CN115099557A - Intelligent power grid analysis method based on intelligent monitoring - Google Patents
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Abstract
The invention relates to the technical field of intelligent power grids, and discloses an intelligent power grid analysis method based on intelligent monitoring. According to the invention, through establishing a three-dimensional multidimensional data system of data, the disadvantages of large storage space and difficulty in data analysis in the prior art can be effectively improved, the storage and data query of big data of the intelligent power distribution network are effectively solved, the provided data structure is combined, an equipment monitoring and retrieval mode is obtained, and more comprehensive and accurate fault solutions can be provided for rapidly locking problem equipment and nodes on line.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a smart power grid analysis method based on intelligent monitoring.
Background
The distribution network receives electric energy from a transmission network or a regional power plant, distributes the electric energy to the power networks of various users on the spot or step by step according to voltage through distribution facilities, is used for supplying power to each distribution station and various electric loads in a certain area, and is a network with the function of distributing the electric energy in the power networks. The smart grid is the intellectualization of the grid, also called as "grid 2.0", is based on an integrated, high-speed two-way communication network, and realizes the goals of reliability, safety, economy, high efficiency, environmental friendliness and safe use of the grid through the application of advanced sensing and measuring technology, advanced equipment technology, advanced control method and advanced decision support system technology, and the main characteristics of the smart grid include self-healing, excitation and user protection, attack resistance, provision of electric energy quality meeting the requirements of users, allowance of access of various different power generation forms, starting of the power market and optimized and efficient operation of assets.
In the existing power distribution network operation state analysis method, due to the fact that a data acquisition channel is narrow, data integration and processing capacity are weak, data mining becomes very difficult, analysis result accuracy is poor, the data volume of the intelligent power distribution network is large, the annual growth speed is high, data storage and analysis are difficult, and the real-time performance is difficult to guarantee.
Disclosure of Invention
In order to achieve the purpose, the invention provides the following technical scheme: an intelligent power grid analysis method based on intelligent monitoring comprises the following steps:
s1, establishing a power distribution network system
The method comprises the steps of establishing a historical fault database, classifying and grading faults, establishing a machine learning algorithm, training the machine learning algorithm by using the fault database, calculating risk indexes of various faults, establishing a solution sharing platform according to fault processing reports of the historical fault database, and carrying out node numbering and data acquisition on the intelligent power distribution network according to a topological structure of the intelligent power distribution network system.
S2, data acquisition
The method comprises the following steps of collecting voltage, current and position information of each node by using a sensor, collecting internal environment data of the distribution equipment by using a monitor, transmitting the collected data information to a monitoring center for integration, storing the collected data information according to a multidimensional stereo unit with a membership relation, wherein a storage unit is in a matrix tensor product form, and the specific method comprises the following steps of:
converting the collected data between serial and parallel according to physical definition, wherein the converted parallel data is as follows:
B＝<B 1,v ，B 2,v ，...，B K,v >and 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:
B 1,v ＝{e 1,v,1 ，e 1,v,2 ，...，e 1,v,M and M represents a geographical membership dimension of data mapping, and a difference value of each node is calculated to obtain a difference value data vector: delta B 1,v ＝{Δe 1,v,1 ，Δe 1,v,2 ，...，Δe 1,v,M Wherein Δ e 1,v,i ＝e 1,v,i -e intial ，e intial ＝Min{e 1,v,1 ，e 1,v,2 ，...，e 1,v,M }。
S3, data query analysis
And performing data query and analysis on the stored data in the step S2 to obtain a query result and an analysis result.
S4, data evaluation early warning
The method comprises the steps of training a machine learning algorithm by using a fault database, calculating risk indexes of various faults, converting received data by a converter, extracting a characteristic vector, inputting the characteristic vector into the machine learning algorithm, obtaining running state information of a corresponding node after the machine learning algorithm is analyzed and processed, automatically evaluating the running state of a power distribution network by an analysis system to obtain abnormal state information, fault state information and normal state information, calling adjacent node state information by the analysis system according to the position of the abnormal node, confirming the state of the abnormal node to prevent false alarm, and classifying the fault state information by the analysis system in a grading mode.
S5, data visualization
The display displays the running state information of the nodes and the equipment of the power distribution network in a chart form, carries out early warning on abnormal state information, carries out classified display on the position information, the fault type and the grade of the fault equipment and the nodes, and is reminded by intelligent voice broadcast.
S6, providing a solution
After the administrator determines the fault, the system automatically locates the corresponding solution according to the fault type and the fault level, downloads the solution from the solution sharing platform, generates a fault report, and the monitoring center sends the fault report to maintenance personnel through wireless communication.
S7, result feedback
And supplementing and updating the solution by the system according to the feedback of the fault processing result of the maintenance personnel.
Preferably, the machine learning algorithm in step S4 is an artificial neural network algorithm.
Preferably, the monitor in step S2 includes a temperature monitor and a humidity monitor, and the data information is transmitted through a wireless network.
Preferably, the eigenvector in step S4 is voltage amplitude, voltage phase angle, current amplitude, current phase angle, distribution equipment environment temperature and distribution equipment environment humidity.
Preferably, the evaluation items in step S4 include safety evaluation, power supply capability evaluation, reliability evaluation and power supply quality evaluation.
Preferably, in step S1, each intelligent distribution network device and the associated meter are numbered and assigned with an IP address according to the topology structure of the intelligent distribution network system, and data information is acquired for the intelligent distribution network device nodes according to the topology map established by the IP address.
Preferably, the IP address uses IPV6 protocol.
Preferably, in step S2, K represents a time membership dimension of the data mapping, and v represents a number of the data collection node.
The invention provides an intelligent power grid analysis method based on intelligent monitoring. The method has the following beneficial effects:
(1) according to the invention, through establishing a three-dimensional multidimensional data system of data, the disadvantages of large storage space and difficulty in data analysis in the prior art can be effectively improved, the problems of storage and data query of big data of the intelligent power distribution network can be effectively solved, an equipment monitoring and retrieval mode is obtained by combining the provided data structure, a more comprehensive and accurate fault solution can be provided by locking problem equipment and nodes on line and rapidly, a result feedback link is designed, and the instantaneity, the advancement and the accuracy of a solution sharing platform can be improved.
(2) According to the invention, node numbering and data acquisition of the intelligent power distribution network are carried out through the application of the membership characteristics of the site and time of data acquisition and the topological structure of the intelligent power distribution network system, and fault early warning and fault analysis can be rapidly and effectively carried out on the power distribution network by acquiring and analyzing the information of each node and the equipment environment information, so that the safety and reliability of the power distribution network are greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are 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.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
As shown in fig. 1, the present invention provides a technical solution: an intelligent power grid analysis method based on intelligent monitoring comprises the following steps:
s1, establishing a power distribution network system
The method comprises the steps of establishing a historical fault database, classifying and grading faults, establishing a machine learning algorithm, training the machine learning algorithm by using the fault database, calculating risk indexes of various faults, establishing a solution sharing platform according to fault processing reports of the historical fault database, carrying out node numbering and data acquisition on an intelligent power distribution network according to a topological structure of the intelligent power distribution network system, numbering each intelligent power distribution network device and a related attached meter according to the topological structure of the intelligent power distribution network system, distributing IP addresses, carrying out data information acquisition on the intelligent power distribution network device nodes according to a topological graph established by the IP addresses, and adopting an IPV6 protocol for the IP addresses.
S2, data acquisition
The method comprises the following steps of collecting voltage, current and position information of each node by using a sensor, collecting internal environment data of the distribution equipment by using a monitor, transmitting the collected data information to a monitoring center for integration processing, storing the collected data information according to a multidimensional stereo unit with a membership relationship, wherein a storage unit is in a matrix tensor product form, the monitor comprises a temperature monitor and a humidity monitor, and transmitting the data information through a wireless network, and the specific method comprises the following steps:
converting the acquired data between serial data and parallel data according to a physical definition, wherein the converted parallel data is as follows:
B＝<B 1,v ，B 2,v ，...，B K,v >and K represents the time membership dimension of data mapping, v represents the number of a data acquisition node, each parallel data is subjected to the transformation of a place membership mode, and the nodes are clustered by adopting the IP addresses of the nodes to obtain:
B 1,v ＝{e 1,v,1 ，e 1,v,2 ，...，e 1,v,M and M represents a geographical membership dimension of data mapping, and a difference value of each node is calculated to obtain a difference value data vector: delta B 1,v ＝{Δe 1,v,1 ，Δe 1,v,2 ，...，Δe 1,v,M Wherein Δ e 1,v,i ＝e 1,v,i -e intial ，e intial ＝Min{e 1,v,1 ，e 1,v,2 ，...，e 1,v,M }。
S3, data query analysis
And performing data query and analysis on the stored data in the step S2 to obtain a query result and an analysis result.
S4, data evaluation early warning
Training a machine learning algorithm by using a fault database, calculating risk indexes of various faults, converting received data by a converter, extracting a characteristic vector, inputting the characteristic vector into the machine learning algorithm, obtaining running state information of corresponding nodes after the machine learning algorithm is analyzed and processed, automatically evaluating the running state of a power distribution network by an analysis system to obtain abnormal state information, fault state information and normal state information, calling adjacent node state information by the analysis system according to the position of the abnormal node, confirming the state of the abnormal node to prevent false alarm, classifying the fault state information by the analysis system in a grading way, wherein the machine learning algorithm is an artificial neural network algorithm, the characteristic vector comprises a voltage amplitude value, a voltage phase angle, a current amplitude value, a current phase angle, the environmental temperature of power distribution equipment and the environmental humidity of the power distribution equipment, the evaluation items comprise safety evaluation, power supply capacity evaluation, reliability evaluation and power supply quality evaluation.
S5, data visualization
The display displays the running state information of the nodes and the equipment of the power distribution network in a chart form, performs early warning on abnormal state information, performs classified display on position information and fault types and grades of fault equipment and nodes, and is reminded by intelligent voice broadcasting.
S6, providing a solution
After the administrator determines the fault, the system automatically locates the corresponding solution according to the fault type and the fault level, downloads the solution from the solution sharing platform, generates a fault report, and the monitoring center sends the fault report to maintenance personnel through wireless communication.
S7, result feedback
And supplementing and updating the solution by the system according to the feedback of the fault processing result of the maintenance personnel.
The machine learning algorithm in step S4 is an artificial neural network algorithm.
When the method is used, the disadvantages of large storage space and difficulty in data analysis in the prior art can be effectively improved by establishing a three-dimensional multi-dimensional data system of data, the storage and data query of big data of the intelligent power distribution network can be effectively solved, an equipment monitoring and retrieval mode is obtained by combining the provided data structure, a more comprehensive and accurate fault solution can be provided by rapidly locking problem equipment and nodes on line, a result feedback link is designed, and the instantaneity, the advancement and the accuracy of a solution sharing platform can be improved; by applying the membership characteristics of the place and time of data acquisition and the topological structure of the intelligent power distribution network system, node numbering and data acquisition of the intelligent power distribution network are performed, and by acquiring and analyzing the node information and the equipment environment information, fault early warning and fault analysis can be rapidly and effectively performed on the power distribution network, so that the safety and reliability of the power distribution network are greatly improved.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An intelligent power grid analysis method based on intelligent monitoring is characterized by comprising the following steps:
s1, establishing a power distribution network system
Establishing a historical fault database, classifying and grading faults, establishing a machine learning algorithm, training the machine learning algorithm by using the fault database, calculating risk indexes of various faults, establishing a solution sharing platform according to fault processing reports of the historical fault database, and carrying out node numbering and data acquisition on the intelligent power distribution network according to a topological structure of the intelligent power distribution network system;
s2, data acquisition
The method comprises the following steps of collecting voltage, current and position information of each node by using a sensor, collecting internal environment data of the distribution equipment by using a monitor, transmitting the collected data information to a monitoring center for integration processing, storing the collected data information according to a multidimensional stereo unit with a membership relationship, wherein a storage unit is in a matrix tensor product form, and the specific method comprises the following steps:
converting the collected data between serial and parallel according to physical definition, wherein the converted parallel data is as follows:
B＝<B 1,v ，B 2,v ，...，B K,v >and carrying out the transformation of the place membership mode on each parallel data, and clustering the nodes by adopting the IP addresses of the nodes to obtain:
B 1,v ＝{e 1,v,1 ，e 1,v,2 ，...，e 1,v,M and (5) wherein M represents a geographical membership dimension of the data mapping, and a difference value of each node is calculated to obtain a difference value data vector: delta B 1,v ＝{Δe 1,v,1 ，Δe 1,v,2 ，...，Δe 1,v,M Wherein Δ e 1,v,i ＝e 1,v,i -e intial ，e intial ＝Min{e 1,v,1 ，e 1,v,2 ，...，e 1,v,M }；
S3, data query analysis
Performing data query and analysis on the stored data in the step S2 to obtain a query result and an analysis result;
s4, data evaluation early warning
Training a machine learning algorithm by using a fault database, calculating risk indexes of various faults, converting received data by a converter, extracting a characteristic vector, inputting the characteristic vector into the machine learning algorithm, obtaining running state information of a corresponding node after the running state information is analyzed and processed by the machine learning algorithm, automatically evaluating the running state of a power distribution network by an analysis system to obtain abnormal state information, fault state information and normal state information, calling adjacent node state information by the analysis system according to the position of the abnormal node, confirming the state of the abnormal node to prevent false alarm, and classifying the fault state information by the analysis system in a grading way;
s5, data visualization
The display displays the running state information of the nodes and the equipment of the power distribution network in a chart form, performs early warning on abnormal state information, performs classified display on position information and fault types and grades of fault equipment and nodes, and is reminded by intelligent voice broadcast;
s6, providing a solution
After the administrator determines the fault, the system automatically locates the corresponding solution according to the fault type and the fault level, downloads the solution from the solution sharing platform, generates a fault report, and sends the fault report to maintenance personnel through wireless communication by the monitoring center;
s7, result feedback
And supplementing and updating the solution by the system according to the feedback of the fault processing result of the maintenance personnel.
2. The intelligent monitoring-based intelligent power grid analysis method according to claim 1, wherein the method comprises the following steps: the machine learning algorithm in the step S4 is an artificial neural network algorithm.
3. The intelligent monitoring-based intelligent power grid analysis method according to claim 1, wherein the method comprises the following steps: the monitor in step S2 includes a temperature monitor and a humidity monitor, and the data information is transmitted through a wireless network.
4. The intelligent monitoring-based intelligent power grid analysis method according to claim 1, wherein the method comprises the following steps: the eigenvectors in the step S4 are voltage amplitude, voltage phase angle, current amplitude, current phase angle, distribution equipment ambient temperature and distribution equipment ambient humidity.
5. The intelligent monitoring-based intelligent power grid analysis method according to claim 1, wherein the method comprises the following steps: the evaluation items in the step S4 include safety evaluation, power supply capability evaluation, reliability evaluation and power supply quality evaluation.
6. The intelligent monitoring-based intelligent power grid analysis method according to claim 1, wherein the method comprises the following steps: in the step S1, each intelligent distribution network device and the relevant attached meter are numbered and assigned with an IP address according to the topological structure of the intelligent distribution network system, and data information collection is performed on the intelligent distribution network device nodes according to the topological graph established by the IP address.
7. The intelligent monitoring-based smart grid analysis method according to claim 6, wherein: the IP address uses IPV6 protocol.
8. The intelligent monitoring-based intelligent power grid analysis method according to claim 1, wherein the method comprises the following steps: in the step S2, K represents a time membership dimension of the data mapping, and v represents a number of the data collection node.
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