CN115423051A - Power inspection data mining, equipment state classification and risk prediction system and method - Google Patents
Power inspection data mining, equipment state classification and risk prediction system and method Download PDFInfo
- Publication number
- CN115423051A CN115423051A CN202211373057.7A CN202211373057A CN115423051A CN 115423051 A CN115423051 A CN 115423051A CN 202211373057 A CN202211373057 A CN 202211373057A CN 115423051 A CN115423051 A CN 115423051A
- Authority
- CN
- China
- Prior art keywords
- data
- power
- risk
- equipment
- data mining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007418 data mining Methods 0.000 title claims abstract description 63
- 238000007689 inspection Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000011156 evaluation Methods 0.000 claims abstract description 26
- 238000007781 pre-processing Methods 0.000 claims abstract description 17
- 238000012502 risk assessment Methods 0.000 claims abstract description 16
- 238000000605 extraction Methods 0.000 claims description 13
- 239000000126 substance Substances 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 5
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 4
- 230000001186 cumulative effect Effects 0.000 claims description 4
- 230000001419 dependent effect Effects 0.000 claims description 4
- 238000003745 diagnosis Methods 0.000 claims description 4
- 238000005315 distribution function Methods 0.000 claims description 4
- 238000005065 mining Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 3
- 239000000463 material Substances 0.000 abstract description 3
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Water Supply & Treatment (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Artificial Intelligence (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of power grids, in particular to a system and a method for data mining, equipment state classification and risk prediction of power routing inspection, which comprises the following steps: the data acquisition module is used for acquiring power patrol data; the data preprocessing module is used for preprocessing the acquired power patrol data; the data mining module is used for constructing a data mining model for the collected power inspection data to perform data mining; the equipment state evaluation module is used for evaluating and classifying the equipment state; and the risk analysis module is used for predicting the risk of the equipment. According to the invention, the mass data is rapidly extracted by constructing the data mining model, so that manpower and material resources are saved; the equipment states are classified through data extracted by the data mining model, and then risk prediction is carried out on factors possibly causing equipment risks, so that reference is provided for optimizing the equipment.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a power inspection data mining, equipment state classification and risk prediction system and method.
Background
The power grid system is an important national infrastructure, safe, stable and efficient operation of power production is related to the national civilians, has great significance for continuous and healthy development of the power industry, and is a constantly-running fundamental task in the whole industry. The state detection of the power grid equipment is the daily key work of a power company, in order to ensure the normal operation of the power equipment, a strict inspection plan needs to be made, and dispatching personnel inspect a transformer substation and a power transmission line so as to find hidden dangers and faults in time, gather the operation condition and the defect information of the equipment and perform regular analysis and statistics, so that the working intensity of the personnel is high, and the work task is heavy. The power equipment can generate mass data in the inspection process, and extraction of the mass data consumes a large amount of manpower and material resources. Therefore, how to improve the efficiency of evaluating the state of the power grid equipment becomes a technical problem to be solved at present.
Disclosure of Invention
The invention aims to solve the problems in the background technology by providing a power inspection data mining, equipment state classification and risk prediction system and a method.
The technical scheme adopted by the invention is as follows:
the utility model provides a data mining, equipment state classification and risk prediction system are patrolled and examined to electric power includes:
a data acquisition module: the power routing inspection system is used for collecting power routing inspection data;
a data preprocessing module: the power routing inspection system is used for preprocessing the collected power routing inspection data;
a data mining module: the system is used for constructing a data mining model for the collected power inspection data to carry out data mining;
an equipment state evaluation module: the device is used for evaluating and classifying the device state;
a risk analysis module: the risk prediction method is used for constructing a risk analysis model and predicting the risk of the equipment.
As a preferred technical scheme of the invention: the data preprocessing module is used for preprocessing the collected power inspection data, and comprises data cleaning and data normalization.
As a preferred technical scheme of the invention: the data mining module processes the power inspection data through Bayesian network parameter discretization, performs dimensionality reduction on the power inspection data, then constructs a data mining model, and further processes the data subjected to dimensionality reduction.
As a preferred technical scheme of the invention: the data mining model is constructed by the following steps:
setting the property capable of being mined, corresponding to different property data samples and numerical value relationship
Comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
represents a classification sample in the power patrol data,
the probability that the attribute of the power patrol data is h is shown, n is the total amount of the power patrol data, i is the ith power patrol data,
and the data set is patrolled for the ith power line.
As a preferred technical scheme of the invention: relationship of said values by Logistic function
And (3) carrying out conversion:
wherein, the first and the second end of the pipe are connected with each other,
which represents the regression parameters introduced by the regression technique,
representing the power patrol data group, e is a regression coefficient,
is a deformation function;
constructing a numerical value extraction relation:
wherein, b is a set threshold value,
for the ith power patrol data group, Q is a set constant, corresponding to the value of the regression parameter, according to the value intervalForming the final extraction process.
As a preferred technical scheme of the invention: when extracting a large amount of power patrol data, expanding the introduced regression parameters
The numerical value of (2) realizes the rapid extraction of a large amount of power patrol data.
As a preferred technical scheme of the invention: in the equipment state evaluation module, equipment data information is extracted based on a data mining model built by the data mining module, an equipment state evaluation matrix is built, an equipment state evaluation training matrix is obtained through an Adam algorithm, training data is obtained through a convolution algorithm, and the equipment state is classified through training a softmax classifier.
As a preferred technical scheme of the invention: the risk analysis module constructs a risk analysis model as follows:
the risk of the equipment is taken as a dependent variable A, and the risk factor is
The functional relationship is:
data patrol with electric power
Merging:
wherein the content of the first and second substances,
are respectively to
A power patrol data set under each risk factor;
determining an average value for each risk factor
And variance
：
Wherein n is the total amount of the power patrol data, k is the kth risk factor, i is the ith power patrol data,
is the ith power patrol data for the kth risk factor;
for each risk factor variable
Generating evenly distributed risk factors
And polling data with electric power
Performing combined analysis to determine probability density function of each risk factor variable
And cumulative probability distribution function
：
Wherein the content of the first and second substances,
the number of the variables of the risk factors,
；
in order to simulate the number of times,
；
calculating risk factors
Probability of occurrence
And predicting the factors causing the equipment risk.
As a preferred technical scheme of the invention: and constructing a Bayesian network for equipment state diagnosis according to the probability of the random number of the risk factor, classifying risk grades according to the equipment states classified by the equipment state evaluation module, and predicting factors causing different equipment risk grades.
The method for mining the data of the power inspection, classifying the equipment state and predicting the risk comprises the following steps:
s1.1: collecting power patrol data through various sensor devices;
s1.2: preprocessing the collected power inspection data;
s1.3: constructing a data mining model to process the preprocessed power inspection data;
s1.4: extracting equipment data according to the constructed data mining model, and classifying the equipment state according to the equipment state evaluation training model;
s1.5: and (4) dividing equipment risk levels according to data mined by the data mining model and equipment states classified by the equipment state evaluation training model, and predicting the risk of the equipment.
Compared with the prior art, the system and the method for mining the power inspection data, classifying the equipment state and predicting the risk have the beneficial effects that:
according to the invention, the mass data is rapidly extracted by constructing the data mining model, so that manpower and material resources are saved; and classifying the equipment state through the data extracted by the data mining model, and then predicting the risk of factors which possibly cause equipment risk so as to provide reference for optimizing the equipment.
Drawings
FIG. 1 is a system block diagram of a preferred embodiment of the present invention;
FIG. 2 is a flow chart of a method in a preferred embodiment of the present invention.
The meaning of each label in the figure is: 100. a data acquisition module; 200. a data preprocessing module; 300. a data mining module; 400. a device state evaluation module; 500. and a risk analysis module.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and the features in the embodiments may be combined with each other, and the technical solution 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. 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.
Referring to fig. 1, a preferred embodiment of the present invention provides a power inspection data mining, equipment state classification, and risk prediction system and method, including:
the data acquisition module 100: the power routing inspection system is used for collecting power routing inspection data;
the data preprocessing module 200: the system is used for preprocessing the collected power patrol data;
the data mining module 300: the data mining system is used for constructing a data mining model for the collected power inspection data to carry out data mining;
device state evaluation module 400: the device is used for evaluating and classifying the state of the device;
risk analysis module 500: the risk prediction method is used for constructing a risk analysis model and predicting the risk of the equipment.
The data preprocessing module 200 preprocesses the collected power inspection data, including data cleaning and data normalization.
The data mining module 300 processes the power inspection data through Bayesian network parameter discretization, performs dimensionality reduction on the power inspection data, then constructs a data mining model, and further processes the dimensionality-reduced data.
The data mining model is constructed by the following steps:
setting the property capable of being mined, corresponding to different property data samples and numerical value relationship
Comprises the following steps:
wherein the content of the first and second substances,
represents a classification sample in the power patrol data,
the probability that the attribute of the power patrol data is h is represented, n is the total amount of the power patrol data, i is the ith power patrol data,
and the data set is patrolled and examined for the ith power.
Relationship of said values by Logistic function
And (3) carrying out transformation:
wherein the content of the first and second substances,
which represents the regression parameters introduced in the form of,
representing the power patrol data group, e is a regression coefficient,
is a deformation function;
constructing a numerical value extraction relation:
wherein, b is a set threshold value,
and (4) forming a final extraction process for the ith power patrol data set according to the numerical value interval, wherein Q is a set constant and corresponds to the numerical value of the regression parameter.
When a large amount of power inspection data are extracted, the numerical values of the introduced regression parameters are expanded, and the rapid extraction of the large amount of power inspection data is realized.
In the device state evaluation module 400, device data information is extracted based on a data mining model constructed by the data mining module 300, a device state evaluation matrix is constructed, a device state evaluation training matrix is obtained through an Adam algorithm, training data is obtained through a convolution algorithm, and the device state is classified through training a softmax classifier.
The risk analysis module 500 constructs a risk analysis model as follows:
taking the risk of the equipment as a dependent variable A, wherein the risk factor is
The functional relationship is:
data patrol with electric power
Merging:
wherein, the first and the second end of the pipe are connected with each other,
are respectively to
A power patrol data set under each risk factor;
determining an average value for each risk factor
And variance
：
Wherein n is the total amount of the power patrol data, k is the kth risk factor, i is the ith power patrol data,
is the ith power patrol data for the kth risk factor;
for each risk factor variable
Generating evenly distributed risk factors
And polling data with power
Performing combined analysis to determine probability density function of each risk factor variable
And cumulative probability distribution function
：
Wherein the content of the first and second substances,
the number of the variables of the risk factors,
；
in order to simulate the number of times,
；
calculating risk factors
Probability of occurrence
And predicting the factors causing the equipment risk.
And constructing a Bayesian network for equipment state diagnosis according to the probability of the random number of the risk factor, dividing risk grades according to the equipment states classified by the equipment state evaluation module 400, and predicting factors causing different equipment risk grades.
The method for mining the power inspection data, classifying the equipment state and predicting the risk comprises the following steps:
s1.1: collecting power patrol data through various sensor devices;
s1.2: preprocessing the collected power patrol data;
s1.3: constructing a data mining model to process the preprocessed power patrol data;
s1.4: extracting equipment data according to the constructed data mining model, and classifying the equipment state according to the equipment state evaluation training model;
s1.5: and (4) dividing equipment risk levels according to data mined by the data mining model and equipment states classified by the equipment state evaluation training model, and predicting the risk of the equipment.
In this embodiment, the data acquisition module 100 acquires the power inspection equipment through various sensors, and performs preprocessing operations such as data cleaning and data normalization on the power inspection data. The data mining module 300 constructs a data mining model, which facilitates extraction of mass data.
Setting mineable attributes such as different temperature, voltage, power and other attributes, taking voltage attribute as an example, corresponding to different attribute data samples and numerical value relationship
Comprises the following steps:
wherein the content of the first and second substances,
represents a classification sample in the power patrol data,
the probability that the attribute of the power patrol data is h is shown, n is the total amount of the power patrol data, i is the ith power patrol data,
and the data set is patrolled and examined for the ith power.
Relationship of said values by Logistic function
And (3) carrying out transformation:
wherein the content of the first and second substances,
which represents the regression parameters introduced by the regression technique,
representing the power patrol data group, e is a regression coefficient,
is a deformation function;
classifying according to the voltage attribute needing to be excavated, and constructing a power patrol numerical value extraction relation of voltage attribute equipment according to the following formula:
wherein, b is a set threshold value,
and forming a final extraction process for the ith power patrol inspection data set according to the value interval, wherein Q is a set constant and corresponds to the value of the regression parameter. When a large amount of power inspection data with low voltage attributes need to be extracted, the numerical values of regression parameters are expanded, and the power inspection data with the low voltage attributes can be rapidly extracted.
The method comprises the steps of extracting power patrol data under a low-voltage working state of equipment according to a data mining model built by a data mining module 300, building a state evaluation matrix of the equipment under the low-voltage working state, obtaining a state evaluation training matrix of the equipment through an Adam algorithm, obtaining training data through a convolution algorithm, and dividing the state of the equipment into a first-stage working state, a second-stage working state, a third-stage working state and a halt/standby state through a training softmax classifier.
The risk analysis model excavates risk factors such as a high-pressure working state, a long-time low-pressure working state, a high-temperature working state and the like according to the data mining model, and still takes the low-pressure working state as an example, according to the constructed risk analysis model:
the risk of the equipment is taken as a dependent variable A, and the risk factor is
The functional relationship is:
data patrol with electric power
Merging:
wherein the content of the first and second substances,
are respectively to
A power patrol data set under each risk factor;
determining the average value of each risk factor
And variance
：
Wherein n is the total amount of the power patrol data, k is the kth risk factor, i is the ith power patrol data,
is the ith power patrol data for the kth risk factor;
for low pressure risk factor variable
To produce a uniform distribution
And performing combined analysis with the power patrol data to determine the probability density function of each risk factor variable
And cumulative probability distribution function
：
Wherein the content of the first and second substances,
the number of the variables of the risk factors,
；
in order to simulate the number of times,
；
determination of risk factors
Probability of occurrence
And predicting low-pressure risk factors causing equipment risks, and timely early warning and taking countermeasures according to the predicted abnormal running state of the equipment.
Constructing a Bayesian network for device status diagnosis based on the probability of the random number of the risk factor, and classifying risk levels based on the device statuses classified by the device status evaluation module 400, for example, classifying risk levels into classes
And predicting the factors causing the risk of the equipment with the four risk levels.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present specification describes embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and it is to be understood that all embodiments may be combined as appropriate by one of ordinary skill in the art to form other embodiments as will be apparent to those of skill in the art from the description herein.
Claims (9)
1. Data mining, equipment state classification and risk prediction system are patrolled and examined to electric power, its characterized in that: the method comprises the following steps:
data acquisition module (100): the power routing inspection system is used for collecting power routing inspection data;
data pre-processing module (200): the power routing inspection system is used for preprocessing the collected power routing inspection data;
data mining module (300): the data mining system is used for constructing a data mining model for the collected power inspection data to carry out data mining;
device status evaluation module (400): the device is used for evaluating and classifying the device state;
risk analysis module (500): the risk prediction method is used for constructing a risk analysis model and predicting the risk of equipment;
the risk analysis module (500) constructs a risk analysis model as follows:
taking the risk of the equipment as a dependent variable A, wherein the risk factor is
The functional relationship is:
data patrol with electric power
Merging:
wherein the content of the first and second substances,
are respectively to
A power patrol data set under each risk factor;
determining the average value of each risk factor
And variance
：
Wherein n is the total amount of the power patrol data, k is the kth risk factor, i is the ith power patrol data,
is the ith power patrol data for the kth risk factor;
for each risk factor variable
Generating evenly distributed risk factors
And polling data with electric power
Performing combined analysis to determine probability density function of each risk factor variable
And cumulative probability distribution function
：
Wherein the content of the first and second substances,
the number of the variables of the risk factors,
；
in order to simulate the number of times,
；
determination of risk factors
Probability of occurrence
And predicting the factors causing the equipment risk.
2. The power inspection data mining, equipment state classification and risk prediction system of claim 1, wherein: the data preprocessing module (200) preprocesses the collected power inspection data, including data cleaning and data normalization.
3. The power inspection data mining, equipment state classification and risk prediction system of claim 1, wherein: the data mining module (300) processes the power inspection data through Bayesian network parameter discretization, performs dimension reduction on the power inspection data, then constructs a data mining model, and further processes the dimension reduced data.
4. The power inspection data mining, equipment status classification, and risk prediction system of claim 3, wherein: the data mining model is constructed by the following steps:
setting mineable attribute, corresponding to different attribute data samples and numerical value relationship
Comprises the following steps:
wherein the content of the first and second substances,
represents a classification sample in the power patrol data,
the probability that the attribute of the power patrol data is h is represented, n is the total amount of the power patrol data, i is the ith power patrol data,
and the data set is patrolled for the ith power line.
5. The power inspection data mining, equipment status classification, and risk prediction system of claim 4, wherein: relationship of said values by Logistic function
And (3) carrying out transformation:
wherein, the first and the second end of the pipe are connected with each other,
which represents the regression parameters introduced in the form of,
representing the power patrol data group, e is a regression coefficient,
is a deformation function;
constructing a numerical extraction relation:
wherein, b is a set threshold value,
and forming a final extraction process for the ith power patrol inspection data set according to the value interval, wherein Q is a set constant and corresponds to the value of the regression parameter.
6. The power inspection data mining, equipment status classification and risk prediction system of claim 5, wherein: when extracting a large amount of power patrol data, expanding the introduced regression parameters
The numerical value of (2) realizes the rapid extraction of a large amount of electric power inspection data.
7. The power inspection data mining, equipment status classification, and risk prediction system of claim 1, wherein: in the equipment state evaluation module (400), equipment data information is extracted based on a data mining model constructed by the data mining module (300), an equipment state evaluation matrix is constructed, an equipment state evaluation training matrix is obtained through an Adam algorithm, training data is obtained through a convolution algorithm, and the equipment state is classified through training a softmax classifier.
8. The power inspection data mining, equipment status classification, and risk prediction system of claim 1, wherein: and constructing a Bayesian network for equipment state diagnosis according to the probability of the random number of the risk factor, dividing risk grades according to the equipment states classified by the equipment state evaluation module (400), and predicting factors causing different equipment risk grades.
9. The method for mining power inspection data, classifying equipment states and predicting risks is characterized by comprising the following steps of: the method comprises the following steps:
s1.1: collecting power patrol data through various sensor devices;
s1.2: preprocessing the collected power inspection data;
s1.3: constructing a data mining model to process the preprocessed power inspection data;
s1.4: extracting equipment data according to the constructed data mining model, and classifying the equipment state according to the equipment state evaluation training model;
s1.5: and (4) dividing equipment risk levels according to data mined by the data mining model and equipment states classified by the equipment state evaluation training model, and predicting the risk of the equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211373057.7A CN115423051A (en) | 2022-11-04 | 2022-11-04 | Power inspection data mining, equipment state classification and risk prediction system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211373057.7A CN115423051A (en) | 2022-11-04 | 2022-11-04 | Power inspection data mining, equipment state classification and risk prediction system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115423051A true CN115423051A (en) | 2022-12-02 |
Family
ID=84208366
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211373057.7A Pending CN115423051A (en) | 2022-11-04 | 2022-11-04 | Power inspection data mining, equipment state classification and risk prediction system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115423051A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116629709A (en) * | 2023-07-21 | 2023-08-22 | 国网山东省电力公司青岛市即墨区供电公司 | Intelligent analysis alarm system of power supply index |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069695A (en) * | 2015-07-28 | 2015-11-18 | 东北电力大学 | Intelligent substation real-time risk analysis system and analysis method |
| CN106026393A (en) * | 2016-06-06 | 2016-10-12 | 国家电网公司 | Intelligent line-cruising and operational safety risk prediction system and method for power grid linef |
| CN111723850A (en) * | 2020-05-28 | 2020-09-29 | 国网河北省电力有限公司电力科学研究院 | Automatic verification equipment state evaluation method based on intelligent inspection system |
| CN112884363A (en) * | 2021-03-19 | 2021-06-01 | 上海核工程研究设计院有限公司 | Nuclear power project economic evaluation probability risk analysis method |
-
2022
- 2022-11-04 CN CN202211373057.7A patent/CN115423051A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069695A (en) * | 2015-07-28 | 2015-11-18 | 东北电力大学 | Intelligent substation real-time risk analysis system and analysis method |
| CN106026393A (en) * | 2016-06-06 | 2016-10-12 | 国家电网公司 | Intelligent line-cruising and operational safety risk prediction system and method for power grid linef |
| CN111723850A (en) * | 2020-05-28 | 2020-09-29 | 国网河北省电力有限公司电力科学研究院 | Automatic verification equipment state evaluation method based on intelligent inspection system |
| CN112884363A (en) * | 2021-03-19 | 2021-06-01 | 上海核工程研究设计院有限公司 | Nuclear power project economic evaluation probability risk analysis method |
Non-Patent Citations (3)
| Title |
|---|
| 吴军英等: "基于大数据的变压器设备状态及风险分析研究与应用", 《电力大数据》 * |
| 李明钰 等: "考虑多价值链协同的电力设备制造企业经营风险预测研究", 《工程管理科技前沿》 * |
| 赵永国: "基于数据挖掘算法的智慧档案馆海量数据快速提取方法", 《控制理论与应用》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116629709A (en) * | 2023-07-21 | 2023-08-22 | 国网山东省电力公司青岛市即墨区供电公司 | Intelligent analysis alarm system of power supply index |
| CN116629709B (en) * | 2023-07-21 | 2023-10-20 | 国网山东省电力公司青岛市即墨区供电公司 | Intelligent analysis alarm system of power supply index |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103198175A (en) | Transformer fault diagnosis method based on fuzzy cluster | |
| BahooToroody et al. | A condition monitoring based signal filtering approach for dynamic time dependent safety assessment of natural gas distribution process | |
| CN110232499A (en) | A kind of power distribution network information physical side method for prewarning risk and system | |
| CN112926749A (en) | Intelligent power grid information equipment monitoring system and method | |
| CN113420162B (en) | Equipment operation chain state monitoring method based on knowledge graph | |
| CN111861211A (en) | System with double-layer anti-electricity-stealing model | |
| CN106682835A (en) | Data-driven complex electromechanical system service quality state evaluation method | |
| CN115423051A (en) | Power inspection data mining, equipment state classification and risk prediction system and method | |
| CN110909895A (en) | Early warning method and system based on special equipment historical periodic inspection report | |
| CN111931849A (en) | Hydroelectric generating set operation data trend early warning method | |
| CN113569459A (en) | Group intelligence-based hydraulic support performance degradation quantitative evaluation method and system | |
| CN110348683A (en) | The main genetic analysis method, apparatus equipment of electrical energy power quality disturbance event and storage medium | |
| CN103617447A (en) | Evaluation system and method for intelligent substation | |
| CN112001511A (en) | Equipment reliability and dynamic risk evaluation method, system and equipment based on data mining | |
| CN110956281A (en) | Power equipment abnormity detection alarm system based on Log analysis | |
| CN114091549A (en) | Equipment fault diagnosis method based on deep residual error network | |
| CN115983507B (en) | Method and system for predicting broadband oscillation risk of section of power grid of transmitting end source | |
| KR102126954B1 (en) | Method for measuring smart sewing works and system for performing the same | |
| CN116881958A (en) | Power grid big data safety protection method, system, electronic equipment and storage medium | |
| CN111506636A (en) | System and method for analyzing residential electricity consumption behavior based on autoregressive and neighbor algorithm | |
| Bond et al. | A hybrid learning approach to prognostics and health management applied to military ground vehicles using time-series and maintenance event data | |
| CN111160490A (en) | Deep learning dangerous rock deformation prediction method and device based on multiple time sequences | |
| Huang et al. | An intelligent operation and maintenance system for power consumption based on deep learning | |
| CN110689241A (en) | Power grid physical asset evaluation system based on big data | |
| Ceschini et al. | Multivariate Time Series Analysis for Electrical Power Theft Detection in the Distribution Grid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20221202 |
|
| RJ01 | Rejection of invention patent application after publication |