CN112308436A

CN112308436A - Power distribution network evaluation diagnosis analysis method, device, equipment and storage medium

Info

Publication number: CN112308436A
Application number: CN202011218524.XA
Authority: CN
Inventors: 殷俊; 耿文逸; 李佳乐; 罗鹏; 周慧
Original assignee: State Grid Jiangsu Electric Power Co ltd Yangzhou Power Supply Branch; Yangzhou Yongmao Electric Power Construction Co ltd; Yangzhou Jiangdu District Power Supply Branch Of State Grid Jiangsu Electric Power Co ltd; State Grid Jiangsu Electric Power Co Ltd
Current assignee: State Grid Jiangsu Electric Power Co ltd Yangzhou Power Supply Branch; Yangzhou Yongmao Electric Power Construction Co ltd; Yangzhou Jiangdu District Power Supply Branch Of State Grid Jiangsu Electric Power Co ltd; State Grid Jiangsu Electric Power Co Ltd
Priority date: 2020-11-04
Filing date: 2020-11-04
Publication date: 2021-02-02

Abstract

A power distribution network evaluation and diagnosis analysis method, a device, equipment and a storage medium are provided, the power distribution network evaluation and diagnosis method comprises the following steps: s1: acquiring a measured state parameter of a measured power distribution network device; s2: and inputting the measured state parameters into a decision tree model which is trained in advance to obtain an output evaluation analysis result and a processing strategy of the measured power distribution network, wherein the strategy classification model is obtained based on the decision tree model. The invention trains the fault processing classification model of the power distribution network equipment based on the C4.5 decision tree algorithm, and solves the problems that the fault problem combing by the power distribution network lacks unified data analysis, the quality deviation of technicians is large, and the problem is not clearly distinguished in degree of urgency, so that the problem cannot be solved in time. And an auxiliary decision method is provided for diagnosis analysis and fault treatment of the power distribution network.

Description

Power distribution network evaluation diagnosis analysis method, device, equipment and storage medium

Technical Field

The invention relates to the field of power distribution network evaluation and diagnosis, in particular to a method, a device, equipment and a storage medium for evaluating, diagnosing and analyzing when a power distribution network has a fault problem.

Background

The distribution network presents the characteristics of multiple points, wide range, quick change and complex structure, the construction management level of the distribution network is insufficient, the problem is disordered and cannot be effectively combed, systematic data analysis is lacked for the fault problem, a solution scheme cannot be provided from the source, the existing problem is analyzed only by people, due to the fact that evaluation and diagnosis relying on manual troubleshooting and combing are influenced by the quality of personnel, certain influence is brought to the necessity and accuracy of decision implementation, the problem exposed in operation cannot be solved orderly according to the degree of urgency, and partial problems are not solved timely to influence the safe operation of equipment.

With the popularization of power grid intelligent equipment and the maturity of data analysis algorithms in recent years, the realization of diagnosis analysis assisted by artificial intelligence algorithms becomes possible gradually. Compared with the traditional subjective judgment and manual inspection modes, the artificial intelligence fully utilizes the internet of things data, exploits the implicit nonlinear relation among the data, provides reliable data support and method support for power distribution network diagnosis analysis and auxiliary decision-making, and can help inspection personnel to detect power grid faults and ensure the orderly and efficient development of power distribution network inspection work along with the research and development deepened application of a power distribution network diagnosis analysis and auxiliary decision-making system.

Disclosure of Invention

In order to solve the above mentioned drawbacks in the background art, the present invention provides a method, an apparatus, a device and a storage medium for power distribution network fault evaluation, diagnosis and analysis, so as to determine a processing scheme of a power distribution network device fault quickly and accurately. Through the auxiliary analysis decision of realizing the distribution network fault treatment, the accuracy of the distribution network fault diagnosis is improved, the orderly and efficient development of the intelligent routing inspection work of the distribution network is ensured, and the resource investment is saved.

The purpose of the invention can be realized by the following technical scheme:

the invention discloses a power distribution network evaluation, diagnosis and analysis method, which comprises the following steps:

s1: acquiring measured state parameters of the measured power distribution network equipment, wherein the measured state parameters comprise the annual maximum load rate, the distribution transformer operation age, the power supply radius, the power supply area, whether a fault, a defect, an abnormity, a model and the distribution transformer capacity of the power distribution network equipment;

s2: and inputting the measured state parameters into a power distribution network evaluation and diagnosis model which is trained in advance to obtain an output processing method of the equipment fault of the measured power distribution network, wherein the power distribution network evaluation and diagnosis model is obtained by training based on a decision tree algorithm.

Preferably, the method for constructing the power distribution network evaluation diagnosis model comprises the following steps:

(1) acquiring sample state parameters of the observed target power distribution network, packaging the sample state parameters into sample data, and storing the sample data in a training sample database;

(2) setting a training sample entry threshold value for a training sample database, and taking a sample as training data of the power distribution network evaluation diagnosis model when the number of newly received samples in the training sample database reaches the threshold value;

(3) classifying according to different training data provided by a training sample database and different characteristics, and comparing the variation degrees of the entropies under different classification methods to obtain a classification rule which finally enables the entropies to be maximum, thereby completing the training of the power distribution network evaluation diagnosis model;

(4) and updating the parameters of the existing power distribution network evaluation and diagnosis model in real time according to the training data set to obtain the power distribution network evaluation and diagnosis model capable of manually modifying the database.

Preferably, the sample state parameters in step (1) include an annual maximum load rate of the observed distribution network equipment, an operation age of the distribution transformer, a power supply radius, a power supply area, whether a fault occurs, a defect, an anomaly, a model and a distribution transformer capacity.

Preferably, the sample data in step (2) may be modified manually, that is, the data in the sample database is not fixed.

The invention also discloses a power distribution network evaluation and diagnosis device which comprises a measured state parameter acquisition module and a fault processing scheme output module;

the measured state parameter acquisition module is used for acquiring measured state parameters of the measured power distribution network equipment, and the measured state parameters comprise the annual maximum load rate, the distribution transformer operation age, the power supply radius, the power supply area, whether a fault occurs, a defect, an anomaly, a model and the distribution transformer capacity of the measured power distribution network equipment.

And the fault processing scheme output module is used for inputting the state parameters of the power distribution network to be tested into a power distribution network evaluation diagnosis model which is trained in advance to obtain an output diagnosis result of the power distribution network to be tested and a corresponding fault processing scheme, and the power distribution network evaluation diagnosis model is obtained based on decision tree model training.

The invention also discloses equipment which comprises a memory and one or more processors, wherein the memory is used for storing at least one program data, and the program data is executed by the processors, so that the processors realize the power distribution network evaluation diagnosis analysis method.

The invention also discloses a storage medium which contains computer executable instructions, and the computer executable instructions are used for executing the power distribution network evaluation diagnosis analysis method when being executed by a computer processor.

The invention has the beneficial effects that:

according to the invention, a decision model of a power distribution network fault processing scheme is established based on a decision tree algorithm, so that the problems that the problem cannot be solved in time due to the lack of unified data analysis for fault problem combing, large quality deviation of technicians and unclear distinction of degree and urgency of the problem in a power distribution network are solved. The orderly and efficient development of the power distribution network inspection work can be guaranteed.

Drawings

The invention will be further described with reference to the accompanying drawings;

fig. 1 is a flowchart of a power distribution network evaluation, diagnosis and analysis method according to an embodiment of the present invention;

fig. 2 is a flowchart of a power distribution network evaluation, diagnosis and analysis method according to a second embodiment of the present invention;

fig. 3 is a flowchart of a power distribution network evaluation, diagnosis and analysis method according to a third embodiment of the present invention;

fig. 4 is a schematic diagram of a specific example of evaluation, diagnosis and analysis of a simulated power distribution network according to a third embodiment of the present invention;

fig. 5 is a schematic diagram of an evaluation, diagnosis and analysis apparatus for a power distribution network according to a fourth embodiment of the present invention;

fig. 6 is a schematic structural diagram of an apparatus according to a fifth embodiment 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 description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.

Example one

Fig. 1 is a flowchart of a power distribution network evaluation, diagnosis and analysis method according to an embodiment of the present invention, where the method is applicable to a situation where a fault handling scheme is determined for a target resource, and the method may be executed by a power distribution network evaluation, diagnosis and analysis apparatus, which may be implemented in a software and/or hardware manner, and the apparatus may be configured in a terminal device. The method specifically comprises the following steps:

and S110, acquiring the measured state parameter of the measured target.

The target resource may be, for example, a hardware device, such as an ultrasound device, an energy source material, such as steel, or a food, such as milk.

The decision tree is a typical machine learning classification model, and the model establishes a mapping relation between object attributes and object values, so that the input of non-numerical classes can be directly processed. The decision tree measures the disorder degree of the system based on an informatics theory, for example, the ID3 algorithm adopts information gain, and the C4.5 algorithm adopts information gain rate for measurement, so that the prediction classification has a good effect. In this embodiment, the input data of the model is the annual maximum load rate, the distribution transformer operating life, the power supply radius, the power supply area, whether a fault, a defect, an anomaly, a model, and the distribution transformer capacity of the observed power distribution network device, and the output data of the output layer is the fault handling scheme of the observed power distribution network device.

In one embodiment, the measured state parameters include annual maximum load rate, distribution transformer operating age, power supply radius, power supply area, fault, defect, anomaly, model and distribution transformer capacity. In practical applications, the data are recorded by an information system of the power distribution network. In this embodiment, the processing scheme corresponding to the recorded failure data can be manually written into the database and modified.

And S120, inputting the measured state parameters into the trained power distribution network evaluation and diagnosis model to obtain an output fault processing scheme.

In one embodiment, optionally, the power distribution network evaluation diagnosis model is trained based on a decision tree algorithm. Wherein the decision tree is a tree structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. The C4.5 algorithm uses the information gain rate to avoid the problem of selecting attributes with more values in a biased way when the attributes are selected by using the information gain, and pruning is carried out in the construction process, so that the tree structure is not excessively expanded, and the discretization processing of continuous data also conforms to the data characteristics of the characteristics such as power supply radius and the like. And training to enable the gain rate of the tree structure to the training data information to be maximum, so as to obtain a corresponding classification rule.

According to the technical scheme, the power distribution network evaluation and diagnosis model is trained based on the decision tree model, so that the problems that the classification of the power distribution network to faults is unclear and cannot be solved in time due to the fact that the quality deviation of technical staff is large are solved. A decision-making means based on data analysis is provided for power distribution network polling work, and power distribution network polling and fault handling work is guaranteed to be carried out orderly and efficiently.

Example two

Fig. 2 is a flowchart of a power distribution network evaluation, diagnosis and analysis method according to a second embodiment of the present invention, and the technical solution of the present embodiment is further detailed based on the above-mentioned embodiment. Optionally, the power distribution network evaluation and diagnosis model is obtained based on decision tree algorithm training, and includes: obtaining sample state parameters of power distribution network observation equipment, and inputting the sample state parameters into a power distribution network evaluation diagnosis analysis model; the sample state parameters comprise the annual maximum load rate of the observed power distribution network equipment, the running age of the distribution transformer, the power supply radius, the power supply area, whether a fault, a defect, an anomaly, a model and the distribution transformer capacity exists; determining sample data according to the state parameters of the observed power distribution network equipment sample collected in real time, and storing the sample data in a training database; the sample data comprises the annual maximum load rate of the observed power distribution network equipment, the running age of the distribution transformer, the power supply radius, the power supply area, whether a fault occurs, a defect, an abnormity, a model and the distribution transformer capacity. And training the initialized model to obtain a power distribution network evaluation diagnosis analysis model based on the sample data in the training database and a preset training database sample entry threshold value.

The specific implementation steps of this embodiment include:

s210, obtaining sample state parameters of the target power distribution network equipment, and performing random initialization on part of parameters of the initial power distribution network evaluation and diagnosis model.

And S220, packaging the acquired sample state parameters into sample data, and storing the sample data in a training sample database.

In one embodiment, optionally, the sample state parameters include an annual maximum load rate of the observed distribution network equipment, an operating age of the distribution transformer, a power supply radius, a power supply area, whether a fault, a defect, an anomaly, a model, and a distribution transformer capacity.

In one embodiment, the sample data should be obtained from an information center related to the power distribution network management terminal device, and preferably includes historical data and real-time data.

And S230, training the existing initialization model according to the training set determined by the training sample database to obtain the power distribution network evaluation diagnosis model.

For example, an initialized training sample entry threshold is set for the training sample database, and the threshold may be 50 or 100. Specifically, when newly received sample entries in the database reach the threshold, a training set is generated by the new sample entries, and the existing power distribution network evaluation and diagnosis model is trained, so that the power distribution network evaluation and diagnosis model can update parameters in real time, and a fault solution corresponding to the observation samples can be output when state parameters of the observation samples are received. Alternatively, the data in the database may be modified manually.

In one embodiment, specifically, different features are selected for classification according to a training data set received from a training sample database, and a classified information gain rate is calculated, specifically, the calculation of the information gain rate may be obtained by the following formula:

the numerator part is information gain, and the denominator part is split information measurement, specifically, the calculation method is as follows:

and recursively constructing a decision tree according to the rules, so that the training of the power distribution network evaluation diagnosis model can be completed.

S240, acquiring a measured state parameter of the observed power distribution network equipment;

and S250, inputting the measured state parameters into the power distribution network evaluation diagnosis model which is trained in advance to obtain the output observed power distribution network diagnosis result and the fault processing scheme.

According to the technical scheme, the decision-making model of the power distribution network fault handling scheme based on the decision-making tree is trained by adopting historical equipment fault handling data, so that the problem that the fault handling problem in the existing power distribution network depends on the quality of technical personnel too much and cannot be solved in time is solved, the speed and the accuracy of power distribution network fault handling are increased, and meanwhile, the investment of partial human resources can be saved.

EXAMPLE III

Fig. 3 is a flowchart of a power distribution network evaluation, diagnosis and analysis method provided in the third embodiment of the present invention, and the technical solution of the present embodiment is further detailed based on the foregoing embodiments. Wherein the resource objects comprise resource objects corresponding to the initial power distribution network assessment diagnosis model; the resource object parameters comprise annual maximum load rate, distribution transformer operation age, power supply radius, power supply area, fault, defect, abnormity, model and distribution transformer capacity.

The specific implementation steps of this embodiment include:

s310, acquiring sample state parameters of target resources through a power distribution network terminal acquisition device, and initializing an initial power distribution network evaluation diagnosis model;

s320, storing sample data in a training sample database according to the acquired target resource sample data;

s330, determining a training data set of the power distribution network evaluation diagnosis model based on a recent sample data set stored in a training sample database according to preset database sample items;

s340, ordering the features according to the training data set of the power distribution network evaluation and diagnosis model and entropy changes obtained by classification according to different features until a classification rule with the maximum entropy is finally obtained, wherein the rule corresponds to the parameters of the model, and the rule generation process is the training process of the power distribution network evaluation and diagnosis model;

initializing partial parameters randomly every time according to different training data provided by a sample database, then performing feature scoring according to the training data to generate a decision tree structure, and finishing the training of the power distribution network evaluation diagnosis model;

and S350, acquiring the detected state parameters of the target power distribution network equipment.

Specifically, the measured state parameter of the target distribution network device may be set artificially. Because the power distribution network evaluation and diagnosis model at the moment is trained, the hypothetical state parameters of the observed power distribution network equipment can be calculated.

And S360, inputting the measured state parameters into the power distribution network evaluation diagnosis model which is trained in advance to obtain the output diagnosis result and the fault processing scheme of the observed power distribution network.

Fig. 4 is a schematic diagram of a training result of a power distribution network evaluation diagnosis model according to a third embodiment of the present invention. Each leaf node of the tree in the figure represents a fault handling scheme, the specific content of the scheme is shown in table 1, and specifically, the handling schemes of the power distribution network faults are divided into five types.

Each non-leaf node in the graph represents a classification rule, when a sample parameter of a new observed power distribution network device is input, the classification rule is pushed downwards from the tree root according to the content of features in the sample parameter until the leaf node is reached, and at the moment, a diagnosis and corresponding fault solution of the observed power distribution network is given through a diagnosis result output module.

TABLE 1 treatment scheme for power distribution network faults

Fault handling scheme sequence number	Fault handling scheme content
		1	Transformation of
2	Capacity increase modification
		3	Stationing
4	Stationing + improvement
		5	Stationing, capacity increasing and reforming

In this specific example, the threshold of the sample entry of the training sample database is set to 100, that is, when the number of samples newly added to the sample database reaches the threshold, the parameters of the power distribution network evaluation diagnosis model are updated accordingly. And the data in the database can be manually changed at any time. According to the different sizes of the training sets, the decision trees have different effects, and the accuracy of the test set is shown in table 2:

TABLE 2 Effect of different size datasets

Number of training set data entries	Test set accuracy
		100	92.57％
1000	96.88％
		4000	97.10％

In this example, the highest accuracy of the model on the test set reached 97.1%.

According to the technical scheme, the power distribution network assessment diagnosis model is trained based on historical collected fault processing, the problems that the fault processing problem in the existing power distribution network depends on the quality of technical personnel and cannot be solved in time are solved, a large amount of existing historical fault data are utilized, the efficiency of assessment diagnosis and decision making of the power distribution network is improved, corresponding human input can be reduced, and the resource utilization efficiency is improved.

Example four

Fig. 5 is a schematic diagram of an evaluation and diagnosis apparatus for a power distribution network according to a fourth embodiment of the present invention. The embodiment can be applied to the situation of evaluating, diagnosing and analyzing the observation power distribution network equipment, the device can be realized in a software and/or hardware mode, and the device can be configured in the terminal equipment. The power distribution network evaluation diagnosis analysis device comprises: the system comprises a measured power distribution network state parameter acquisition module 410 and a fault processing scheme output module 420.

The measured distribution network state parameter obtaining module 410 is configured to obtain measured state parameters of the distribution network, where the measured node parameters include an annual maximum load rate, a distribution transformer operating age, a power supply radius, a power supply area, whether a fault, a defect, an abnormality, a model, and a distribution transformer capacity of the distribution network device in an observation state;

and the diagnosis result and processing scheme output module 420 is configured to input the measured state parameters into a power distribution network evaluation diagnosis model which is trained in advance, so as to obtain an output evaluation result and a fault processing scheme of the observed power distribution network. The power distribution network evaluation diagnosis model is obtained based on decision tree model training.

According to the technical scheme, the power distribution network evaluation and diagnosis model is trained based on the decision tree model, so that the problems that the classification of the power distribution network to faults is unclear and cannot be solved in time due to the fact that the quality deviation of technical staff is large are solved. On the basis of the technical scheme, optionally, the target device is a device in a power distribution network, and the measured state parameters of the network nodes are annual maximum load rate, distribution transformer operation age, power supply radius, power supply area, fault, defect, abnormality, model and distribution transformer capacity.

On the basis of the above technical solution, optionally, the apparatus further includes a power distribution network evaluation and diagnosis model training module, where the power distribution network evaluation and diagnosis model training module includes:

the sample state parameter acquisition unit is used for acquiring sample state parameters of the target node and inputting the sample state parameters into the power distribution network evaluation and diagnosis model;

the sample storage unit is used for packaging the actual sample data acquired by the sample state parameter acquisition unit and storing the sample data in a training data sample database; wherein, the sample data comprises the observed distribution network equipment.

And the power distribution network evaluation diagnosis model training unit is used for determining internal parameters of the power distribution network evaluation diagnosis model based on the sample data of the corresponding item number stored in the training sample database according to the preset training data item number.

The power distribution network evaluation, diagnosis and analysis device provided by the embodiment of the invention can be used for executing the power distribution network evaluation, diagnosis and analysis method provided by the embodiment of the invention, and has corresponding functions and beneficial effects of the execution method.

It should be noted that, in the embodiment of the power distribution network evaluation, diagnosis and analysis apparatus, the included units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.

EXAMPLE five

Fig. 6 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention, where the fifth embodiment of the present invention provides a service for implementing evaluation, diagnosis and analysis of a power distribution network according to the foregoing embodiment of the present invention, and the power distribution network evaluation, diagnosis and analysis apparatus according to the foregoing embodiment may be configured. Fig. 6 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 6 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present invention.

As shown in FIG. 6, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM30) and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.

Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with device 12, and/or with any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown in FIG. 6, the network adapter 20 communicates with the other modules of the device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing by executing program data stored in the system memory 28, for example, to implement the power distribution network evaluation diagnosis analysis method provided by the embodiment of the present invention.

Through above-mentioned equipment, solved the problem that the fault handling problem was too much to rely on technical staff's quality, can't in time solve in the present distribution network for distribution network fault handling's speed and accuracy have been practiced thrift partial human resource's input simultaneously, make the utilization efficiency of resource and income maximize.

EXAMPLE six

An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for power distribution network evaluation, diagnosis and analysis, the method including:

and acquiring the measured state parameters of the observed power distribution network, wherein the measured state parameters comprise the annual maximum load rate, the distribution transformer operation age, the power supply radius, the power supply area, whether a fault, a defect, an abnormality, a model and the distribution transformer capacity of each device.

And inputting the measured state parameters into a power distribution network evaluation and diagnosis model which is trained in real time to obtain an output diagnosis result and a corresponding fault processing scheme, wherein the power distribution network evaluation and diagnosis model is obtained based on decision tree model training.

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

Of course, the storage medium provided by the embodiments of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the above method operations, and may also perform related operations in the power distribution network evaluation diagnosis analysis method provided by any embodiment of the present invention.

In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims

1. A power distribution network evaluation, diagnosis and analysis method is characterized by comprising the following steps:

s1: acquiring the measured state parameters of the measured power distribution network equipment,

s2: and inputting the measured state parameters into a pre-trained power distribution network evaluation diagnosis model to obtain the output equipment fault types and corresponding processing schemes.

2. The power distribution network evaluation, diagnosis and analysis method according to claim 1, wherein in step S1, the measured state parameters include annual maximum load rate, distribution transformer operating age, power supply radius, power supply area, fault, defect, anomaly, model, distribution transformer capacity of the power distribution network equipment.

3. The power distribution network evaluation and diagnosis analysis method according to claim 1, wherein the power distribution network evaluation and diagnosis model is constructed by the method comprising the following steps:

(1) acquiring sample state parameters of observed target power distribution network equipment from a historical database, performing data cleaning processing on the sample state parameters, packaging the sample state parameters into sample data, and storing the sample data in a training sample database;

(4) and updating the parameters of the existing power distribution network evaluation and diagnosis model in real time according to the training data set to obtain a real-time power distribution network evaluation and diagnosis model.

4. The power distribution network evaluation, diagnosis and analysis method according to claim 3, wherein the sample state parameters in step (1) include annual maximum load rate, distribution transformer operation age, power supply radius, power supply area, fault, defect, anomaly, model, distribution transformer capacity of the observed power distribution network equipment.

5. The power distribution network assessment diagnostic analysis method according to claim 3, wherein the sample data in step (2) is stored in a training sample database, and the data entries in the training sample database can be modified manually, and the parameters of the power distribution network assessment diagnostic model are modified by retraining the power distribution network assessment diagnostic model.

6. The power distribution network evaluation diagnosis analysis device is characterized by comprising a measured state parameter acquisition module and an evaluation diagnosis result output module;

the measured state parameter acquisition module is used for acquiring the measured state parameters of the measured power distribution network equipment;

and the evaluation and diagnosis result output module is used for inputting the state parameters of the tested power distribution network equipment into a power distribution network evaluation and diagnosis analysis model which is trained in advance to obtain the output evaluation result of the tested power distribution network and a corresponding fault processing scheme.

7. An electric distribution network evaluation diagnostic analysis apparatus comprising a memory and one or more processors, the memory for storing one or more program data, the one or more program data being executable by the one or more processors to cause the processors to implement the electric distribution network evaluation diagnostic analysis method of any of claims 1-5.

8. A storage medium containing computer-executable instructions for performing the power distribution network assessment diagnostic analysis method of any of claims 1-5 when executed by a computer processor.