CN109103991B - Big data analysis method for intelligent power distribution network - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The invention discloses a big data analysis method of an intelligent power distribution network, which comprises the steps of carrying out node numbering and data acquisition on the intelligent power distribution network according to a topological structure of an intelligent power distribution network system; storing the acquired data information according to the multi-dimensional stereo unit with membership, wherein the storage unit is in a matrix tensor product form; and carrying out data query and analysis on the stored data to obtain a query result and an analysis result. The invention utilizes the membership characteristics of the intelligent power distribution network structure and the membership characteristics of the place and time of the acquired data to establish a three-dimensional multidimensional data system of the data, can effectively improve the disadvantages of large traditional storage space and difficult data analysis, and combines the provided data structure to obtain a mode of monitoring and retrieving equipment, and can quickly lock the problematic equipment and nodes on line. The storage and data query of big data of the intelligent power distribution network can be effectively solved; the method is not only suitable for the intelligent power distribution network, but also suitable for analyzing the data of the intelligent power grid.
Description
Technical Field
The invention belongs to the field of intelligent power distribution network control, and relates to a big data analysis method of an intelligent power distribution network.
Background
With the development of the smart power grid, the power supply reliability and the power quality provided by the traditional power distribution network cannot meet the requirements of users; from the statistical grid faults, about 80% of the grid faults are from the power distribution network, and with the access of new energy nodes, the fault risk of the power distribution network is increased by various factors such as voltage fluctuation in the active power distribution network. The intelligent power distribution network adopting various acquisition and communication modes (wireless communication, optical fiber, microwave communication and the like) can analyze according to mass data, grasp the network state in real time, predict and inquire abnormal operation states, and effectively monitor and repair the power distribution network, so that the power supply reliability and the power quality of the power distribution network are improved. However, unlike the traditional power distribution network control decision, the smart power distribution network needs to collect a large amount of associated data, and needs to perform real-time data query and decision on the collected large amount of associated data, so that the traditional data storage method and data analysis method cannot be applied to the big data analysis of the smart power distribution network.
The characteristics of the collection of the big data of the intelligent power distribution network can be summarized as follows: 1) the data acquisition and analysis have real-time performance; 2) data analysis needs to be performed at multiple spatial and temporal scales; 3) the network nodes of the power distribution network have membership, so that the data structure collected by the network nodes has membership.
In the existing big data analysis method, a big data storage and analysis method which can be directly applied to the intelligent power distribution network does not exist; firstly, the intelligent power distribution network has large data volume and high annual growth speed, so that data storage and analysis are difficult to bring, and the real-time performance is difficult to guarantee; secondly, the data of the intelligent power distribution network needs to be updated continuously, and the storage and processing speed of the data needs to be matched with the acquisition speed; third, although data in a plurality of physical meanings is required, data in each physical meaning requires data provided by different devices at different places and different times.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a big data analysis method of an intelligent power distribution network.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a big data analysis method of an intelligent power distribution network comprises the following steps:
step 1: node numbering and data acquisition of the intelligent power distribution network are carried out according to the topological structure of the intelligent power distribution network system;
step 2: storing the data information acquired in the step 1 according to the multi-dimensional stereo unit with membership, wherein the storage unit is in a matrix tensor product form;
and step 3: and (4) performing data query and analysis on the stored data in the step (2) to obtain a query result and an analysis result.
The invention further improves the following steps:
the specific method of the step 1 comprises the following steps:
numbering each intelligent power distribution network device and a related attached meter according to a topological structure of the intelligent power distribution network system, allocating an IP address, and acquiring data information of nodes of the intelligent power distribution network device according to a topological graph established by the IP address, wherein the acquired data information B is as follows:
B＝<m,t,d>
wherein m represents the IP identification of the equipment node, t is the acquisition time, and d represents the acquired data.
The IP address in step 1 adopts IPV6 protocol.
The specific method of the step 2 comprises the following steps:
step 2-1: converting the collected data between serial and parallel according to physical definition, wherein the converted parallel data is as follows:
B＝<B1,v,B2,v,…,BK,v>
k represents a time membership dimension of data mapping, and v represents the number of a data acquisition node;
step 2-2: and (3) carrying out the transformation of a place membership mode on each parallel data, and clustering the nodes by adopting the IP addresses of the nodes to obtain:
B1,v＝{e1,v,1,e1,v,2,…,e1,v,M}
wherein M represents a geographic membership dimension of the data mapping;
step 2-3: calculating the difference value of each node to obtain a difference value data vector:
ΔB1,v＝{Δe1,v,1,Δe1,v,2,…,Δe1,v,M}
wherein, Δ e1,v,i＝e1,v,i-eintial，eintial＝Min{e1,v,1,e1,v,2,…,e1,v,M}；
Step 2-4: by Δ B1,v＝{Δe1,v,1,Δe1,v,2,…,Δe1,v,MAnd B1,v＝{e1,v,1,e1,v,2,…,e1,v,MAdopting a tensor calculation mode to establish a tensor matrix:
step 2-5: carrying out tensor low-rank decomposition on the tensor matrix to obtain a decomposition matrix:
wherein, (.)(m)Representing the operation of modulus m on the multidimensional matrix;
wherein the content of the first and second substances,
and
is independent data, and the elements therein obey a standard normal distribution;
wherein, tmSubject to a standard normal distribution of the distribution,
is an identity matrix.
And 2, clustering the nodes in the step 2 by adopting a fuzzy membership clustering mode.
The specific method of the step 3 comprises the following steps:
step 3-1: performing eigenvalue decomposition on the decomposition matrix in the step 2 to obtain E(m)Characteristic value of
And D(m)Z(m)Characteristic value of
Step 3-2: computing a random indication scalar
Wherein N represents the number of characteristic values;
step 3-3: the calculation indicates the scalar share tau,
step 3-4: and obtaining abnormal indications of time and place positions according to the indication scalar ratio tau, and carrying out equipment alarm through sequencing the tau.
Further comprising the steps of:
and 4, step 4: carrying out relevant decision on the query result and the analysis result to obtain a decision result;
and 5: and sending the decision result to an execution device for operation maintenance and fault elimination of the intelligent power distribution network.
Compared with the prior art, the invention has the following beneficial effects:
the invention utilizes the membership characteristics of an intelligent distribution network structure, applies the membership characteristics of the place and time of data acquisition, carries out node numbering and data acquisition on the intelligent distribution network through the topological structure of the intelligent distribution network system, stores the acquired data information according to multidimensional stereo units with membership, adopts a storage unit in a matrix tensor product form, then carries out data query and analysis on the stored data to obtain a query result and an analysis result, establishes a stereo multidimensional data system of the data, can effectively improve the disadvantages of large storage space and difficult data analysis in the prior art, effectively solves the problems of storage and data query of big data of the intelligent distribution network, combines the provided data structure to obtain an equipment monitoring and retrieval mode, and can lock the problem equipment and nodes quickly on line.
Drawings
FIG. 1 is a block flow diagram of the method of the present invention;
FIG. 2 is a block diagram of a data storage model building process of the present invention;
FIG. 3 is a block diagram of a data analysis process of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, the big data analysis method for the intelligent power distribution network of the invention comprises the following steps:
step 1: according to the topological structure of the intelligent power distribution network system, node numbering and data acquisition of the intelligent power distribution network are carried out, and the specific method comprises the following steps:
the nodes of each power distribution network are endowed with IP addresses according to the topological structure, and the IP addresses adopt an IPV6 protocol, so that the membership of the addresses is ensured; and acquiring data information of the intelligent distribution network equipment node according to the obtained address relation, wherein the acquired data is B ═ m, t, d >, m represents the IP identification of the equipment node, t represents the acquisition time, and d represents the acquired data.
Step 2: storing the data information acquired in the step 1 according to the multi-dimensional stereo unit with membership, wherein the storage unit is in a matrix tensor product form, and referring to fig. 2, the specific method is as follows:
step 2-1: the server carries out conversion between serial and parallel data according to physical definition according to the collected and transmitted data, wherein the converted parallel data is expressed as B ═<B1,v,B2,v,…,BK,v>K represents the time membership dimension of data mapping, and v represents the number of the data acquisition node;
step 2-2: each parallel data is transformed in a place membership mode, and B1,v＝{e1,v,1,e1,v,2,…,e1,v,MThe nodes are clustered by adopting the IP addresses of the nodes, and the clustering mode adopts fuzzy membership clustering; wherein M represents a geographic membership dimension of the data map;
step 2-3: calculating the difference value of each node, wherein the initial value is B1,v＝{e1,v,1,e1,v,2,…,e1,v,MMinimum value e ofintial＝Min{e1,v,1,e1,v,2,…,e1,v,MGet difference data vector delta B1,v＝{Δe1,v,1,Δe1,v,2,…,Δe1,v,MIn which Δ e1,v,i＝e1,v,i-eintial；
Step 2-4: tong (Chinese character of 'tong')Over Delta B1,v＝{Δe1,v,1,Δe1,v,2,…,Δe1,v,MAnd B1,v＝{e1,v,1,e1,v,2,…,e1,v,MAdopting tensor calculation mode to establish final tensor matrix
Step 2-5: to tensor matrix
Carrying out tensor low-rank decomposition to obtain the final decomposition result of
Wherein (·)(m)Representing the modulo m operation on a multi-dimensional matrix,
wherein
And
is independent data, and the elements therein obey a standard normal distribution;
tmsubject to a standard normal distribution of the distribution,
is an identity matrix.
And step 3: and (3) performing data query and analysis on the storage data in the step (2) to obtain a query result and an analysis result, and referring to fig. 3, wherein the specific method comprises the following steps:
step 3-1: the decomposition matrix obtained according to step 2
Performing eigenvalue decomposition to obtain E(m)Characteristic value of
And D(m)Z(m)Characteristic value of
Step 3-2: computing a random indication scalar of
Wherein N represents the number of characteristic values;
step 3-3: according to
Calculating an indicated scalar fraction τ;
step 3-4: and obtaining abnormal indications of time and place positions according to the indication scalar ratio tau, and carrying out equipment alarm through sequencing the tau.
The invention adopts a multidimensional lattice-shaped hierarchical tensor product data structure for the first time, and provides a big data mining algorithm suitable for the data structure, and the algorithm can effectively solve the storage and data query of the big data of the intelligent power distribution network. The method is not only suitable for the intelligent power distribution network, but also suitable for the analysis of the data of the intelligent power distribution network.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (5)
1. A big data analysis method of an intelligent power distribution network is characterized by comprising the following steps:
step 1: node numbering and data acquisition of the intelligent power distribution network are carried out according to the topological structure of the intelligent power distribution network system;
step 2: storing the data information acquired in the step 1 according to a multi-dimensional stereo unit with a membership relationship, wherein the storage unit is in a matrix tensor product form, and the specific method in the step 2 comprises the following steps:
step 2-1: converting the collected data between serial and parallel according to physical definition, wherein the converted parallel data is as follows:
B＝<B1,v,B2,v,…,BK,v>
k represents a time membership dimension of data mapping, and v represents the number of a data acquisition node;
step 2-2: and (3) carrying out the transformation of a place membership mode on each parallel data, and clustering the nodes by adopting the IP addresses of the nodes to obtain:
B1,v＝{e1,v,1,e1,v,2,…,e1,v,M}
wherein M represents a geographic membership dimension of the data mapping;
step 2-3: calculating the difference value of each node to obtain a difference value data vector:
ΔB1,v＝{Δe1,v,1,Δe1,v,2,…,Δe1,v,M}
wherein, Δ e1,v,i＝e1,v,i-eintial，eintial＝Min{e1,v,1,e1,v,2,…,e1,v,M}；
Step 2-4: by Δ B1,v＝{Δe1,v,1,Δe1,v,2,…,Δe1,v,MAnd B1,v＝{e1,v,1,e1,v,2,…,e1,v,MAdopting a tensor calculation mode to establish a tensor matrix:
step 2-5: carrying out tensor low-rank decomposition on the tensor matrix to obtain a decomposition matrix:
wherein, (.)(m)Representing the operation of modulus m on the multidimensional matrix;
wherein the content of the first and second substances,
and
is independent data, and the elements therein obey a standard normal distribution;
wherein, tmSubject to a standard normal distribution of the distribution,
is an identity matrix;
and step 3: and (3) performing data query and analysis on the stored data in the step (2) to obtain a query result and an analysis result, wherein the specific method in the step (3) comprises the following steps:
step 3-1: performing eigenvalue decomposition on the decomposition matrix in the step 2 to obtain E(m)Characteristic value of
And D(m)Z(m)Characteristic value of
Step 3-2: computing a random indication scalar
Wherein N represents the number of characteristic values;
step 3-3: the calculation indicates the scalar share tau,
step 3-4: and obtaining abnormal indications of time and place positions according to the indication scalar ratio tau, and carrying out equipment alarm through sequencing the tau.
2. The big data analysis method for the intelligent power distribution network according to claim 1, wherein the specific method in the step 1 is as follows:
numbering each intelligent power distribution network device and a related attached meter according to a topological structure of the intelligent power distribution network system, allocating an IP address, and acquiring data information of nodes of the intelligent power distribution network device according to a topological graph established by the IP address, wherein the acquired data information B is as follows:
B＝<m,t,d>
wherein m represents the IP identification of the equipment node, t is the acquisition time, and d represents the acquired data.
3. The big data analysis method for the intelligent power distribution network according to claim 2, wherein the IP address in the step 1 adopts IPV6 protocol.
4. The big data analysis method for the intelligent power distribution network according to claim 1, wherein the clustering manner for clustering the nodes in the step 2 is fuzzy membership clustering.
5. The big data analysis method for the intelligent power distribution network according to claim 1, further comprising the steps of:
and 4, step 4: carrying out relevant decision on the query result and the analysis result to obtain a decision result;
and 5: and sending the decision result to an execution device for operation maintenance and fault elimination of the intelligent power distribution network.
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| CN110601227A (en) * | 2019-10-31 | 2019-12-20 | 国网陕西省电力公司铜川供电公司 | Three-phase load unbalance adjusting system and method applied to power distribution network |
| CN110880152A (en) * | 2019-12-20 | 2020-03-13 | 国网湖北省电力公司咸宁供电公司 | Power supply method for intelligent power distribution network |
| CN111697691B (en) * | 2020-05-22 | 2023-06-27 | 国网河北省电力有限公司石家庄市藁城区供电分公司 | Power distribution network planning monitoring system based on cloud computing |
| CN113124929A (en) * | 2021-04-13 | 2021-07-16 | 国网陕西省电力公司铜川供电公司 | Transformer substation multi-parameter signal acquisition comprehensive analysis system and method |
| CN113312348B (en) * | 2021-04-22 | 2022-07-22 | 山东马斯克智能科技有限公司 | Analysis and application method and system for industrial park electric power big data |
| CN115099557A (en) * | 2022-05-09 | 2022-09-23 | 广东电网有限责任公司电力调度控制中心 | Intelligent power grid analysis method based on intelligent monitoring |
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