CN116799741B - Precision equipment short-circuit monitoring protection method and system based on slope detection - Google Patents
Precision equipment short-circuit monitoring protection method and system based on slope detection Download PDFInfo
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Abstract
The invention discloses a short-circuit monitoring protection method and a short-circuit monitoring protection system for precision equipment based on slope detection, wherein the method comprises the following steps: data acquisition, algorithm optimization, short circuit monitoring model establishment, protection latching and visual display. The invention relates to the technical field of circuits, in particular to a short-circuit monitoring protection method and system for precision equipment based on slope detection.
Description
Technical Field
The invention relates to the technical field of circuits, in particular to a short-circuit monitoring protection method and system for precision equipment based on slope detection.
Background
In the research and development process of university and large-scale company hardware, high-precision experimental equipment including high-precision machining equipment, signal analysis processing equipment, high-precision sensor equipment and the like is often used. In the working process of the precision equipment, short-circuit faults can be inevitably generated, and the traditional short-circuit protection method is used for short-circuit protection when the absolute value of current exceeds a preset value, so that the problems of low cutting-off speed and low efficiency exist; the general short circuit monitoring model has the problem of low test accuracy due to over fitting or under fitting caused by improper parameters; meanwhile, the problem that the short circuit monitoring model repeatedly and mistakenly triggers the short circuit signal exists.
Disclosure of Invention
Aiming at the problems that the current absolute value exceeds a preset value and then short-circuit protection is carried out, the cutting-off speed is low and the efficiency is low in the traditional short-circuit protection method, the monitoring protection method based on the slope detection is adopted, and when real-time operation data are identified as suspected short-circuit data by a model, the cutting-off protection is carried out immediately, so that the cutting-off efficiency is high and the speed is high; aiming at the problem that the test accuracy is low due to the fact that the general short circuit monitoring model is excessively fitted or underfitted due to improper parameters, the scheme adopts algorithm optimization for modeling, so that the problem that the parameters used for modeling are excessively fitted or underfitted is avoided, and the test accuracy is improved; aiming at the problem that the short circuit monitoring model repeatedly and mistakenly triggers the short circuit signal, the scheme adopts a protection latch mechanism to avoid repeated and mistaken triggering.
The technical scheme adopted by the invention is as follows: the invention provides a precision equipment short-circuit monitoring protection method based on slope detection, which comprises the following steps:
step S1: collecting data;
step S2: optimizing an algorithm;
step S3: establishing a short circuit monitoring model;
step S4: protecting and latching;
step S5: and (5) visual display.
Further, in step S1, the data acquisition includes acquiring historical operation data and corresponding tags and real-time operation data, and the acquired real-time operation data is a voltage differenceAnd a current, said voltage difference->Correspond to->Difference in current in time ∈ ->Said voltage difference->I.e. the current slope, the corresponding tag comprises normal data and suspected short circuit data.
Further, in step S2, the algorithm optimization includes step S21, step S22, step S23, step S24, step S25, step S26, step S27, and step S28;
step S21: data classification, namely taking the historical operation data acquired in the step S1 and the corresponding labels as sample data, taking 70% of the sample data as a training data set and the other 30% of the sample data as a test data set, and presetting a range of kernel parameters sigma, a range of penalty factors C, a test threshold, the number n of candidate solutions and the maximum iteration number max;
step S22: initializing a location, randomly generatingInitial position as n candidate solutions, wherein +.>Is the initial position of the 1 st candidate solution, +.>Is the initial position of the 2 nd candidate solution, +.>，/>Is the initial position of the nth candidate solution; />Is the response within the penalty factor CNumber of machines and/or items>Is a random number within the kernel parameter sigma range;
step S23: calculating an fitness function value, using python to import a sklearn library, respectively taking the position coordinates of the candidate solutions as parameters, calling an SVM function to establish a quasi-SVM model by using a training data set, taking the recognition accuracy of the quasi-SVM model to the test data set as the fitness function value of the candidate solutions, and taking the candidate solution with the largest fitness function value as a target solution;
step S24: detecting whether the fitness function value of the target solution is not lower than a test threshold value or not or whether the maximum iteration number is reached, if the fitness function value of the target solution is not lower than the test threshold value, outputting the position of the target solution and ending; if the maximum iteration number is reached and the fitness function value of the target solution is lower than the test threshold, the step S22 is performed; otherwise go to step S25;
step S25: the vector distance between the candidate solution and the target solution is calculated by the following formula:
，
where D is the vector distance of the candidate solution from the target solution, C is the wobble factor, C is a random number in the range of 0 to 2,is the position of the target solution in the t-th iteration,/->Is the position of the candidate solution in the t-th iteration;
step S26: the convergence factor is calculated using the formula:
，
wherein a is a convergence factor, and t is the current iteration number;
step S27: the location of the candidate solution is updated using the following formula:
，
in the method, in the process of the invention,is the position of the candidate solution after being updated in the t-th iteration, and r is a random number in the range of 0 to 1;
step S28: go to step S23.
Further, in step S3, the short-circuit monitoring model is built by using python to import a sklearn library, using the position of the target solution as a parameter, calling an SVM function to build the short-circuit monitoring model with a training data set, the short-circuit monitoring model identifies real-time operation data and outputs a data type, the data type includes normal data and suspected short-circuit data, and when the short-circuit monitoring model outputs the suspected short-circuit data, the hardware responds, and then the semiconductor device closes a circuit, thereby protecting precision equipment.
Further, in step S4, the protection latch is triggered when the short circuit monitoring model outputs suspected short circuit data, and the circuit keeps the off state until the protection latch is actively closed, so as to avoid repeated false triggering.
Further, in step S5, the visual display is to visually display the recognition result of the short-circuit monitoring model on the real-time operation data along with the time sequence, so as to facilitate manual supervision.
The invention provides a precision equipment short-circuit monitoring protection system based on slope detection, which comprises a data acquisition module, an algorithm optimization module, a short-circuit monitoring model module, a protection latch module and a visual display module, wherein the data acquisition module acquires historical operation data and corresponding labels and real-time operation data, sends the historical operation data to the algorithm optimization module, and sends the historical operation data and the real-time operation data to the short-circuit monitoring model module; the algorithm optimization module receives the historical operation data sent by the data acquisition module and the corresponding label to perform algorithm optimization, and sends the position of the target solution to the short-circuit monitoring model module; the short circuit monitoring model module establishes a short circuit monitoring model by utilizing the position of the target solution sent by the algorithm optimization module, the historical operation data sent by the data acquisition module and the corresponding label, identifies the real-time operation data, closes a circuit of suspected short circuit data, and sends an identification result to the protection latch module and the visual display module; the protection latch module receives the identification result sent by the short circuit monitoring model module, triggers signal latch when the identification result is suspected short circuit data, and keeps the circuit in a turn-off state until the protection latch is actively closed; the visual display module receives the identification result sent by the short-circuit monitoring model module, and performs visual display on the identification result along with the time sequence, and the visual display module is a Tatany Legionella 27G1 display screen.
By adopting the scheme, the beneficial effects obtained by the invention are as follows:
(1) Aiming at the problems that the traditional short-circuit protection method is to perform short-circuit protection when the absolute value of current exceeds a preset value and has low cutting-off speed and low efficiency, the method adopts a monitoring protection method based on slope detection, and immediately performs cutting-off protection after real-time operation data is identified as suspected short-circuit data by a model, so that the cutting-off efficiency is high and the speed is high.
(2) Aiming at the problem that the test accuracy is low due to the fact that the general short circuit monitoring model is excessively fitted or under fitted due to improper parameters, the scheme adopts algorithm optimization for modeling, so that the problem that the parameters used for modeling are excessively fitted or under fitted is avoided, and the test accuracy is improved.
(3) Aiming at the problem that the short circuit monitoring model repeatedly and mistakenly triggers the short circuit signal, the scheme adopts a protection latch mechanism to avoid repeated and mistaken triggering.
Drawings
FIG. 1 is a schematic flow chart of a short-circuit monitoring protection method for precision equipment based on slope detection;
FIG. 2 is a schematic flow chart of a short-circuit monitoring protection system for precision equipment based on slope detection;
FIG. 3 is a flow chart of step S2;
FIG. 4 is a graph comparing the off current speed of the present scheme with the conventional scheme;
fig. 5 is a diagram showing the classification effect of the short-circuit monitoring model on a part of training data.
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate orientation or positional relationships based on those shown in the drawings, merely to facilitate description of the invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
Referring to fig. 1, the method for protecting short circuit monitoring of precision equipment based on slope detection provided by the invention comprises the following steps:
step S1: collecting data;
step S2: optimizing an algorithm;
step S3: establishing a short circuit monitoring model;
step S4: protecting and latching;
step S5: and (5) visual display.
Embodiment two, referring to fig. 1 and 3, the algorithm optimization includes step S21, step S22, step S23, step S24, step S25, step S26, step S27 and step S28 in step S2 based on the above embodiments;
step S21: data classification, namely taking the historical operation data acquired in the step S1 and the corresponding labels as sample data, taking 70% of the sample data as a training data set and the other 30% of the sample data as a test data set, and presetting a range of kernel parameters sigma, a range of penalty factors C, a test threshold, the number n of candidate solutions and the maximum iteration number max;
step S22: initializing a location, randomly generatingInitial position as n candidate solutions, wherein +.>Is the initial position of the 1 st candidate solution, +.>Is the initial position of the 2 nd candidate solution, +.>，/>Is the initial position of the nth candidate solution; />Is a random number within the penalty factor C, < +.>Is a random number within the kernel parameter sigma range;
step S23: calculating an fitness function value, using python to import a sklearn library, respectively taking the position coordinates of the candidate solutions as parameters, calling an SVM function to establish a quasi-SVM model by using a training data set, taking the recognition accuracy of the quasi-SVM model to the test data set as the fitness function value of the candidate solutions, and taking the candidate solution with the largest fitness function value as a target solution;
step S24: detecting whether the fitness function value of the target solution is not lower than a test threshold value or not or whether the maximum iteration number is reached, if the fitness function value of the target solution is not lower than the test threshold value, outputting the position of the target solution and ending; if the maximum iteration number is reached and the fitness function value of the target solution is lower than the test threshold, the step S22 is performed; otherwise go to step S25;
step S25: the vector distance between the candidate solution and the target solution is calculated by the following formula:
，
where D is the vector distance of the candidate solution from the target solution, C is the wobble factor, C is a random number in the range of 0 to 2,is the position of the target solution in the t-th iteration,/->Is the position of the candidate solution in the t-th iteration;
step S26: the convergence factor is calculated using the formula:
，
wherein a is a convergence factor, and t is the current iteration number;
step S27: the location of the candidate solution is updated using the following formula:
，
in the method, in the process of the invention,is the position of the candidate solution after being updated in the t-th iteration, and r is a random number in the range of 0 to 1;
step S28: go to step S23.
By executing the operation, the scheme adopts algorithm optimization to model aiming at the problem that the test accuracy is low due to over-fitting or under-fitting of the general short circuit monitoring model caused by improper parameters, so that the problem that the over-fitting or under-fitting of the parameters used for modeling cannot occur is solved, and the test accuracy is improved.
In step S1, the data acquisition includes acquiring historical operation data and corresponding tag and real-time operation data, the acquired real-time operation data being a voltage difference, referring to fig. 1And a current, said voltage difference->Correspond to->Difference in current in time ∈ ->Said voltage difference->I.e. the current slope, the corresponding tag comprises normal data and suspected short circuit data.
Referring to fig. 1, in step S3, a short-circuit monitoring model is built, using python to import a sklearn library, using the position of the target solution as a parameter, and calling a training data set for an SVM function to build the short-circuit monitoring model, where the short-circuit monitoring model identifies real-time operation data and outputs data types including normal data and suspected short-circuit data, and when the short-circuit monitoring model outputs the suspected short-circuit data, the hardware responds, and the semiconductor device turns off the circuit, thereby protecting the precision equipment.
In step S4, the protection latch is triggered when the short circuit monitoring model outputs suspected short circuit data, and the circuit keeps the off state until the protection latch is actively closed, so as to avoid repeated false triggering.
By executing the operation, the scheme adopts a protection latch mechanism to solve the problem that the short circuit monitoring model repeatedly and mistakenly triggers the short circuit signal, so that the repeated and mistaken triggering is avoided.
In step S5, the visual display is performed by using the taitan Legion 27G1 display screen, the recognition result of the real-time operation data by the short-circuit monitoring model is visually displayed along with the time sequence, and the data is uploaded to the cloud end, and the cloud end server synchronizes the data to the user terminal for display, so that manual supervision is facilitated.
By executing the operation, the traditional short-circuit protection method aims at the problems that the current absolute value exceeds a preset value and the short-circuit protection is performed, and the cutting-off speed is low and the efficiency is low.
Embodiment seven, referring to fig. 4, based on the above-described embodiment,is->Current at time->Is->Current at time->Is->And->Difference of->Is->And->Difference of->Is the time of complete turn-off of the circuit based on slope detection in the scheme, +.>Is the time of hardware shutdown triggering of the traditional scheme, +.>Is the off peak current of the hardware off trigger of the traditional scheme, < >>Is the time when the circuit is completely turned off in the traditional scheme; the horizontal axis is a time axis, the unit is microseconds us, and the turn-off speed is marked; the vertical axis is a bus current unit, ampere A, and the off current peak value is marked; the conventional solution requires reaching an absolute short threshold, shown in the figure as +.>The switch-off is triggered at the moment until +.>The switch is completely turned off at the moment, the total turn-off time is about 50us, and the turn-off peak current can reach +.>About 100A; the scheme detects that->To->Is->Difference in rising current in time +.>Obtaining a voltage difference->Namely, the current slope, the short circuit monitoring model is identified as suspected short circuit data and output, and the circuit is at +.>The power supply is completely turned off at the moment, the total turn-off time is about 3us, and the turn-off peak current is about 18A; the short circuit monitoring method based on slope detection adopted by the method is better than the traditional method, and the protection effect on precision equipment is better.
An eighth embodiment, referring to fig. 5, is based on the above embodiment, inputs a part of training data into a short-circuit monitoring model, and the short-circuit monitoring model identifies and classifies the part of training data, and the part of training data is classified into normal data and suspected short-circuit data.
An embodiment nine, referring to fig. 2, based on the embodiment, the precision equipment short-circuit monitoring protection system provided by the invention includes a data acquisition module, an algorithm optimization module, a short-circuit monitoring model module, a protection latch module and a visual display module, wherein the data acquisition module acquires historical operation data and corresponding labels and real-time operation data, sends the historical operation data to the algorithm optimization module, and sends the historical operation data and the real-time operation data to the short-circuit monitoring model module; the algorithm optimization module receives the historical operation data sent by the data acquisition module and the corresponding label to perform algorithm optimization, and sends the position of the target solution to the short-circuit monitoring model module; the short circuit monitoring model module establishes a short circuit monitoring model by utilizing the position of the target solution sent by the algorithm optimization module, the historical operation data sent by the data acquisition module and the corresponding label, identifies the real-time operation data, closes a circuit of suspected short circuit data, and sends an identification result to the protection latch module and the visual display module; the protection latch module receives the identification result sent by the short circuit monitoring model module, triggers signal latch when the identification result is suspected short circuit data, and keeps the circuit in a turn-off state until the protection latch is actively closed; the visual display module receives the identification result sent by the short-circuit monitoring model module, and performs visual display on the identification result along with the time sequence, and the visual display module is a Tatany Legionella 27G1 display screen.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The invention and its embodiments have been described above with no limitation, and the actual construction is not limited to the embodiments of the invention as shown in the drawings. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.
Claims (3)
1. A precision equipment short circuit monitoring protection method based on slope detection is characterized in that: the method comprises the following steps:
step S1: collecting data;
step S2: optimizing an algorithm;
step S3: establishing a short circuit monitoring model;
step S4: protecting and latching;
step S5: visual display;
in step S2, the algorithm optimization includes step S21, step S22, step S23, step S24, step S25, step S26, step S27, and step S28;
step S21: data classification, namely taking the historical operation data acquired in the step S1 and the corresponding labels as sample data, taking 70% of the sample data as a training data set and the other 30% of the sample data as a test data set, and presetting a range of kernel parameters sigma, a range of penalty factors C, a test threshold, the number n of candidate solutions and the maximum iteration number max;
step S22: initializing positions, randomly generating (C1, σ1), (C2, σ2) … (Cn, σ) as initial positions of n candidate solutions, wherein (C1, σ1) is the initial position of the 1 st candidate solution, (C2, σ2) is the initial position of the 2 nd candidate solution, … …, (Cn, σn) is the initial position of the n-th candidate solution; c1 C2, … …, cn is a random number within the range of penalty factor C, σ1, σ2, … …, σn is a random number within the range of kernel parameter σ;
step S23: calculating an fitness function value, using python to import a sklearn library, respectively taking the position coordinates of the candidate solutions as parameters, calling an SVM function to establish a quasi-SVM model by using a training data set, taking the recognition accuracy of the quasi-SVM model to the test data set as the fitness function value of the candidate solutions, and taking the candidate solution with the largest fitness function value as a target solution;
step S24: detecting whether the fitness function value of the target solution is not lower than a test threshold value or not or whether the maximum iteration number is reached, if the fitness function value of the target solution is not lower than the test threshold value, outputting the position of the target solution and ending; if the maximum iteration number is reached and the fitness function value of the target solution is lower than the test threshold, the step S22 is performed; otherwise go to step S25;
step S25: the vector distance between the candidate solution and the target solution is calculated by the following formula:
D=C*Xp（t）-X（t）；
wherein D is the vector distance between the candidate solution and the target solution, C is the wobble factor, C is a random number in the range of 0 to 2, xp (t) is the position of the target solution in the t-th iteration, and X (t) is the position of the candidate solution in the t-th iteration;
step S26: the convergence factor is calculated using the formula:
a=2-2（t/max）；
wherein a is a convergence factor, and t is the current iteration number;
step S27: the location of the candidate solution is updated using the following formula:
X（t+1）=Xp（t）-（2a*r-a）*D；
wherein X (t+1) is the position updated by the candidate solution in the t-th iteration, and r is a random number in the range of 0 to 1;
step S28: go to step S23;
in step S1, the data acquisition includes acquiring historical operation data, corresponding labels and real-time operation data, the acquired real-time operation data is a voltage difference Δv and a current, the voltage difference Δv corresponds to a difference value Δi of the current in Δt time, the voltage difference Δv is a current slope, and the corresponding labels include normal data and suspected short circuit data;
in step S3, the short-circuit monitoring model is built by using python to import a sklearn library, taking the position of a target solution as a parameter, calling a training data set for an SVM function, and building the short-circuit monitoring model for real-time operation data, wherein the short-circuit monitoring model identifies and outputs data types including normal data and suspected short-circuit data, and when the short-circuit monitoring model outputs the suspected short-circuit data, the hardware responds, and then the semiconductor device turns off a circuit, so that precision equipment is protected;
in step S4, the protection latch is triggered when the short circuit monitoring model outputs suspected short circuit data, and the circuit keeps the off state until the protection latch is actively closed;
in step S5, the visual display is to visually display the recognition result of the short-circuit monitoring model on the real-time operation data along with the time sequence.
2. A slope detection-based short-circuit monitoring protection system for precision equipment, for implementing the slope detection-based short-circuit monitoring protection method for precision equipment as set forth in claim 1, characterized in that: the system comprises a data acquisition module, an algorithm optimization module, a short circuit monitoring model module, a protection latch module and a visual display module.
3. The precision equipment short-circuit monitoring protection system based on slope detection according to claim 2, wherein: the data acquisition module acquires historical operation data, corresponding labels and real-time operation data, transmits the historical operation data to the algorithm optimization module, and transmits the historical operation data and the real-time operation data to the short circuit monitoring model module; the algorithm optimization module receives the historical operation data sent by the data acquisition module and the corresponding label to perform algorithm optimization, and sends the position of the target solution to the short-circuit monitoring model module; the short circuit monitoring model module establishes a short circuit monitoring model by utilizing the position of the target solution sent by the algorithm optimization module, the historical operation data sent by the data acquisition module and the corresponding label, identifies the real-time operation data, closes a circuit of suspected short circuit data, and sends an identification result to the protection latch module and the visual display module; the protection latch module receives the identification result sent by the short circuit monitoring model module, triggers signal latch when the identification result is suspected short circuit data, and keeps the circuit in a turn-off state until the protection latch is actively closed; and the visual display module receives the identification result sent by the short circuit monitoring model module and performs visual display on the identification result along with the time sequence.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109657720A (en) * | 2018-12-20 | 2019-04-19 | 浙江大学 | A kind of inline diagnosis method of power transformer shorted-turn fault |
| JP2019091124A (en) * | 2017-11-10 | 2019-06-13 | 株式会社東芝 | Reliability evaluation system, reliability evaluation method and program |
| CN114019298A (en) * | 2021-09-28 | 2022-02-08 | 中电华创(苏州)电力技术研究有限公司 | PCC-SVM-based generator rotor turn-to-turn short circuit online monitoring method |
| CN115079007A (en) * | 2022-06-24 | 2022-09-20 | 上海玫克生储能科技有限公司 | Lithium-ion battery micro-short circuit identification method, identification system and battery management system |
| CN115389812A (en) * | 2022-10-28 | 2022-11-25 | 国网信息通信产业集团有限公司 | Artificial neural network short-circuit current zero prediction method and prediction terminal |
| JP2023042527A (en) * | 2021-09-08 | 2023-03-27 | 山東大学 | Data drive active power distribution network abnormal state sensing method and system |
| CN115859616A (en) * | 2022-12-01 | 2023-03-28 | 大连理工大学 | Aero-engine sensor fault diagnosis method based on multi-target fault detection observer and improved LSSVM |
| WO2023054928A1 (en) * | 2021-10-01 | 2023-04-06 | 주식회사 엘지에너지솔루션 | Short-circuit current prediction device and method |
| CN116008815A (en) * | 2022-12-28 | 2023-04-25 | 华人运通(山东)科技有限公司 | Method, device, equipment, storage medium and vehicle for detecting short circuit in battery cell |
| CN116109017A (en) * | 2023-04-12 | 2023-05-12 | 国网信息通信产业集团有限公司 | A fast and accurate prediction method and system for short-circuit current zero point |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210390396A1 (en) * | 2018-07-11 | 2021-12-16 | The Board Of Trustees Of The Leland Stanford Junior University | Systems and Methods for Generative Models for Design |
-
2023
- 2023-05-24 CN CN202310589889.0A patent/CN116799741B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2019091124A (en) * | 2017-11-10 | 2019-06-13 | 株式会社東芝 | Reliability evaluation system, reliability evaluation method and program |
| CN109657720A (en) * | 2018-12-20 | 2019-04-19 | 浙江大学 | A kind of inline diagnosis method of power transformer shorted-turn fault |
| JP2023042527A (en) * | 2021-09-08 | 2023-03-27 | 山東大学 | Data drive active power distribution network abnormal state sensing method and system |
| CN114019298A (en) * | 2021-09-28 | 2022-02-08 | 中电华创(苏州)电力技术研究有限公司 | PCC-SVM-based generator rotor turn-to-turn short circuit online monitoring method |
| WO2023054928A1 (en) * | 2021-10-01 | 2023-04-06 | 주식회사 엘지에너지솔루션 | Short-circuit current prediction device and method |
| CN115079007A (en) * | 2022-06-24 | 2022-09-20 | 上海玫克生储能科技有限公司 | Lithium-ion battery micro-short circuit identification method, identification system and battery management system |
| CN115389812A (en) * | 2022-10-28 | 2022-11-25 | 国网信息通信产业集团有限公司 | Artificial neural network short-circuit current zero prediction method and prediction terminal |
| CN115859616A (en) * | 2022-12-01 | 2023-03-28 | 大连理工大学 | Aero-engine sensor fault diagnosis method based on multi-target fault detection observer and improved LSSVM |
| CN116008815A (en) * | 2022-12-28 | 2023-04-25 | 华人运通(山东)科技有限公司 | Method, device, equipment, storage medium and vehicle for detecting short circuit in battery cell |
| CN116109017A (en) * | 2023-04-12 | 2023-05-12 | 国网信息通信产业集团有限公司 | A fast and accurate prediction method and system for short-circuit current zero point |
Non-Patent Citations (1)
| Title |
|---|
| 一种新的短路电流预测方法;黄旭 等;《电力系统及其自动化学报》;第29卷(第1期);第24-29页 * |
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