CN114383652A - Method, system and device for identifying potential fault online risk of power distribution network - Google Patents
Method, system and device for identifying potential fault online risk of power distribution network Download PDFInfo
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Abstract
The invention provides a method, a system and a device for identifying potential fault online risks of a power distribution network, wherein the method, the system and the device comprise the following steps: collecting relevant data of the operation of power equipment of the power distribution network; extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data, and calculating statistical index values of the characteristic parameters based on the relevant characteristic parameters; comparing the statistical index values of the characteristic parameters with the predetermined threshold intervals of the statistical index values of the characteristic parameters, and judging whether the power distribution network has potential fault risks; the method solves the problem that the running state of the power distribution network equipment is diagnosed by only adopting a single characteristic parameter and is easy to miss and judge and misjudge in the prior art, and provides a solution with good practicability, strong applicability and low cost for identifying the potential fault risk of the power equipment under different running working conditions and different monitoring device configuration conditions; meanwhile, the invention provides support for realizing distributed on-site monitoring and diagnosis, centralized comprehensive verification and judgment of potential faults of power distribution network power equipment.
Description
Technical Field
The invention relates to the field of analysis of running states of power distribution network equipment, in particular to a method, a system and a device for identifying potential fault online risks of a power distribution network.
Background
The power supply reliability of the power system is related to the national civilization, how to effectively ensure the safe and reliable operation of the power system is always an important subject of the power department, and the safe operation of the power equipment is the basis of the safe operation of the whole system. The main power equipment in the power distribution network comprises a transformer, a switch cabinet, a line and the like, and if potential defects caused by poor manufacture, aging and external force damage exist in the power equipment during operation, electrical accidents influencing the safe operation of the equipment and the power grid can occur. Once a power device fails, a power failure accident in a local area or a whole area is caused, the daily life of people and the conventional production of enterprises are endangered, and even the life safety of people is endangered in serious cases, so that the national economy is greatly lost.
In order to ensure the safe and reliable operation of the power distribution network and reduce the occurrence of the power equipment faults of the power distribution network, the premonitory operation characteristics before the power equipment faults must be accurately mastered in time, potential fault risks existing in the power equipment are monitored, identified and early warned, the existing hidden dangers are eliminated in time, and the power distribution network accidents are eliminated in the bud. At present, the traditional potential fault detection means of the power equipment comprises post-accident maintenance, periodical preventive test maintenance and condition maintenance. The maintenance cost after the accident is high, the cost is high, and the consequences caused by the accident can not be retrieved and compensated; the regular preventive test maintenance needs to be performed with power failure arrangement in advance and multiple departments to be closely matched, so that manpower and material resources are wasted, the pertinence is poor, and overhauls are easy to realize; the condition maintenance is an important means for discovering potential faults of power equipment and maintaining safe operation of a power grid, but due to the characteristics of complex power distribution network lines, multiple points and wide range of equipment and the like, the traditional complex and expensive online detection system and scheme are difficult to implement and have low applicability. In actual operation, certain premonitory phenomena and characteristics exist before the power equipment fails, a large amount of electric quantity and non-electric quantity information is generated, and potential failure hidden dangers are difficult to find under the condition that the power equipment is not powered off.
At present, the diagnosis of the potential fault of the power equipment comprises an off-line diagnosis mode and an on-line diagnosis mode, wherein the off-line diagnosis mode belongs to the post-maintenance mode and the periodic maintenance mode, and the on-line diagnosis mode belongs to the on-line diagnosis mode of the equipment state, and the on-line monitoring mode and the live detection mode belong to the on-line diagnosis mode of the equipment state. With the increasing requirement of users on power supply reliability, online fault monitoring and charged fault detection technologies have become widely adopted potential fault diagnosis technologies. However, the traditional charged fault detection technology has low integration degree, low data utilization rate, asynchronous and limited detection data, low accuracy of the diagnosis result of the potential fault diagnosis of the equipment and serious misjudgment and missed judgment; for the power equipment without the characteristic parameter acquisition sensor, the on-line monitoring and risk identification of potential faults cannot be realized, and the manufacturing, installation, operation and maintenance costs are high; the existing electrified fault detection technology has poor universality and high cost. Therefore, in order to realize all-weather and all-around monitoring, sensing and early warning of the operating state of the power equipment, a technical scheme of more efficient, more economical, more reliable and more accurate on-line monitoring and risk sensing identification of the power equipment is urgently needed.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an online risk identification method for potential faults of a power distribution network, which comprises the following steps:
collecting relevant data of the operation of power equipment of the power distribution network;
extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data of the operation of the power equipment of the power distribution network, and calculating statistic index values of the characteristic parameters based on the relevant characteristic parameters;
and comparing the statistical index value of each characteristic parameter with the threshold interval of the statistical index of each characteristic parameter, and judging whether the power distribution network has potential fault risk.
Preferably, the collecting of the relevant data of the operation of the power equipment of the power distribution network includes:
collecting relevant data of power distribution network power equipment operation based on a multi-source information system and/or a device entity;
cleaning the collected related data and storing the cleaned data in a historical database and a real-time database;
wherein the device entity comprises: an intelligent terminal or an information acquisition sensor;
the multi-source information system includes at least one of: the system comprises a power distribution automation system, a power utilization information acquisition and management system, a weather forecast system and an environment monitoring system;
the power device includes at least one of: looped netowrk cabinet, transformer substation's cubical switchboard, low-voltage distribution box, low-voltage distribution cabinet and distribution lines.
Preferably, the data comprises original data of electrical quantity information and original data of non-electrical quantity information;
the electrical quantity information raw data comprises at least one of the following data: voltage, current, active and reactive;
the non-electrical quantity information raw data comprises at least one of the following data: sound, light, temperature and humidity.
Preferably, the characteristic parameters at least include one or more of the following: voltage, current, voltage to ground of the metal enclosure of the power equipment, contact voltage of the metal enclosure of the power equipment, electric field, magnetic field, sound wave, temperature, humidity, gas composition, optical image, power frequency phase-ground of the distribution line, power frequency phase-phase leakage current of the distribution line, high frequency phase-ground partial discharge current, non-power frequency phase-ground partial discharge current, high frequency phase-phase partial discharge current, non-power frequency phase-phase partial discharge current, and line impedance.
Preferably, each characteristic parameter statistical indicator at least includes one of the following: the amplitude value of each characteristic parameter, the amplitude value out-of-limit degree, the amplitude value change rate, the amplitude value out-of-limit duration and the amplitude value out-of-limit frequency.
Preferably, the method for determining the threshold interval of each characteristic parameter statistical indicator includes: and the threshold interval is determined in a preset threshold interval range according to the operation experience or in a self-adaptive mode based on a statistical pattern recognition method.
Preferably, the method for adaptively determining the threshold interval based on the statistical pattern recognition includes:
extracting relevant characteristic parameter statistical index values reflecting potential faults of the power distribution network from data stored in a historical database;
and counting the index values based on the characteristic parameter values, and calculating the optimal threshold value interval of each characteristic parameter statistical index in each fault risk mode by adopting a statistical mode identification method according to the pre-divided potential fault risk modes.
Preferably, the latent failure risk mode is a plurality of latent failure risk modes divided according to different influencing factors causing the abnormal operation of the equipment;
wherein the influencing factors include at least one or more of the following: temperature, humidity, load level, grid voltage, and meteorological conditions.
Preferably, the comparing the statistical index value of each characteristic parameter with the threshold interval of each characteristic parameter to determine whether the distribution network has a potential fault risk includes:
and comparing the statistical index value of each characteristic parameter with the optimal threshold interval of each characteristic parameter in each potential fault risk mode, and judging whether the power distribution network has potential fault risks or not by combining preset power distribution network potential fault online monitoring and risk identification rules.
Preferably, the power distribution network latent fault risk identification rule includes:
aiming at a single characteristic parameter, if all statistical index values of the characteristic parameter exceed a threshold value, judging that the potential fault risk exists in the power grid; otherwise, judging that the power grid is in a normal operation state;
synthesizing the judgment results of a plurality of single characteristic parameters, and judging that the potential fault risk exists in the whole power grid if all the characteristic parameters are judged to be potential fault risks in the power grid; otherwise, the power grid is judged to be in a normal operation state.
Based on the same invention concept, the invention also provides an online risk identification system for potential faults of the power distribution network, which comprises the following steps: a sensing layer, a data layer and an application layer;
the sensing layer is used for: collecting relevant data of the operation of power equipment of the power distribution network;
the data layer is used for: extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data, and calculating statistical index values of the characteristic parameters based on the relevant characteristic parameters;
the application layer is to: and comparing the statistical index value of each characteristic parameter with the threshold interval of the statistical index of each characteristic parameter, and judging whether the power distribution network has potential fault risk.
Preferably, the system further comprises a physical layer; the physical layer includes one or more of: the system comprises power equipment, a multi-source system, an acquisition channel and a device entity;
the power device includes at least one of: the system comprises a ring main unit, a transformer substation switch cabinet, a low-voltage distribution box, a low-voltage distribution cabinet and a distribution line;
the multi-source system includes at least one of: the system comprises a power distribution automation system, a power utilization information acquisition and management system, a weather forecast system and an environment monitoring system;
the acquisition channel comprises a data interface or an information interaction bus;
the device entity comprises an intelligent terminal or an information acquisition sensor.
Preferably, the sensing layer is specifically configured to:
and collecting all relevant data provided by the physical layer, cleaning all relevant data and storing the cleaned data in a historical database and a real-time database.
Preferably, the data layer is specifically configured to:
extracting relevant characteristic parameter statistical index values reflecting potential faults of the power distribution network from data stored in a historical database;
and counting the index values based on the characteristic parameter values, and calculating the optimal threshold value interval of each characteristic parameter statistical index in each fault risk mode by adopting a statistical mode identification method according to the pre-divided potential fault risk modes.
Preferably, the application layer is specifically configured to:
and comparing the statistical index value of each characteristic parameter with the optimal threshold interval of each characteristic parameter in each potential fault risk mode, and judging whether the power distribution network has potential fault risks or not by combining preset power distribution network potential fault online monitoring and risk identification rules.
Preferably, the system further comprises: the communication layer is used for transmitting data flow between the sensing layer and the data layer;
the communication layer includes a communication network or a communication module.
The invention also provides a device for identifying the potential fault online risk of the power distribution network based on the same invention concept, wherein the device is provided with the system for identifying the potential fault online risk of the power distribution network, and is used for realizing the method for identifying the potential fault online risk of the power distribution network.
Preferably, the apparatus comprises one or more of the following: the intelligent terminal, the distribution automation system collection unit and the distribution network substation.
Preferably, the devices deployed with the power distribution network potential fault online risk identification system are in communication connection, and interaction and verification are performed on the power distribution network potential fault online risk identification results among the devices.
Preferably, the device further comprises a power distribution network master station;
the distribution network main station is in communication connection with the device with the distribution network potential fault online risk identification system, and is used for interacting and verifying the distribution network potential fault online risk identification result.
Preferably, the device comprises a distribution network master station.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method, a system and a device for identifying potential fault online risks of a power distribution network, wherein the method, the system and the device comprise the following steps:
collecting relevant data of the operation of power equipment of the power distribution network; extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data, and calculating statistical index values of the characteristic parameters based on the relevant characteristic parameters; comparing the statistical index values of the characteristic parameters with the threshold intervals of the statistical indexes of the characteristic parameters, and judging whether the power distribution network has potential fault risks; the method disclosed by the invention is used for identifying the potential fault online risk of the power distribution network based on multi-characteristic parameter identification, solves the problem that the insulation state diagnosis of the power distribution network is easy to miss and misjudge by adopting a single characteristic parameter, and provides a solution with good practicability, strong applicability and low cost for the insulation state monitoring and risk identification of the power equipment under different operating conditions and different monitoring device configuration conditions.
Drawings
FIG. 1 is a schematic diagram of an online risk identification method for potential faults of a power distribution network according to the present invention;
FIG. 2 is a flowchart of a method for identifying potential fault online risks of a power distribution network according to the present invention;
FIG. 3 is a graph illustrating a statistical analysis of the variation trend of leakage current in different voltage modes according to the present invention;
FIG. 4 is a graph of leakage current characteristic thresholds for various voltage modes of the present invention;
FIG. 5 is a diagram of a power distribution network potential fault online risk identification system according to the present invention;
Detailed Description
Current charged detection techniques include electrical and non-electrical detection methods. The electric quantity detection method mainly realizes the charged detection of the state of the electric power equipment by analyzing the change of characteristic parameters such as voltage, current, electric field and magnetic field of the electric power equipment, and common electric quantity detection technologies comprise a leakage current analysis method, a pulse current method, a dielectric loss method, a radio interference voltage method (radio frequency detection method), an ultrahigh frequency electromagnetic wave detection method, a transient ground voltage detection method and the like. The non-electric quantity detection method mainly realizes insulation live detection by analyzing the change of characteristic parameters such as abnormal sound, luminescence, heating and gas components generated when insulation is abnormal, and common non-electric quantity detection methods comprise an ultrasonic detection method, a photometric method, a chemical detection method, an infrared temperature measurement method and the like. The electric quantity detection method has weak anti-electromagnetic and corona interference capability, extremely high requirement on the sensitivity of the characteristic parameter acquisition sensor, high cost and difficult maintenance; the non-electric quantity detection method has strong anti-electromagnetic interference capability, but the detection technology is related to the performance of a used sensor and a propagation medium, and only can qualitatively judge the insulation state of the power equipment.
The existing electrification detection technology has certain limitation in practical application, and mainly comprises the following aspects:
(1) leakage current, partial discharge signals and other characteristic parameter signals reflecting abnormal operation of the power equipment are weak, the waveform is complex and changeable, background noise and electromagnetic interference are large, and accurate extraction is difficult;
(2) the characteristic parameter data representing the abnormal operation of the power equipment are multi-source, large in quantity, various in format and high in comprehensive analysis and processing difficulty;
(3) the installation configuration of different types of sensors for extracting multiple characteristic quantities is dispersed and not concentrated, the integration degree is low, and the operation and maintenance cost is high;
(4) the universality is poor, the applicability is not strong, and the on-line monitoring and risk identification of potential faults of the power equipment cannot be realized for the power equipment without the characteristic parameter acquisition sensor;
(5) the utilization rate of potential fault diagnosis data of equipment is low, various detection data are asynchronous and have limitations, single characteristic parameters are often adopted, the threshold setting often depends on expert experience, the accuracy rate of diagnosis results is low, and misjudgment and missed judgment are serious.
In order to solve the problems in the prior art, the invention provides a method, a system and a device for identifying the potential fault online risk of a power distribution network, which can accurately monitor the running state of power equipment by data analysis, feature extraction, statistical index calculation, mode matching and diagnosis identification of a plurality of characteristic parameters such as temperature, humidity, leakage current, partial discharge and the like, solve the problem that the running state of the power distribution network equipment is diagnosed by adopting a single characteristic parameter and is easy to be missed and judged and misjudged in the prior art, provide solutions with good practicability, strong applicability and low cost for the online monitoring and risk identification of the running state of the power equipment under different running conditions and different monitoring device configuration conditions, provide theoretical method support for realizing the distributed on-site monitoring and diagnosis and the centralized comprehensive verification and judgment of the potential fault of the power equipment, and provide theoretical method support for intelligent terminals with the functions of online monitoring and risk identification of the potential fault, The research and development of the software system provide technical support, and a referable solution is provided for the establishment of the insulation detection test platform of the power equipment. The method and the device can timely and accurately sense the premonitory sign of the abnormal operation condition of the power equipment in the initial stage of the accident so as to timely remind the operating personnel to control the failure premonitory sign of the power equipment within a normal range, avoid bringing the failure hidden trouble to the operation, effectively avoid the occurrence of the power failure accident, reduce the operation risk of the power grid, reduce the power failure loss, and have very important significance for ensuring the normal operation of the power grid and the safe production of enterprises. For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1:
the invention provides a method for identifying potential fault online risks of a power distribution network, which comprises the following steps of:
s1, collecting relevant data of operation of power equipment of the power distribution network;
s2, extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data, and calculating statistical index values of the characteristic parameters based on the relevant characteristic parameters;
and S3, comparing the characteristic parameter statistical index values with the threshold intervals of the characteristic parameter statistical indexes, and judging whether the distribution network has potential fault risks.
The specific process is shown in FIG. 2;
step S1, collecting all relevant data of the operation of the power distribution network power equipment, which specifically comprises the following steps:
acquiring various electrical data acquired in real time by intelligent sensors installed in equipment such as a ring main unit, a transformer substation switch cabinet, a low-voltage distribution box/power distribution cabinet and the like; the electrical data includes various electrical quantity and non-electrical quantity information raw data.
Step S2, extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data, and calculating statistical index values of the characteristic parameters, wherein the method specifically comprises the following steps:
and extracting relevant characteristic parameters reflecting potential faults of the power distribution network, calculating specific statistical index values of the characteristic parameters according to preset statistical indexes of the characteristic parameters, and storing the statistical index values in a real-time database and a historical database respectively.
And for each characteristic parameter statistical index stored in the historical database, dividing a potential fault identification mode by adopting a statistical mode identification method, and calculating the optimal threshold value interval of each characteristic parameter statistical index in different modes.
And calculating and analyzing the abnormal change condition and the change trend of each characteristic parameter statistical index stored in the real-time database.
Step S3, comparing the statistical index values of the characteristic parameters with the threshold intervals of the statistical indexes of the characteristic parameters, and determining whether the distribution network has a potential failure risk, including:
and comparing the multi-characteristic parameter statistical indexes with the optimal threshold interval, judging whether the power equipment has a potential fault risk according to preset power distribution network potential fault online monitoring and risk identification rules, and giving the current and future power equipment operation states and risk levels.
The various electrical quantity and non-electrical quantity raw data collected in real time comprise various electrical quantity information and non-electrical quantity information raw data collected by sensors installed at a switch cabinet/distribution line/transformer substation and the like in the operation process of power distribution network equipment, wherein the electrical quantity information comprises but is not limited to voltage, current, active power, reactive power and the like, and the non-electrical quantity information comprises but is not limited to sound, light, temperature, humidity and the like.
The relevant characteristic parameters reflecting the potential faults of the power distribution network comprise direct acquisition and indirect calculation, the relevant characteristic parameters reflecting the potential faults of the power distribution network obtained through direct acquisition comprise, but are not limited to, voltage, current, voltage to earth/contact voltage of a metal outer shell of power equipment, an electric field, a magnetic field, sound waves, temperature, humidity, gas components, light images and the like, and the relevant characteristic parameters reflecting the potential faults of the power distribution network obtained through indirect measurement comprise, but are not limited to, power frequency phase-ground/phase-phase leakage current (comprising resistive leakage current and capacitive leakage current), high frequency/non-power frequency phase-ground/phase-phase partial discharge current, line impedance and the like.
The statistical indexes of the characteristic parameters include but are not limited to amplitude magnitude, amplitude out-of-limit degree, amplitude change rate, amplitude out-of-limit duration, amplitude out-of-limit frequency and the like of the characteristic parameters.
The latent fault identification mode refers to multiple equipment operation state identification modes which are divided according to different influence factors causing equipment operation abnormity, such as temperature, humidity, load level, power grid voltage, meteorological conditions and the like.
The historical database stores statistical calculation indexes of each characteristic parameter within a period of time, and based on the statistical calculation indexes, the statistical data have normal, out-of-limit and different fluctuation ranges, so a statistical mode identification method is adopted to process the data, the data are classified into one type of normal data, the data are classified into several types of abnormal data according to different out-of-limit degrees, each type is used as a mode, and an interval quantization is given to the mode, and the interval quantization is a threshold interval.
The relationship among the fault mode, the grid operation characteristic parameter and the threshold interval is described by taking the analysis of the variation trend of the leakage current characteristic parameter and the setting of the threshold interval in the voltage mode as an example.
Voltage mode division UN(rated voltage of line)
Voltage mode one: (0.9U)N～UN)
Voltage mode two: (U)N～1.1UN)
Voltage mode three: (1.1U)N～1.2UN)
Statistical analysis of the trend of the leakage current in different voltage modes is shown in fig. 3.
The inter-class variance maximum method is used in the voltage mode to set the leakage current characteristic threshold corresponding to the load mode, and several voltage modes are used in correspondence to several leakage current characteristic thresholds, as shown in fig. 4.
After determining whether the distribution network has the potential fault risk at step S3, the method further includes:
providing the current and future power distribution network operation states and risk levels; early warning is carried out based on the running state and the risk level of the power distribution network; and simultaneously, visually displaying the running state and risk level of the power distribution network and the early warning information.
The determination of the optimal threshold interval in the invention can be preset or can be the optimal threshold interval obtained by self-adaptive setting calculation according to the collected historical data.
Example 2:
based on the same invention concept, the invention also provides an online risk identification system for the potential fault of the power distribution network, as shown in fig. 5, the system architecture comprises 5 layers of a physical layer, a sensing layer, a communication layer, a data layer and an application layer, and specifically comprises a multi-source information system, a multi-characteristic intelligent acquisition terminal physical entity, a data interface or an information interaction bus, an acquisition raw data preprocessing module, a communication network or communication module, a data storage module, a characteristic parameter data intelligent analysis module, an online potential fault monitoring and risk identification module for the power distribution network and the like. The system has the advantages of high integration and intelligence degree, good universality, strong applicability, low installation cost and high accuracy of potential fault diagnosis, can realize online and real-time analysis and judgment of the operation state of the power equipment, and provides decision support for live operation and maintenance of the power equipment.
The physical layer of the power distribution network potential fault on-line monitoring and risk identification system comprises a multi-source information system (including but not limited to automation/informatization systems such as a power distribution automation system, a power utilization information acquisition and management system, a weather forecast system, an environment monitoring system and the like) and a data interface of the power distribution network potential fault on-line monitoring and risk identification system; the system comprises various electric quantity and non-electric quantity information acquisition sensors or intelligent terminal physical entities installed on power equipment such as a ring main unit, a transformer substation switch cabinet, a low-voltage distribution box/power distribution cabinet, a distribution line and the like.
The sensing layer of the power distribution network potential fault on-line monitoring and risk identification system comprises an acquisition original data preprocessing module and mainly realizes the functions of preprocessing multiple characteristic quantity original data such as data cleaning and the like.
The communication layer of the power distribution network potential fault on-line monitoring and risk identification system comprises a communication network or a communication module, and is used for transmitting data streams between a sensing layer and a data layer.
The data layer of the power distribution network potential fault on-line monitoring and risk identification system comprises a data analysis and processing module and a data storage module, and aims to realize functions of characteristic parameter extraction, characteristic parameter statistical index calculation, insulation identification mode division, threshold setting and the like.
The application layer of the power distribution network potential fault on-line monitoring and risk identification system comprises a power distribution network potential fault on-line monitoring and risk identification module, and has the main functions of fusing a multi-characteristic parameter statistical index calculation result obtained by calculation of a data layer and realizing final diagnosis, early warning and visualization of the power distribution network potential fault risk through certain comprehensive diagnosis criteria and rules.
Example 3
The invention also provides a device for identifying the potential fault online risk of the power distribution network, which can be an intelligent terminal, a power distribution automation system collection unit, a power distribution network substation or a power distribution network main station. The device provided by the invention can deploy the power distribution network potential fault online risk identification system provided by the invention on one or more devices by using any one of the following deployment modes, so as to realize the power distribution network potential fault online risk identification method provided by the invention.
The deployment mode provided by the invention comprises the following three modes:
the first mode is as follows: in-situ deployment mode
The on-site deployment mode is that the power distribution network potential fault on-line monitoring and risk identification system is deployed in the intelligent terminal/power distribution automation system collection unit/power distribution network substation, and on-site power distribution network/local potential fault on-line monitoring and risk identification are carried out by utilizing the on-site acquired characteristic parameter information. The deployment mode has high performance requirements on data processing, local calculation, intelligent analysis and the like of the intelligent terminal/collection unit/substation, the intelligent terminal/collection unit/substation has the function of intercommunication, insulation monitoring and risk identification rapid diagnosis can be realized, but the accuracy rate of potential fault diagnosis is low, and judgment and misjudgment are easy to miss.
And a second mode: distributed and centralized combined deployment mode
The distribution centralized combination deployment mode is that a distribution network potential fault on-line monitoring and risk identification system is deployed in intelligent terminals/distribution automation system collection units/distribution network substations and a distribution network master station, and the intelligent terminals/distribution automation system collection units/distribution network substations and the distribution network master station can be communicated with each other; the method can realize the fusion of each characteristic parameter and the mutual verification of the identification result of the insulation state, finally realize the quick and accurate identification of the local and global potential fault states of the power distribution network, and has high accuracy and good timeliness.
And a third mode: centralized deployment mode
The centralized deployment mode is that the power distribution network latent fault on-line monitoring and risk identification system is deployed in a power distribution network main station, fusion of all characteristic parameters and mutual verification of a latent fault state identification result can be realized, the identification of a global latent fault state of a power distribution network can be realized, the timeliness is not high, and the identification effect on local latent faults is poor.
The various electrical quantity and non-electrical quantity raw data collected in real time comprise various electrical quantity information and non-electrical quantity information raw data collected by sensors installed at a switch cabinet/distribution line/transformer substation and the like in the operation process of power distribution network equipment, wherein the electrical quantity information comprises but is not limited to voltage, current, active power, reactive power and the like, and the non-electrical quantity information comprises but is not limited to sound, light, temperature, humidity and the like.
The relevant characteristic parameters reflecting the potential faults of the power distribution network comprise direct acquisition and indirect calculation, the relevant characteristic parameters reflecting the potential faults of the power distribution network obtained through direct acquisition comprise, but are not limited to, voltage, current, voltage to earth/contact voltage of a metal outer shell of power equipment, an electric field, a magnetic field, sound waves, temperature, humidity, gas components, light images and the like, and the relevant characteristic parameters reflecting the potential faults of the power distribution network obtained through indirect measurement comprise, but are not limited to, power frequency phase-ground/phase-phase leakage current (comprising resistive leakage current and capacitive leakage current), high frequency/non-power frequency phase-ground/phase-phase partial discharge current, line impedance and the like.
The statistical indexes of the characteristic parameters include but are not limited to amplitude magnitude, amplitude out-of-limit degree, amplitude change rate, amplitude out-of-limit duration, amplitude out-of-limit frequency and the like of the characteristic parameters.
The latent fault identification mode refers to multiple equipment operation state identification modes which are divided according to different influence factors causing equipment operation abnormity, such as temperature, humidity, load level, power grid voltage, meteorological conditions and the like.
The optimal threshold interval comprises a preset threshold interval and an optimal threshold interval obtained by self-adaptive setting calculation according to collected historical data.
The device for identifying the potential fault online risk of the power distribution network provides theoretical method support for realizing distributed on-site monitoring and diagnosis and centralized comprehensive verification and judgment of power equipment insulation, provides technical support for research and development of intelligent terminals and software systems with insulation monitoring and risk identification functions, and provides a referable solution for building a power equipment insulation detection test platform.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (17)
1. A method for identifying potential fault online risks of a power distribution network is characterized by comprising the following steps:
collecting relevant data of the operation of power equipment of the power distribution network;
extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data, and calculating statistical index values of the characteristic parameters based on the relevant characteristic parameters;
and comparing the statistical index value of each characteristic parameter with the threshold interval of the statistical index of each characteristic parameter, and judging whether the power distribution network has potential fault risk.
2. The method of claim 1, wherein collecting data regarding operation of power distribution network power equipment comprises:
collecting relevant data of power distribution network power equipment operation based on a multi-source information system and/or a device entity;
cleaning the collected related data and storing the cleaned data in a historical database and a real-time database;
wherein the device entity comprises: an intelligent terminal or an information acquisition sensor;
the multi-source information system includes at least one of: the system comprises a power distribution automation system, a power utilization information acquisition and management system, a weather forecast system and an environment monitoring system;
the power device includes at least one of: looped netowrk cabinet, transformer substation's cubical switchboard, low-voltage distribution box, low-voltage distribution cabinet and distribution lines.
3. The method of claim 2, wherein the data comprises electrical quantity information raw data and non-electrical quantity information raw data;
the electrical quantity information raw data comprises at least one of the following data: voltage, current, active and reactive;
the non-electrical quantity information raw data comprises at least one of the following data: sound, light, temperature and humidity.
4. The method of claim 1, wherein the characteristic parameters include at least one or more of the following: voltage, current, voltage to ground of the metal enclosure of the power equipment, contact voltage of the metal enclosure of the power equipment, electric field, magnetic field, sound wave, temperature, humidity, gas composition, optical image, power frequency phase-ground of the distribution line, power frequency phase-phase leakage current of the distribution line, high frequency phase-ground partial discharge current, non-power frequency phase-ground partial discharge current, high frequency phase-phase partial discharge current, non-power frequency phase-phase partial discharge current, and line impedance.
5. The method of claim 1, wherein the characteristic parameter statistics include at least one of: the amplitude value of each characteristic parameter, the amplitude value out-of-limit degree, the amplitude value change rate, the amplitude value out-of-limit duration and the amplitude value out-of-limit frequency.
6. The method of claim 1, wherein the determining of the threshold interval for each characteristic parameter statistic comprises: presetting a threshold interval range according to operation experience; or the threshold interval is determined adaptively based on a statistical pattern recognition method.
7. The method of claim 6, wherein the method for adaptively determining the threshold interval based on statistical pattern recognition comprises:
extracting relevant characteristic parameter statistical index values reflecting potential faults of the power distribution network from data stored in a historical database;
and counting the index values based on the characteristic parameter values, and calculating the optimal threshold value interval of each characteristic parameter statistical index in each fault risk mode by adopting a statistical mode identification method according to the pre-divided potential fault risk modes.
8. The method of claim 7, wherein the latent failure risk pattern is a plurality of latent failure risk patterns divided according to different influencing factors causing the device to operate abnormally;
wherein the influencing factors include at least one or more of the following: temperature, humidity, load level, grid voltage, and meteorological conditions.
9. The method of claim 1, wherein comparing the statistical indicator value of each characteristic parameter with the threshold interval of each characteristic parameter to determine whether the distribution network has a potential failure risk comprises:
and comparing the statistical index value of each characteristic parameter with the optimal threshold interval of each characteristic parameter in each potential fault risk mode, and judging whether the power distribution network has potential fault risks or not by combining preset power distribution network potential fault online monitoring and risk identification rules.
10. The method of claim 1, wherein the distribution network risk of latent fault identification rule comprises:
aiming at a single characteristic parameter, if all statistical index values of the characteristic parameter exceed a threshold value, judging that the potential fault risk exists in the power grid; otherwise, judging that the power grid is in a normal operation state;
synthesizing the judgment results of a plurality of single characteristic parameters, and judging that the potential fault risk exists in the whole power grid if all the characteristic parameters are judged to be potential fault risks in the power grid; otherwise, the power grid is judged to be in a normal operation state.
11. An online risk identification system for potential faults of a power distribution network is characterized by comprising: a sensing layer, a data layer and an application layer;
the sensing layer is used for: collecting relevant data of the operation of power equipment of the power distribution network;
the data layer is used for: extracting relevant characteristic parameters reflecting potential faults of the power distribution network based on the relevant data, and calculating statistical index values of the characteristic parameters based on the relevant characteristic parameters;
the application layer is to: and comparing the statistical index value of each characteristic parameter with the threshold interval of the statistical index of each characteristic parameter, and judging whether the power distribution network has potential fault risk.
12. The system of claim 11, wherein the system further comprises a physical layer; the physical layer includes one or more of: the system comprises power equipment, a multi-source system, an acquisition channel and a device entity;
the power device includes at least one of: the system comprises a ring main unit, a transformer substation switch cabinet, a low-voltage distribution box, a low-voltage distribution cabinet and a distribution line;
the multi-source system includes at least one of: the system comprises a power distribution automation system, a power utilization information acquisition and management system, a weather forecast system and an environment monitoring system;
the acquisition channel comprises a data interface or an information interaction bus;
the device entity comprises an intelligent terminal or an information acquisition sensor.
13. The system of claim 12, wherein the sensing layer is specifically configured to:
and collecting all relevant data provided by the physical layer, cleaning all relevant data and storing the cleaned data in a historical database and a real-time database.
14. The system of claim 13, wherein the data layer is specifically configured to:
extracting relevant characteristic parameter statistical index values reflecting potential faults of the power distribution network from data stored in a historical database;
and counting the index values based on the characteristic parameter values, and calculating the optimal threshold value interval of each characteristic parameter statistical index in each fault risk mode by adopting a statistical mode identification method according to the pre-divided potential fault risk modes.
15. The system of claim 13, wherein the application layer is specifically configured to:
and comparing the statistical index value of each characteristic parameter with the optimal threshold interval of each characteristic parameter in each potential fault risk mode, and judging whether the power distribution network has potential fault risks or not by combining preset power distribution network potential fault online monitoring and risk identification rules.
16. The system of claim 13, wherein the system further comprises: the communication layer is used for transmitting data flow between the sensing layer and the data layer;
the communication layer includes a communication network or a communication module.
17. An online risk identification device for potential faults of a power distribution network, wherein the online risk identification system for potential faults of the power distribution network according to any claim 11 to 16 is deployed on the device and used for realizing the online risk identification method for potential faults of the power distribution network according to any claim 1 to 10.
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