CN113988518A - Distribution network equipment health diagnosis method for multi-source information fusion analysis - Google Patents
Distribution network equipment health diagnosis method for multi-source information fusion analysis Download PDFInfo
- Publication number
- CN113988518A CN113988518A CN202111137265.2A CN202111137265A CN113988518A CN 113988518 A CN113988518 A CN 113988518A CN 202111137265 A CN202111137265 A CN 202111137265A CN 113988518 A CN113988518 A CN 113988518A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- signals
- network equipment
- health
- distribution
- 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
- 238000009826 distribution Methods 0.000 title claims abstract description 83
- 238000003745 diagnosis Methods 0.000 title claims abstract description 51
- 230000036541 health Effects 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000004927 fusion Effects 0.000 title claims abstract description 30
- 238000004458 analytical method Methods 0.000 title claims abstract description 26
- 238000011156 evaluation Methods 0.000 claims abstract description 53
- 230000008447 perception Effects 0.000 claims abstract description 12
- 230000005540 biological transmission Effects 0.000 claims abstract description 11
- 238000000605 extraction Methods 0.000 claims abstract description 6
- 230000003862 health status Effects 0.000 claims abstract description 5
- 238000007689 inspection Methods 0.000 claims description 62
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000007500 overflow downdraw method Methods 0.000 abstract description 5
- 238000012423 maintenance Methods 0.000 description 28
- 230000006870 function Effects 0.000 description 25
- 230000007547 defect Effects 0.000 description 20
- 238000005516 engineering process Methods 0.000 description 18
- 238000001514 detection method Methods 0.000 description 16
- 238000012545 processing Methods 0.000 description 14
- 238000012544 monitoring process Methods 0.000 description 13
- 238000009529 body temperature measurement Methods 0.000 description 12
- 230000006378 damage Effects 0.000 description 7
- 230000008901 benefit Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 5
- 238000009413 insulation Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000002950 deficient Effects 0.000 description 2
- 230000006866 deterioration Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000011900 installation process Methods 0.000 description 2
- 238000013021 overheating Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 229910000975 Carbon steel Inorganic materials 0.000 description 1
- 241000834199 Centropogon australis (White, 1790) Species 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000035508 accumulation Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 238000013398 bayesian method Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000010962 carbon steel Substances 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000002893 slag Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/083—Locating faults in cables, transmission lines, or networks according to type of conductors in cables, e.g. underground
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1272—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- 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
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Mathematical Physics (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Computational Mathematics (AREA)
- Development Economics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Algebra (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Evolutionary Computation (AREA)
- Primary Health Care (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
Abstract
The utility model provides a distribution network equipment health diagnosis method for multisource information fusion analysis, is used for obtaining distribution equipment health status grade in distribution cable health diagnostic system, and it contains the step: obtaining signals of each influence factor dimension of the distribution network equipment through a perception layer; the signals influencing the factor dimension comprise terahertz time-domain signals, partial discharge signals and temperature signals; transmitting the signals of each influencing factor dimension to a data layer through a transmission layer; completing the feature extraction of the signals of each influencing factor dimension in a data layer to obtain an influencing factor vector of each dimension; and obtaining a comprehensive evaluation result taking a distribution network device as an evaluated object by using a fuzzy evaluation method. The system health state is evaluated by adopting a fuzzy data fusion method, and a high-quality diagnosis system can be obtained in the actual application of multi-source information fusion analysis.
Description
Technical Field
The invention belongs to the technical field of digital active power distribution networks, and particularly relates to a computer processing method for a power distribution cable health diagnosis system.
Background
In a digital active power distribution network taking a power cable as a main body, power distribution equipment such as the power cable and the like are mainly distributed in an underground comprehensive pipe gallery and an overground operating environment. Due to the fact that the cable installation process levels are uneven, the operation condition of power equipment is complex, and the urban distribution network fault risk is continuously increased. If power equipment breaks down, such as insulation breakdown, the power supply reliability and the electric energy quality of a power distribution network are affected, and large-scale power failure can be caused in severe cases, so that the urban production and life suffer great loss.
The traditional distribution network operation and maintenance multi-side is more important to the equipment state detection under the overdue service or abnormal working condition. However, the design requirement of the digital active power distribution network considers that the source of the urban power cable failure is mainly due to poor installation and construction process and accumulation and development of early defects. At present, similar products are available for systems such as cable joint temperature measurement, video monitoring inside a cable tunnel, fire prevention monitoring and the like. However, the existing systems often have the following problems: (1) the power supply mode of the monitoring terminal is single; (2) collected data are difficult to transmit; (3) the product integration level is poor; (4) background data analysis processing is lacking.
The Chinese patent application 202010895141.X discloses a power distribution network fault diagnosis method and system based on multi-source information fusion, wherein a fault area is analyzed by utilizing the operation of a fault judgment matrix, analog quantity information after a power distribution network fault is analyzed by utilizing a Fortesque method, phase selection conditions for various faults are generated, an objective function fusing analog quantity and digital quantity information is solved by utilizing an optimization method based on a genetic algorithm, a visual management platform of a power distribution network is constructed by utilizing a geographic information system, the running states of elements of the power distribution network can be monitored in real time, and fault element information diagnosed by the power distribution network geographic information system by utilizing the optimization algorithm after the power distribution network has the fault. Chinese patent application 202010966294.9 discloses a low-voltage distribution network fault diagnosis method based on multi-source information fusion, which comprises preprocessing collected low-voltage distribution network fault information to obtain a fault probability representation; constructing an information fusion model by using fault probability representation and based on a D-S evidence theory synthesis rule; combining a fast density peak search algorithm to quickly select an initial clustering center to improve a C-mean algorithm; and establishing a diagnosis decision model based on a gamma function, performing initial classification on the elements by combining an information fusion model, correcting the initial classification by using an improved C-mean algorithm, and judging the fault elements. However, none of the technical solutions of the above-mentioned publications relate to a distribution equipment health status grading technology, and do not relate to technical details such as diagnosis for obtaining distribution network equipment health information through a multi-source information fusion analysis process.
Disclosure of Invention
The invention aims to provide a distribution network equipment health diagnosis method for multi-source information fusion analysis, which adopts a fuzzy data fusion method to evaluate the health state of a system, can consider the respective defects of the fusion method in the actual application of the multi-source information fusion analysis, can also play the advantages of the fusion method and obtain a high-quality diagnosis system.
The technical scheme provided by the invention is a distribution network equipment health diagnosis method for multi-source information fusion analysis, which is used for obtaining the distribution equipment health state grade in a distribution cable health diagnosis system and comprises the following steps:
s10, obtaining signals of each influence factor dimension of the distribution network equipment through a perception layer; the signals of the influencing factor dimension comprise terahertz time-domain signals, partial discharge signals and temperature signals;
s20, respectively transmitting the signals of each influencing factor dimension to a data layer through a transmission layer;
and S30, completing feature extraction of the signals of each influencing factor dimension in the data layer to obtain an influencing factor vector of each dimension.
And S40, obtaining a comprehensive evaluation result taking a distribution network device as an evaluated object by using a fuzzy evaluation method. Preferably, the fuzzy evaluation method comprises the steps of:
s100, determining an evaluated object X;
s200, establishing an influence factor set U;
s300, establishing an evaluation set V representing the evaluation result grade;
s400, calculating a weight set W;
s500, calculating a comprehensive evaluation matrix R;
s600, carrying out fuzzy comprehensive evaluation operation;
and S700, calculating a comprehensive evaluation result.
Preferably, the fuzzy evaluation method further comprises the steps of: and S800, making corresponding decision according to the comprehensive evaluation result of the evaluated object.
Preferably, the sensing layer of the distribution cable health diagnosis system comprises a terahertz time-domain spectrometer, a partial discharge sensor and a temperature sensor which are deployed on the inspection robot.
Preferably, the inspection robot is a four-foot robot provided with a mechanical arm, and the terahertz time-domain spectrometer is arranged at the free end of the mechanical arm.
Preferably, the inspection robot is provided with a perception host, and the perception host is used as a data layer to complete the feature extraction of the signals of the dimensions of each influence factor;
preferably, the sensing layer of the distribution cable health diagnosis system comprises a dynamically deployed terahertz time-domain spectrometer, a partial discharge sensor and a temperature sensor.
Preferably, the signal of the influencing factor dimension further comprises at least one of a humidity signal, a wind speed signal, a PM2.5 signal, a visible light image signal.
Preferably, the health status grade of the power distribution equipment of the comprehensive evaluation result comprises a functional index and a repairability index.
Preferably, in step S700, after the fuzzy comprehensive evaluation vector of the influence factor set is obtained through calculation, according to the maximum membership rule, the comprehensive evaluation result of the factor set is the comment corresponding to the maximum membership.
The technical scheme of the invention has the effects that: the technical bottlenecks of intelligent autonomous inspection and key equipment state diagnosis of the urban distribution network are broken through a high-efficiency non-contact test technology, a data fusion technology and the like, the method for evaluating and diagnosing the full-life cycle state of the distribution equipment and detecting the cable installation process is changed by constructing a distribution network intelligent autonomous inspection operation and maintenance system, so that the accurate monitoring of the full-life cycle operation state of the distribution equipment is realized, the fault risk and the operation and maintenance cost of the distribution equipment are greatly reduced, and the reliability and the operation and maintenance precision level of the distribution network are improved. The technical scheme of the invention is beneficial to completing the framework design of the multi-source heterogeneous data perception and data fusion technology, and provides data support for the accurate power distribution equipment health diagnosis technology.
Drawings
FIG. 1 is a block diagram of a distribution cable health diagnostic system in an embodiment of a distribution network equipment health diagnostic method for multi-source information fusion analysis according to the present invention;
FIG. 2 is a data flow diagram of a distribution cable health diagnostic system in accordance with an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a structural configuration of an inspection robot for realizing dynamic deployment for realizing the distribution network equipment health diagnosis method for multi-source information fusion analysis according to an embodiment of the present invention;
fig. 4 is a data flow diagram of the embodiment of fig. 3 when the distribution network equipment health diagnosis method for multi-source information fusion analysis according to the present invention is implemented.
Detailed Description
First, it should be noted that, at present, the data fusion method for fault diagnosis mainly includes: the method comprises a Bayesian theorem data fusion fault diagnosis method, a fuzzy data fusion fault diagnosis method, a D-S evidence theory data fusion fault diagnosis method and a neural network data fusion fault diagnosis method. Data fusion fault diagnosis has unique advantages in improving diagnosis accuracy, but has limitations. Prior probability in a Bayesian method is difficult to determine; in the fuzzy fault diagnosis, certain subjective factors are included when the influence weight of each sensor is selected, and if the influence weight is not properly selected, the diagnosis accuracy is influenced; human factors also exist in the determination of the fault reliability function in the D-S evidence theory; the neural network data fusion not only has difficulty in determining the fault membership value, but also has the problem that training samples are difficult to obtain.
Secondly, the first major cause of cable insulation damage is that external force damage accounts for 61%, the quality problem of the cable itself and accessories accounts for 22%, damage accounts for 10% during laying, and the other accounts for 7%. The fault reasons can be specifically divided into mechanical damage, insulation moisture, insulation aging deterioration and the like, wherein the aging deterioration is difficult to avoid in reality, and the aging reasons comprise internal discharge and overheating; cable overload is an important factor in cable overheating, accelerating the damage of the insulation. If the source factor and the hidden defect of monitoring cable fault can be strengthened, the accurate analysis of the cable installation construction process and the operation state after commissioning is realized, and the method is of great benefit to reducing the risk of cable fault and improving the operation safety and reliability.
The invention relates to a fuzzy evaluation method, which is a comprehensive evaluation method based on fuzzy mathematics. The comprehensive evaluation method converts qualitative evaluation into quantitative evaluation according to the membership theory of fuzzy mathematics, namely, fuzzy mathematics is used for making overall evaluation on objects or objects restricted by various factors. The method has the characteristics of clear result and strong systematicness, can better solve the problems of fuzziness and difficult quantization, and is suitable for solving various non-determinacy problems. In each embodiment of the invention, the health state grade of the power distribution equipment is quantitatively and comprehensively judged by using a fuzzy evaluation method comprising a terahertz imaging index. In an exemplary embodiment, the specific evaluation steps of the fuzzy evaluation method herein are first described:
s100, determining an evaluated object X;
s200, establishing an influence factor set (namely an evaluation index system) U;
specifically, let the object X to be evaluated have n influencing factors (indices), u1、u2、…，unThen factor set U ═ U1、u2、…，unRecording the corresponding factor set vector of the factor set as
S300, establishing an evaluation set V representing the evaluation result grade.
Specifically, let viIn order to evaluate the evaluation grade of each influencing factor in the set, and the grade number is m, the evaluation set V is { V ═ V1、v2、…，vmRecording the evaluation set vector corresponding to the evaluation set as
S400, calculating a weight set W.
Specifically, the weight set is the degree of importance (i.e., weight) of a certain influence factor to the object to be evaluated, and u is set1、u2、…，unAre respectively weighted as w1、w2、…，wnAnd satisfies the following relation:
define weight set W ═ W1、w2、…，wnRecord its corresponding weight vector as
And S500, calculating a comprehensive evaluation matrix R.
Exemplarily, R is a fuzzy relation matrix of U and V, defined as:
R＝(rij)n×m (15)
wherein r isiiI rows and j columns of elements of the matrix R.
To make the result satisfy the normalization requirement, let rijThe following relationship is satisfied:
in the above formula, rijAs influencing factor ui in the evaluation scale vjDegree of membership of (A) or (B), i.e. rijIs represented by uiBelong to vjQuantitative index of degree.
S600, carrying out fuzzy comprehensive evaluation operation:
B＝W·R＝(w1，w2，...，wn)·(rij)n×m＝(b1，b2，...，bm) (17)
wherein, the jth element B of BjAnd multiplying the elements equal to W by the corresponding elements in the jth column of R respectively, and accumulating to obtain the product.
S700, calculating a comprehensive evaluation result Vresult
Vresult＝vk (19)
k＝{j|max(bj，j＝1～m)} (20)
After the fuzzy comprehensive evaluation vector of the influence factor set is obtained through calculation, according to the maximum membership principle, the comprehensive evaluation result of the factor set is the comment v corresponding to the maximum membershipj。
S800, according to the comprehensive evaluation result V of the evaluated objectresultA corresponding decision is made.
In an exemplary embodiment, the comprehensive assessment result is specifically a health index as a criterion for evaluating the health status. Before implementation, a health index H of the power distribution equipment is definedi. Firstly, the health index H of the power distribution equipmentiIs limited to (0, 5)]In the meantime. At 4 < HiWhen the maintenance time is less than or equal to 5, the maintenance time is a health state, meaning that the functions are complete, the performance is excellent, and a corresponding decision is a maintenance schedule capable of being delayed properly. At 3 < HiWhen the number is less than or equal to 4, the state is in a sub-health state, the meaning is that the functions are complete, the performance is reduced, and the corresponding decision is to overhaul according to the original plan. At 2 < HiWhen the defect is less than or equal to 3, the general defect means complete functions and slight defects of performance, and a corresponding decision is to arrange a maintenance plan in advance. At 1 < HiWhen the defect is less than or equal to 2, the defect is serious, the functions are complete, the performance is obviously out of limit, and the corresponding decision is to arrange a maintenance plan in time. At 0 < HiWhen the number is less than or equal to 1, the meaning is crisis defect, function is lost, but the repairing can be carried out, and the corresponding decision is that the maintenance is required to be arranged immediately. The comprehensive evaluation result does not contain Hi0, but HiThe operational parameter is set to 0 by the system, at which time, it is indicated that the equipment has not been repaired, and the expert studies the distribution equipment as a standard according to the given health index definition.
As shown in fig. 1, the distribution network equipment health diagnosis method for multi-source information fusion analysis provided by the present invention is implemented in a distribution cable health diagnosis system. The system architecture includes four device levels: the system comprises a perception layer (comprising perception devices such as terminal sensors and data acquisition devices), a transmission layer (comprising transmission devices such as wireless, wired, Bluetooth and internet), a data layer (comprising information processing devices such as data processing, output storage and data cloud), and a platform layer (comprising platform devices such as a visual platform, a mobile terminal and user interaction).
Referring to fig. 2, when the system is operated to implement the distribution network device health diagnosis method for multi-source information fusion analysis provided by the present invention, firstly, various sensors statically or dynamically deployed at a cable joint to be measured are used to collect signals of various influencing factor dimensions, such as a terahertz time-domain spectrometer, a partial discharge sensor, a temperature sensor (including a temperature sensor in an infrared CCD form), and other sensing devices, and then the collected data are transmitted to an intelligent terminal on the device site as transmission devices of the influencing factor dimensions through respective data processing units in a wired or wireless manner, and then the collected data are preprocessed (data analysis/reconstruction) through a fuzzy data fusion processing unit of the intelligent terminal on the data layer, and are transmitted to platform devices such as a cloud platform through the internet of things, or transmitted to a state monitoring terminal on the platform layer for signal reconstruction, and then the fault early warning is accurately carried out in time. In some embodiments, the cloud platform on the platform layer performs transverse comparison and comprehensive processing on various data, and if fault information is found, alarm information is sent to the remote monitoring terminal and the mobile monitoring terminal.
Exemplarily, a working process of the distribution network equipment health diagnosis method for the multi-source information fusion analysis implemented by the distribution cable health diagnosis system of the embodiment is as follows:
s10, data acquisition: the terahertz time-domain spectrograph collects terahertz time-domain spectral information of a cable detection point, the partial discharge sensor collects partial discharge information of the cable detection point, and the temperature sensor collects temperature information of the cable detection point.
S20, data transmission: the terahertz time-domain spectrograph transmits the collected terahertz time-domain signals to the terahertz data processing unit, the partial discharge sensor transmits the partial discharge signals to the partial discharge data processing unit, and the temperature sensor transmits the temperature signals to the temperature data processing unit.
S30, feature information extraction: in the terahertz data processing unit, several characteristic parameters of a positive peak value, a negative peak value, a peak-to-peak value, an energy value, a positive peak time delay, a negative peak time delay, a zero delay and a positive peak-negative peak relative time delay are extracted from a terahertz time-domain signal, meanwhile, Fourier transform is carried out, the time-domain signal is converted into a frequency-domain signal, and several characteristic parameters of a frequency spectrum peak value, a characteristic frequency peak value, an amplitude peak delay and an absorption peak value are extracted from the frequency-domain signal. The partial discharge data processing unit extracts characteristic parameters such as an ultrahigh frequency signal energy value and an amplitude value. The temperature data processing unit extracts the characteristic parameter of the temperature value of the cable detection point.
S40, fault diagnosis and decision: and according to the fuzzy evaluation method and the fault set obtained by data training/clustering, substituting the characteristic parameters extracted in the last step into an algorithm to identify the fault type, presenting the result in a terminal in a visual mode, and giving different decision opinions according to different health levels.
Specifically, in other embodiments, the fault diagnosis and decision of S40 may share the computation cost and the communication cost in a distributed manner at the data layer and the platform layer, so as to synchronously increase the robustness and the economy of the system as a whole.
In the embodiment of dynamically deploying the sensor at the joint of the cable to be detected, platform support is considered to be provided for autonomous inspection of a plurality of inspection sites and complex terrains such as a distribution network ground station house, an underground comprehensive pipe gallery and the like; the method has the advantages that the framework design of the multi-source heterogeneous data sensing and data fusion technology is completed, environment detection, temperature detection, defect identification, partial discharge detection, terahertz detection and the like are realized, and data support is provided for the accurate power distribution equipment health diagnosis technology; an autonomous inspection platform is established based on an edge computing technology, an elastic management environment, interface support and the like are provided for deployment and upgrading of distribution network AI services, and a high-motion-performance autonomous walking inspection robot in a distribution scene is established to help a distribution cable health diagnosis system to realize a specific distribution network equipment health diagnosis method for multi-source information fusion analysis.
Referring to fig. 3, the inspection robot adopts a quadruped robot structure, an integral structure customized design is adopted between the inspection robot body and each module, the integral structure is firm and reliable, and the connecting part has no obvious defects of unfused, air holes, slag inclusion, cracks and the like; the whole operation is safe and stable, and the contact part is specially treated to prevent the damage to the on-site power distribution equipment.
The edge intelligent module meets the requirements of integration and secondary development of inspection functions of a power distribution station/room and an underground comprehensive pipe rack, has the edge control and management functions of the cable inspection function of the comprehensive pipe rack, and has the control capability and the repeated positioning precision of less than 1 cm; the specific customization comprises the following steps: the functions of high-reliability autonomous navigation, path planning and the like are realized based on the fusion of multiple navigation technologies; the embedded artificial intelligence analysis and control function is realized based on the edge intelligence technology; the environment detection integration function is realized based on the environment perception technology; the functions of on-site detection, identification and the like are realized based on a power distribution detection technology; the data transmission function under a specific scene is realized based on the communication technology of the Internet of things.
The intelligent inspection system can realize the functions of autonomous recognition and analysis based on AI technology, supports the real-time recognition of human, machine and ring, and has the target detection accuracy rate of more than 95% and the time delay of less than 50 milliseconds when the target has the resolution of more than 80 pixels or 1/5 pixels; realizing an image processing function based on a machine vision technology; the method has the advantages that the flexible deployment function is realized based on the micro-service technology, and the functions of power distribution equipment defect identification and health diagnosis evaluation can be integrated; and realizing a customized human-computer interaction function based on a visualization technology.
The robot adopts a case/cabinet made of common carbon steel, the size of a module configured on the robot does not exceed the bearing range of the inspection robot body, and the inspection system meets the flexible deployment requirement; all the wire inlet and outlet and plug interfaces adopt aviation plugs.
The inspection robot is provided with the ultra-light mechanical arm, so that the electric power is saved, the inspection robot is convenient to carry, and the inspection robot is an ideal choice for mobile application; the mechanical dog can be assisted to carry out various switching and lifting operations on the pipe gallery inspection, such as robot motion control, autonomous positioning navigation, mobile grabbing, robot sensing and the like;
the inspection robot adopts a 16-line laser radar, is a small laser radar leading in the world, and mainly faces the fields of unmanned automobile environment sensing, robot environment sensing, unmanned aerial vehicle surveying and mapping and the like. A mixed solid laser radar mode is adopted, 16 laser transceiving components are integrated, the measuring distance is up to 150 meters, the measuring precision is within +/-2cm, the number of the outgoing points is up to 300,000 points/second, the horizontal angle measurement is 360 degrees, and the vertical angle measurement is-15 degrees. The 16 laser emission assemblies rapidly rotate and simultaneously emit high-frequency laser beams to continuously scan the external environment, three-dimensional space point cloud data and object reflectivity are provided through a distance measurement algorithm, a machine can see the surrounding world, and powerful guarantee is provided for positioning, navigation, obstacle avoidance and the like.
In order to improve the power inspection efficiency and solve the problems that manual inspection is high in strength and unbalanced in quality, a wheeled robot cannot adapt to the complex terrain of a transformer substation and the like, the project starts from the aspects of mechanism design and optimization, motion planning and control, state estimation, environment perception and the like, the intelligent inspection robot can perform routine inspection, meter reading, automatic storage comparison analysis, infrared accurate temperature measurement, background automatic archiving analysis and other operations under the complex terrain of the transformer substation, and intelligent power inspection is explored.
The robot inspection area is greatly enlarged, and outdoor and indoor inspection scenes are opened: in the past, different robots are needed for outdoor and indoor environments of a transformer substation, and one inspection robot can be competent for different indoor and outdoor terrains, and can cross boundary obstacles, so that inspection cost is reduced, and management efficiency is improved.
Compared with the traditional wheeled robot, the inspection robot can cross obstacles, shorten the inspection distance and select a more appropriate observation position. The omnidirectional autonomous navigation, intelligent identification and intelligent routing inspection can be realized.
Referring to fig. 4, the distribution cable health diagnosis system implements SLAM (simultaneous localization and mapping) based on the laser radar, implements model-free point cloud mapping of the substation, can rapidly acquire three-dimensional point cloud data in the substation, implements omnidirectional navigation path autonomous planning according to a task model, and reports coordinate information in real time. The AI computing power realizes small closed loop of in-situ target detection, can tolerate deviation caused by environmental disturbance factors, and realizes target state identification and temperature detection based on an algorithm model of deep learning.
The inspection robot can be provided with 2 sensing hosts and various sensors, wherein the sensors can be provided with a depth camera, a monocular wide-angle camera, a six-microphone array, an ultrasonic radar, a single-line or 16-line laser radar. The aware host may configure a CPU processor of X86 architecture, UP Board, and a GPU processor of Arm architecture, NVIDIA Xavier NX. Both hosts can use remote desktop software for visualization.
The inspection function is realized:
(1) autonomous navigation
The inspection robot sequentially reaches the designated position to perform tasks such as instrument identification, temperature measurement and the like according to the inspection task requirements by manually planning a path or automatically calculating the shortest path.
(2) Inspection task
The inspection task is the most common work in the daily operation and maintenance work of the pipe gallery. The content comprises appearance inspection, sound judgment, various meter inspection, switch blade state judgment and the like of the equipment. Traditional manual work is patrolled and examined, needs the operation and maintenance personnel regularly to carry all kinds of hand-held type and patrols and examines equipment and patrol and examine, can alleviate the work burden of patrolling and examining personnel greatly behind the robot is patrolled and examined in the adoption, plays the effect that subtracts the staff and increase.
After the intelligent inspection robot is equipped in the pipe gallery, the inspection tasks can be freely set by operation and maintenance personnel, various inspection tasks such as comprehensive inspection, routine inspection, infrared temperature measurement, oil level meter reading, SF6 pressure meter reading, switch position state identification and the like are supported, and custom inspection can be performed according to specific inspection requirements. When the task is established, the inspection content can be customized according to various modes such as equipment types, function types and the like. When the device types are classified, the device types of the pipe gallery can be checked; when selected according to the function type, the polling can be performed according to the meter type, the infrared type or the sound type.
The operation and maintenance personnel can formulate a polling task, so that the robot can regularly carry out comprehensive polling or special polling tasks, and can also carry out self-defined polling tasks by selecting the instrument to be detected.
(3) Automatic recording of table notes
The inspection robot can replace manual work to automatically complete the reading work of all meters in the pipe gallery, automatically upload the reading result to a background monitoring system in real time and form a report form. The method is characterized in that a high-definition visible light camera and an omni-directional rotating holder which are carried by the system are matched with an ultra-computing platform to capture a high-definition image of a meter in a station in real time, various instruments such as pointer type instruments and digital instruments in a pipe gallery are identified, a digital image processing algorithm and a pattern identification detection algorithm are adopted, and a method of combining an image registration technology of SURF with local invariant characteristics, nearest neighbor matching, coarse matching and RANSAC precise matching is adopted, so that high-precision identification is achieved, and compared with manual work, the error is not more than 3%.
(4) Infrared temperature measurement
The advanced imported infrared camera carried by the inspection robot carries out infrared general measurement and accurate temperature measurement on a monitored area, and transmits a temperature measurement result back to the background monitoring center for displaying, and alarms on abnormal temperature in real time.
The robot infrared general survey is through setting up a plurality of check points in advance, sets up the infrared general survey task by fortune dimension personnel, replaces artifical carry out wholeness scanning formula temperature acquisition to the interior equipment of piping lane to effectively avoid regional equipment to be omitted. The inspection robot is used for accurately measuring the temperature, operation and maintenance personnel synthesize the inspection capability, the temperature measurement coverage rate and the accuracy of the robot, analyze and summarize key temperature measurement points of each type of equipment which are possibly defective, and set corresponding temperature measurement points at a robot client. The robot can accurately measure the temperature, can automatically detect and diagnose equipment in multiple directions and multiple angles, has higher temperature measurement precision, can ensure high consistency in the aspects of position, angle and configuration parameters for the same equipment every time, and has strong result contrast.
While regular monitoring of equipment for the presence of important or emergency defects is performed. The system can automatically store temperature measurement data, form a historical analysis curve and diversified analysis reports, and is convenient for operation and maintenance personnel to carry out diagnosis and analysis. If obvious mutation is found, the operation and maintenance personnel receive the prompt information to perform manual checking.
(5) Environmental anomaly monitoring
The inspection robot provides monitoring data of various environments such as temperature, humidity, wind speed, PM2.5 and the like through various advanced data acquisition sensors; simultaneously through visible light camera shooting, radio station transmission, video transmission to the backstage surveillance center demonstration that the robot was shot in the environment, whether check out equipment junction has the outward appearance to damage, whether have gas leakage oil leak phenomenon, discovery abnormal state realizes reporting alarm information automatically, ensures the safety of piping lane operational environment in real time.
(6) Auxiliary functions
Remote confirmation of abnormalities: under the robot mode of patrolling and examining, after the operation and maintenance personnel obtain the warning of all kinds of equipment in the station, can call the robot in the very first time and arrive appointed equipment fast, in time look over and verify alarm information to formulate the strategy of coping rapidly.
Security linkage alarm: the robot background system receives security alarm signals in the unmanned station in real time through linkage with the security system in the station, automatically judges the types of the alarm signals and starts a corresponding security inspection task. The operation and maintenance personnel can know the field situation at the first time through the visual angle of the robot, quickly make the best strategy for dealing with and guarantee the personal, equipment and property safety to the maximum extent.
Remote state identification: after the robot patrols and examines, the robot can accurately judge equipment divide-shut brake state, in time feeds back equipment information, presents the running state of equipment fast. And the operation and maintenance personnel are assisted to check the remote equipment in the switching operation process.
Defect fixed point tracking: the inspection robot can automatically track and monitor the defective equipment in real time. The operation and maintenance personnel remotely select corresponding equipment through the client, set a defect tracking task and select a corresponding period to perform tracking repeated inspection; or the robot is controlled to monitor the defect equipment at fixed points all day, so that the data of the defect equipment can be acquired in real time, and the workload of operation and maintenance personnel is reduced. And operation and maintenance personnel can master the operation condition of the defect equipment at the operation and maintenance master station according to the defect report automatically generated and uploaded by the robot. And according to the alarm information of the robot, checking and checking the equipment state in time and reporting the scheduling processing.
Assisting emergency accident handling: after receiving accident information in a management corridor, operation and maintenance personnel can select designated equipment to establish a special patrol task through a robot client navigation chart and send instructions, the robot goes deep into an accident site in the first time, and after the accident site arrives at the designated position, the robot is switched to a manual remote control mode, the position of a vehicle body is adjusted in a remote control mode, the direction of a cradle head is rotated, a fault area is quickly positioned, site data are recorded and read in real time, adjacent equipment is checked, site information is quickly transmitted to the operation and maintenance personnel through robot video transmission and information interaction, the operation and maintenance personnel can quickly know site conditions, grasp site dynamics and timely determine a processing scheme at a remote place, and personal safety of the operation and maintenance personnel is guaranteed.
The robot is used as a mobile real-time monitoring platform, inspection personnel can assist field personnel to make professional judgment without arriving at the field, and technical support is provided for safe operation and power supply reliability of a power grid. The method is beneficial to building a remote network expert joint analysis team, enhances the real-time performance of difficult and complicated defect analysis of the equipment, and improves the efficiency and the scientificity of field professional conclusions during accident handling.
Claims (10)
1. A distribution network equipment health diagnosis method for multi-source information fusion analysis is used for obtaining the health state grade of distribution equipment in a distribution cable health diagnosis system and comprises the following steps:
s10, obtaining signals of each influence factor dimension of the distribution network equipment through a perception layer; the signals of the influencing factor dimension comprise terahertz time-domain signals, partial discharge signals and temperature signals;
s20, respectively transmitting the signals of each influencing factor dimension to a data layer through a transmission layer;
s30, completing feature extraction of the signals of each influencing factor dimension in the data layer to obtain an influencing factor vector of each dimension;
and S40, obtaining a comprehensive evaluation result taking a distribution network device as an evaluated object by using a fuzzy evaluation method.
2. The method for diagnosing the health of distribution network equipment according to claim 1, wherein the fuzzy evaluation method comprises the steps of:
s100, determining an evaluated object X;
s200, establishing an influence factor set U;
s300, establishing an evaluation set V representing the evaluation result grade;
s400, calculating a weight set W;
s500, calculating a comprehensive evaluation matrix R;
s600, carrying out fuzzy comprehensive evaluation operation;
and S700, calculating a comprehensive evaluation result.
3. The method for diagnosing the health of distribution network equipment according to claim 1, wherein the fuzzy evaluation method further comprises the steps of: and S800, making corresponding decision according to the comprehensive evaluation result of the evaluated object.
4. The distribution network equipment health diagnosis method according to claim 1, wherein the sensing layer of the distribution cable health diagnosis system comprises a terahertz time-domain spectrometer, a partial discharge sensor and a temperature sensor deployed in the inspection robot.
5. The distribution network equipment health diagnosis method according to claim 4, wherein the inspection robot is a four-legged robot provided with a mechanical arm, and the terahertz time-domain spectrometer is arranged at the free end of the mechanical arm.
6. The distribution network equipment health diagnosis method according to claim 4, wherein the inspection robot is provided with a perception host, and the perception host is used as a data layer to complete feature extraction of the signals of each influencing factor dimension.
7. The distribution network equipment health diagnosis method according to claim 1, wherein the sensing layer of the distribution cable health diagnosis system comprises a dynamically deployed terahertz time-domain spectrometer, a partial discharge sensor and a temperature sensor.
8. The method for diagnosing the health of distribution network equipment of claim 1, wherein the signals of the influencing factor dimension further comprise at least one of a humidity signal, a wind speed signal, a PM2.5 signal, and a visible light image signal.
9. The distribution network equipment health diagnosis method according to claim 1, wherein the distribution equipment health status level of the comprehensive evaluation result comprises a functional index and a repairability index.
10. The distribution network equipment health diagnosis method according to claim 2, wherein in step S700, after the fuzzy comprehensive evaluation vector of the influence factor set is obtained through calculation, according to a maximum membership rule, the comprehensive evaluation result of the factor set is a comment corresponding to the maximum membership.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111137265.2A CN113988518A (en) | 2021-09-27 | 2021-09-27 | Distribution network equipment health diagnosis method for multi-source information fusion analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111137265.2A CN113988518A (en) | 2021-09-27 | 2021-09-27 | Distribution network equipment health diagnosis method for multi-source information fusion analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113988518A true CN113988518A (en) | 2022-01-28 |
Family
ID=79736918
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111137265.2A Pending CN113988518A (en) | 2021-09-27 | 2021-09-27 | Distribution network equipment health diagnosis method for multi-source information fusion analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113988518A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114714376A (en) * | 2022-05-06 | 2022-07-08 | 湖南工业大学 | Intelligent train detection robot system and detection method thereof |
| CN114944023A (en) * | 2022-05-17 | 2022-08-26 | 内蒙古呼和浩特抽水蓄能发电有限责任公司 | Equipment induction type inspection system based on NFC and GPS positioning |
| CN115564203A (en) * | 2022-09-23 | 2023-01-03 | 杭州国辰智企科技有限公司 | Equipment real-time performance evaluation system and method based on multi-dimensional data cooperation |
| CN117007984A (en) * | 2023-09-27 | 2023-11-07 | 南通国轩新能源科技有限公司 | Dynamic monitoring method and system for operation faults of battery pack |
-
2021
- 2021-09-27 CN CN202111137265.2A patent/CN113988518A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114714376A (en) * | 2022-05-06 | 2022-07-08 | 湖南工业大学 | Intelligent train detection robot system and detection method thereof |
| CN114944023A (en) * | 2022-05-17 | 2022-08-26 | 内蒙古呼和浩特抽水蓄能发电有限责任公司 | Equipment induction type inspection system based on NFC and GPS positioning |
| CN115564203A (en) * | 2022-09-23 | 2023-01-03 | 杭州国辰智企科技有限公司 | Equipment real-time performance evaluation system and method based on multi-dimensional data cooperation |
| CN117007984A (en) * | 2023-09-27 | 2023-11-07 | 南通国轩新能源科技有限公司 | Dynamic monitoring method and system for operation faults of battery pack |
| CN117007984B (en) * | 2023-09-27 | 2023-12-15 | 南通国轩新能源科技有限公司 | Dynamic monitoring method and system for operation faults of battery pack |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113988518A (en) | Distribution network equipment health diagnosis method for multi-source information fusion analysis | |
| CN103235562B (en) | Transformer station is based on the comprehensive parameters detection system of crusing robot and method for inspecting | |
| CN110908370A (en) | Unmanned inspection task planning method and system for thermal power plant | |
| CN206194076U (en) | Substation equipment detecting system | |
| CN108956640A (en) | Vehicle-mounted detection apparatus and detection method suitable for distribution line inspection | |
| CN112466000A (en) | Inspection system based on power inspection robot and inspection control method | |
| CN111555178B (en) | Sky-ground cooperative intelligent inspection method and system for power transmission line | |
| CN207268846U (en) | Electric inspection process robot | |
| CN102566552B (en) | Road tunnel intelligent overhaul robot facing Internet of things and 3D GIS | |
| CN116755451B (en) | Intelligent patrol robot path planning method and system | |
| CN115793649B (en) | Automatic cable pit inspection device and inspection method | |
| CN115876655B (en) | System and method for monitoring whole process of explosion-related dust in limited space | |
| CN110608716A (en) | Substation maintenance safety distance monitoring method based on SLAM | |
| CN210605473U (en) | Power plant inspection system | |
| CN116231504A (en) | Remote intelligent inspection method, device and system for booster station | |
| CN114744756A (en) | Intelligent power transmission line inspection system based on machine vision | |
| CN114838810A (en) | Inspection robot | |
| CN111025325A (en) | Unmanned aerial vehicle laser radar aerial data telemetering and analyzing system based on satellite communication | |
| CN116363397A (en) | Equipment fault checking method, device and inspection system | |
| CN115857536A (en) | Unmanned aerial vehicle intelligent inspection method, device, equipment and medium for workshop equipment | |
| CN114062302A (en) | Distribution network autonomous inspection method for terahertz imaging detection | |
| CN109977181A (en) | A method of based on bridge stereoscopic monitoring | |
| CN116227849B (en) | Standardized management and early warning system for enterprise dangerous operation | |
| TWI833549B (en) | Sunshine value estimation system | |
| Zhang et al. | Research on Vision Accurate Positioning and Algorithm of Automatic Maintenance Robot for Unmanned Power Distribution Room |
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 |