CN113435781A - Auxiliary judgment method and system for equipment running state - Google Patents
Auxiliary judgment method and system for equipment running state Download PDFInfo
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- G01L19/00—Details of, or accessories for, apparatus for measuring steady or quasi-steady pressure of a fluent medium insofar as such details or accessories are not special to particular types of pressure gauges
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Abstract
The invention provides an auxiliary judgment method and an auxiliary judgment system for equipment running state. It can effectively avoid artifical careless omission, simultaneously, can raise the efficiency, alleviates the work load of transformer fortune dimension personnel in the course of the work, can reduce the problem that artifical judgement probably appears.
Description
Technical Field
The invention relates to the field of maintenance of electrical equipment, in particular to an auxiliary judgment method and an auxiliary judgment system for an equipment running state.
Background
At present, the operation and maintenance personnel usually patrol the hydraulic pressure of equipment in a station by a patrol equipment pressure recording mode, and judge whether the equipment is in a normal working state or not by comparing the normal pressure of the equipment after patrol.
The station hydraulic pressure meter is diversified, the hydraulic rated values of different devices are different, and the alarm values are also different, so, the operation and maintenance personnel need to firstly confirm the hydraulic pressure meter value in order to judge whether the hydraulic pressure of the device is normal after inspecting the device, and then evaluate according to different judgment standards, and the process has the following defects: the evaluation process is completely subjective, and once the operation and maintenance personnel misjudge, the risk of error, leakage and fault can be caused; in addition, the process is time consuming, time consuming and labor intensive.
Disclosure of Invention
The invention provides an auxiliary judgment method and an auxiliary judgment system for the running state of equipment aiming at the defects and shortcomings in the prior art.
The technical scheme is as follows:
an auxiliary judgment method for equipment running state is characterized in that: the initial pressure value of the electrical equipment is listed in a database, the extended Kalman filtering algorithm is used for pressure estimation, when the pressure is suddenly changed, the sudden change value is deviated from the estimated value of the Kalman filtering algorithm, and at the moment, an alarm is given out.
Further, the extended kalman filter algorithm is an EKF algorithm.
Further, the method comprises the following steps:
step S1: constructing hydraulic pressure basic databases of different devices;
step S2: constructing a hydraulic pressure estimation matrix;
step S3: inputting the hydraulic pressure estimation matrix into an extended Kalman filter for hydraulic pressure estimation, and storing a pressure estimation value;
step S4: and comparing the newly measured hydraulic pressure value of the equipment with the pressure estimated value, and if the deviation exceeds a threshold value, giving an alarm.
Further, in the extended kalman filter algorithm, the state equation and the measurement equation of the nonlinear system are assumed as follows:
Xk+1＝f(Xk)+ωk
Zk＝h(Xk)+υk
wherein, Xk+1Is the n X1 dimensional state matrix, f (X), of the system at time kk) Is a state transition matrix of dimension n × 1 at time k; zkIs the m x 1 dimension measurement vector of the system at the time k; h (X)k) Is a nonlinear measurement function of the mx 1-dimensional quantity; omegakAnd upsilonkIs white Gaussian noise with dimension of n multiplied by 1 and dimension of m multiplied by 1 for simulating system interference;
thereafter, the nonlinear system is linearized, in particular by f (X)k) And h (X)k) Performing Taylor expansion, neglecting high-order terms, and linearizing a state transition matrix of the state equation to obtain:
Xk+1＝FkXk+ωk+uk
in the formula, FkState transition matrix of dimension n x n, ukIs an external-action entry in the unfolding process;
in the same way, the measurement equation is subjected to Taylor expansion, and after high-order terms are omitted, a linearized measurement function is obtained as follows:
Zk+1＝Hk+1Xk+1+υk+1+Yk+1
in the formula, Hk+1Is a m × n dimensional measurement function Jacobian matrix:
after each variable and the transfer function are linearized, the solution is carried out by the Kalman filtering algorithm, and dynamic estimation is carried out.
And, an equipment running state assists the judgement system, characterized by, including: the system comprises a database module, an extended Kalman filtering module and a comparison module:
the database module is used for storing hydraulic pressure basic data of different electrical equipment and estimated data after extended Kalman filtering;
the extended Kalman filtering module is used for estimating pressure basic data and acquiring an estimated value;
and the comparison module is used for comparing the newly measured hydraulic pressure value of the equipment with the pressure estimation value and judging whether the pressure value exceeds a threshold value.
Further, the device also comprises an alarm module used for giving an alarm when the judgment result of the comparison module exceeds the threshold value.
Further, the extended kalman filtering module is an EKF filtering module.
And an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the device operation state auxiliary judgment method as described above when executing the program.
And a non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the device operation state auxiliary judgment method as described above.
The invention and the optimal selection scheme thereof automatically judge whether the hydraulic pressure of the equipment is normal by utilizing the database technology, thereby avoiding the omission and the error which are possibly caused by manual judgment. Can effectively avoid artifical careless omission, simultaneously, can raise the efficiency, alleviate the work load of transformer fortune dimension personnel in the course of the work, can reduce the problem that artifical judgement probably appears.
Detailed Description
In order to make the features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail as follows:
according to the scheme, the initial pressure value of the equipment is listed in the database through the Kalman filtering algorithm, the hydraulic pressure estimation is carried out through the expanded Kalman filtering algorithm, when the hydraulic pressure is suddenly changed, the Kalman filtering algorithm is optimal filtering, so that the pressure sudden change of the equipment is necessarily deviated from the estimation value of the Kalman filtering algorithm, and at the moment, the system can automatically give an alarm.
The method specifically comprises the following steps:
1. constructing hydraulic pressure basic databases of different devices;
2. a matrix is constructed from the hydraulic pressure estimates. The classical Kalman filtering algorithm exists for the most optimal filtering of a system with linear state equations and linear measurement equations, and for a strong nonlinear system such as a power system, the state equations and the measurement equations are nonlinear, so that a set of nonlinear filtering method is required for applying the Kalman filtering algorithm to the state estimation of the power system. The extended Kalman filter algorithm is an effective method for solving the nonlinear filtering problem, and is fundamental, the extended Kalman filter is a suboptimal nonlinear filter, the nonlinear filtering problem is converted into a linear problem by using a first-order Taylor expansion for intercepting a transfer function, and the algorithm principle is as follows:
the state equation and the measurement equation of the nonlinear system are assumed as follows.
Xk+1＝f(Xk)+ωk (1)
Zk＝h(Xk)+υk (2)
Xk+1Is the n X1 dimensional state matrix, f (X), of the system at time kk) Is a state transition matrix of dimension n × 1 at time k; zkIs the m x 1 dimension measurement vector of the system at the time k; h (X)k) Is a nonlinear measurement function of the mx 1-dimensional quantity; omegakAnd upsilonkIs white gaussian noise of dimensions n × 1 and m × 1 simulating system interference.
Next, the nonlinear system is linearized by linearizing f (X)k) And h (X)k) Taylor expansion is performed and the higher order terms are ignored, the equation of state isThe state transition matrix is linearized to yield:
Xk+1＝FkXk+ωk+uk (3)
in the formula, FkState transition matrix of dimension n x n, ukIs an externally-acting entry in the unfolding process.
In the same way, the measurement equation is subjected to Taylor expansion, and after high-order terms are omitted, a linearized measurement function is obtained as follows:
Zk+1＝Hk+1Xk+1+υk+1+Yk+1 (5)
in the formula, Hk+1Is a m x n dimensional measurement function Jacobian matrix
After each variable and the transfer function are linearized, the solution can be carried out by using a classic Kalman filtering algorithm, and dynamic estimation is carried out.
The extended Kalman filtering omits more than two Taylor expansion terms, so that the error is predicted in one step
And filtering error
When the difference is small, a relatively good effect can be obtained, otherwise, a linearization error caused by linearization and an overlarge noise deviation between the noise statistical characteristic and the actual noise may cause inaccurate gain matrix calculation and step loss prediction, so that a filtering error is overlarge.
3. And then, inputting the hydraulic pressure estimation matrix into an extended Kalman filter for hydraulic pressure estimation, and storing the pressure estimation value.
4. And comparing the newly measured hydraulic pressure value of the equipment with the pressure estimation value, and if the deviation exceeds a threshold value, giving an alarm.
The above method provided by this embodiment can be stored in a computer readable storage medium in a coded form, and implemented in a computer program, and inputs basic parameter information required for calculation through computer hardware, and outputs the calculation result.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
The present invention is not limited to the above preferred embodiments, and other various types of auxiliary determination methods and systems for the operation status of the equipment can be obtained by anyone with the benefit of the present invention.
Claims (9)
1. An auxiliary judgment method for equipment running state is characterized in that: the initial pressure value of the electrical equipment is listed in a database, the extended Kalman filtering algorithm is used for pressure estimation, when the pressure is suddenly changed, the sudden change value is deviated from the estimated value of the Kalman filtering algorithm, and at the moment, an alarm is given out.
2. The device operation state auxiliary judgment method according to claim 1, characterized in that: the extended Kalman filtering algorithm is an EKF algorithm.
3. The auxiliary judgment method for the equipment operation state according to claim 1, characterized by comprising the following steps:
step S1: constructing hydraulic pressure basic databases of different devices;
step S2: constructing a hydraulic pressure estimation matrix;
step S3: inputting the hydraulic pressure estimation matrix into an extended Kalman filter for hydraulic pressure estimation, and storing a pressure estimation value;
step S4: and comparing the newly measured hydraulic pressure value of the equipment with the pressure estimated value, and if the deviation exceeds a threshold value, giving an alarm.
4. The device operation state auxiliary judgment method according to claim 2, characterized in that: in the extended kalman filter algorithm, the state equation and the measurement equation of the nonlinear system are assumed as follows:
Xk+1＝f(Xk)+ωk
Zk＝h(Xk)+υk
wherein, Xk+1Is the n X1 dimensional state matrix, f (X), of the system at time kk) Is a state transition matrix of dimension n × 1 at time k; zkIs the m x 1 dimension measurement vector of the system at the time k; h (X)k) Is a nonlinear measurement function of the mx 1-dimensional quantity; omegakAnd upsilonkIs white Gaussian noise with dimension of n multiplied by 1 and dimension of m multiplied by 1 for simulating system interference;
thereafter, the nonlinear system is linearized, in particular by f (X)k) And h (X)k) Performing Taylor expansion, neglecting high-order terms, and linearizing a state transition matrix of the state equation to obtain:
Xk+1＝FkXk+ωk+uk
in the formula, FkState transition matrix of dimension n x n, ukIs an external-action entry in the unfolding process;
in the same way, the measurement equation is subjected to Taylor expansion, and after high-order terms are omitted, a linearized measurement function is obtained as follows:
Zk+1＝Hk+1Xk+1+υk+1+Yk+1
in the formula, Hk+1Is a m × n dimensional measurement function Jacobian matrix:
after each variable and the transfer function are linearized, the solution is carried out by the Kalman filtering algorithm, and dynamic estimation is carried out.
5. An auxiliary judgment system for equipment running state is characterized by comprising: the system comprises a database module, an extended Kalman filtering module and a comparison module:
the database module is used for storing hydraulic pressure basic data of different electrical equipment and estimated data after extended Kalman filtering;
the extended Kalman filtering module is used for estimating pressure basic data and acquiring an estimated value;
and the comparison module is used for comparing the newly measured hydraulic pressure value of the equipment with the pressure estimation value and judging whether the pressure value exceeds a threshold value.
6. The device operation state auxiliary judgment system according to claim 5, wherein: the device also comprises an alarm module which is used for giving an alarm when the judgment result of the comparison module exceeds the threshold value.
7. The device operation state auxiliary judgment system according to claim 5, wherein: the extended Kalman filtering module is an EKF filtering module.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for assisting in determining the operational status of a device according to any one of claims 1-4 when executing the program.
9. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the method for assisting in determining an operational status of an apparatus according to any one of claims 1 to 4.
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Citations (4)
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| CN102227532A (en) * | 2008-12-01 | 2011-10-26 | Abb研究有限公司 | Procedure and system for control of refiner to improve energy efficiency and pulp quality |
| CN104390657A (en) * | 2014-11-05 | 2015-03-04 | 浙江大学 | Generator set operating parameter measuring sensor fault diagnosis method and system |
| CN112345841A (en) * | 2020-09-09 | 2021-02-09 | 中国电力科学研究院有限公司 | Kalman filtering fault detection method applied to coastal city alternating current charging pile |
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- 2021-07-14 CN CN202110798116.4A patent/CN113435781A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7392696B1 (en) * | 2007-04-10 | 2008-07-01 | Toyota Motor Engineering & Manufacturing North America, Inc. | Sensorless tire pressure estimating method for vehicle with ABS brake control |
| CN102227532A (en) * | 2008-12-01 | 2011-10-26 | Abb研究有限公司 | Procedure and system for control of refiner to improve energy efficiency and pulp quality |
| CN104390657A (en) * | 2014-11-05 | 2015-03-04 | 浙江大学 | Generator set operating parameter measuring sensor fault diagnosis method and system |
| CN112345841A (en) * | 2020-09-09 | 2021-02-09 | 中国电力科学研究院有限公司 | Kalman filtering fault detection method applied to coastal city alternating current charging pile |
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