CN113239623B - Fault positioning method suitable for electric power equipment - Google Patents
Fault positioning method suitable for electric power equipment Download PDFInfo
- Publication number
- CN113239623B CN113239623B CN202110539549.8A CN202110539549A CN113239623B CN 113239623 B CN113239623 B CN 113239623B CN 202110539549 A CN202110539549 A CN 202110539549A CN 113239623 B CN113239623 B CN 113239623B
- Authority
- CN
- China
- Prior art keywords
- transformer
- inversion
- power equipment
- field
- temperature
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Biology (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computing Systems (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a fault positioning method suitable for electric power equipment, which comprises the steps of constructing a forward calculation model based on a multi-physical field simulation strategy, and performing forward simulation calculation on each single physical field in the electric power equipment through the forward calculation model; performing inversion calculation according to the observable data of the transformer acquired by the monitoring system to obtain the loss distribution in the transformer; constructing an electric power equipment inversion model according to the forward calculation model and the loss distribution; inputting the surface temperature of the transformer into an inversion model of the electric power equipment, and performing multi-physical-field multi-parameter inversion optimization on the multi-point temperature of each component in the transformer by adopting a genetic algorithm to obtain the parameter distribution condition of the temperature field in the transformer; fault positioning is carried out on the electric equipment according to the parameter distribution condition of the temperature field in the transformer; the method and the device can predict the fault abnormality of the electric equipment timely and accurately, and provide important basis for operation and maintenance personnel to handle the abnormal phenomenon.
Description
Technical Field
The invention relates to the technical field, in particular to a fault positioning method suitable for electric equipment.
Background
When the power equipment runs, equipment heating faults caused by insulation aging, poor contact and other various reasons, such as heating of a common isolating switch, heating of resistance loss, heating of a wire clamp and the like, bring great hidden dangers to the running safety of a power grid, and cause equipment to quit running or even fire disasters in serious conditions. Therefore, fault detection of electric power equipment is one of important issues in electric power equipment safety research.
Traditional power equipment fault detection adopts the mode that has a power failure to overhaul, and is laborious in time-consuming, along with infrared thermal imager's introduction, thermal fault detection mode now generally adopts infrared thermal imager to detect power equipment. Compared with the traditional method, the thermal infrared imager can realize long distance, no sampling, no contact and no power failure, and has the characteristics of accuracy, rapidness, intuition and the like. Nowadays, fixed infrared thermal imaging probes are popularized in various transformer substations and other places, but still workers need to manually judge faults at a data receiving end, so that the accuracy and efficiency of whole intelligent diagnosis are seriously reduced.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a fault positioning method suitable for electric power equipment, and can solve the problems of inaccurate fault positioning and high detection difficulty of the electric power equipment in the prior art.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of constructing a forward calculation model based on a multi-physics field simulation strategy, and performing forward simulation calculation on each single physics field in the electric equipment through the forward calculation model; performing inversion calculation according to the transformer observable data acquired by the monitoring system to obtain loss distribution inside the transformer; constructing an electric power equipment inversion model according to the forward calculation model and the loss distribution; inputting the surface temperature of the transformer into the power equipment inversion model, and performing multi-physical-field multi-parameter inversion optimization on the multi-point temperature of each component in the transformer by adopting a genetic algorithm to obtain the parameter distribution condition of the temperature field in the transformer; and carrying out fault location on the electric equipment according to the temperature field parameter distribution condition in the transformer.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: the forward computing model comprises the following steps:
wherein t is the fluid surface temperature, ρ is the fluid density, V is the fluid velocity, and k is the unit vector of the external normal of the infinitesimal area vector dS; f is the mass force to which the unit mass of fluid is subjected; pi is the stress tensor of the infinitesimal area vector dS; c is the specific heat capacity of the fluid; t is the temperature of the control body; lambda is the coefficient of thermal conductivity; q is the heat generation quantity of corresponding infinitesimal; ^ is a harmonic operator; x, y, z represent three axes of a spatial coordinate system; u, V, w are the velocity components of the fluid velocity V in the x, y, z directions, respectively.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: the density of the fluid may include,
wherein g is the thermal conductivity of the fluid.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: the transformer observable data includes voltage, current, and load information.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: the inversion calculation includes performing an inversion calculation by:
where H is the magnetic field strength, J is the current density, t is the time, D is the electric flux density, E is the electric field strength, B is the magnetic flux density, and σ is the charge density.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: further comprising constraining the magnetic field boundary values and the field normal inverse by boundary conditions:
where Φ is the distribution of the boundary region potentials, m is the external normal vector at the boundary, and Γ 1 and Γ 2 are the noremann boundaries.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: constructing the power equipment inversion model includes,
wherein f (x) is an objective function, c p The constant-voltage heat capacity of air, T is the temperature of the transformer, T is the time, x is the node temperature, and P is the thermal power of the transformer.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: the multi-physics field multi-parameter inversion optimization comprises the steps of randomly generating N individuals in a feasible domain of a variable x to obtain a population size N; setting a visual field, a step length, a congestion factor delta and the number of attempts of x; calculating a population adaptation value, and assigning the maximum adaptation value to a bulletin board; updating x through foraging behavior, clustering behavior and rear-end collision behavior to generate a new population; if a certain individual is better than the bulletin board, updating the bulletin board to the individual; when the optimal solution on the bulletin board reaches the satisfied error limit, the optimization is finished; otherwise, continuing to update x.
As a preferable aspect of the fault location method applicable to electric power equipment according to the present invention, wherein: the updating x comprises that the foraging behavior: let the current state of the node temperature X be X i Randomly selecting a state X within said visual field j If f (X) i )＞f(X j ) Then, go one step forward in the direction; otherwise, re-randomly selecting state X j Judging whether a forward condition is met; if the judgment times reach the trial times and do not meet the advancing condition, randomly moving for one step; the clustering behavior is: setting the current state of the node temperature X as X i Exploring the number of buddies x of x within the visual domain f And a central position X c If the target function f (X) of the center position c ) Big (a)Value of objective function f (X) at current position i ) Then towards the central position X c The direction is advanced one step; otherwise, executing the foraging behavior; the rear-end collision behavior is as follows: exploring the number of buddies x within the visual domain f And the maximum value of the objective function f (X) j ) If the maximum value of the objective function f (X) j ) An objective function value f (X) greater than the current position i ) Then, the step is further proceeded towards the central position direction of the partner; otherwise, foraging is performed.
The invention has the beneficial effects that: according to the method, a model which is high in prediction accuracy and suitable for time-lag characteristic prediction is constructed according to historical operating data and internal temperature field inversion data of the electric equipment, the fault abnormality of the electric equipment can be predicted in time, an important basis is provided for operation and maintenance personnel to handle the abnormality, and precious time is created for the advance handling of the fault.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a fault location method for electric power equipment according to a first embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures of the present invention are described in detail below, and it is apparent that the described embodiments are a part, not all or all of the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Also in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a fault location method for electric power equipment, including:
s1: and constructing a forward calculation model based on a multi-physical field simulation strategy, and performing forward simulation calculation on each single physical field in the electric power equipment through the forward calculation model.
When temperature field analysis is carried out, the influence of various factors such as fluid, solid, heat transfer and the like on temperature needs to be considered at the same time, a forward calculation model of the temperature field in the transformer is established on the basis of a multi-physics field simulation strategy and meeting the mass conservation law, the momentum conservation law and the energy conservation law:
the fluid density ρ is:
wherein g is the thermal conductivity of the fluid, t is the surface temperature of the fluid, ρ is the density of the fluid, V is the velocity of the fluid, and k is the unit vector of the external normal of the infinitesimal area vector dS; f is the mass force to which the unit mass of fluid is subjected; pi is the stress tensor of the infinitesimal area vector dS; c is the specific heat capacity of the fluid; t is the temperature of the control body; λ is the coefficient of thermal conductivity; q is the heat generation quantity of corresponding infinitesimal; ^ is a harmonic operator; x, y, z represent three axes of a spatial coordinate system; u, V, w are the velocity components of the fluid velocity V in the x, y, z directions, respectively.
S2: and performing inversion calculation according to the observable data of the transformer acquired by the monitoring system to obtain the loss distribution in the transformer.
Acquiring voltage, current and load information of the transformer through a monitoring system, and performing inversion calculation through a Maxwell equation set:
where H is the magnetic field strength, J is the current density, t is the time, D is the electric flux density, E is the electric field strength, B is the magnetic flux density, and σ is the charge density.
Meanwhile, setting a homogeneous boundary condition constraint magnetic field boundary value and a field normal reciprocal according to Dirichlet boundary conditions (Dirichlet) and Noumann boundary conditions (Neumann);
dirichlet boundary conditions:
Φ| Γ ＝g(Γ)
noelman boundary conditions:
homogeneous boundary conditions:
where Φ is the distribution of the boundary region potentials, m is the external normal vector at the boundary, and Γ 1 and Γ 2 are the norhman boundaries.
S3: and constructing an electric power equipment inversion model according to the forward calculation model and the loss distribution.
In order to ensure the accuracy of inversion calculation, loss distribution in the transformer is led into a forward calculation model of a temperature field in the transformer, and a target function and constraint conditions are established to obtain an electric power equipment inversion model.
The objective function and constraint conditions are as follows:
wherein f (x) is an objective function, c p The constant-voltage heat capacity of air, T is the temperature of the transformer, T is the time, x is the node temperature, and P is the thermal power of the transformer.
S4: inputting the surface temperature of the transformer into an inversion model of the power equipment, and performing multi-physical-field multi-parameter inversion optimization on the multi-point temperature of each component in the transformer by adopting a genetic algorithm to obtain the parameter distribution condition of the temperature field in the transformer; and carrying out fault location on the electric equipment according to the temperature field parameter distribution condition in the transformer.
The multi-physics field multi-parameter inversion optimizing method comprises the following steps:
(1) Randomly generating N individuals in the feasible region of the variable x to obtain a population size N; wherein, the feasible fields of the variable x are: x is more than or equal to 0.
(2) Setting a visual field, a step length, a congestion factor delta and the number of attempts of x;
the specific parameter settings are shown in table 1.
| Parameter(s) | Visual field | Step size | Congestion factor | Number of trials | Number of individuals |
| Value taking | [0，1] | 0.1 | 0.618 | 100 | 200 |
(3) Calculating a population adaptive value, and assigning the maximum adaptive value to a bulletin board;
fitness is calculated by the following formula:
further, fit (x) max The bulletin board is given, wherein it should be noted that the bulletin board is where the optimal value of f (x) is recorded.
(4) Updating x through foraging behavior, clustering behavior and rear-end collision behavior to generate a new population;
(1) foraging behavior: setting the current state of the node temperature X as X i Randomly selecting a state X in the visual field j If f (X) i )＞f(X j ) Then advance one unit step to the direction; otherwise, re-randomly selecting state X j Judging whether or not a forward condition f (X) is satisfied i )＞f(X j ) (ii) a If the judgment times reach 100 times and the advancing condition is not met, randomly moving one unit step length;
(2) clustering behavior: setting the current state of the node temperature X as X i Exploring the number of partners x in the visual domain f And a central position X c If the target function f (X) of the center position c ) Value f (X) of the objective function greater than the current position i ) I.e. by
Towards the central position X c The direction is advanced by one unit step; otherwise, executing foraging behavior;
(3) and (3) rear-end collision behavior: let the current state of the node temperature X be X i Exploring the number of buddies x in the visual domain f And the maximum value of the objective function f (X) j ) If the maximum value of the objective function f (X) j ) Value f (X) of the objective function greater than the current position i ) I.e. by
Proceeding one step towards the central position of the partner; otherwise executeForaging behavior.
(5) If a certain individual is superior to the bulletin board, updating the bulletin board to the individual;
after each individual executes one iteration, the current state of the individual is compared with the state recorded in the bulletin board, if the current state is superior to the state in the bulletin board, the current state is updated by the state of the individual, and otherwise, the state of the bulletin board is unchanged.
(6) When the optimal solution on the bulletin board reaches a satisfactory error limit, the optimization is finished; otherwise, returning to the step (3).
Setting the error to 0.01; and when the whole optimizing iteration is finished, outputting the value of the bulletin board at the moment, namely obtaining the optimal solution of the objective function.
Example 2
In order to verify and explain the technical effects adopted in the method, the embodiment selects the traditional power equipment fault positioning method and adopts the method to perform comparison test, and compares the test results by means of scientific demonstration to verify the real effect of the method.
The traditional power equipment fault positioning method is used for simply carrying out threshold segmentation or watershed algorithm segmentation on the infrared thermal image of the power equipment to obtain a heating part, the heating part obtained by segmentation by the method usually comprises a plurality of non-interested region images or loses a plurality of interested region images, the accuracy of the subsequent heating part identification is seriously influenced, and the accuracy and the efficiency of the whole intelligent diagnosis are seriously reduced.
In order to verify that the method has higher fault location accuracy compared with the conventional power equipment fault location method, in this embodiment, the conventional power equipment fault location method and the method are respectively used for fault location of the capacitor box of a certain substation.
Respectively acquiring infrared thermographs of capacitor cabinets CC1, CC2 and CCC3 by adopting a traditional power equipment fault positioning method, segmenting heating parts through a threshold value, positioning the faults of the capacitor cabinets according to the heating parts, and counting the fault positioning accuracy; the surface temperatures of the capacitor cabinets CC1, CC2 and CCC3 are respectively input into the power equipment inversion model of the method, and a fault positioning result is obtained, wherein the specific data are as shown in the following table.
Table 2: and comparing the fault positioning results of the capacitor cabinets.
As can be seen from the above table, compared with the conventional power equipment fault location method, the fault location accuracy is higher.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (6)
1. A fault location method suitable for electric equipment is characterized by comprising the following steps: comprises the steps of (a) preparing a substrate,
constructing a forward modeling calculation model based on a multi-physical field simulation strategy, and performing forward modeling simulation calculation on each single physical field in the electric power equipment through the forward modeling calculation model;
performing inversion calculation according to the observable data of the transformer acquired by the monitoring system to obtain the loss distribution in the transformer;
constructing an electric power equipment inversion model according to the forward calculation model and the loss distribution;
inputting the surface temperature of the transformer into the power equipment inversion model, and performing multi-physical-field multi-parameter inversion optimization on the multi-point temperature of each component in the transformer by adopting a genetic algorithm to obtain the parameter distribution condition of the temperature field in the transformer;
carrying out fault location on the electric equipment according to the temperature field parameter distribution condition in the transformer;
the forward computing model may include a forward computing model,
the forward calculation model is as follows:
wherein t is the fluid surface temperature, ρ is the fluid density, V is the fluid velocity, and k is the unit vector of the external normal of the infinitesimal area vector dS; f is the mass force to which the unit mass of fluid is subjected; pi is the stress tensor of the infinitesimal area vector dS; c is the specific heat capacity of the fluid; t is the temperature of the control body; λ is the coefficient of thermal conductivity; q is the heat generation quantity of corresponding infinitesimal; ^ is a harmonic operator; x, y, z represent three axes of a spatial coordinate system; u, V, w are the velocity components of the fluid velocity V in the x, y, z directions, respectively;
the inversion calculation includes the calculation of the inverse of the model,
the inversion calculation is performed by:
wherein H is magnetic field strength, J is current density, t is time, D is electric flux density, E is electric field strength, B is magnetic flux density, and sigma is charge density;
constructing the power plant inversion model includes,
s.t.x≥0
wherein f (x) is an objective function, c p The constant-voltage heat capacity of air, T is the temperature of the transformer, T is time, x is the node temperature, and P is the thermal power of the transformer.
2. The method of fault location for electrical equipment of claim 1, wherein: the density of the fluid may include,
wherein g is the thermal conductivity of the fluid.
3. A fault location method applicable to electric power equipment according to claim 1 or 2, characterized in that: the transformer observable data includes voltage, current, and load information.
4. A fault location method suitable for use in electrical equipment according to claim 3, wherein: also comprises the following steps of (1) preparing,
constraining the magnetic field domain boundary value and the field domain normal reciprocal by boundary conditions:
where Φ is the distribution of the boundary region potentials, m is the external normal vector at the boundary, and Γ 1 and Γ 2 are the norhman boundaries.
5. The method of fault location for electrical equipment according to claim 4, wherein: the multi-physics multi-parameter inversion optimization comprises,
randomly generating N individuals in the feasible region of the variable x to obtain a population size N;
setting a visual field, a step length, a congestion factor delta and the number of attempts of x;
calculating a population adaptation value, and assigning the maximum adaptation value to a bulletin board;
updating x through foraging behavior, clustering behavior and rear-end collision behavior to generate a new population;
if a certain individual is better than the bulletin board, updating the bulletin board to the individual;
when the optimal solution on the bulletin board reaches the satisfied error limit, the optimization is finished; otherwise, continuing to update x.
6. The fault location method for electrical equipment according to claim 5, wherein: the update x comprises the information of the number of the bits,
the foraging behavior is as follows: setting the current state of the node temperature X as X i Randomly selecting a state X within said visual field j If f (X) i )>f(X j ) Then, go one step forward in the direction; otherwise, re-randomly selecting state X j Judging whether a forward condition is met; if the judgment times reach the trial times and do not meet the advancing condition, randomly moving for one step;
the clustering behavior is: setting the current state of the node temperature X as X i Exploring the number of buddies x of x within the visual domain f And a central position X c If the target function f (X) of the center position c ) A value of the objective function f (X) greater than the current position i ) Then towards the central position X c The direction is advanced by one step; otherwise, executing the foraging behavior;
the rear-end collision behavior is as follows: exploring the number of buddies x within the visual domain f And the maximum value of the objective function f (X) j ) If said maximum value of the objective function f (X) j ) An objective function value f (X) greater than the current position i ) Then, the step is further proceeded towards the central position direction of the partner; otherwise, foraging is performed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110539549.8A CN113239623B (en) | 2021-05-17 | 2021-05-17 | Fault positioning method suitable for electric power equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110539549.8A CN113239623B (en) | 2021-05-17 | 2021-05-17 | Fault positioning method suitable for electric power equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113239623A CN113239623A (en) | 2021-08-10 |
| CN113239623B true CN113239623B (en) | 2022-11-18 |
Family
ID=77134891
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110539549.8A Active CN113239623B (en) | 2021-05-17 | 2021-05-17 | Fault positioning method suitable for electric power equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113239623B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114279573A (en) * | 2021-12-14 | 2022-04-05 | 国网电力科学研究院武汉南瑞有限责任公司 | Internal fault inversion detection method based on surface temperature rise of lightning arrester |
| CN116413541A (en) * | 2023-03-28 | 2023-07-11 | 国网山东省电力公司德州供电公司 | Transformer state sensing method based on digital twin and closed loop optimization |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107273687A (en) * | 2017-06-16 | 2017-10-20 | 浙江大学 | KbAFSA supercritical water oxidation kinetic parameters method of estimation |
| CN110687397A (en) * | 2019-09-30 | 2020-01-14 | 上海电力大学 | Active power distribution network fault positioning method based on improved artificial fish swarm algorithm |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106549394B (en) * | 2016-12-07 | 2018-11-27 | 东北大学 | Electric power idle work optimization system and method based on double fish-swarm algorithms |
| CN112528546B (en) * | 2020-12-29 | 2022-02-15 | 中国地质大学（武汉） | Gravity and magnetic data three-dimensional forward and backward modeling method for unstructured grid |
-
2021
- 2021-05-17 CN CN202110539549.8A patent/CN113239623B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107273687A (en) * | 2017-06-16 | 2017-10-20 | 浙江大学 | KbAFSA supercritical water oxidation kinetic parameters method of estimation |
| CN110687397A (en) * | 2019-09-30 | 2020-01-14 | 上海电力大学 | Active power distribution network fault positioning method based on improved artificial fish swarm algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113239623A (en) | 2021-08-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113239623B (en) | Fault positioning method suitable for electric power equipment | |
| CN112149873B (en) | Low-voltage station line loss reasonable interval prediction method based on deep learning | |
| CN113204922B (en) | Transformer running state monitoring and predicting method | |
| CN107526872B (en) | Method for calculating thermal stress and deformation of 500kV ultrahigh-voltage cable | |
| CN112180204A (en) | Power grid line fault diagnosis method based on electric quantity information | |
| CN115659799B (en) | Lithium battery energy storage power station fault diagnosis method with threshold self-adaption function | |
| CN108918111A (en) | Breaker mechanical method for diagnosing faults based on the classification of k- neighbour's Bayes's multi-tag | |
| CN114091231A (en) | Switch cabinet modeling method based on digital twin and error adaptive optimization | |
| CN109142991A (en) | A kind of infrared survey zero-temperature coefficient threshold determination method of porcelain insulator based on Burr distribution | |
| CN108181891A (en) | Industrial big data method for diagnosing faults based on intelligent core pivot element analysis | |
| CN114910756A (en) | Insulation performance evaluation method and system for low-voltage bus duct | |
| CN114156916B (en) | Superconducting energy storage system control method and device based on magnet state prediction | |
| CN114186437A (en) | Multi-physical-field coupling degradation model order reduction method for power system reliability simulation analysis | |
| CN116485049B (en) | Electric energy metering error prediction and optimization system based on artificial intelligence | |
| CN113378375A (en) | Fault prediction method, device, equipment and medium for transformer insulation structure | |
| CN115640732B (en) | Power distribution network arc fault positioning method based on magnetic field distribution | |
| CN116400266A (en) | Transformer fault detection method, device and medium based on digital twin model | |
| CN107766660B (en) | Method for rapidly acquiring steady-state temperature rise of single tunnel cable | |
| Zhang et al. | Classification of building electrical system faults based on Probabilistic Neural Networks | |
| Luo et al. | A method for hot spot temperature monitoring of oil-immersed transformers combining physical simulation and intelligent neural network | |
| CN112507577B (en) | GIS breaker contact steady-state temperature calculation method and system based on pattern recognition | |
| Bai et al. | Abnormal Detection Scheme of Substation Equipment based on Intelligent Fusion Terminal | |
| Selzer et al. | Physics-guided machine learning techniques for improving temperature calculations of high-voltage transmission lines | |
| Ji et al. | Research on Fault Identification Method of Dry-type Transformers Based on Support Vector Machine | |
| CN117374977B (en) | Load prediction and risk analysis method for energy storage system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |