CN110727669A - Device and method for cleaning sensor data of power system - Google Patents
Device and method for cleaning sensor data of power system Download PDFInfo
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Abstract
The invention belongs to the technical field of wireless sensor networks and neural networks, and particularly relates to a device and a method for cleaning sensor data of a power system, which are mainly used for distinguishing abnormal data caused by an event from abnormal data caused by other factors such as environment. The invention comprises the following steps: the device comprises an abnormal data detection module, an abnormal data classification module and a noise data restoration module. The invention constructs a sensor data cleaning model for cleaning sensor data, classifies the sensor data into normal data, noise data and fault data, completes the restoration of the noise data, removes the interference of the noise data, and can greatly enhance the accuracy rate by using the sensor data cleaning model to perform fault diagnosis and classification. The method has real-time performance and can quickly clear data transmitted in real time; and the method has universality and can be suitable for most industrial sensor networks.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks and neural networks, and particularly relates to a device and a method for cleaning sensor data of a power system, which are mainly used for distinguishing abnormal data caused by an event from abnormal data caused by other factors such as environment.
Background
Real-time status monitoring of various facilities of an electrical power system is very important for safety production management. The monitoring data of the device can be used to determine whether the operating device is functioning properly or what kind of failure has occurred. The rapid development of the internet enables many devices to be automatically monitored by various types of sensors, and wireless sensor network technology has also been successfully applied in various large factories, and sensor data can be uniformly transmitted through the network, thereby greatly saving labor cost and improving real-time monitoring. However, various factors, such as environmental factors, sensor hardware performance, wireless transmission interference, etc., cause the monitoring data to be easily abnormal. Secondly, due to the change of the state of the monitoring equipment, such as the occurrence of a fault, the sensor data can also be abnormal. Since these two anomalies are very similar, the accuracy of the state analysis and the fault detection is greatly reduced when data cleaning is performed, and the fault data and the noise data belong to the anomaly data. Obviously, these data are important to determine if a device is malfunctioning.
Since noisy and faulty data tend to be very similar, anomalous data should not be smoothed or discarded directly during data cleansing.
Disclosure of Invention
In order to solve the problem that accuracy of precision state analysis and fault detection performed by using sensor data is low due to the fact that noise data and fault data in abnormal sensor data are very similar, the invention provides a device and a method for cleaning the sensor data of a power system, which can classify the fault data and the noise data, repair the noise data and improve accuracy of the precision state analysis and the fault detection.
In order to realize the purpose, the invention adopts the following technical scheme:
an abnormal data cleaning device for a power system sensor comprises: the abnormal data detection module, the abnormal data classification module and the noise data restoration module;
the abnormal data detection module detects abnormal data of a sensor of the power system;
the abnormal data classification module is used for dividing the sensor abnormal data into noise data and fault data;
and the noise data restoration module fits the noise data into normal data, and then replaces the noise data with the data fitted by the corresponding SDAE to finish restoration.
The abnormal data detection module trains the SDAE by using the normal data of the sensor, learns the characteristics of the normal data of the sensor, fits the normal data of the sensor, and takes the maximum value of the difference value between the SDAE fitted data and the original data as a threshold value for judging whether the data is normal or not; and when new data is input, inputting the new data into SDAE for fitting, solving the difference value between the fitting data and the original data, and judging the data with the difference value larger than the threshold value as abnormal data.
The abnormal data classification module is used for dividing the sensor data set X into m windows X ═ L according to time1,...,LmCalculating the correlation degree between the sensor data in each window, finding out all abnormal data at the same time for the abnormal data, and recording the correlation degree between the sensor where the abnormal data is located and the sensor i in the window; when at least w-1 abnormal data exist at the time t and the correlation degree of the sensor time sequence where the abnormal data exist and the sensor time sequence is larger than the minimum correlation degree threshold value, the data are determined to be fault data; if the condition is not met, the data is noise data; a data category matrix is established that includes normal data, fault data, and noise data.
Correlation RT between the sensors i and jijThe calculation formula of (2) is as follows:
wherein x isijData representing the i-th sensor at time j, X \ uitIs the t-th data, X _, of the sensor within the windowjtIs the t-th data of sensor j in a window, each window being s in length.
The noise data restoration module is used for establishing a data matrix Y ═ YijLet Y be X; finding k in data category matrixijData x of 1ijLet us order
The data matrix Y is a repaired data set.
The data cleaning method of the abnormal data cleaning device for the sensor of the power system comprises the following steps:
inputting new data to be cleaned into the trained SDAE, and outputting the fitted data; making a difference between the fitted data and the original data, wherein the data with the difference value exceeding a specified threshold value is abnormal data;
for each abnormal data, searching whether other sensors have abnormal data at the same moment, and recording other sensors with abnormal data at the same moment; calculating the correlation degree between the sensor and other recorded sensors, and if the correlation degree is greater than a specified threshold value, judging that the sensor and other recorded sensors have strong correlation; if the number of the sensors with strong correlation with the sensors is larger than a specified number threshold, the abnormal data of the sensors at the moment is fault data; otherwise, it is noise data;
for each noise data, finding out the corresponding SDAE fitting data; and replacing the noise data with the fitting data to complete the restoration.
The specific steps of the abnormal data detection comprise:
s1, collecting normal data of the sensor, learning the characteristics of the normal data of the sensor, and fitting the normal data of the sensor;
s2, calculating the difference between the SDAE fitting data and the original data when fitting the normal data, making a histogram of the difference sequence, drawing a normal distribution curve, taking a confidence interval of 0.99, and obtaining an upper threshold ThLAnd a lower threshold ThU；
S3: suppose the new data is X ═ XijIn which xijData representing the j time of the ith sensor is input into SDAE and fitted to obtain fitted data
Wherein
Fitting data representing the j moment of the ith sensor is obtained, the fitting data is subtracted from the original data, and a difference value is obtained
Wherein d isijRepresenting the difference between the fitted data of the ith sensor at the time j and the original data;
s4: comparing the difference with a threshold value, if ThL＜dij＜ThUThen xijIs normal data, kij0, otherwise, xijAs abnormal data, kij＝1。
The specific steps of the abnormal data classification include:
s1: time-wise partitioning of a sensor data set X into m windows X ═ L1,...,LmIn which each window is provided with
Lj＝{X1j,...,Xnj}T,Xij＝{xi,(j-1)×s+1,...,xi,j×s}，
Each window has a length s;
s2, calculating the correlation degree between the sensor data in each window;
s3 for kitAbnormal data x of 1itFinding out all abnormal data at t moment and recording the abnormal data in the windowIntra-oral correlation; when at least w-1 abnormal data exist at the time t and the correlation degree of the sensor time sequence where the abnormal data exist and the sensor time sequence is larger than the minimum correlation degree threshold value, namely Num ((RT)ij＞RTmin)&(kjt1) is greater than w-1, the data is considered as fault data, and k is madeit2; if this condition is not satisfied, the data is noise data, kit1 is unchanged;
s4: establishing a data category matrix K ═ Kij},kij∈{0,1,2}，kij0 indicates data xijIs normal data, kij1 denotes the data xijFor noisy data, kij2 denotes the data xijIs failure data.
Correlation RT between the sensors i and jijThe calculation formula of (2) is as follows:
wherein x isijData representing the i-th sensor at time j, X \ uitIs the t-th data, X _, of the sensor within the windowjtIs the t-th data of sensor j in a window, each window being s in length.
The specific steps of the noise data restoration are as follows:
s1: establishing a data matrix Y ═ YijLet Y be X;
s2: finding k in data category matrixijData x of 1ijLet us order
S3: the data matrix Y is used as the repair-completed data set.
The invention has the following advantages and beneficial effects:
the invention constructs a sensor data cleaning model for cleaning sensor data, classifies the sensor data into normal data, noise data and fault data, completes the restoration of the noise data, removes the interference of the noise data, and can greatly enhance the accuracy rate by using the sensor data cleaning model to perform fault diagnosis and classification. The method has real-time performance and can quickly clean data transmitted in real time. And the method has universality and can be suitable for most industrial sensor networks.
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The following detailed description of the present invention is provided to facilitate one of ordinary skill in the art to understand and practice the present invention, and it should be understood that the scope of the present invention is not limited by the specific embodiments.
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a schematic diagram of the present invention dividing 5 sensor time-series data into several windows.
Detailed Description
The invention relates to a device and a method for cleaning sensor data of a power system, and is shown in fig. 1, wherein fig. 1 is a flow chart of the method according to the embodiment of the invention.
The sensor data cleaning device of the power system is divided into three stages: the device comprises an abnormal data detection module, an abnormal data classification module and a noise data restoration module. The three stages are sequentially operated from front to back to finish the task of data cleaning.
The abnormal data detection module detects abnormal data of a sensor of the power system by adopting a three-layer superposition denoising self-encoding machine SDAE;
the abnormal data classification module is used for dividing the sensor abnormal data into noise data and fault data, classifying the abnormal data by utilizing the correlation among the sensor data, and determining the sensor data as the fault data if the sensor data at the same moment are simultaneously judged to be the abnormal data, or else, determining the sensor data as the noise data;
and the noise data restoration module fits the noise data into normal data by using the SDAE, and then replaces the noise data with the data fitted by the corresponding SDAE to finish restoration.
The abnormal data detection stage is responsible for detecting abnormal data in the sensor data; the three-layer superposition denoising self-encoder SDAE is trained by applying normal data of the sensor, so that the three-layer superposition denoising self-encoder SDAE can learn the characteristics of the sensor data and can fit the sensor data. And recording the maximum value of the difference value when the SDAE of the three-layer superposition denoising self-encoder is fitted with normal data, and taking the maximum value as a threshold value for judging the normal data and the abnormal data. When new data comes, the data is input into SDAE for fitting, the difference value between the fitting data and the original data is calculated, and the data with the difference value larger than the threshold value is judged as abnormal data.
The abnormal data classification stage is responsible for dividing the abnormal data into noise data and fault data, classifying the detected abnormal data by utilizing the correlation among the sensor data, and determining the plurality of related sensor data at the same time as the abnormal data if the plurality of related sensor data are simultaneously judged as the abnormal data, or determining the plurality of related sensor data as the noise data if the plurality of related sensor data are not judged as the abnormal data.
And the noise data restoration stage is responsible for restoring the noise data judged in the previous stage into normal data so as not to influence the subsequent data analysis and processing work. The noise can be fitted to the characteristic of normal data by utilizing the three-layer superposition denoising self-encoding machine SDAE, and the noise data is replaced by the data fitted by the corresponding three-layer superposition denoising self-encoding machine SDAE to complete the restoration.
The invention relates to a data cleaning method of an abnormal data cleaning device of a power system sensor. The method specifically comprises the following steps:
detecting abnormal data, and training a three-layer superposition denoising self-encoder SDAE by using normal sensor data; inputting new data to be cleaned into the trained SDAE, and outputting the fitted data; making a difference between the fitted data and the original data, wherein the data with the difference value exceeding a specified threshold value is abnormal data;
and (3) abnormal data classification: for each abnormal data, searching whether other sensors have abnormal data at the same moment, and recording other sensors with abnormal data at the same moment; calculating the correlation degree between the sensor and other recorded sensors, and if the correlation degree is greater than a specified threshold value, judging that the sensor and other recorded sensors have strong correlation; if the number of the sensors with strong correlation with the sensors is larger than a specified number threshold, the abnormal data of the sensors at the moment is fault data; otherwise, it is noise data;
abnormal data restoration: for each noise data, finding out the corresponding SDAE fitting data; and replacing the noise data with the fitting data to complete the restoration.
The abnormal data detection module comprises the following specific steps:
the method is characterized in that normal data of the sensor are collected in advance and used for training the SDAE, the SDAE can learn the characteristics of the normal data of the sensor, and the normal data of the sensor can be fitted.
And recording the maximum value of the difference value when the SDAE of the three-layer superposition denoising self-encoder is fitted with normal data, and taking the maximum value as a threshold value for judging the normal data and the abnormal data. Because of the existence of errors, the maximum value can not be directly selected, firstly, the difference value between three-layer superposition denoising self-encoder SDAE fitting data and original data when fitting normal data is calculated, a difference value sequence is made into a histogram, a normal distribution curve is drawn, a confidence interval is taken to be 0.99, and an upper threshold Th is obtainedLAnd a lower threshold ThU。
Suppose the new data is X ═ XijIn which xijData representing the j moment of the ith sensor is input into a three-layer superposition denoising self-encoding machine SDAE, and the three-layer superposition denoising self-encoding machine SDAE can fit the data according to the learned characteristics to obtain fitting data
WhereinFitting data at time j for the ith sensor is shown. The fitting data and the original data are subjected to difference to obtain a difference value
Wherein d isijOf fitting data representing time j of the ith sensor to raw dataThe difference value.
Comparing the difference with a threshold value, if ThL＜dij＜ThUThen xijIs normal data, kij0, otherwise, xijAs abnormal data, kij＝1。
The abnormal data classification module comprises the following specific steps:
time-wise partitioning of a sensor data set X into m windows X ═ L1,...,LmIn which each window Lj＝{X1j,...,Xnj}T,XijEach window length is s, and fig. 2 shows division of 5 sensor data windows, where the window length is 200. It can be seen that L3The correlation between the sensors in the window is obviously different from the correlation of other windows, so that it is necessary to divide the window and then perform correlation calculation.
Calculating the correlation degree between the sensor data in each window, the correlation degree RT between the sensor i and the sensor jijThe calculation formula of (2) is as follows:
for kitAbnormal data x of 1itAnd finding out all abnormal data at the time t and recording the correlation degree of the sensor where the abnormal data are located and the sensor i in the window. Only at time t, there are at least w-1 abnormal data, and the correlation between the sensor time series and the sensor time series is greater than the minimum correlation threshold, that is
Num((RTij＞RTmin)&(kjt1) is greater than w-1, the data is considered as fault data, and k is madeit2; if this condition is not satisfied, the data is noise data, kit1 is unchanged.
From here, the data class matrix K ═ { K ═ Kij},kijE {0,1,2} is built, and the classification of abnormal data is completed: k is a radical ofij0 indicates data xijIs normal data, kij1 denotes the data xijFor noisy data, kij2 denotes the data xijIs the noisy data.
The specific steps of the noise data restoration module include:
establishing a data matrix Y ═ YijLet Y be X. Finding k in data category matrixijData x of 1ijLet us order
The matrix Y is the repair-completed data set.
Those skilled in the art will understand that: embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. The utility model provides an abnormal data cleaning device of electric power system sensor which characterized in that: the method comprises the following steps: the abnormal data detection module, the abnormal data classification module and the noise data restoration module;
the abnormal data detection module detects abnormal data of a sensor of the power system;
the abnormal data classification module is used for dividing the sensor abnormal data into noise data and fault data;
and the noise data restoration module fits the noise data into normal data, and then replaces the noise data with the data fitted by the corresponding SDAE to finish restoration.
2. The abnormal data cleaning device for the sensor of the power system according to claim 1, wherein: the abnormal data detection module trains the SDAE by using the normal data of the sensor, learns the characteristics of the normal data of the sensor, fits the normal data of the sensor, and takes the maximum value of the difference value between the SDAE fitted data and the original data as a threshold value for judging whether the data is normal or not; and when new data is input, inputting the new data into SDAE for fitting, solving the difference value between the fitting data and the original data, and judging the data with the difference value larger than the threshold value as abnormal data.
3. The abnormal data cleaning device for the sensor of the power system according to claim 1, wherein: the abnormal data classification module is used for dividing the sensor data set X into m windows X ═ L according to time1,...,LmCalculating the correlation degree between the sensor data in each window, finding out all abnormal data at the same time for the abnormal data, and recording the correlation degree between the sensor where the abnormal data is located and the sensor i in the window; when at least w-1 abnormal data exist at the time t and the correlation degree of the sensor time sequence where the abnormal data exist and the sensor time sequence is larger than the minimum correlation degree threshold value, the data are determined to be fault data; if the condition is not met, the data is noise data; a data category matrix is established that includes normal data, fault data, and noise data.
4. The device for cleaning abnormal data of sensor in power system as claimed in claim 3, wherein correlation RT between sensor i and sensor j isijThe calculation formula of (2) is as follows:
wherein x isijData representing the i-th sensor at time j, X \ uitIs the t-th data, X _, of the sensor within the windowjtIs the t-th data of sensor j in a window, each window being s in length.
5. The abnormal data cleaning device for the sensor of the power system as claimed in claim 3, wherein the noise data repairing module is used for establishing a data matrix Y ═ { Y ═ Y }ijLet Y be X; finding k in data category matrixijData x of 1ijLet us order
The data matrix Y is a repaired data set.
6. A data cleaning method using the abnormal data cleaning apparatus for a power system sensor according to any one of claims 1 to 5, comprising:
inputting new data to be cleaned into the trained SDAE, and outputting the fitted data; making a difference between the fitted data and the original data, wherein the data with the difference value exceeding a specified threshold value is abnormal data;
for each abnormal data, searching whether other sensors have abnormal data at the same moment, and recording other sensors with abnormal data at the same moment; calculating the correlation degree between the sensor and other recorded sensors, and if the correlation degree is greater than a specified threshold value, judging that the sensor and other recorded sensors have strong correlation; if the number of the sensors with strong correlation with the sensors is larger than a specified number threshold, the abnormal data of the sensors at the moment is fault data; otherwise, it is noise data;
for each noise data, finding out the corresponding SDAE fitting data; and replacing the noise data with the fitting data to complete the restoration.
7. The data cleansing method according to claim 6, characterized in that: the specific steps of abnormal data detection comprise:
s1, collecting normal data of the sensor, learning the characteristics of the normal data of the sensor, and fitting the normal data of the sensor;
s2, calculating the difference between the SDAE fitting data and the original data when fitting the normal data, making a histogram of the difference sequence, drawing a normal distribution curve, taking a confidence interval of 0.99, and obtaining an upper threshold ThLAnd a lower threshold ThU；
S3: suppose the new data is X ═ XijIn which xijData indicating the j time of the i-th sensor is input to SDAE, andfitting the data to obtain fitting data
Wherein
Fitting data representing the j moment of the ith sensor is obtained, the fitting data is subtracted from the original data, and a difference value is obtained
Wherein d isijRepresenting the difference between the fitted data of the ith sensor at the time j and the original data;
s4: comparing the difference with a threshold value, if ThL＜dij＜ThUThen xijIs normal data, kij0, otherwise, xijAs abnormal data, kij＝1。
8. The data cleansing method according to claim 7, characterized in that:
the specific steps of the abnormal data classification include:
s1: time-wise partitioning of a sensor data set X into m windows X ═ L1,...,LmIn which each window Lj＝{X1j,...,Xnj}T,Xij＝{xi,(j-1)×s+1,...,xi,j×sH, the length of each window is s;
s2, calculating the correlation degree between the sensor data in each window;
s3 for kitAbnormal data x of 1itFinding out all abnormal data at the time t and recording the correlation degree of the sensor where the abnormal data are located and the sensor i in the window; when at least w-1 abnormal data exist at the time t and the correlation degree of the sensor time sequence where the abnormal data exist and the sensor time sequence is larger than the minimum correlation degree threshold value, namely Num ((RT)ij＞RTmin)&(kjt1) is greater than w-1, the data is considered as fault data, and k is madeit2; if this condition is not satisfied, the data is noise data, kit1 is unchanged;
s4: establishing a data category matrix K ═ Kij},kij∈{0,1,2}，kij0 indicates data xijIs normal data, kij1 denotes the data xijFor noisy data, kij2 denotes the data xijIs failure data.
9. The data cleansing method according to claim 8, characterized in that:
correlation RT between sensor i and sensor jijThe calculation formula of (2) is as follows:
wherein x isijData representing the i-th sensor at time j, X \ uitIs the t-th data, X _, of the sensor within the windowjtIs the t-th data of sensor j in a window, each window being s in length.
10. The data cleansing method according to claim 7, characterized in that:
the specific steps of the noise data restoration are as follows:
s1: establishing a data matrix Y ═ YijLet Y be X;
s2: finding k in data category matrixijData x of 1ijLet us order
S3: the data matrix Y is used as the repair-completed data set.
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