CN113742650A - Distributed sensing data processing method and device - Google Patents
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Abstract
The invention relates to a distributed sensing data processing method and a distributed sensing data processing device, which are used for acquiring data acquired by a power distribution network intelligent sensor in real time, processing links such as data interference point removal, data matrix construction and data matrix mapping on the acquired data, and performing classification and aggregation according to different classification dimension data so as to provide the data for functional modules such as subsequent analysis and diagnosis. According to the invention, the data monitored by the distributed sensor of the power distribution network is processed in each link, massive multidimensional monitoring information is normalized and sorted, part of unreliable monitoring data possibly caused by interference is eliminated, and data aggregation is realized according to different classification dimensions, so that a practical, reliable and structured data basis is provided for operation analysis of the power distribution network.
Description
Technical Field
The invention relates to the technical field of online monitoring of power distribution network data, in particular to a distributed sensing data processing method and device.
Background
The power distribution network is characterized by large total scale and numerous devices. Along with the scale of distribution network is bigger and bigger, can bring the unmatched problem of distribution network an organic whole fortune dimension management manpower resources and distribution network acceleration rate, simultaneously, huge scale distribution network equipment operation condition is analyzed by the manual work completely, and fortune dimension work load is great, also can produce certain deviation. Nowadays, the novel distribution network of using big data, thing networking, intellectuality and information-based technology on a large scale has become the mainstream direction of distribution network construction, and novel distribution network realizes that distribution network leap formula development plays apparent effect to promoting distribution network construction, fortune dimension, management level. The novel power distribution network is constructed in a large scale by adopting novel information means such as an intelligent sensing technology and an internet of things technology, massive data are obtained for information such as the running state, equipment working conditions and running environment of the power distribution network, the data are support sources of the running of the power distribution network on the one hand, massive monitoring data are scattered and have insufficient normative performance on the other hand, and relative difficulty is caused for the effectiveness of comprehensive diagnosis of the power distribution network. Meanwhile, the monitoring data of the power distribution network, especially the monitoring data of the working condition of the equipment obtained by various intelligent sensing equipment additionally arranged on a switch cabinet, a transformer and a circuit indirectly reflect the health condition of the main equipment, the main means of supporting state maintenance is analysis and diagnosis of the main equipment, the insulation deterioration of the main equipment is slowly changed, the meaning of distinguishing the real-time sampled data is usually not large, the real-time collected data is possibly caused by interference, the actual working condition of the main equipment cannot be truly reflected, and even misjudgment is caused.
How to fully use the mass data monitored by the power distribution network, clean the data to remove possible interference points and aggregate the data, and provide practical, credible and structured data for the operation analysis of the power distribution network is a problem which needs to be solved urgently.
Disclosure of Invention
Based on the above situation in the prior art, the present invention aims to provide a distributed sensing data processing method and apparatus, which provide a reliable and structured data base for power distribution network operation analysis through processing of data acquisition, data interference point removal, data matrix construction, data matrix mapping, and the like.
To achieve the above object, according to one aspect of the present invention, there is provided a distributed sensing data processing method, including the steps of:
acquiring distributed sensing data;
removing data interference points in the acquired data;
constructing a data matrix and carrying out matrix transformation;
and mapping the transformed matrix into a two-dimensional array.
Further, the removing the data interference points in the acquired data includes:
obtaining the maximum value and the minimum value in the acquired data in a preset time period through comparison;
dividing a plurality of value intervals between the maximum value and the minimum value;
counting the number of the data in each value interval, and taking the interval with the maximum probability as a confidence interval;
and counting the mean value of the data in the confidence interval, and taking the mean value as the effective value of the acquired data.
Further, the constructing the data matrix comprises constructing the data matrix into a multi-dimensional column matrix a according to different classification dimensions of the data:
A＝{yi1,yi2,yi3,yi4,…}
wherein, i is 1, 2, 3, and m is the number of monitoring data; y isi1,yi2,yi3，yi4And … represent different classification dimensions of the data.
Further, the performing matrix transformation includes transforming the multi-dimensional column matrix a into a concentration matrix B concentrated according to different classification dimensions.
Further, the classification dimension at least comprises a monitoring object, monitoring data, a data type and an applicable algorithm.
Further, the mapping the transformed matrix into a two-dimensional array includes: mapping the transformed matrix into a two-dimensional array, wherein each row of the two-dimensional array corresponds to one monitoring data; each column of the two-dimensional array represents a sort dimension.
According to a second aspect of the present invention, a distributed sensing data processing apparatus is provided, which includes a data acquisition module, a data processing module, a matrix construction module, and an array mapping module; wherein the content of the first and second substances,
the data acquisition module is used for acquiring distributed sensing data;
the data processing module is used for removing data interference points in the acquired data;
the matrix construction module is used for constructing a data matrix and performing matrix transformation;
and the array mapping module is used for mapping the transformed matrix into a two-dimensional array.
Further, the removing, by the data processing module, data interference points in the acquired data includes:
obtaining the maximum value and the minimum value in the acquired data in a preset time period through comparison;
dividing a plurality of value intervals between the maximum value and the minimum value;
counting the number of the data in each value interval, and taking the interval with the maximum probability as a confidence interval;
and counting the mean value of the data in the confidence interval, and taking the mean value as the effective value of the acquired data.
Further, the matrix construction module constructs a data matrix and performs matrix transformation, including:
constructing the data into a multi-dimensional column matrix A according to different classification dimensions of the data:
A＝{yi1,yi2，yi3,yi4,…}
wherein, i is 1, 2, 3, and m is the number of monitoring data; y isi1，yi2，yi3，yi4… represent different classification dimensions of the data;
the multi-dimensional column matrix a is transformed into a concentration matrix B concentrated by different classification dimensions.
According to a third aspect of the present invention, there is provided a storage medium storing a computer program, characterized in that the computer program realizes the method according to the first aspect of the present invention when executed by a processor.
In summary, the invention provides a distributed sensing data processing method and device, which are used for acquiring data acquired by a power distribution network smart sensor in real time, performing data interference point removal, data matrix construction, data matrix mapping and other links on the acquired data, and performing classification and aggregation according to different classification dimension data so as to provide the data for subsequent functional modules such as analysis and diagnosis. According to the invention, the data monitored by the distributed sensor of the power distribution network is processed in each link, massive multidimensional monitoring information is normalized and sorted, part of unreliable monitoring data possibly caused by interference is eliminated, and data aggregation is realized according to different classification dimensions, so that a practical, reliable and structured data basis is provided for operation analysis of the power distribution network.
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FIG. 1 is a flow chart of a distributed sensing data processing method of the present invention;
FIG. 2 is a schematic diagram of a data acquisition and processing flow of each device of the power distribution network;
fig. 3 is a block diagram showing the configuration of the distributed sensing data processing apparatus according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings. According to an embodiment of the present invention, there is provided a distributed sensing data processing method, a flowchart of which is shown in fig. 1, including the steps of:
and S1, acquiring the distributed sensing data. The distributed sensing data acquisition means acquiring data acquired by the intelligent sensor of the power distribution network in real time in a wireless and/or wired mode. Fig. 2 is a schematic diagram illustrating a data acquisition and processing flow of each device of the power distribution network. The distribution network equipment includes, for example, a transformer, a line, a switching station, a distribution room, and the like, and distributed sensing data is acquired by an intelligent sensor provided on each equipment. The acquired data comprises electric quantity (such as parameters of current, voltage, power, phase angle and the like), environment sensing quantity (such as parameters of pressure, flow, temperature, vibration and the like), energy consumption measurement (such as parameters of electricity consumption, gas consumption, heat consumption, price and the like), and hydrological microclimate information (such as parameters of weather, temperature and humidity, water level and the like). If the original sampling value is transmitted by the smart sensor, the calculation of the effective value needs to be performed first, and the calculation method of the effective value may adopt a commonly used calculation method of the effective value, which is not limited herein.
And S2, removing the data interference points in the acquired data. The method mainly aims to effectively filter interference information on the premise of maximally retaining useful information of the monitoring data of the power distribution network. In this embodiment, the data interference point is removed based on the fact that the change of the monitored data value is a slow variable, so that the data preprocessing can be performed by adopting a probability statistics method. The specific steps can be carried out as follows:
firstly, data sampled by an intelligent sensor in real time within a preset time period are counted, and a monitoring data sequence is generated, for example: y (n) ═ y1，y2，y3，...ynComparing the data in the sequence to obtain the maximum value and the minimum value in the acquired data in a preset time period; the predetermined time period may be set as desired and may be variable;
dividing a plurality of value intervals between the maximum value and the minimum value; specifically, the value intervals should be generally equal to or greater than 3.
Counting the number of the data in each value interval, taking the interval with the maximum probability as a confidence interval, and discarding the data falling into other intervals;
and counting the mean value of the data in the confidence interval, and taking the mean value as the effective value of the acquired data.
It should be noted that the data sequence in this embodiment is sliding in real time, so that the magnitude change of the monitoring data in a period of time can be reflected while the interference points can be removed to the maximum extent.
And S3, constructing a data matrix and performing matrix transformation. In the step, the data matrix is constructed by selecting different classification dimensions to construct a multi-dimensional column matrix, wherein the dimension of the matrix is consistent with the number of the selected parameters; and the data matrix mapping performs row transformation on the constructed multi-dimensional column matrix, and centralizes the data with the same classification dimension, thereby realizing the standardized and structured processing of mass data.
Firstly, constructing the data into a multi-dimensional column matrix A according to different classification dimensions of the data:
A＝{yi1，yi2，yi3，yi4，…}
wherein, i is 1, 2, 3, and m is the number of monitoring data; y isi1,yi2,yi3,yi4And … represent different classification dimensions of the data.
For example, according to an embodiment, the classification dimensions are selected as monitoring objects, monitoring data, data types, and applicable algorithms. For 1 switchgear, the temperature data of 3 temperature measuring points are monitored (data type y)tempThe applicable algorithm is yalgo1) 1-way partial discharge data (data type y)pdThe applicable algorithm is yalgo2) 1 way separating brake speed data (data type y)spThe applicable algorithm is yalgo3) For example, a matrix A is constructed1Comprises the following steps:
wherein, y11Indicating the object to be monitored as a switchgear apparatus, y1-y5A value indicative of the monitored data is obtained,
ytemp、ypdand yspExpressed as temperature data, partial discharge data, and opening speed data, yalgo1-yalgo3Respectively, representing different algorithms applicable.
Then, matrix transformation is performed to transform the multi-dimensional column matrix a into a concentration matrix B concentrated by different classification dimensions. For the constructed matrix A, y isi1The information of the monitored object is line-transformed by firstly yi1The same rows are concentrated to realize the concentration of all parameters of the monitored object; then based on yi3And the data types are concentrated, so that the concentration of different monitoring data of the same monitoring object is realized. Let pm1For monitoring data, tiFor monitoring the object, pjIs a data type, akTo apply the algorithm, a new matrix Bp can be formedm1,ti,pj,ak|：
Taking the above-mentioned exemplary embodiment as an example, the multi-dimensional column matrix A is1Performing a matrix transformation, set y1～y5For monitoring data, it can be considered as p in the matrix Bm1One instantiation of the monitoring data, y11Is tiInstantiation of the monitoring object, ytemp、ypd、yspIs pjInstantiation of a data type, yalgo1～yalgo3Is akInstantiation of the applicable algorithm. Thereby transforming into B1：
It should be noted that the construction and transformation of the matrix are not only 4 classification dimensions, but the increase and decrease of the classification dimensions can be performed according to the situation. For the power distribution network, if the data types of the same kind of devices are the same, the applicable algorithms are basically consistent.
And S4, mapping the transformed matrix into a two-dimensional array. Matrix mapping is to map the matrix B into a two-dimensional array for subsequent analysis and diagnosis functions, and the subsequent modules call an adaptive algorithm to perform diagnosis and analysis according to the centralized equipment type and data type.
Taking the above-mentioned exemplary embodiment as an example, the transformed matrix B is1Matrix mapping for computer processing, the mapped two-dimensional array C[5][4]As shown in table 1:
TABLE 1
| Line number | Monitoring an object | Monitoring data | Data type | Adaptive algorithm |
| 1 | y11 | y1 | ytemp | yalgo1 |
| 2 | y11 | y5 | ytemp | yalgo1 |
| 3 | y11 | y3 | ytemp | yalgo1 |
| 4 | y11 | y2 | ypd | yalgo2 |
| 5 | y11 | y4 | ysp | yalgo3 |
The two-dimensional array C[5][4]The subsequent processing is a common two-dimensional array, and the subsequent diagnostic analysis calls an applicable algorithm according to the column parameters to perform corresponding analysis, for example, an analysis algorithm combining a threshold value and longitudinal analysis is adopted for ultrasonic measurement data of partial discharge, and an algorithm combining a threshold value and transverse analysis is comprehensively adopted for transient earth electric wave data of partial discharge.
And the data matrix mapping performs row transformation on the constructed multi-dimensional column matrix to realize the concentration of the same monitoring object and data type. It should be noted that the parameters of the selected main line transformation are adjusted according to different application scenarios. For monitoring data of a power distribution network, monitoring objects such as a transformer, a switch cabinet, a transformer area and a line and different data types such as voltage, temperature and partial discharge are collectively explained.
In the embodiment, the data processing method is described by taking the monitoring data of the power distribution network as an example, but the method provided by the invention is also suitable for processing the multidimensional monitoring data in occasions such as a high-voltage power grid, a power plant, an industrial and mining enterprise and the like.
According to a second embodiment of the present invention, a distributed sensing data processing apparatus is provided, and the apparatus is configured as shown in fig. 3, and includes a data acquisition module, a data processing module, a matrix construction module, and an array mapping module.
The data acquisition module is used for acquiring distributed sensing data.
The data processing module is used for removing data interference points in the acquired data.
The matrix construction module is used for constructing a data matrix and performing matrix transformation.
The array mapping module is used for mapping the transformed matrix into a two-dimensional array.
The specific process of each module in the device to realize its function is the same as each step of the fault location method in the first embodiment provided by the present invention, and is not described herein again.
According to a third embodiment of the invention, a storage medium is provided, which stores a computer program which, when executed by a processor, carries out the method as described in the first embodiment of the invention.
In summary, the present invention relates to a distributed sensing data processing method and device, which are used for acquiring data acquired by a power distribution network smart sensor in real time, performing data interference point removal, data matrix construction, data matrix mapping and other processes on the acquired data, and performing classification and aggregation according to different classification dimension data, so as to provide the data for subsequent functional modules such as analysis and diagnosis. According to the invention, the data monitored by the distributed sensor of the power distribution network is processed in each link, massive multidimensional monitoring information is normalized and sorted, part of unreliable monitoring data possibly caused by interference is eliminated, and data aggregation is realized according to different classification dimensions, so that a practical, reliable and structured data basis is provided for operation analysis of the power distribution network.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting the protection scope thereof, and although the present invention has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present invention, they can make various changes, modifications or equivalents to the specific embodiments of the present invention, but these changes, modifications or equivalents are within the protection scope of the appended claims.
Claims (10)
1. A distributed sensing data processing method is characterized by comprising the following steps:
acquiring distributed sensing data;
removing data interference points in the acquired data;
constructing a data matrix and carrying out matrix transformation;
and mapping the transformed matrix into a two-dimensional array.
2. The method of claim 1, wherein removing data interference points in the acquired data comprises:
obtaining the maximum value and the minimum value in the acquired data in a preset time period through comparison;
dividing a plurality of value intervals between the maximum value and the minimum value;
counting the number of the data in each value interval, and taking the interval with the maximum probability as a confidence interval;
and counting the mean value of the data in the confidence interval, and taking the mean value as the effective value of the acquired data.
3. The method of claim 2, wherein constructing the data matrix comprises constructing the data matrix into a multi-dimensional column matrix a according to different classification dimensions of the data:
A＝{yi1，yi2，yi3，yi4，...}
wherein, i is 1, 2, 3, and m is the number of monitoring data; y isi1，yi2，yi3，yi4,.. represent different classification dimensions of the data.
4. The method of claim 3, wherein performing a matrix transformation comprises transforming the multi-dimensional column matrix A into a concentration matrix B concentrated by different classification dimensions.
5. The method of claim 3, wherein the classification dimensions include at least monitoring objects, monitoring data, data types, and applicable algorithms.
6. The method of claim 4 or 5, wherein mapping the transformed matrix into a two-dimensional array comprises: mapping the transformed matrix into a two-dimensional array, wherein each row of the two-dimensional array corresponds to one monitoring data; each column of the two-dimensional array represents a sort dimension.
7. A distributed sensing data processing device is characterized by comprising a data acquisition module, a data processing module, a matrix construction module and an array mapping module; wherein the content of the first and second substances,
the data acquisition module is used for acquiring distributed sensing data;
the data processing module is used for removing data interference points in the acquired data;
the matrix construction module is used for constructing a data matrix and carrying out matrix transformation;
and the array mapping module is used for mapping the transformed matrix into a two-dimensional array.
8. The apparatus of claim 7, wherein the data processing module removes data interference points from the acquired data, comprising:
obtaining the maximum value and the minimum value in the acquired data in a preset time period through comparison;
dividing a plurality of value intervals between the maximum value and the minimum value;
counting the number of the data in each value interval, and taking the interval with the maximum probability as a confidence interval;
and counting the mean value of the data in the confidence interval, and taking the mean value as the effective value of the acquired data.
9. The apparatus of claim 8, wherein the matrix construction module constructs a data matrix and transforms the data matrix, comprising:
constructing the data into a multi-dimensional column matrix A according to different classification dimensions of the data:
A＝{yi1，yi2，yi3，yi4，...}
wherein, i is 1, 2, 3, and m is the number of monitoring data; y isi1，yi2，yi3，yi4,.. representing different classification dimensions of the data;
the multi-dimensional column matrix a is transformed into a concentration matrix B concentrated by different classification dimensions.
10. A storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1-6.
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