CN116455059A - Power distribution cabinet control method and device - Google Patents

Power distribution cabinet control method and device Download PDF

Info

Publication number
CN116455059A
CN116455059A CN202310212818.9A CN202310212818A CN116455059A CN 116455059 A CN116455059 A CN 116455059A CN 202310212818 A CN202310212818 A CN 202310212818A CN 116455059 A CN116455059 A CN 116455059A
Authority
CN
China
Prior art keywords
fault
power distribution
distribution cabinet
vector
monitoring data
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.)
Withdrawn
Application number
CN202310212818.9A
Other languages
Chinese (zh)
Inventor
梁文辉
饶南
周同宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority to CN202310212818.9A priority Critical patent/CN116455059A/en
Publication of CN116455059A publication Critical patent/CN116455059A/en
Withdrawn legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9027—Trees
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/90335—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
    • H02B1/00—Frameworks, boards, panels, desks, casings; Details of substations or switching arrangements
    • H02B1/24—Circuit arrangements for boards or switchyards
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
    • H02H1/00—Details of emergency protective circuit arrangements
    • H02H1/0007—Details of emergency protective circuit arrangements concerning the detecting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
    • H02H1/00—Details of emergency protective circuit arrangements
    • H02H1/0092—Details of emergency protective circuit arrangements concerning the data processing means, e.g. expert systems, neural networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
    • H02H7/00—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
    • H02H7/22—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for distribution gear, e.g. bus-bar systems; for switching devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • 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

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computational Linguistics (AREA)
  • Power Engineering (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Molecular Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Human Computer Interaction (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to the field of power distribution cabinet control, and discloses a power distribution cabinet control method and device, wherein the method comprises the following steps: analyzing the fault type and the fault reason of the historical power distribution cabinet, constructing a fault tree of the historical power distribution cabinet by using a fault tree analysis method based on the fault type and the fault reason, and configuring a control strategy of each fault node in the fault tree; collecting monitoring data of a power distribution cabinet to be controlled, denoising the monitoring data to obtain denoising monitoring data, and orthonormal processing the denoising monitoring data to obtain orthonormal monitoring data; detecting the fault type of the power distribution cabinet to be controlled by using a trained fault identification model according to the orthogonal monitoring data; according to the fault category, inquiring a target fault node corresponding to the fault category in a fault tree, and taking a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled. The invention can improve the control efficiency of the power distribution cabinet.

Description

Power distribution cabinet control method and device
Technical Field
The invention relates to the field of power distribution cabinet control, in particular to a power distribution cabinet control method and device.
Background
The power distribution cabinet is a generic name of a motor control center, directly relates to power supply conditions and power supply quality, and needs to perform fault diagnosis when controlling the power distribution cabinet, wherein the fault diagnosis is a technology for knowing and grasping the state of a machine in the running process, determining the whole or partial normal or abnormal state of the machine, finding faults and reasons of the faults in time and forecasting the development trend of the faults, and oil monitoring, vibration monitoring, noise monitoring, performance trend analysis, nondestructive inspection and the like are main diagnosis technical modes, so that the faults of the power distribution cabinet are found early and the reasons of the faults are diagnosed, and a control strategy is effectively implemented for the power dispatching center and time, so that the recovery of the faults is guaranteed.
At present, fault diagnosis methods of the power distribution cabinet are various, such as a BP neural network fault diagnosis method, a fuzzy mathematic judgment method, a deep learning method, an expert system and the like are widely applied to fault diagnosis of the power distribution cabinet, but the fault diagnosis is often realized by adopting a single method, and the single method is often limited and has defects, so that the control efficiency of the power distribution cabinet is low.
Disclosure of Invention
The invention provides a control method and a device for a power distribution cabinet, and mainly aims to improve the control efficiency of the power distribution cabinet.
In order to achieve the above object, the present invention provides a control method for a power distribution cabinet, including:
analyzing the fault type and the fault reason of a historical power distribution cabinet, constructing a fault tree of the historical power distribution cabinet by using a fault tree analysis method based on the fault type and the fault reason, and configuring a control strategy of each fault node in the fault tree;
collecting monitoring data of a power distribution cabinet to be controlled, denoising the monitoring data to obtain denoising monitoring data, and performing orthogonal normalization processing on the denoising monitoring data to obtain orthogonal monitoring data;
detecting the fault type of the power distribution cabinet to be controlled by using a trained fault identification model according to the orthogonal monitoring data;
And according to the fault category, inquiring a target fault node corresponding to the fault category in the fault tree, and taking a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled.
Optionally, the analyzing the fault type and the fault cause of the historical power distribution cabinet includes:
inquiring the fault influence of a history power distribution cabinet from a pre-constructed history power distribution cabinet fault database;
based on the fault impact, all possible fault types and their fault causes leading to the fault impact are analyzed.
Optionally, the constructing a fault tree of the historical power distribution cabinet by using a fault tree analysis method based on the fault type and the fault cause includes:
inquiring fault influence corresponding to the fault type from a pre-constructed historical power distribution cabinet fault database, and calculating fault occurrence probability of the fault type;
analyzing the fault grade of the fault type according to the fault occurrence probability;
according to the fault level, the fault occurrence probability and the fault reason, constructing an event node of the historical power distribution cabinet, and configuring a logic gate of the event node;
and generating a fault tree of the historical power distribution cabinet according to the event node and the logic gate.
Optionally, the constructing the event node of the historical power distribution cabinet according to the fault level, the fault occurrence probability and the fault reason includes:
carrying out weighted summation on the fault grade and the fault occurrence probability to obtain a fault score of the historical power distribution cabinet;
descending order sorting is carried out on the fault scores to obtain score sorting, and the top event node of the historical power distribution cabinet is determined according to the score sorting;
inquiring the node fault reason of the top event node from the fault reasons, and constructing a lower event node of the top event node according to the node fault reason;
and determining the event node of the historical power distribution cabinet according to the top event node and the lower event node.
Optionally, before the detecting the fault type of the power distribution cabinet to be controlled by using the trained fault recognition model according to the orthogonal monitoring data, the method further includes:
acquiring the fault tree of the power distribution cabinet to be controlled, and screening a learning sample of the fault identification model from a pre-constructed historical power distribution cabinet fault database by using a fault tree analysis method;
vectorizing the learning sample to obtain an initial sample vector, and performing bipolar conversion on the initial sample vector to obtain a bipolar sample vector;
Carrying out orthogonal normalization on the initial sample vector according to the bipolar sample vector to obtain a normalized orthogonal vector;
calculating an associative memory matrix of the fault recognition model according to the orthonormal vector;
and determining a trained fault recognition model based on the associative memory matrix.
Optionally, the screening, by using a fault tree analysis method, the learning sample of the fault identification model from a pre-constructed historical power distribution cabinet fault database includes:
calculating the minimum cut set of the fault tree by using a minimum cut set solving algorithm;
calculating a corresponding intermediate event when the minimum cut set occurs by using the fault tree analysis method, combining the intermediate event into a combined event, taking the combined event as a fault monitoring event of the power distribution cabinet to be controlled, and taking the fault tree bottom event corresponding to the fault monitoring event as a fault analysis event;
and screening corresponding historical fault data from the pre-constructed historical power distribution cabinet fault database according to the fault monitoring event and the fault analysis event, and taking the corresponding historical fault data as a learning sample of the fault recognition model.
Optionally, the performing bipolar conversion on the initial sample vector to obtain a bipolar sample vector includes:
Performing bipolar conversion on the initial sample vector by using the following formula to obtain the bipolar sample vector:
wherein,,representing the bipolar sample vector, which is a vector representation of states on a fault analysis event set, 1 representing a fault state, -1 representing a normal state; x represents the initial sample vector, which is a vector representation of the states on the fault-monitoring event set, 1 represents the fault state, and 0 represents the normal state.
Optionally, the orthogonal normalization is performed on the initial sample vector according to the bipolar sample vector to obtain a normalized orthogonal vector, which includes:
according to the bipolar sample vector, the initial sample vector is orthogonally normalized by using the following formula to obtain the normalized orthogonal vector:
H 1 ＝[11；1-1]
H k ＝[H k-1 H k-1 ；H k-1 -H k-1 ]
wherein,,representing the orthonormal vector, H 1 Representing a 2 nd order Hadamard transform matrix, H k Representation 2 k Order Hadamard transform matrix, X * Representing the approximate orthogonal vector transformed by Hadamard, ">Representing the bipolar sample vector, τ (·) representing a normalization function, ++>Representing an approximate orthogonal vector X * K represents the input layer neuron parameters of the failure recognition model.
Optionally, the calculating the associative memory matrix of the fault recognition model according to the orthonormal vector includes:
According to the orthonormal vector, calculating an associative memory matrix of the fault recognition model by using the following formula:
T：R n (S X )→R n (S Y )
Q＝X T Y
wherein Q represents the associative memory matrix, T represents the spatial transformation relationship from the input layer to the output layer of the failure recognition model, S X Representing input layer neuron set, S Y Representing output layer neuronsA set representing an input vector, X representing a transpose of the input vector, Y representing an output vector, R n Representing the input layer neuron space.
In order to solve the above problems, the present invention further provides a control device for a power distribution cabinet, the device comprising:
the fault tree construction module is used for analyzing the fault type and the fault reason of the historical power distribution cabinet, constructing a fault tree of the historical power distribution cabinet by utilizing a fault tree analysis method based on the fault type and the fault reason, and configuring a control strategy of each fault node in the fault tree;
the orthogonal monitoring data generation module is used for collecting monitoring data of the power distribution cabinet to be controlled, denoising the monitoring data to obtain denoising monitoring data, and carrying out orthogonal normalization on the denoising monitoring data to obtain orthogonal monitoring data;
the fault type detection module is used for detecting the fault type of the power distribution cabinet to be controlled by utilizing a trained fault identification model according to the orthogonal monitoring data;
And the control strategy generation module is used for inquiring a target fault node corresponding to the fault category in the fault tree according to the fault category, and taking a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled.
It can be seen that, according to the embodiment of the invention, through analyzing the fault type and the fault reason of the historical power distribution cabinet, a fault tree can be built for the follow-up fault type and the fault reason, and a corresponding control strategy can be adopted, based on the fault type and the fault reason, a logic relationship among various faults of the power distribution cabinet can be built by constructing the fault tree of the historical power distribution cabinet by utilizing a fault tree analysis method, and important guarantee is provided for fault diagnosis, so that the accuracy and the efficiency of fault diagnosis of the power distribution cabinet are improved, and a corresponding implementation strategy can be provided for fault recovery by configuring the control strategy of each fault node in the fault tree, so that intelligent control of the power distribution cabinet is completed; secondly, the embodiment of the invention can acquire real-time monitoring data by acquiring the monitoring data of the power distribution cabinet to be controlled to provide a data object for the subsequent judgment of fault types, and denoising the monitoring data to obtain denoising monitoring data, which can improve the data quality of the monitoring data to provide a good implementation object for the subsequent processing, thereby improving the detection precision, and orthogonalizing the denoising monitoring data to obtain orthogonalizing monitoring data which can be used as an input object of a subsequent trained fault identification model to obtain a fault diagnosis result of the power distribution cabinet to be controlled; further, according to the embodiment of the invention, the current fault diagnosis result of the power distribution cabinet to be controlled can be obtained by detecting the fault type of the power distribution cabinet to be controlled by utilizing the trained fault identification model according to the orthogonal monitoring data, a support is provided for the follow-up adoption of a corresponding control strategy, a target fault node corresponding to the fault type is inquired in the fault tree according to the fault type, and the control strategy corresponding to the target fault node is used as a control strategy of the power distribution cabinet to be controlled, so that a response scheme is timely made for the fault diagnosis result of the power distribution cabinet, intelligent control of the power distribution cabinet is realized, and the control efficiency of the power distribution cabinet is ensured. Therefore, the power distribution cabinet control method and device provided by the embodiment of the invention can improve the power distribution cabinet control efficiency.
Drawings
Fig. 1 is a schematic flow chart of a control method of a power distribution cabinet according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a control device of a power distribution cabinet according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a control method of a power distribution cabinet according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a control method of a power distribution cabinet. The execution main body of the power distribution cabinet control method comprises, but is not limited to, at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the invention. In other words, the power distribution cabinet control method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a control method of a power distribution cabinet according to an embodiment of the invention is shown. In an embodiment of the present invention, the power distribution cabinet control method includes:
s1, analyzing the fault type and the fault reason of a historical power distribution cabinet, constructing a fault tree of the historical power distribution cabinet by using a fault tree analysis method based on the fault type and the fault reason, and configuring a control strategy of each fault node in the fault tree.
According to the embodiment of the invention, the fault type and the fault reason of the historical power distribution cabinet are analyzed, so that a basis can be provided for constructing a fault tree aiming at the fault type and the fault reason and adopting a corresponding control strategy. The fault type refers to a fault expression form of the power distribution cabinet, and the fault expression form comprises a fault occurrence mode or an influence of the fault on operation, such as incapability of normally opening and closing a circuit breaker, incapacity of a control loop, phase failure, short circuit, incapacity of locking and the like; the failure cause is the sum of internal factors and external factors that lead to failure of the system and the product.
Further, as an optional embodiment of the present invention, the analyzing the fault type and the fault cause of the historical power distribution cabinet includes: inquiring the fault influence of a history power distribution cabinet from a pre-constructed history power distribution cabinet fault database; based on the fault impact, all possible fault types and their fault causes leading to the fault impact are analyzed.
Wherein the fault impact refers to the impact of a certain fault type on a system, a subsystem, a unit operation, a function or a state.
Further, according to the embodiment of the invention, the fault tree analysis method is utilized to construct the fault tree of the historical power distribution cabinet based on the fault type and the fault cause, so that the logic relationship among various faults of the power distribution cabinet can be established, and important guarantee is provided for fault diagnosis, thereby improving the accuracy and efficiency of fault diagnosis of the power distribution cabinet. The fault tree analysis method is used for analyzing hardware, software, environment and human factors which possibly cause product faults and drawing a fault tree so as to determine various possible combination modes of product fault reasons or occurrence probability of the product fault reasons; the fault tree is a special inverted tree logic causal relationship graph that describes causal relationships between various events in the system with event symbols, logic gate symbols, and transition symbols to indicate which component faults of the product or external events or combinations thereof will cause the product to experience a given fault.
Further, as an optional embodiment of the present invention, the constructing a fault tree of the historical power distribution cabinet based on the fault type and the fault cause by using a fault tree analysis method includes: inquiring fault influence corresponding to the fault type from a pre-constructed historical power distribution cabinet fault database, and calculating fault occurrence probability of the fault type; analyzing the fault grade of the fault type according to the fault occurrence probability; according to the fault level, the fault occurrence probability and the fault reason, constructing an event node of the historical power distribution cabinet, and configuring a logic gate of the event node; and generating a fault tree of the historical power distribution cabinet according to the event node and the logic gate.
The historical power distribution cabinet fault database is an organized collection of structural information or data (generally stored in a computer system in an electronic form) of the historical power distribution cabinet faults, and comprises fault types, fault reasons, fault influences, fault grades, fault type probability, historical monitoring data and the like; the fault influence refers to the influence of a certain fault type on a system, a subsystem, unit operation, a function or a state; optionally, analyzing the fault level of the fault type by using a fault mode, an influence and harm analysis method, wherein the fault mode, the influence and the harm analysis method are used for all possible faults of a product, determining the influence of each fault mode on the work of the product according to the analysis of the fault mode, finding out a single-point fault, and determining the harmfulness of the fault mode according to the severity of the fault mode and the occurrence probability of the fault mode, wherein the single-point fault refers to a local fault which causes the fault of the product and has no redundant or alternative working procedure as remedy; the fault grade refers to the grade classification of faults according to the severity of the fault on property or system, and comprises a mild grade, a critical grade, a severe grade and a deadly grade; the event nodes comprise a top event node, a middle event node and a bottom event node; alternatively, the configuration logic gate between the event nodes with causal relation can be constructed according to the logic causal relation between the upper event node and the lower event node, wherein the logic causal relation refers to logic AND relation or logic OR relation.
Further, as an optional embodiment of the present invention, the constructing the event node of the historical power distribution cabinet according to the fault level, the fault occurrence probability and the fault cause includes: carrying out weighted summation on the fault grade and the occurrence type probability to obtain a fault score of the historical power distribution cabinet; descending order sorting is carried out on the fault scores to obtain score sorting, and the top event node of the historical power distribution cabinet is determined according to the score sorting; inquiring the node fault reason of the top event node from the fault reasons, and constructing a lower event node of the top event node according to the node fault reason; and determining the event node of the historical power distribution cabinet according to the top event node and the lower event node.
Furthermore, according to the embodiment of the invention, the control strategy of each fault node in the fault tree can be configured to provide a corresponding implementation strategy for fault recovery, so that intelligent control of the power distribution cabinet is completed.
Further, as an optional embodiment of the present invention, the configuring a control policy of each fault node in the fault tree includes: inquiring the fault reason of each fault node in the fault tree; and analyzing a solution of the fault reason according to the fault reason, and taking the solution as a control strategy of the fault node.
S2, collecting monitoring data of the power distribution cabinet to be controlled, denoising the monitoring data to obtain denoising monitoring data, and carrying out orthogonal normalization on the denoising monitoring data to obtain orthogonal monitoring data.
According to the embodiment of the invention, the real-time monitoring data can be obtained by collecting the monitoring data of the power distribution cabinet to be controlled so as to provide a data object for the subsequent judgment of the fault type.
Further, as an optional embodiment of the present invention, the collecting monitoring data of the power distribution cabinet to be controlled may be implemented by identifying a power distribution cabinet fault monitoring event in a trained fault identification model, and erecting a sensor at a position of a component corresponding to the fault monitoring event.
The sensor is a detection device, can sense the measured information, and can convert the sensed information into an electric signal or other information output in a required form according to a certain rule so as to meet the requirements of information transmission, processing, storage, display, recording, control and the like.
Further, according to the embodiment of the invention, the denoising processing is carried out on the monitoring data, so that the denoising monitoring data can be obtained, the data quality of the monitoring data can be improved, a good implementation object is provided for subsequent processing, and the detection precision is improved.
Further, as an optional embodiment of the present invention, the denoising processing is performed on the monitoring data, so as to obtain denoising monitoring data, which may be obtained by performing median filtering on the monitoring data.
Further, in the embodiment of the invention, through carrying out orthogonal normalization processing on the denoising monitoring data, the obtained orthogonal monitoring data can be used as an input object of a fault identification model trained later so as to obtain a fault diagnosis result of the power distribution cabinet to be controlled. The orthogonal normalization algorithm is an algorithm formed by two parts of functions of orthogonalization and normalization, and is used for enabling different vector inner products in a vector group to be 0, enabling the same vector inner products to be 1, enabling vectors in the vector group to have a normalization form through the same transformation, such as a Schmidt orthogonal normalization algorithm, a Hadamard orthogonal normalization algorithm and the like.
Further, as an optional embodiment of the present invention, the implementation principle of performing orthogonal normalization processing on the denoising monitoring data to obtain orthogonal monitoring data is similar to that of performing orthogonal normalization on the initial sample vector in S3 to obtain a normalized orthogonal vector, which is not described herein.
And S3, detecting the fault type of the power distribution cabinet to be controlled by using a trained fault identification model according to the orthogonal monitoring data.
According to the embodiment of the invention, the current fault diagnosis result of the power distribution cabinet to be controlled can be obtained by detecting the fault type of the power distribution cabinet to be controlled by utilizing the trained fault identification model according to the orthogonal monitoring data, and support is provided for the follow-up adoption of a corresponding control strategy.
As an optional embodiment of the present invention, the detecting the fault class of the power distribution cabinet to be controlled by using a trained fault recognition model according to the orthogonal monitoring data, before further includes a training process of the fault recognition model, including: acquiring the fault tree of the power distribution cabinet to be controlled, and screening a learning sample of the fault identification model from a pre-constructed historical power distribution cabinet fault database by using a fault tree analysis method; vectorizing the learning sample to obtain an initial sample vector, and performing bipolar conversion on the initial sample vector to obtain a bipolar sample vector; carrying out orthogonal normalization on the initial sample vector according to the bipolar sample vector to obtain a normalized orthogonal vector; calculating an associative memory matrix of the fault recognition model according to the orthonormal vector; and determining a trained fault recognition model based on the associative memory matrix.
Optionally, the process of determining the trained fault recognition model based on the associative memory matrix may be: and determining a two-way associative memory model through the associative memory matrix, and taking the two-way associative memory model as a trained fault recognition model.
Further, as an optional embodiment of the present invention, the screening, by using a fault tree analysis method, the learning sample of the fault identification model from the pre-constructed historical power distribution cabinet fault database includes: calculating the minimum cut set of the fault tree by using a minimum cut set solving algorithm; calculating a corresponding intermediate event when the minimum cut set occurs by using the fault tree analysis method, combining the intermediate event into a combined event, taking the combined event as a fault monitoring event of the power distribution cabinet to be controlled, and taking the fault tree bottom event corresponding to the fault monitoring event as a fault analysis event; and screening corresponding historical fault data from the pre-constructed historical power distribution cabinet fault database according to the fault monitoring event and the fault analysis event, and taking the corresponding historical fault data as a learning sample of the fault recognition model.
The minimum cut set solving algorithm refers to a method for solving a minimum cut set, such as a downlink method, an uplink method, a determinant method and a Boolean algebra method. The minimal cut set is the set of minimal bottom events that are necessary to cause the top event to occur.
In yet another embodiment of the present invention, the bipolar converting the initial sample vector to obtain a bipolar sample vector includes: performing bipolar conversion on the initial sample vector by using the following formula to obtain the bipolar sample vector:
wherein,,representing the bipolar sample vector, which is divided at the faultA vector representation of states on the event set is analyzed, 1 representing a fault state, -1 representing a normal state; x represents the initial sample vector, which is a vector representation of the states on the fault-monitoring event set, 1 represents the fault state, and 0 represents the normal state.
Further, as an optional embodiment of the present invention, the orthogonal normalization of the initial sample vector according to the bipolar sample vector to obtain a normalized orthogonal vector includes: according to the bipolar sample vector, the initial sample vector is orthogonally normalized by using the following formula to obtain the normalized orthogonal vector:
H 1 ＝[1 1；1-1]
H k ＝[H k-1 H k-1 ；H k-1 -H k-1 ]
wherein,,representing the orthonormal vector, H 1 Representing a 2 nd order Hadamard transform matrix, H k Representation 2 k Order Hadamard transform matrix, X * Representing the approximate orthogonal vector transformed by Hadamard, ">Representing the bipolar sample vector, τ (·) representing a normalization function, ++ >Representing an approximate orthogonal vector X * K represents the input layer neuron parameters of the failure recognition model.
Further, as an optional embodiment of the present invention, the calculating the associative memory matrix of the fault recognition model according to the orthonormal vector includes: calculating an associative memory matrix of the fault recognition model using the formula:
T：R n (S X )→R n (S Y )
Q＝X T Y
wherein Q represents the associative memory matrix, T represents the spatial transformation relationship from the input layer to the output layer of the failure recognition model, S X Representing input layer neuron set, S Y Represents the output layer neuron set, represents the input vector, X represents the transpose of the input vector, Y represents the output vector, R n Representing the input layer neuron space.
Further, according to the embodiment of the invention, the fault diagnosis result of the power distribution cabinet to be controlled can be obtained by detecting the fault type of the power distribution cabinet to be controlled by using the trained fault identification model according to the orthogonal monitoring data.
Further, as an optional embodiment of the present invention, the detecting the fault class of the power distribution cabinet to be controlled by using the trained fault recognition model according to the orthogonal monitoring data may first perform bipolar conversion on the orthogonal monitoring data according to the orthogonal monitoring data, then calculate a corresponding fault analysis event by using a spatial transformation relationship in the trained fault recognition model, and use the fault analysis event as the fault class of the power distribution cabinet to be controlled.
S4, inquiring a target fault node corresponding to the fault category in the fault tree according to the fault category, and taking a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled.
It should be appreciated that, according to the fault category, querying the fault tree for a target fault node corresponding to the fault category indicates querying the fault tree for an event node matched with the fault category, and taking the matched event node as the target fault node.
It can be seen that, according to the embodiment of the invention, through analyzing the fault type and the fault reason of the historical power distribution cabinet, a fault tree can be built for the follow-up fault type and the fault reason, and a corresponding control strategy can be adopted, based on the fault type and the fault reason, a logic relationship among various faults of the power distribution cabinet can be built by constructing the fault tree of the historical power distribution cabinet by utilizing a fault tree analysis method, and important guarantee is provided for fault diagnosis, so that the accuracy and the efficiency of fault diagnosis of the power distribution cabinet are improved, and a corresponding implementation strategy can be provided for fault recovery by configuring the control strategy of each fault node in the fault tree, so that intelligent control of the power distribution cabinet is completed; secondly, the embodiment of the invention can acquire real-time monitoring data by acquiring the monitoring data of the power distribution cabinet to be controlled to provide a data object for the subsequent judgment of fault types, and denoising the monitoring data to obtain denoising monitoring data, which can improve the data quality of the monitoring data to provide a good implementation object for the subsequent processing, thereby improving the detection precision, and orthogonalizing the denoising monitoring data to obtain orthogonalizing monitoring data which can be used as an input object of a subsequent trained fault identification model to obtain a fault diagnosis result of the power distribution cabinet to be controlled; further, according to the embodiment of the invention, the current fault diagnosis result of the power distribution cabinet to be controlled can be obtained by detecting the fault type of the power distribution cabinet to be controlled by utilizing the trained fault identification model according to the orthogonal monitoring data, a support is provided for the follow-up adoption of a corresponding control strategy, a target fault node corresponding to the fault type is inquired in the fault tree according to the fault type, and the control strategy corresponding to the target fault node is used as a control strategy of the power distribution cabinet to be controlled, so that a response scheme is timely made for the fault diagnosis result of the power distribution cabinet, intelligent control of the power distribution cabinet is realized, and the control efficiency of the power distribution cabinet is ensured. Therefore, the power distribution cabinet control method, the power distribution cabinet control device and the electronic equipment can improve the power distribution cabinet control efficiency.
Fig. 2 is a functional block diagram of the control device of the power distribution cabinet of the present invention.
The power distribution cabinet control device 100 of the present invention may be installed in an electronic apparatus. The power distribution cabinet control device may include a fault tree construction module 101, an orthogonal monitoring data generation module 102, a fault category detection module 103, and a control strategy generation module 104 according to the implemented functions. The module according to the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the fault tree construction module 101 is configured to analyze a fault type and a fault cause of a historical power distribution cabinet, construct a fault tree of the historical power distribution cabinet by using a fault tree analysis method based on the fault type and the fault cause, and configure a control strategy of each fault node in the fault tree;
the orthogonal monitoring data generating module 102 is configured to collect monitoring data of a power distribution cabinet to be controlled, denoise the monitoring data to obtain denoised monitoring data, and orthonormal-process the denoised monitoring data to obtain orthonormal monitoring data;
The fault class detection module 103 is configured to detect, according to the orthogonal monitoring data, a fault class of the power distribution cabinet to be controlled by using a trained fault identification model;
the control strategy generation module 104 is configured to query, according to the fault category, a target fault node corresponding to the fault category in the fault tree, and take a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled.
In detail, the modules in the power distribution cabinet control device 100 in the embodiment of the present invention use the same technical means as the power distribution cabinet control method described in fig. 1 and can produce the same technical effects, which are not described herein.
Fig. 3 is a schematic structural diagram of an electronic device 1 for implementing a control method of a power distribution cabinet according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a power distribution cabinet control program, stored in the memory 11 and executable on the processor 10.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects the respective components of the entire electronic device 1 using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes a power distribution cabinet Control program or the like), and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various data, such as codes of a control program of a power distribution cabinet, etc., but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and an employee interface. Optionally, the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices 1. The employee interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual staff interface.
Fig. 3 shows only an electronic device 1 with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited in scope by this configuration.
The power distribution cabinet control program stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs, which when run in the processor 10 can realize:
analyzing the fault type and the fault reason of a historical power distribution cabinet, constructing a fault tree of the historical power distribution cabinet by using a fault tree analysis method based on the fault type and the fault reason, and configuring a control strategy of each fault node in the fault tree;
Collecting monitoring data of a power distribution cabinet to be controlled, denoising the monitoring data to obtain denoising monitoring data, and performing orthogonal normalization processing on the denoising monitoring data to obtain orthogonal monitoring data;
detecting the fault type of the power distribution cabinet to be controlled by using a trained fault identification model according to the orthogonal monitoring data;
and according to the fault category, inquiring a target fault node corresponding to the fault category in the fault tree, and taking a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1 may be stored in a non-volatile computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
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 characteristics 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 signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the invention can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims (10)

1. A power distribution cabinet control method, characterized in that the method comprises:
analyzing the fault type and the fault reason of a historical power distribution cabinet, constructing a fault tree of the historical power distribution cabinet by using a fault tree analysis method based on the fault type and the fault reason, and configuring a control strategy of each fault node in the fault tree;
collecting monitoring data of a power distribution cabinet to be controlled, denoising the monitoring data to obtain denoising monitoring data, and performing orthogonal normalization processing on the denoising monitoring data to obtain orthogonal monitoring data;
detecting the fault type of the power distribution cabinet to be controlled by using a trained fault identification model according to the orthogonal monitoring data;
and according to the fault category, inquiring a target fault node corresponding to the fault category in the fault tree, and taking a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled.
2. The method for controlling a power distribution cabinet according to claim 1, wherein the analyzing the fault type and the fault cause of the historical power distribution cabinet comprises:
inquiring the fault influence of a history power distribution cabinet from a pre-constructed history power distribution cabinet fault database;
Based on the fault impact, all possible fault types and their fault causes leading to the fault impact are analyzed.
3. The method for controlling a power distribution cabinet according to claim 1, wherein said constructing a fault tree of said historical power distribution cabinet using a fault tree analysis method based on said fault type and said fault cause comprises:
inquiring fault influence corresponding to the fault type from a pre-constructed historical power distribution cabinet fault database, and calculating fault occurrence probability of the fault type;
analyzing the fault grade of the fault type according to the fault occurrence probability;
according to the fault level, the fault occurrence probability and the fault reason, constructing an event node of the historical power distribution cabinet, and configuring a logic gate of the event node;
and generating a fault tree of the historical power distribution cabinet according to the event node and the logic gate.
4. A power distribution cabinet control method according to claim 3, wherein said constructing an event node of said historical power distribution cabinet according to said fault level, said fault occurrence probability and said fault cause comprises:
carrying out weighted summation on the fault grade and the occurrence type probability to obtain a fault score of the historical power distribution cabinet;
Descending order sorting is carried out on the fault scores to obtain score sorting, and the top event node of the historical power distribution cabinet is determined according to the score sorting;
inquiring the node fault reason of the top event node from the fault reasons, and constructing a lower event node of the top event node according to the node fault reason;
and determining the event node of the historical power distribution cabinet according to the top event node and the lower event node.
5. The method for controlling a power distribution cabinet according to claim 1, wherein before the detecting the fault class of the power distribution cabinet to be controlled by using the trained fault identification model according to the orthogonal monitoring data, the method further comprises:
acquiring the fault tree of the power distribution cabinet to be controlled, and screening a learning sample of the fault identification model from a pre-constructed historical power distribution cabinet fault database by using a fault tree analysis method;
vectorizing the learning sample to obtain an initial sample vector, and performing bipolar conversion on the initial sample vector to obtain a bipolar sample vector;
carrying out orthogonal normalization on the initial sample vector according to the bipolar sample vector to obtain a normalized orthogonal vector;
Calculating an associative memory matrix of the fault recognition model according to the orthonormal vector;
and determining a trained fault recognition model based on the associative memory matrix.
6. The method for controlling a power distribution cabinet according to claim 5, wherein the screening the learning sample of the fault recognition model from the pre-constructed historical power distribution cabinet fault database by using a fault tree analysis method comprises:
calculating the minimum cut set of the fault tree by using a minimum cut set solving algorithm;
calculating a corresponding intermediate event when the minimum cut set occurs by using the fault tree analysis method, combining the intermediate event into a combined event, taking the combined event as a fault monitoring event of the power distribution cabinet to be controlled, and taking the fault tree bottom event corresponding to the fault monitoring event as a fault analysis event;
and screening corresponding historical fault data from the pre-constructed historical power distribution cabinet fault database according to the fault monitoring event and the fault analysis event, and taking the corresponding historical fault data as a learning sample of the fault recognition model.
7. The method for controlling a power distribution cabinet according to claim 5, wherein performing bipolar conversion on the initial sample vector to obtain a bipolar sample vector comprises:
Performing bipolar conversion on the initial sample vector by using the following formula to obtain the bipolar sample vector:
wherein,,representing the bipolar sample vector, which is a vector representation of states on a fault analysis event set, 1 representing a fault state, -1 representing a normal state; x represents the initial sample vector, which is a vector representation of the states on the fault-monitoring event set, 1 represents the fault state, and 0 represents the normal state.
8. The method for controlling a power distribution cabinet according to claim 5, wherein the orthogonal normalization of the initial sample vector according to the bipolar sample vector to obtain a normalized orthogonal vector comprises:
according to the bipolar sample vector, the initial sample vector is orthogonally normalized by using the following formula to obtain the normalized orthogonal vector:
H 1 ＝[1 1；1-1]
H k ＝[H k-1 H k-1 ；H k-1 -H k-1 ]
wherein,,representing the orthonormal vector, H 1 Representing a 2 nd order Hadamard transform matrix, H k Representation 2 k Order Hadamard transform matrix, X * Represents an approximately orthogonal vector transformed by Hadamard, X represents the bipolar sample vector, τ (·) represents a normalization function,>representing an approximate orthogonal vector X * K represents the input layer neuron parameters of the failure recognition model.
9. The method of claim 5, wherein said calculating an associative memory matrix of said fault identification model based on said canonical orthogonal vector comprises:
according to the orthonormal vector, calculating an associative memory matrix of the fault recognition model by using the following formula:
T:R n (S X )→R n (S Y )
Q＝X T Y
wherein Q represents the associative memory matrix, T represents the spatial transformation relationship from the input layer to the output layer of the failure recognition model, S X Representing input layer neuron set, S Y Represents the output layer neuron set, represents the input vector, X represents the transpose of the input vector, Y represents the output vector, R n Representing the input layer neuron space.
10. A power distribution cabinet control device, characterized in that the device comprises:
the fault tree construction module is used for analyzing the fault type and the fault reason of the historical power distribution cabinet, constructing a fault tree of the historical power distribution cabinet by utilizing a fault tree analysis method based on the fault type and the fault reason, and configuring a control strategy of each fault node in the fault tree;
the orthogonal monitoring data generation module is used for collecting monitoring data of the power distribution cabinet to be controlled, denoising the monitoring data to obtain denoising monitoring data, and carrying out orthogonal normalization on the denoising monitoring data to obtain orthogonal monitoring data;
The fault type detection module is used for detecting the fault type of the power distribution cabinet to be controlled by utilizing a trained fault identification model according to the orthogonal monitoring data;
and the control strategy generation module is used for inquiring a target fault node corresponding to the fault category in the fault tree according to the fault category, and taking a control strategy corresponding to the target fault node as a control strategy of the power distribution cabinet to be controlled.
CN202310212818.9A 2023-03-07 2023-03-07 Power distribution cabinet control method and device Withdrawn CN116455059A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310212818.9A CN116455059A (en) 2023-03-07 2023-03-07 Power distribution cabinet control method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310212818.9A CN116455059A (en) 2023-03-07 2023-03-07 Power distribution cabinet control method and device

Publications (1)

Publication Number Publication Date
CN116455059A true CN116455059A (en) 2023-07-18

Family

ID=87131046

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310212818.9A Withdrawn CN116455059A (en) 2023-03-07 2023-03-07 Power distribution cabinet control method and device

Country Status (1)

Country Link
CN (1) CN116455059A (en)

Similar Documents

Publication Publication Date Title
CN109447461B (en) User credit evaluation method and device, electronic equipment and storage medium
CN106021771A (en) Method and device for diagnosing faults
CN110162445A (en) The host health assessment method and device of Intrusion Detection based on host log and performance indicator
CN111782900B (en) Abnormal service detection method and device, electronic equipment and storage medium
CN116186594B (en) Method for realizing intelligent detection of environment change trend based on decision network combined with big data
CN115791174B (en) Rolling bearing abnormality diagnosis method, system, electronic equipment and storage medium
CN108827671A (en) A kind of Trouble Diagnostic Method of Machinery Equipment
CN112148566B (en) Method and device for monitoring computing engine, electronic equipment and storage medium
CN117155771B (en) Equipment cluster fault tracing method and device based on industrial Internet of things
CN117036732B (en) Electromechanical equipment detection system, method and equipment based on fusion model
CN116823437A (en) Access method, device, equipment and medium based on configured wind control strategy
Wu et al. Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning
CN116455059A (en) Power distribution cabinet control method and device
CN111860661B (en) Data analysis method and device based on user behaviors, electronic equipment and medium
CN111651652B (en) Emotion tendency identification method, device, equipment and medium based on artificial intelligence
CN114742412A (en) Software technology service system and method
CN115372752A (en) Fault detection method, device, electronic equipment and storage medium
CN109558258B (en) Method and device for positioning root fault of distributed system
CN115078952B (en) IGBT driving fault detection method and system
CN113688924B (en) Abnormal order detection method, device, equipment and medium
CN115018366B (en) Energy storage system working state monitoring method and device, storage medium and electronic equipment
CN113591477B (en) Fault positioning method, device, equipment and storage medium based on associated data
CN117235480B (en) Screening method and system based on big data under data processing
CN116723083B (en) Cloud server online fault diagnosis method and device
CN113191455B (en) Edge computing box election method and device, electronic equipment and medium

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
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20230718