CN111428772B - Photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting - Google Patents
Photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting Download PDFInfo
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Abstract
The invention provides a photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting, which comprises the steps of obtaining one-dimensional time sequence data inside a photovoltaic array accumulated in a photovoltaic power generation system, utilizing a sliding window to cut the original data at a fixed length, converting the original data into a two-dimensional frequency domain data image through fast Fourier transform, classifying external data by combining with the slicing time, and giving a label to construct a data set; and training a classification model on the data set by using a deep neural network, and extracting implicit characteristics as anomaly detection input. The method comprises the steps of carrying out data conversion on input data, extracting hidden layer coding features, firstly selecting voting points in a local hidden layer coding feature set by using double k-neighbors based on distance degrees, and obtaining voting weights for determining whether the voting points are abnormal or not through distance difference self-adaption between the voting points and a second k-neighbor point set, so that the method is robust and improves detection accuracy. The method does not need to compare the real data at the next moment, and improves the real-time detection efficiency.
Description
Technical Field
The invention relates to a photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting, and belongs to the technical field of photovoltaic systems.
Background
The photovoltaic power generation system is a complex system influenced by multiple natural factors such as temperature, irradiance and haze, and mainly comprises a grid-connected inverter, a junction box, a photovoltaic array and the like. As a dc power generation unit, a photovoltaic array plays an essential role in the normal operation of the entire photovoltaic power generation system. When the abnormity detection and fault location of related equipment in a photovoltaic system are researched, on one hand, various natural factors are considered, and on the other hand, various types of fault factors inside the photovoltaic power generation system are also considered.
The existing photovoltaic array anomaly detection and early warning method is characterized in that data generated by a photovoltaic array is used as input, various models are built, an anomaly rule evaluation method is set, an anomaly part in the data is analyzed, and anomaly detection of the photovoltaic array is achieved. On one hand, by constructing a theoretical model, learning, inducing and judging parameter rules in the operation process of the power generation system based on the circuit principle and the engineering model of the photovoltaic power generation system. However, in the mode, the actual scene and the operation process of the photovoltaic power station under the condition of complicated and variable natural conditions are difficult to completely reproduce, the deviation is large during actual application, and the detection accuracy and the positioning accuracy of the abnormal detection and the fault diagnosis are affected. On the other hand, the model can be constructed in a mode required by data mining, such as calculating the maximum value, the mean value, the standard deviation and the like of the photovoltaic array data, for example, algorithms such as supervised SVM, LSTM and the like, and algorithms such as unsupervised k-means and the like. Starting from the state data such as open circuit voltage, the historical data in the accumulated time t is used for making a prediction of t +1, and whether an abnormality or a fault is generated is identified. However, the state data of the method is sensitive to the external environment, and the analysis result is easily interfered by complex natural factors such as temperature, irradiance, haze and the like, so that the defects of insufficient detection accuracy and positioning accuracy exist.
Disclosure of Invention
The invention aims to solve the technical problem of providing a photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting, which overcomes the defects of the prior art.
The invention provides a photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting, which comprises the following steps of:
s1, preprocessing data, converting the one-dimensional time sequence data of the photovoltaic array into two-dimensional frequency domain data, generating a label by using external data, and constructing a labeled data set;
s2, taking the two-dimensional frequency domain data as the input of a deep neural network, and implicitly extracting features;
and S3, deep anomaly detection, namely realizing real-time anomaly detection by improving an original k-nearest neighbor algorithm and utilizing a self-adaptive weight voting method.
The method of the invention designs a complete process from data preprocessing to real-time anomaly detection by combining and improving the deep learning and the k-nearest neighbor algorithm. The method comprises the steps of obtaining one-dimensional time sequence data inside a photovoltaic array accumulated in a photovoltaic power generation system, cutting original data at a fixed length by using a sliding window, converting the original data into a two-dimensional frequency domain data image through fast Fourier transform, classifying recorded external data such as temperature and the like by combining with the cutting time, and giving a label to construct a data set; and training a classification model on the constructed data set by using a deep neural network, and extracting implicit characteristics as anomaly detection input. The method comprises the steps of carrying out data conversion on input data to be detected and extracting hidden layer coding features, firstly selecting voting points in a local hidden layer coding feature set by using double k-neighbors based on distance degrees, and adaptively determining whether the voting weight is abnormal or not according to the distance difference between the voting points and a second k-neighbor set, so that the deep anomaly detection method is more robust, and the detection accuracy is improved. In addition, the method does not need to compare the real data at the next moment, and improves the real-time detection efficiency.
The further technical scheme of the invention is as follows:
preferably, in step S1, the data preprocessing includes data transformation and data set construction; the process of the deep anomaly detection comprises feature extraction, voting detection and self-adaptive weight calculation.
Preferably, the specific method of the data conversion is as follows:
the data to be converted is internal one-dimensional time sequence data in the photovoltaic power generation system, the data conversion is carried out by slicing the one-dimensional time sequence data by using a sliding window, partial overlapping is allowed among the slices, and then the multi-dimensional characteristic vector is extracted through fast Fourier transform to obtain a two-dimensional frequency domain characteristic diagram.
Preferably, the constructed data set comprises two-dimensional frequency domain data and a label, wherein the two-dimensional frequency domain data is obtained by preprocessing the internal one-dimensional time sequence data; the label category is constructed by different numerical combinations of external data, namely, the two-dimensional data is obtained by processing the slices by data conversion, the external data corresponding to the time is found, and the external data is classified into one of the label categories.
Preferably, in the step S2, the feature extraction is to use a deep neural network in computer vision to train on the data set constructed above to extract implicit features of the data inside the photovoltaic array as input data for anomaly detection.
Preferably, the specific method of voting detection is as follows:
one-dimensional through data preprocessingAfter the time sequence data is converted into a two-dimensional frequency domain image, m points closest to the test data in the local data set are selected as voting points and the corresponding distance d is calculated i Then calculating the nearest n points in each voting point and calculating the average distance D, and finally comparing D i And D size to determine if an anomaly has occurred.
Preferably, the method of adaptive weight calculation is as follows:
by using d i The distance difference of the sum D calculates an interpretable dynamic coefficient as a weight when the final anomaly detection sum votes.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
according to the invention, two-dimensional frequency domain data conversion based on sliding window segmentation is realized for accumulated one-dimensional time sequence historical data in a photovoltaic power generation system, the implicit extraction of characteristics from two-dimensional input data by using a deep neural network is realized, the abnormal condition of a photovoltaic array is detected in real time based on a k-nearest neighbor self-adaptive voting method, the abnormity or fault is responded timely and accurately, and the abnormal or fault post-processing is accelerated.
Detailed Description
The technical scheme of the invention is further explained in detail as follows: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection authority of the present invention is not limited to the following embodiments.
The embodiment provides a photovoltaic system depth anomaly detection method based on k-neighbor adaptive voting, which comprises two parts, namely a data preprocessing part and a depth anomaly detection part.
The first section introduces a photovoltaic data pre-processing procedure. The process comprises the steps of converting one-dimensional time sequence data of the photovoltaic array into two-dimensional frequency domain data, generating a label by utilizing external data, and constructing a labeled data set.
The flow includes data transformation and data set construction. The data conversion is carried out by slicing internal one-dimensional time sequence data in the photovoltaic power generation system by using a sliding window, wherein partial overlapping is allowed between slices, and then a two-dimensional frequency domain characteristic diagram is obtained by extracting multi-dimensional characteristic vectors through fast Fourier transform. The constructed data set comprises two-dimensional frequency domain data and a label, wherein the two-dimensional frequency domain data is obtained by preprocessing internal one-dimensional time sequence data; the category of the label is constructed by different numerical combinations of external data; and processing the slices by using data conversion to obtain two-dimensional data, finding external data corresponding to time of the two-dimensional data, and classifying the external data into a certain category in the label categories.
The internal data of the photovoltaic array comprises time domain one-dimensional continuous data such as current, voltage, power and the like, and the external data comprises quantitative data such as temperature, irradiance and the like. The internal and external data together form the state condition of the photovoltaic array expressed by each parameter value under a certain natural condition at a certain moment. Since there is a linear relationship between the internal data, the method uses the current in the internal data as the data in the dataset to be processed, and the temperature, irradiance, etc. as the label in the dataset to be processed. Converting the one-dimensional current in the time domain into two-dimensional data in the frequency domain, and realizing the data preprocessing and constructing a data set by the following processes:
s101, filtering internal and external data in normal operation from the original data, and combining the external data such as temperature, irradiance and the like into C categories through different numerical values;
step S102, segmenting continuous current into sub-current data with equal time by using a sliding window, and calling the sub-current data into frames;
step S103, performing left-right end continuity processing on each frame f, and setting N as the frame size and a as the continuity processing coefficient, then
F (n, a) is a function, the function F (n, a) is to take a part of continuous data in the whole one-dimensional linear data, similar to the function of the window function, n is a coefficient, and the value range of n is (0, n);
step S104, by using fast Fourier transform DFT (DFT is an algorithm for converting one-dimensional time domain data into two-dimensional frequency spectrum by using a computer to realize Fast Fourier Transform (FFT)), one-dimensional current data f extracts multi-dimensional vector features to obtain required two-dimensional data m, wherein f is input data, m is output data, the conversion process is realized by the following formula,
in the above formula, X a (k) Representing the final output data (i.e. the above-mentioned m-value), x (n) representing the sampled input data (i.e. the above-mentioned f-value), e -j2πk/N Is a standard formula of DFT for data conversion;
step S105, classifying the external data corresponding to the one-dimensional current data f into a certain class C in the C classes of the structure i Combining them into a tagged data tuple (m, c) i ) The data set X required for the second part is constructed.
The second part introduces the deep anomaly detection of the photovoltaic power generation system based on hidden layer coding, and the anomaly detection application principle realized by the method is described in detail by combining pseudo codes.
And (4) taking the two-dimensional frequency domain data as the input of the deep neural network, and implicitly extracting the features. The feature extraction is to train on a constructed data set by utilizing a deep neural network in computer vision so as to extract implicit features of data inside the photovoltaic array as input data of anomaly detection. After feature extraction, real-time deep anomaly detection is realized by improving an original k-nearest neighbor algorithm and utilizing a self-adaptive weight voting method. Thus, the anomaly detection process includes feature extraction, voting detection processes, and adaptive weight calculation. When voting detection is carried out, m points closest to test data in a local data set are selected as voting points, and corresponding distances d are calculated i Then calculating the nearest n points in each voting point and calculating the average distance D, and finally comparing D i And D size to determine if an anomaly has occurred. The adaptive weight calculation is based on d i The distance difference of D and D is used to calculate the dynamic coefficient with interpretability as the final differenceThe weights at the time of the sum voting are often detected.
In addition, the relevant pseudo code is as follows:
parameters are as follows: coefficients m, n of the twofold kNN;
inputting: testing a sample X, a constructed tagged data set X, and a model f;
and (3) outputting: x is abnormal.
The method comprises the following specific steps:
1. training a model f with high classification accuracy based on the tagged data set X;
2. initialization
3. Firstly, f (x) is calculated to obtain a hidden layer code z;
4. find m nearest z point sets A (a) in the local sample set X 1 ,a 2 ,...,a m )；
5.FOR a i IN A：
6. Calculating z to a i Distance d of i ；
7. Finding n distances a in a local sample set X i Nearest set of points B (B) 1 ,b 2 ,...,b n )；
8. Calculating a i And a set of points B (B) 1 ,b 2 ,...,b n ) The average distance D of;
9. calculating the weight w of the normal sample i ＝-exp(-|D-d i |)+1；
10.IF d i <D，THEN：
11. Receiving the sample, p i ＝w i ，P←<a i :p i >；
12.ELSE：
13. Reject the sample, n i ＝w i ，N←<a i :n i >；
Sum of weights in IF N set
Is greater thanSum of weights in P set
THEN：
15. The test sample x is abnormal;
16.ELSE：
17. no abnormality occurred in the test sample x.
In the above steps, P, N is a collection for voting, which indicates normal or abnormal; (x) is an output value of the training model in the first step, x is two-dimensional input data similar to a picture, and a vector is calculated and output through the model f, namely z is f (x); p is i The ith ticket is judged to be normal and is assigned with weight, and the ith ticket is put into a P (Positive) set; n is a radical of an alkyl radical i The ith ticket is judged to be abnormal and the weight is assigned, and the ith ticket is put into a set of N (negative). For example, there are (0.1, 0.5, 1) in P and (0.2, 0.5, 0.8) in N, where P is i ，n i The voting is the voting with the weight assigned in the set, the voting means that the sum of P is 1.6, the sum of N is 1.5, and the voting result is 1.6>1.5, so the sample is judged to be P, namely a normal sample.
The photovoltaic power generation system anomaly detection method provided by the embodiment is specifically explained by combining the pseudo code as follows:
step S201, training a classification model f based on a constructed data set X in a deep neural network training image mode to serve as a method for implicitly extracting hidden layer coding features, and extracting hidden layer codes from all data in the data set X to obtain a feature set Z;
step S301, in an anomaly detection stage, firstly, input one-dimensional current data x are subjected to data preprocessing of a first part to complete one-dimensional to two-dimensional data conversion, and a hidden layer code z is obtained through a classification model f;
step S302, m point sets with the nearest distance to the test data hidden layer code Z are searched in the hidden layer code set Z extracted from the data set and are used as m points A (a) to be voted 1 ,a 2 ,...,a m )；
Step S303, traverse step S302And finds n point sets B (B) closest to each point 1 ,b 2 ,...,b n ) Then calculate z to a i Distance d of i And a i Average distance D to B;
step S304, utilize d i Distance difference calculation weight w of sum D i I.e. w i ＝-exp(-|D-d i |)) + 1. Then compare d i And the size of D, if D i <D, the test data is considered to be normal, and the sample is accepted, p i ＝w i ，P←<a i :p i >(ii) a Otherwise, the test data is considered abnormal, the sample is rejected, n i ＝w i ，N←<a i :n i >；
Step S305 obtains m weighted inference results through steps S303 and S304, votes whether the point is an abnormal point or not, and determines whether the point is abnormal or not. Namely, judging the weight sum in the N sets
Whether is greater than the sum of weights in the P set
If yes, the test sample x is abnormal, otherwise, the test sample x is not abnormal.
In summary, the weight calculation utilizes z to a i Distance d of i 、a i The distance difference between the average distances D to B is larger, and the confidence that the sample point is determined to be normal/abnormal is larger when the distance difference is larger, and the weight of the sample point in voting is also larger. The weight calculation formula is specifically as follows:
w i ＝-exp(-|D-d i |)+1
through the content, the adaptive voting depth anomaly detection method based on the k-nearest neighbor is realized. Compared with the method for judging the abnormality by comparing whether the classification result output by the model inference is the same as the real result at the time t +1 or not, the method provided by the invention does not need to compare the data at the time t +1 on one hand, and improves the response speed of the model abnormality detection; on the other hand, hidden layer codes with higher original data information retention degree are applied to anomaly detection of the photovoltaic power generation system, and the accuracy of the anomaly detection of the model is improved.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions should be included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. The photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting is characterized by comprising the following steps of:
s1, preprocessing data, converting the one-dimensional time sequence data of the photovoltaic array into two-dimensional frequency domain data, generating a label by using external data, and constructing a labeled data set;
s2, taking the two-dimensional frequency domain data as the input of a deep neural network, and implicitly extracting features;
s3, deep anomaly detection, namely, realizing real-time anomaly detection by improving an original k-nearest neighbor algorithm and utilizing a self-adaptive weight voting method; the deep anomaly detection comprises the processes of feature extraction, voting detection and self-adaptive weight calculation; the specific method for voting detection is as follows:
after one-dimensional time sequence data are converted into two-dimensional frequency domain images through data preprocessing, m points closest to test data in a local data set are selected as voting points and corresponding distances d are calculated i Then calculating the nearest n points in each voting point and calculating the average distance D, and finally comparing D i And D size to determine if an anomaly has occurred, if D i If < D, the test data is considered normal, and the sample is accepted, p i ＝w i ，P←<a i :p i >(ii) a Otherwise, the test data is considered abnormal, the sample is rejected, n i ＝w i ，N←<a i :n i >；
The method for calculating the self-adaptive weight comprises the following steps:
w i ＝-exp(-|D-d i |)+1
by d i The distance difference of the sum D calculates a dynamic coefficient having interpretability as a weight at the time of the final abnormality detection sum voting.
2. The photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting according to claim 1, wherein in step S1, the data is preprocessed, and the process includes data conversion and data set construction.
3. The photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting according to claim 2, wherein the specific method of data conversion is as follows:
the data to be converted are internal one-dimensional time sequence data in the photovoltaic power generation system, the one-dimensional time sequence data are sliced by using a sliding window in the data conversion process, and then a two-dimensional frequency domain characteristic diagram is obtained by extracting multi-dimensional characteristic vectors through fast Fourier transform.
4. The photovoltaic system depth anomaly detection method based on k-nearest neighbor adaptive voting is characterized in that a constructed data set comprises two-dimensional frequency domain data and a label, wherein the two-dimensional frequency domain data are obtained by preprocessing one-dimensional time sequence data; the category of the tag is constructed by different combinations of values of the external data.
5. The photovoltaic system deep anomaly detection method based on k-nearest neighbor adaptive voting is characterized in that in the step S2, a deep neural network in computer vision is used to train on the constructed data set so as to extract implicit features of data inside the photovoltaic array as input data for anomaly detection.
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