anomaly factor directly represents the possibility that a data point is an anomaly point, the LOF value is equal to 1 or about less than 1, the local reachable density of the data point is close to the local reachable density of other points in the neighborhood, the possibility that the point is an isolated point is low, and the larger the LOF value is, the higher the isolation degree is, the more the data point is likely to be the anomaly point.
2. An Isolated Forest algorithm (IF) is an anomaly detection algorithm based on a Tree Embedding (Tree Embedding). The IF algorithm defines the distribution as sparse and the points farther from the area where dense distribution exists as outliers. The basic principle of the IF algorithm is as follows: the data space is divided by using a random hyperplane, and the data subspace is divided again by adopting a recursive random number, so that after repeated cutting, the cutting is stopped until only one sample point is left in each subspace, namely all data points are isolated. Therefore, the data clusters with high distribution density can be isolated after being segmented for multiple times, and the sample points with sparse distribution can be segmented only by few segmentation times.
The detection process of the abnormal data of the algorithm comprises the following steps:
defining the number of edges which are traversed by the measured point x from the root node to traverse each isolated binary tree iTree until the measured point meets an external node as a path length h (x), calculating the average path length of the measured point, and carrying out abnormal scoring, wherein the value range of the abnormal scoring s is (0,1), and the abnormal scoring value s is set as follows:
1) when s is close to 1, the smaller the branch of the data point in the iTree is, the higher the possibility that the data is judged to be abnormal;
2) when s is close to 0, the greater the branching of the data point in the iTree, the less likely the data is judged to be abnormal;
3) when s is close to 0.5, the abnormal features of the data points are not obvious, and whether the data points are abnormal or not cannot be judged.
In order to verify the effect of the abnormal data detection method provided by the invention, in this embodiment, for a certain wind farm with a rated capacity of 16MW, the sampling interval is 15min from 2/1/2012 to 2/30/2012, and the acquired 2880 group data is subjected to abnormal detection.
The algorithm parameters are set as follows: taking 10 as k in an LOF algorithm, and setting an LOF threshold value to be 1.5; in the IF algorithm, the number of trees, i.e., the integration size, is set to 100, and the number of branches, i.e., the sampling size of each tree, is set to 256.
The experimental results shown in fig. 3 to 5 prove that: the LOF algorithm has a good recognition effect on scattered T3 type abnormal data around a curve, and due to the fact that other two types of abnormal data are in the phenomenon that a large amount of data are locally accumulated and densely distributed in a certain area, the LOF algorithm is not good in recognition effect, abnormal data are seriously missed, power is not abnormal in fact for a plurality of points with the maximum wind speed, but due to the fact that the data amount is small and the abnormal data are sparse, the LOF algorithm reports the abnormal data as abnormal points. The IF algorithm separates data points by dividing a data set for multiple times in a tree model, the isolation degree of abnormal data in the tree is obvious, the identification effect of the IF algorithm on the densely distributed abnormal data is obvious from the graph shown in FIG. 5, but the false alarm condition that some normal data at the edge of a data cluster are judged as abnormal data also exists.
The wind power abnormal data detection method combining the LOF algorithm and the IF algorithm can better identify different types of abnormal data of the wind power plant.
The electronic device of the present invention includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) or computer program instructions loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input/output (I/O) interface is also connected to the bus.
A plurality of components in the device are connected to the I/O interface, including: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, speakers, and the like; storage units such as magnetic disks, optical disks, and the like; and a communication unit such as a network card, modem, wireless communication transceiver, etc. The communication unit allows the device to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processing unit performs the various methods and processes described above, such as methods S1-S3. For example, in some embodiments, the methods S1-S3 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via ROM and/or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more of the steps of methods S1-S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform methods S1-S3 in any other suitable manner (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for detecting abnormal power of a wind power plant is characterized by comprising the following steps:
s1, acquiring a power and wind speed scattered point data set D of the wind power plant;
s2, identifying discrete abnormal data in the power and wind speed scattered data set by adopting a local abnormal factor LOF algorithm to obtain an intermediate data set D';
and step S3, identifying the stacking abnormal data in the intermediate data set D' by adopting an isolated forest algorithm IF.
2. The method for detecting abnormal power of a wind farm according to claim 1, wherein the data of the power and wind speed scatter data set of the wind farm in the step S1 comprises scattered data and stacked data.
3. The method for detecting abnormal power of a wind farm according to claim 1, wherein the step S2 specifically comprises: and calculating a local abnormal factor LOF corresponding to each data point in the data set, regarding the data points with the local abnormal factor LOF values higher than the LOF threshold value as abnormal data, and removing the abnormal data.
4. The method for detecting abnormal power of a wind power plant according to claim 3, wherein the local abnormality factor LOF is an average value of a ratio of a local reachable density of the data points in a k-distance neighborhood to a local reachable density of the data points.
5. The method for detecting abnormal power of a wind farm according to claim 1, wherein the step S3 specifically comprises:
1) randomly sampling the intermediate data set D' to construct an isolated binary tree and construct an isolated forest model;
2) for any sample point in each isolated binary tree, traversing the isolated binary tree from a root node until reaching a leaf node, calculating the average path length c (p) of the sample point, and carrying out abnormal scoring on the sample based on an abnormal index function;
3) and (4) regarding the measured points with the abnormal scoring values higher than the scoring threshold values as abnormal points and removing the abnormal points.
6. A method for detecting abnormal power of a wind farm according to claim 5, characterized in that the expression of the abnormal index function is:
Figure FDA0003523211280000011
in the formula, h x From leaf node for sample point xPath Length from Point to root node, E (h) x ) The path length mean value of the sample point x in all isolated binary trees iTree; c (p) the average path length of the isolated binary tree constructed for p sample data.
7. The method for detecting abnormal power of wind power plant according to claim 6, wherein the path length h of the sample point from the leaf node to the root node is x The expression of (a) is:
Figure FDA0003523211280000021
8. method for detecting abnormal power of a wind farm according to claim 6, characterized in that the average path length c (p) is expressed by:
Figure FDA0003523211280000022
in the formula, H p-1 Where ═ ln (p-1) + ξ, ξ is the euler constant and p is the number of samples.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 8.
CN202210185544.4A 2022-02-28 2022-02-28 Method and device for detecting abnormal power of wind power plant and storage medium Pending CN114997256A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340063A (en) * 2020-02-10 2020-06-26 北京华电天仁电力控制技术有限公司 Coal mill data anomaly detection method
CN116365519A (en) * 2023-06-01 2023-06-30 国网山东省电力公司微山县供电公司 Power load prediction method, system, storage medium and equipment
CN116859902A (en) * 2023-09-04 2023-10-10 西安热工研究院有限公司 Database abnormal point detection method and system for hydropower control system
CN118051796A (en) * 2024-04-16 2024-05-17 自贡市第一人民医院 Intelligent analysis method for monitoring data of disinfection supply center

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340063A (en) * 2020-02-10 2020-06-26 北京华电天仁电力控制技术有限公司 Coal mill data anomaly detection method
CN111340063B (en) * 2020-02-10 2023-08-29 国能信控互联技术有限公司 Data anomaly detection method for coal mill
CN116365519A (en) * 2023-06-01 2023-06-30 国网山东省电力公司微山县供电公司 Power load prediction method, system, storage medium and equipment
CN116365519B (en) * 2023-06-01 2023-09-26 国网山东省电力公司微山县供电公司 Power load prediction method, system, storage medium and equipment
CN116859902A (en) * 2023-09-04 2023-10-10 西安热工研究院有限公司 Database abnormal point detection method and system for hydropower control system
CN118051796A (en) * 2024-04-16 2024-05-17 自贡市第一人民医院 Intelligent analysis method for monitoring data of disinfection supply center
CN118051796B (en) * 2024-04-16 2024-06-18 自贡市第一人民医院 Intelligent analysis method for monitoring data of disinfection supply center

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