CN107274088B - Risk field calculation method and system for underground power distribution room - Google Patents
Risk field calculation method and system for underground power distribution room Download PDFInfo
- Publication number
- CN107274088B CN107274088B CN201710432790.4A CN201710432790A CN107274088B CN 107274088 B CN107274088 B CN 107274088B CN 201710432790 A CN201710432790 A CN 201710432790A CN 107274088 B CN107274088 B CN 107274088B
- Authority
- CN
- China
- Prior art keywords
- power equipment
- fault rate
- risk field
- rate
- environmental
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a risk field calculation method and a risk field calculation system for an underground power distribution room, wherein the method comprises the following steps: calculating the benchmark fault rate of the electrical equipment and the benchmark fault rate of the environmental position according to historical statistical data of the service life of the electrical equipment in the underground power distribution room; calculating a standard deviation of the power equipment and a standard deviation of the environment position according to a sampling value of the reference fault rate of the power equipment and a sampling value of the reference fault rate of the environment position respectively; generating normally distributed power equipment fault rates and environment position fault rates by using a Weber-just-Taiyun generator according to the power equipment reference fault rates and the power equipment standard deviations and according to the environment position reference fault rates and the environment position standard deviations; respectively calculating the fault probability of the fault rate of the power equipment and the fault rate of the environmental position, and generating a power equipment risk field and an environmental position risk field of an underground power distribution room; and calculating a risk field with space-time distribution characteristics, and considering the temperature, the service time and the dispersion of the fault rate distribution.
Description
Technical Field
The invention relates to the technical field of electricity, in particular to a risk field calculation method and system for an underground power distribution room.
Background
With the increase of population and the popularization of intelligent equipment, the demand of people on energy sources is gradually increased, but the urban land is more and more tense, and underground indoor substations provide a feasible way for solving the problem. The lifetime of the electrical equipment and whether it can operate without failure depend to a large extent on the operating conditions of the equipment. For an underground substation, the operation environment is worse than that of a common substation. Where temperature is the most critical factor causing equipment failure.
The current fault analysis methods for underground distribution room electrical equipment are too general. When calculating the failure rate, the power equipment is taken as a whole, and the distribution characteristic of the failure rate is not considered. Therefore, how to reasonably and accurately calculate the failure rate of the power equipment is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a risk field calculation method and a risk field calculation system for an underground power distribution room, wherein a risk field with a space-time distribution characteristic is obtained through calculation, the temperature, the service time, the dispersity, the randomness and the uncertainty of fault rate distribution are considered, and the fault rate of power equipment can be accurately calculated.
In order to solve the technical problem, the invention provides a risk field calculation method for an underground power distribution room, which comprises the following steps:
calculating the reference fault rate of the power equipment and the reference fault rate of the environmental position, which take the temperature and the service time into consideration, according to historical statistical data of the service life of the power equipment in the underground power distribution room;
calculating to obtain a standard deviation of the power equipment and a standard deviation of the environment position according to the sampling value of the reference fault rate of the power equipment and the sampling value of the reference fault rate of the environment position respectively;
generating a normally distributed power equipment fault rate by using a weber-positive cloud generator according to the power equipment reference fault rate and the power equipment standard deviation, and generating a normally distributed environment position fault rate by using the weber-positive cloud generator according to the environment position reference fault rate and the environment position standard deviation;
and respectively calculating the fault probability of the fault rate of the power equipment and the fault rate of the environmental position, and generating a power equipment risk field and an environmental position risk field of an underground power distribution room.
Optionally, calculating the reference fault rate of the electrical equipment considering the temperature and the service time includes:
using formulas
Calculating the reference failure rate lambda of the power equipmentb(t,x,y,z)；
Wherein beta is a shape parameter, A and B are empirical constants, T is temperature, T is time, and (x, y, z) are position coordinates.
Optionally, calculating the environmental location reference fault rate considering the temperature and the service time includes:
using formulas
Computing environmental position reference failure rate λbr(t,x,y,z)；
Wherein A isr，BrIs an empirical constant, T is temperature, in deg.C, T is time, and (x, y, z) is position coordinates.
Optionally, the power equipment risk field Fj(t, x, y, z) is specifically:
where λ' is the power equipment failure rate and j represents a switch or transformer assembly.
Optionally, the environmental location risk field Fr1(t, x, y, z) is specifically:
wherein, λ'rIs the environmental location failure rate.
The present invention also provides a risk field calculation system for an underground electrical distribution room, the system comprising:
the reference fault rate calculation module is used for calculating the reference fault rate of the electric power equipment and the reference fault rate of the environmental position, which take the temperature and the service time into consideration, according to historical statistical data of the service life of the electric power equipment in the underground power distribution room;
the standard deviation calculation module is used for calculating to obtain a standard deviation of the power equipment and a standard deviation of the environment position according to the sampling value of the reference fault rate of the power equipment and the sampling value of the reference fault rate of the environment position;
the fault rate calculation module is used for generating normally distributed power equipment fault rates by using a Weber-Positive cloud generator according to the power equipment reference fault rate and the power equipment standard deviation, and generating normally distributed environment position fault rates by using the Weber-Positive cloud generator according to the environment position reference fault rates and the environment position standard deviation;
and the risk field calculation module is used for calculating the fault probability of the power equipment fault rate and the environmental position fault rate respectively and generating a power equipment risk field and an environmental position risk field of the underground power distribution room.
Optionally, the reference fault rate calculating module includes:
a first calculation unit for using the formula
Calculating the reference failure rate lambda of the power equipmentb(t,x,y,z)；
Wherein beta is a shape parameter, A and B are empirical constants, T is temperature, T is time, and (x, y, z) are position coordinates.
Optionally, the reference fault rate calculating module includes:
a second calculation unit for using the formula
Computing environmental position reference failure rate λbr(t,x,y,z)；
Wherein A isr，BrIs an empirical constant, T is temperature, in deg.C, T is time, and (x, y, z) is position coordinates.
Optionally, the power equipment risk field Fj(t, x, y, z) is specifically:
the environment isLocation risk field Fr1(t, x, y, z) is specifically:
where λ 'is the power equipment failure rate, j represents the switch or transformer assembly, λ'rIs the environmental location failure rate.
The invention provides a risk field calculation method for an underground power distribution room, which comprises the following steps: calculating the reference fault rate of the power equipment and the reference fault rate of the environmental position, which take the temperature and the service time into consideration, according to historical statistical data of the service life of the power equipment in the underground power distribution room; calculating to obtain a standard deviation of the power equipment and a standard deviation of the environment position according to a sampling value of the reference fault rate of the power equipment and a sampling value of the reference fault rate of the environment position respectively; generating normally distributed power equipment fault rates by using a Weber-regular cloud generator according to the power equipment reference fault rates and the power equipment standard deviations, and generating normally distributed environment position fault rates by using the Weber-regular cloud generator according to the environment position reference fault rates and the environment position standard deviations; and respectively calculating the fault probability of the fault rate of the power equipment and the fault rate of the environmental position, and generating a power equipment risk field and an environmental position risk field of the underground power distribution room.
Therefore, the method calculates the power equipment risk field and the environmental position risk field with the space-time distribution characteristic, fully considers the influence of temperature and service time on the fault rate of the power equipment, and the dispersity, randomness and uncertainty of the fault rate distribution, and can accurately calculate the fault rate of the power equipment; the invention also provides a risk field calculation system of the underground power distribution room, which has the beneficial effects and is not repeated herein.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a risk field calculation method for an underground electric distribution room according to an embodiment of the present invention;
FIG. 2 is a schematic view of the spatial temperature field distribution of an underground electric power distribution room provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a weber-normal cloud generator according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the spatial distribution of failure rates (number of failures per unit time) in a cross-sectional view of a power distribution room provided by an embodiment of the present invention;
fig. 5 is a block diagram of a risk field calculation system of an underground power distribution room according to an embodiment of the present invention.
Detailed Description
The core of the invention is to provide a risk field calculation method and a risk field calculation system for an underground power distribution room, wherein a risk field with a space-time distribution characteristic is obtained through calculation, the temperature, the service time, the dispersity, the randomness and the uncertainty of fault rate distribution are considered, and the fault rate of power equipment can be accurately calculated.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The risk field concept is provided, the failure rate of each part of the equipment can be accurately described, and the damage of the environment to the normal operation of the equipment can be known. The method and the device consider that the failure rate of the device has randomness, and then calculate the failure rate of the device by using the cloud theory.
Referring to fig. 1, fig. 1 is a flowchart illustrating a risk field calculation method for an underground electric power distribution room according to an embodiment of the present invention; the method may further comprise:
s100, calculating the reference fault rate of the electric power equipment and the reference fault rate of the environment position, which take the temperature and the service time into consideration, according to historical statistical data of the service life of the electric power equipment in the underground power distribution room.
In this embodiment, the power equipment is not limited, and may include any equipment in the underground power distribution room. The subsequent power devices may be simply referred to as devices. The present embodiment also does not limit the age of the historical statistical data, and generally, the richer the historical data is, the more accurate the obtained reference failure rate is.
Specifically, the step is a power equipment fault rate calculation method taking temperature distribution and service time into consideration when calculating the power equipment reference fault rate and the environmental location reference fault rate. Referring to fig. 2, a spatial temperature field profile of an underground utility room is shown. It can be seen that the temperatures at various locations in the distribution room are different, and the spatially dispersed nature of the temperatures contributes to the spatially dispersed nature of the failure rate. The establishment of the risk field is thus related to the temperature distribution. In addition, the failure rate is also related to the time of use; namely, the failure rate lambda of the power equipment is related to the service time T and the temperature T, and according to the definition of the Weber distribution, the lambda calculation formula is as follows:
wherein beta is a shape parameter, eta is a scale parameter, and t is time. When β is 1, the instantaneous failure rate does not change with the use time t. Beta >1, beta <1 represents the rising and falling trend of the failure rate over time, respectively. The scale parameter η is independent of the shape parameter β and is a measure of the lifetime of the device. The relationship between the scale parameter η and the temperature T is as follows:
wherein T is temperature, T is time, L (T) is rated life length, TrefIs rated temperature, ηrefAre the respective nominal dimensional parameters, A, BIs an empirical constant, defined by IEEE C57.91-1995. Specifically, T (x, y, z) can be obtained by using a finite element analysis method to obtain the temperature distribution characteristics with space. The reference fault rate lambda of the power equipment can be obtainedb(t, x, y, z), wherein the power equipment reference failure rate λb(t, x, y, z) has a spatio-temporal distribution characteristic.
I.e. can use the formula
Calculating the reference failure rate lambda of the power equipmentb(t,x,y,z)；
Using formulas
Computing environmental position reference failure rate λbr(t, x, y, z); that is, the reference failure rate of the remaining spatial location (environment) of the distribution room can be calculated from this equation, excluding the electric devices.
Wherein β is a shape parameter, A and B are empirical constants, T is temperature, T is time, T is a position coordinate, and A is a position coordinater，BrThe empirical constant can be estimated by a maximum likelihood method according to relevant historical data, wherein T is temperature and is measured in DEG C, T is time, and (x, y, z) is position coordinates.
Specifically, the reference failure rate of each element of the transformer and the switchgear in the distribution room can be calculated by the following two equations:
wherein λ isbsAnd λbtiThe failure rates of the switches and transformers are given in the following table i ═ d, c, w represent the dielectric, core and winding respectively, μtiIndicating the fault proportion, mu, of each part of the transformertd+μtc+μ tw1. According to the fault research of the transformerThe failure of the tap switch and tap of the transformer is classified as a winding failure, mutd＝0.129，μtc0.142 and μtw0.729. The scale parameters and empirical constants in the above equation may be estimated from historical data.
The parameters β, a and B in the above formulas may also be obtained according to historical statistical data of the service life of the device, and the cumulative failure probability and failure rate per year may be calculated. And then can be estimated according to the maximum likelihood method.
And S110, calculating to obtain a standard deviation of the power equipment and a standard deviation of the environment position according to the sampling value of the reference fault rate of the power equipment and the sampling value of the reference fault rate of the environment position.
And S120, generating normally distributed power equipment fault rates by using a Weber-positive cloud generator according to the power equipment reference fault rates and the power equipment standard deviations, and generating normally distributed environment position fault rates by using the Weber-positive cloud generator according to the environment position reference fault rates and the environment position standard deviations.
Specifically, after the reference fault rate (including the reference fault rate of the power equipment and the reference fault rate of the environmental location) is obtained in the two steps, the standard deviation is calculated according to the sampling value of the reference fault rate, namely the standard deviation is calculated by using the sampling value with the reference fault rate as the mean value; a weber-normal cloud generator is used to generate a normally distributed failure rate. The failure rate has randomness and uncertainty, and for the sampling failure rate λ', a positive-probability distribution is satisfied: lambda' to N (lambda)b,Hλ 2). Hyper entropy HλThe standard deviation equal to the sampling of the reference fault rate can be calculated according to the fault rate of historical statistics, and then the reference fault rate is taken as the mean value according to a Weber-normal cloud generator, HλA failure rate with spatio-temporal distribution characteristics that produces a normal distribution for the standard deviation. The weber-normal cloud generator is a fault rate calculation method based on a cloud theory, the membership degree mu (x) of a number x to the concept C represents the probability that the number x can realize the concept C, and the mu (x) can be calculated according to the following formula:
the above formula is equivalently expressed as x to N (E)x,E’n 2). The failure rate of the equipment also meets the normal distribution of lambda' to N (lambda)b,Hλ 2)。
And S130, respectively calculating the fault probability of the fault rate of the power equipment and the fault rate of the environmental position, and generating a power equipment risk field and an environmental position risk field of the underground power distribution room.
Specifically, after the failure rate of each position in the distribution room is obtained through calculation, the failure probability can be further calculated, so that a risk field of the distribution room is formed. The power equipment risk field Fj(t, x, y, z) is specifically:
the environmental position risk field Fr1(t, x, y, z) is specifically:
wherein, λ'rIs the environmental location failure rate, where λ' is the power equipment failure rate and j represents the switch or transformer assembly. That is, j ═ s denotes a switch, and j ═ ti denotes a transformer assembly. Fj(t, x, y, z) represents the probability of a fault occurring where the power equipment is in use for time t and the switchgear or transformer primary is in a position (x, y, z). Removing electric equipment Fr1And (t, x, y, z) represents the probability of damage to the electric power equipment caused by the point with the service time t and the position (x, y, z) in the environment. I.e. the risk field in the electricity distribution room exhibits a spatio-temporal distribution characteristic. The value of the risk field on the equipment represents the failure rate of the part of the equipment, the value of the risk field in the space represents the probability that the surrounding environment causes danger to the equipment, and the influence of the environment on the equipment is reflected. The spatial distribution of the risk field is mainly a result of the spatial distribution of the temperature. Failure rate is notIs only affected by temperature and is also affected by service time. The risk is therefore long and time-distributed. After the risk field of the power distribution room is obtained, the accumulated fault probability of the power utilization equipment and the accumulated danger probability of the environmental temperature to the equipment can be calculated.
The risk field calculation method for the underground power distribution room mainly comprises the following three aspects: a device fault rate calculation method considering temperature distribution and service time; the randomness and the uncertainty of the fault rate are considered, and a fault rate calculation method based on a cloud theory is provided; in consideration of the dispersion of the failure rate distribution, a risk field establishment method with space-time distribution characteristics is provided. Referring to fig. 4, the spatial distribution of the failure rate (number of failures per unit time) on the cross-sectional view of the distribution room is shown. It can be seen that the failure rates of the transformer windings, core and insulation media and surrounding space vary. The method applies the concept of the field, and the distribution characteristics of the fault rate in the space are used for constructing the risk field, so that the fault analysis method is more accurate. When on the device, the risk field reflects the probability of failure of the device components. When in space, the risk field reflects the probability that the environmental conditions pose a hazard to the equipment.
Based on the technical scheme, the risk field with the space-time distribution characteristic is obtained through calculation by the risk field calculation method for the underground power distribution room, the temperature, the service time, the dispersity, the randomness and the uncertainty of the fault rate distribution are considered, and the fault rate of the power equipment can be accurately calculated.
In the following, the risk field calculation system for an underground power distribution room according to the embodiments of the present invention is described, and the risk field calculation system for an underground power distribution room described below and the risk field calculation method for an underground power distribution room described above may be referred to correspondingly.
Referring to fig. 5, fig. 5 is a block diagram of a risk field computing system of an underground power distribution room according to an embodiment of the present invention; the system may include:
the reference fault rate calculation module 100 is used for calculating a reference fault rate of the power equipment and a reference fault rate of an environment position, which take temperature and service time into consideration, according to historical statistical data of service life of the power equipment in the underground power distribution room;
the standard deviation calculation module 200 is configured to calculate a standard deviation of the power device and a standard deviation of the environmental location according to the sampling value of the reference fault rate of the power device and the sampling value of the reference fault rate of the environmental location, respectively;
the fault rate calculation module 300 is configured to generate a normally distributed power equipment fault rate by using a weber-positive cloud generator according to the power equipment reference fault rate and the power equipment standard deviation, and generate a normally distributed environment position fault rate by using the weber-positive cloud generator according to the environment position reference fault rate and the environment position standard deviation;
and the risk field calculation module 400 is used for calculating the failure probability of the failure rate of the electrical equipment and the failure rate of the environmental location respectively and generating an electrical equipment risk field and an environmental location risk field of an underground power distribution room.
Based on the above embodiments, the reference fault rate calculation module 300 may include:
a first calculation unit for using the formula
Calculating the reference failure rate lambda of the power equipmentb(t,x,y,z)；
Wherein beta is a shape parameter, A and B are empirical constants, T is temperature, T is time, and (x, y, z) are position coordinates.
Based on the above embodiments, the reference fault rate calculation module 300 may include:
a second calculation unit for using the formula
Computing environmental position reference failure rate λbr(t,x,y,z)；
Wherein A isr，BrIs an empirical constant, T is temperature, in deg.C, T is time, and (x, y, z) is position coordinates.
Based on the above embodiment, the electricityForce equipment risk field Fj(t, x, y, z) is specifically:
the environmental position risk field Fr1(t, x, y, z) is specifically:
where λ 'is the power equipment failure rate, j represents the switch or transformer assembly, λ'rIs the environmental location failure rate.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method and the system for calculating the risk field of the underground power distribution room provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (2)
1. A method of risk field calculation for an underground electrical distribution room, the method comprising:
calculating the reference fault rate of the power equipment and the reference fault rate of the environmental position, which take the temperature and the service time into consideration, according to historical statistical data of the service life of the power equipment in the underground power distribution room;
calculating to obtain a standard deviation of the power equipment and a standard deviation of the environment position according to the sampling value of the reference fault rate of the power equipment and the sampling value of the reference fault rate of the environment position respectively;
generating a normally distributed power equipment fault rate by using a weber-positive cloud generator according to the power equipment reference fault rate and the power equipment standard deviation, and generating a normally distributed environment position fault rate by using the weber-positive cloud generator according to the environment position reference fault rate and the environment position standard deviation;
respectively calculating the fault probability of the fault rate of the power equipment and the fault rate of the environmental position, and generating a power equipment risk field and an environmental position risk field of an underground power distribution room;
wherein the calculating the power equipment reference fault rate considering the temperature and the service time comprises:
using formulas
Computing electricityReference failure rate lambda of force equipmentb(t,x,y,z)；
Wherein beta is a shape parameter, A and B are empirical constants, T is temperature, the unit is temperature, T is time, and (x, y, z) are position coordinates;
the calculating of the environmental position reference fault rate considering temperature and service time comprises the following steps:
using formulas
Computing environmental position reference failure rate λbr(t,x,y,z)；
Wherein A isr，BrIs an empirical constant, T is temperature, in units of deg.C, T is time, and (x, y, z) is position coordinates;
the power equipment risk field Fj(t, x, y, z) is specifically:
wherein λ' is the power equipment failure rate, j represents a switch or transformer assembly;
the environmental position risk field Fr1(t, x, y, z) is specifically:
wherein, λ'rIs the environmental location failure rate.
2. A risk field computing system for an underground electrical distribution room, the system comprising:
the reference fault rate calculation module is used for calculating the reference fault rate of the electric power equipment and the reference fault rate of the environmental position, which take the temperature and the service time into consideration, according to historical statistical data of the service life of the electric power equipment in the underground power distribution room;
the standard deviation calculation module is used for calculating to obtain a standard deviation of the power equipment and a standard deviation of the environment position according to the sampling value of the reference fault rate of the power equipment and the sampling value of the reference fault rate of the environment position;
the fault rate calculation module is used for generating normally distributed power equipment fault rates by using a Weber-Positive cloud generator according to the power equipment reference fault rate and the power equipment standard deviation, and generating normally distributed environment position fault rates by using the Weber-Positive cloud generator according to the environment position reference fault rates and the environment position standard deviation;
the risk field calculation module is used for calculating the fault probability of the power equipment fault rate and the environmental position fault rate respectively and generating a power equipment risk field and an environmental position risk field of an underground power distribution room;
the reference fault rate calculation module includes:
a first calculation unit for using the formula
Calculating the reference failure rate lambda of the power equipmentb(t,x,y,z)；
Wherein beta is a shape parameter, A and B are empirical constants, T is temperature, the unit is temperature, T is time, and (x, y, z) are position coordinates;
the reference fault rate calculation module includes:
a second calculation unit for using the formula
Computing environmental position reference failure rate λbr(t,x,y,z)；
Wherein A isr，BrIs an empirical constant, T is temperature, in units of deg.C, T is time, and (x, y, z) is position coordinates;
the power equipment risk field Fj(t, x, y, z) is specifically:
the environmental position risk field Fr1(t, x, y, z) is specifically:
where λ 'is the power equipment failure rate, j represents the switch or transformer assembly, λ'rIs the environmental location failure rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710432790.4A CN107274088B (en) | 2017-06-09 | 2017-06-09 | Risk field calculation method and system for underground power distribution room |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710432790.4A CN107274088B (en) | 2017-06-09 | 2017-06-09 | Risk field calculation method and system for underground power distribution room |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107274088A CN107274088A (en) | 2017-10-20 |
| CN107274088B true CN107274088B (en) | 2020-10-02 |
Family
ID=60066005
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710432790.4A Active CN107274088B (en) | 2017-06-09 | 2017-06-09 | Risk field calculation method and system for underground power distribution room |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107274088B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110398651B (en) * | 2019-08-07 | 2022-05-10 | 广东科鉴检测工程技术有限公司 | Reliability test method for instrument electronic control system |
| CN111274409B (en) * | 2020-01-20 | 2024-02-27 | 山东大学 | Knowledge graph-based engine valve mechanism assembly process control method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102854461A (en) * | 2012-08-24 | 2013-01-02 | 中国电力科学研究院 | Probability forecasting method and system of switch equipment faults |
| CN102945316A (en) * | 2012-10-25 | 2013-02-27 | 华北电力大学 | Failure rate calculation method for relay protection device in consideration of covariates |
| CN104573361A (en) * | 2015-01-04 | 2015-04-29 | 深圳供电局有限公司 | GIS (Gas Insulated Switchgear) evaluation method and device |
| CN106295963A (en) * | 2016-07-29 | 2017-01-04 | 国网江苏省电力公司镇江供电公司 | Reliability assessment method for secondary system of intelligent substation based on the physics of failure |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120078678A1 (en) * | 2010-09-23 | 2012-03-29 | Infosys Technologies Limited | Method and system for estimation and analysis of operational parameters in workflow processes |
-
2017
- 2017-06-09 CN CN201710432790.4A patent/CN107274088B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102854461A (en) * | 2012-08-24 | 2013-01-02 | 中国电力科学研究院 | Probability forecasting method and system of switch equipment faults |
| CN102945316A (en) * | 2012-10-25 | 2013-02-27 | 华北电力大学 | Failure rate calculation method for relay protection device in consideration of covariates |
| CN104573361A (en) * | 2015-01-04 | 2015-04-29 | 深圳供电局有限公司 | GIS (Gas Insulated Switchgear) evaluation method and device |
| CN106295963A (en) * | 2016-07-29 | 2017-01-04 | 国网江苏省电力公司镇江供电公司 | Reliability assessment method for secondary system of intelligent substation based on the physics of failure |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107274088A (en) | 2017-10-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Pukhrem et al. | Probabilistic risk assessment of power quality variations and events under temporal and spatial characteristic of increased PV integration in low-voltage distribution networks | |
| Gray et al. | Power quality assessment in distribution systems embedded with plug-in hybrid and battery electric vehicles | |
| Torquato et al. | A Monte Carlo simulation platform for studying low voltage residential networks | |
| US20160043548A1 (en) | Rolling stochastic optimization based operation of distributed energy systems with energy storage systems and renewable energy resources | |
| Koksal et al. | Improved transformer maintenance plan for reliability centred asset management of power transmission system | |
| Jain et al. | Quasi-static time-series PV hosting capacity methodology and metrics | |
| Qureshi et al. | A fast scalable quasi-static time series analysis method for PV impact studies using linear sensitivity model | |
| CN105866526B (en) | A kind of monitoring method and system fluctuating diagnosis electrical equipment exception using power consumption | |
| Park et al. | Voltage quality assessment considering low voltage ride‐through requirement for wind turbines | |
| Guo et al. | Toward efficient cascading outage simulation and probability analysis in power systems | |
| Urquhart et al. | Impacts of demand data time resolution on estimates of distribution system energy losses | |
| CN109462225B (en) | Insulation matching method and system for series compensation device | |
| Stahlhut et al. | A preliminary assessment of the impact of ambient temperature rise on distribution transformer loss of life | |
| Derafshian Maram et al. | Event‐based remedial action scheme against super‐component contingencies to avert frequency and voltage instabilities | |
| CN107274088B (en) | Risk field calculation method and system for underground power distribution room | |
| Aljohani et al. | Matlab code to assess the reliability of the smart power distribution system using monte carlo simulation | |
| Lu et al. | Smart load management of distribution‐class toroidal transformers using a dynamic thermal model | |
| Patsalides et al. | Simplified distribution grid model for power quality studies in the presence of photovoltaic generators | |
| Ouyang et al. | Test and analysis on sensitivity of low‐voltage releases to voltage sags | |
| Wawrzyniak et al. | Methodology of risk assessment and decomposition in power grid applications | |
| Atkinson et al. | Leveraging advanced metering infrastructure for distribution grid asset management | |
| WO2015042793A1 (en) | Method for determining dynamic overload curve of transformer based on operating data | |
| CN107767060B (en) | Theoretical line loss calculation system and method for distribution network line | |
| CN115579845A (en) | Relay protection fixed value analysis and verification method based on big data analysis | |
| Safitri et al. | Monte Carlo-based stochastic analysis results for coordination of single-phase rooftop PVs in low voltage residential networks |
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 | ||
| CB02 | Change of applicant information |
Address after: 311199 No. 165, Star Bridge Road, Star Bridge Street, Yuhang District, Hangzhou, Zhejiang Applicant after: ZHEJIANG TRULY ELECTRIC Co.,Ltd. Applicant after: STATE GRID ZHEJIANG HANGZHOU YUHANG POWER SUPPLY Co. Address before: 311199 No. 165, Star Bridge Road, Star Bridge Street, Yuhang District, Hangzhou, Zhejiang Applicant before: ZHEJIANG TRULY ELECTRIC Co.,Ltd. Applicant before: STATE GRID ZHEJIANG HANGZHOU YUHANG POWER SUPPLY Co. |
|
| CB02 | Change of applicant information | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20180530 Address after: 310018 No. 11 street, Hangzhou economic and Technological Development Zone, Zhejiang 91 Applicant after: HANGZHOU ELECTRIC EQUIPMENT MANUFACTURING Co.,Ltd. Applicant after: STATE GRID ZHEJIANG HANGZHOU YUHANG POWER SUPPLY Co. Address before: 311199 No. 165, Star Bridge Road, Star Bridge Street, Yuhang District, Hangzhou, Zhejiang Applicant before: ZHEJIANG TRULY ELECTRIC Co.,Ltd. Applicant before: STATE GRID ZHEJIANG HANGZHOU YUHANG POWER SUPPLY Co. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |