CN106124933B - A kind of fault diagnosis method of electric power system based on input nonlinearities method of equal value - Google Patents
A kind of fault diagnosis method of electric power system based on input nonlinearities method of equal value Download PDFInfo
- Publication number
- CN106124933B CN106124933B CN201610539740.1A CN201610539740A CN106124933B CN 106124933 B CN106124933 B CN 106124933B CN 201610539740 A CN201610539740 A CN 201610539740A CN 106124933 B CN106124933 B CN 106124933B
- Authority
- CN
- China
- Prior art keywords
- equal value
- group
- value
- input nonlinearities
- nonlinearities
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
Abstract
The invention discloses a kind of electric power system fault judgment methods based on input nonlinearities method of equal value, include the following steps：S1, entire electric system is rationally layered, and divides group on the basis of every layer, group divides subgroup again, calculates the power of equal value of each group；S2, the input nonlinearities observer of equal value for building each group；S3, the input nonlinearities threshold value of equal value for finding out group；S4, since first layer, the situation of change of the input nonlinearities observer of equal value of group is successively observed down, if there is its value the case where certain period exceeding input nonlinearities threshold value of equal value, it can determine whether to break down, detect most bottom group down, the specific node of failure is determined, it realizes and location of fault is judged, if not occurring the case where beyond threshold value, continue to monitor, to not occurring the group beyond threshold value, subgroup need not be detected again.The invention avoids traditional corresponding failure diagnostic methods based on the defect caused by protective device and breaker information, and failure diagnosis time is short and precision is high.
Description
Technical field
The present invention relates to a kind of electric power system fault judgment methods based on input nonlinearities method of equal value more precisely to relate to
A kind of and direct method for utilizing basic electrical quantity progress breakdown judge in electric system.
Background technology
Power system failure diagnostic is the important technology that electric system maintains stability, fast and accurately judges system
Present in failure be requirement that latter step makes failure respective treated basis namely fault diagnosis.
Power system failure diagnostic mainly has expert knowledge system, Petri network, genetic algorithm, multi-agent technology etc. at present
Deng some correlation methods, they depend on the information of protective device and breaker in system, and there are two major defects：
1), in the case of sensor fault, due to cannot get complete information, the accurate of fault diagnosis can be seriously affected
Property；
2) it, in above method research, needs to model system whole-network, and when network size is larger, system mould
Shape parameter will become huge, and Diagnostic Time is elongated and accuracy decline.
It can be seen that there is also certain deficiencies for the prior art.
Invention content
In view of this, in order to solve the technical problem that power system failure diagnostic is long in the prior art and precision is low, this hair
It is bright to propose a kind of electric power system fault judgment method based on input nonlinearities method of equal value, rely only on basic electrical quantity in electric system
Just the input nonlinearities hierarchical fault diagnosis method of equal value that fault diagnosis can be carried out, has the raising of power system failure diagnostic important
Meaning.
The present invention is solved the above problems by following technological means：
A kind of electric power system fault judgment method based on input nonlinearities method of equal value, includes the following steps：
S1, entire electric system is rationally layered, and divides group on the basis of every layer, group divides subgroup again, meter
Calculate the power of equal value of each group；
S2, the input nonlinearities observer of equal value for building each group；
S3, the input nonlinearities threshold value of equal value for finding out group；
S4, since first layer, successively down observation group input nonlinearities observer of equal value situation of change, if there is it
Value can determine whether to break down, detect most bottom group down, i.e., really the case where certain period exceeding input nonlinearities threshold value of equal value
Determine the specific node of failure, realized and location of fault is judged, if not occurring the case where beyond threshold value, has continued to monitor, it is right
Do not occur the group beyond threshold value, subgroup need not be detected again.
Further, further include：
S5, the minimum subgroup to finally determining failure, take its state variable to be observed, and carry out corresponding differential to it
Algorithm process, determines whether current phase and amplitude break down, and realizes and judges the type of failure.
Further, in step S1, the logic by network topology structure and each node is layered to entire electric system
Relationship determines that the power of equal value of group is realized by forward-backward sweep method Load flow calculation.
Further, in step S2, input nonlinearities observer parameter of equal value calculates as follows：
Wherein,WithIt is equivalent resistance and electricity of the node represented by j groups, kth layer to circuit between a node thereon
It is anti-,WithIt is this group of equivalent resistance and reactance；
Further, in step S2, input nonlinearities observer gain value of equal value is determined by quadratic performance optimization.
Further, in step S2, input nonlinearities observer gain value of equal value is determined by quadratic performance optimization, tool
Body is as follows：
To determine input nonlinearities observer gain value K of equal value, performance index function uses Quadratic Function Optimization, namely uses error
Integrated square
The quality for carrying out evaluation system Control platform, since controlled quentity controlled variable is constrained, in integrand consider increase by one with
The related penalty term of power is controlled, rewriteeing its performance index function is
Infinite Time standing state adjuster：If the state equation and quadratic performance index of Linear Time Invariant controlled system
Respectively
In formula：X (t) is the state variable of n dimensions, and u (t) is the dominant vector of p dimensions, and A, B, Q and R are then to have appropriate dimension
Constant matrices；And weighted matrix R=RT＞ 0, Q=QT＞ 0 or Q=QT>=0, and { A, D } complete Observable, among these D be
Make DDT=Q set up Arbitrary Matrix, from allow control in determine an optimum control u*(t), so that performance indicator reaches minimum；
In Matlab, Infinite Time standing state regulator problem is solved using function lqr (), i.e.,
[K, P, E]=lqr (A, B, Q, R) (5)
If the coefficient matrices A of input linear controlled system state equation, the weighting matrices Q of B and quadratic performance index,
R can acquire the state feedback matrix K of optimal output adjusting system, i.e., input nonlinearities observer gain value K of equal value.
Further, in step S3, input nonlinearities threshold value of equal value is passed through using the greatest measure of power system load curve
The output valve of input nonlinearities observer of equal value determines.
Further, in step S5, differential algorithm includes that parity price input nonlinearities observer waveform peak carries out at differential
Reason, current amplitude are calculated by the maximin of load, and current phase is calculated by power factor.
Compared with prior art, it is big by one that the present invention is based on the electric power system fault judgment methods of input nonlinearities method of equal value
Power system network, be divided into multistage multilayer, by way of being successively unfolded, failure diagnosed and is positioned, it is of the invention
Fault diagnosis technology directly uses the direct electrical quantity in electric system to carry out model construction, avoids traditional corresponding failure diagnosis
For method based on the defect caused by protective device and breaker information, fault diagnosis is accurate, and Diagnostic Time is short and precision is high.
Description of the drawings
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, other are can also be obtained according to these attached drawings
Attached drawing.
Fig. 1 is the structure chart for illustrating IEEE33 node power distributions network used in specific implementation mode；
Fig. 2 is that the present invention is based on the flow charts of the electric power system fault judgment method of input nonlinearities method of equal value；
Fig. 3 is IEEE33 network hierarchy illustratons of model；
Fig. 4 is input nonlinearities Observer Structure figure of equal value.
Specific implementation mode
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, below in conjunction with attached drawing and specifically
Embodiment technical scheme of the present invention is described in detail.It should be pointed out that described embodiment is only this hair
Bright a part of the embodiment, instead of all the embodiments, based on the embodiments of the present invention, those of ordinary skill in the art are not having
There is the every other embodiment obtained under the premise of making creative work, shall fall within the protection scope of the present invention.
Embodiment
Specific implementation details to illustrate the invention, by taking IEEE33 node power distributions network (as shown in Figure 1) as an example, to illustrate
The present invention specifically carries out the process of fault diagnosis.
As shown in Fig. 2, a kind of electric power system fault judgment method based on input nonlinearities method of equal value, includes the following steps：
S1, entire electric system is rationally layered, and divides group on the basis of every layer, group divides subgroup again, such as
Shown in Fig. 3, the power of equal value of each group is calculated；
Entire electric system is layered and is determined by the logical relation of network topology structure and each node, the work(of equal value of group
Rate is realized by forward-backward sweep method Load flow calculation；
S2, the input nonlinearities observer of equal value of each group of structure are as shown in Figure 4；
Input nonlinearities observer parameter of equal value calculates as follows：
Wherein,WithIt is equivalent resistance and electricity of the node represented by j groups, kth layer to circuit between a node thereon
It is anti-,WithIt is this group of equivalent resistance and reactance；
Input nonlinearities observer gain value of equal value is determined by quadratic performance optimization, specific as follows：
To determine input nonlinearities observer gain value K of equal value, within the system, performance index function uses quadratic form letter
Number, namely with the integral of square-error
The quality for carrying out evaluation system Control platform, since controlled quentity controlled variable is constrained, in integrand consider increase by one with
The related penalty term of power is controlled, rewriteeing its performance index function is
In Practical Project, optimal adjustment system should asymptotically stability, and its transient state quality should be it is excellent,
Feedback gain matrix is also permanent, thus could constitute a permanent reponse system, and Infinite Time standing state is adjusted
It is exactly so system that device problem was provided, which most has control system to be proved to,；
Infinite Time standing state adjuster：If the state equation and quadratic performance index of Linear Time Invariant controlled system
Respectively
In formula：X (t) is the state variable of n dimensions, and u (t) is the dominant vector of p dimensions, and A, B, Q and R are then to have appropriate dimension
Constant matrices；And weighted matrix R=RT＞ 0, Q=QT＞ 0 or Q=QT>=0, and { A, D } complete Observable, among these D be
Make DDT=Q set up Arbitrary Matrix, our target be from allow control in determine an optimum control u*(t), so that performance
Index reaches minimum；
In Matlab, Infinite Time standing state regulator problem is solved using function lqr (), i.e.,
[K, P, E]=lqr (A, B, Q, R) (5)
If the coefficient matrices A of input linear controlled system state equation, the weighting matrices Q of B and quadratic performance index,
R can acquire the state feedback matrix K of optimal output adjusting system, i.e., input nonlinearities observer gain value K of equal value；
S3, the input nonlinearities threshold value of equal value for finding out group；
Input nonlinearities threshold value of equal value passes through input nonlinearities observer of equal value using the greatest measure of power system load curve
Output valve determine；
S4, since first layer, successively down observation group input nonlinearities observer of equal value situation of change, if there is it
Value can determine whether to break down, detect most bottom group down, i.e., really the case where certain period exceeding input nonlinearities threshold value of equal value
Determine the specific node of failure, realized and location of fault is judged, if not occurring the case where beyond threshold value, has continued to monitor, it is right
Do not occur the group beyond threshold value, subgroup need not be detected again；
S5, the minimum subgroup to finally determining failure, take its state variable to be observed, and carry out corresponding differential to it
Algorithm process, determines whether current phase and amplitude break down, and realizes and judges the type of failure；
Differential algorithm includes carrying out differential process to state observer waveform peak, current amplitude by load it is maximum most
Small value calculates, and current phase calculated by power factor.
The present invention provides a kind of method for diagnosing faults independent of protective device in electric system and breaker information, main
Structure observer is carried by input nonlinearities method of equal value carries out breakdown judge.Input nonlinearities method of equal value is the one kind in modern scientist field
New hot technology uses extensive in the controls.Input nonlinearities method of equal value is intended to explanation to be existed in classical reponse system
One input nonlinearities of equal value can have consistent effect with original interference to the output that system generates, and dry by being inputted to the equivalence
The inhibition disturbed make system more stablize.Electric system is considered as classical control system by the present invention, and will be in electric system
Failure is equivalent to the interference in control system, to estimate interference by the input nonlinearities of equal value in input nonlinearities method of equal value,
Namely failure judgement.Due to the complexity of power system network, complete modeling is carried out in the presence of tired to entire power system network
Difficulty, the present invention proposes a kind of power system failure diagnostic model based on layered distribution type thought, and breakdown judge is exactly base
In the model.
One big power system network is divided into multistage multilayer according to the present invention, it is right by way of being successively unfolded
Failure is diagnosed and is positioned.The calculating including system interior joint parameter, the determination of observer gain, input of equal value are dry among these
The a series of program of determination of threshold value etc. is disturbed, fault diagnosis technology of the invention directly uses direct electrical in electric system
Amount carries out model construction, avoids traditional corresponding failure diagnostic method based on lacking caused by protective device and breaker information
It falls into, fault diagnosis is accurate, and Diagnostic Time is short and precision is high.
Several embodiments of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Cannot the limitation to the scope of the claims of the present invention therefore be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the guarantor of the present invention
Protect range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (4)
1. a kind of electric power system fault judgment method based on input nonlinearities method of equal value, which is characterized in that include the following steps：
S1, entire electric system is layered, and divides group on the basis of every layer, group divides subgroup again, calculates each group
Power of equal value；Entire electric system is layered and is determined by the logical relation of network topology structure and each node, group etc.
Valence power is realized by forward-backward sweep method Load flow calculation；
S2, the input nonlinearities observer of equal value for building each group；
Input nonlinearities observer parameter of equal value calculates as follows：
Wherein,WithIt is node represented by j groups, kth layer to the equivalent resistance of circuit and reactance between a node thereon,WithIt is this group of equivalent resistance and reactance；
S3, the input nonlinearities threshold value of equal value for finding out group；
Input nonlinearities threshold value of equal value passes through the defeated of input nonlinearities observer of equal value using the greatest measure of power system load curve
Go out value determination；
S4, since first layer, successively down observation group input nonlinearities observer of equal value situation of change, exist if there is its value
Certain period exceeds the case where input nonlinearities threshold value of equal value, can determine whether to break down, detects most bottom group down, that is, determine
The specific node of failure is realized and is judged location of fault, if not occurring the case where beyond threshold value, continues to monitor, to not going out
Now exceed the group of threshold value, subgroup need not be detected again.
2. the electric power system fault judgment method according to claim 1 based on input nonlinearities method of equal value, which is characterized in that
Further include：
S5, the minimum subgroup to finally determining failure, take its state variable to be observed, and carry out corresponding differential algorithm to it
Processing, determines whether current phase and amplitude break down, and realizes and judges the type of failure；
Differential algorithm include parity price input nonlinearities observer waveform peak carry out differential process, current amplitude by load most
Big minimum value calculates, and current phase is calculated by power factor.
3. the electric power system fault judgment method according to claim 1 based on input nonlinearities method of equal value, which is characterized in that
In step S2, input nonlinearities observer gain value of equal value is determined by quadratic performance optimization.
4. the electric power system fault judgment method according to claim 1 based on input nonlinearities method of equal value, which is characterized in that
In step S2, input nonlinearities observer gain value of equal value is determined by quadratic performance optimization, specific as follows：
To determine input nonlinearities observer gain value K of equal value, performance index function uses Quadratic Function Optimization, namely uses square-error
Integral
The quality for carrying out evaluation system Control platform considers to increase by one and control in integrand since controlled quentity controlled variable is constrained
The related penalty term of power, rewriteeing its performance index function is
Infinite Time standing state adjuster：If the state equation and quadratic performance index of Linear Time Invariant controlled system are distinguished
For
In formula：X (t) is the state variable of n dimensions, and u (t) is the dominant vector of p dimensions, and A, B, Q and R are then have appropriate dimension normal
Matrix number；And weighted matrix R=RT＞ 0, Q=QT＞ 0 or Q=QT>=0, and { A, D } complete Observable, D is to make among these
DDT=Q set up Arbitrary Matrix, from allow control in determine an optimum control u*(t), so that performance indicator reaches minimum；
In Matlab, Infinite Time standing state regulator problem is solved using function lqr (), i.e.,
[K, P, E]=lqr (A, B, Q, R) (5)
As long as the coefficient matrices A of input linear controlled system state equation, the weighting matrices Q of B and quadratic performance index, R, just
The state feedback matrix K of optimal output adjusting system can be acquired, i.e., input nonlinearities observer gain value K of equal value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610539740.1A CN106124933B (en) | 2016-07-11 | 2016-07-11 | A kind of fault diagnosis method of electric power system based on input nonlinearities method of equal value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610539740.1A CN106124933B (en) | 2016-07-11 | 2016-07-11 | A kind of fault diagnosis method of electric power system based on input nonlinearities method of equal value |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106124933A CN106124933A (en) | 2016-11-16 |
| CN106124933B true CN106124933B (en) | 2018-08-24 |
Family
ID=57282812
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610539740.1A Active CN106124933B (en) | 2016-07-11 | 2016-07-11 | A kind of fault diagnosis method of electric power system based on input nonlinearities method of equal value |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106124933B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110019450B (en) * | 2017-09-27 | 2021-09-07 | 许昌许继软件技术有限公司 | Wave recording data selecting, storing and uploading method and system |
| CN107822833B (en) * | 2017-11-21 | 2023-08-18 | 中国地质大学（武汉） | Foot-operated multiple training mode rehabilitation device for lower limbs |
| CN114578793A (en) * | 2022-03-21 | 2022-06-03 | 杭州电力设备制造有限公司 | Observer-based nonlinear robust fault detection method for electric power generation vehicle |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103235224A (en) * | 2013-04-25 | 2013-08-07 | 国家电网公司 | Grid fault analytical method |
| CN105116291A (en) * | 2015-09-07 | 2015-12-02 | 国电南瑞科技股份有限公司 | Power grid operating steady state monitoring information-based fault diagnosis system and method |
| CN105606919A (en) * | 2015-11-06 | 2016-05-25 | 国网福建省电力有限公司 | Simulation closed-loop test method for layered distributed wireless network of intelligent transformer station |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10401417B2 (en) * | 2013-02-13 | 2019-09-03 | General Electric Technology Gmbh | Electrical fault location determination in a distribution system based on phasor information |
| US20160116522A1 (en) * | 2014-10-27 | 2016-04-28 | King Fahd University Of Petroleum And Minerals | Fully adaptive fault location method |
-
2016
- 2016-07-11 CN CN201610539740.1A patent/CN106124933B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103235224A (en) * | 2013-04-25 | 2013-08-07 | 国家电网公司 | Grid fault analytical method |
| CN105116291A (en) * | 2015-09-07 | 2015-12-02 | 国电南瑞科技股份有限公司 | Power grid operating steady state monitoring information-based fault diagnosis system and method |
| CN105606919A (en) * | 2015-11-06 | 2016-05-25 | 国网福建省电力有限公司 | Simulation closed-loop test method for layered distributed wireless network of intelligent transformer station |
Non-Patent Citations (3)
| Title |
|---|
| 利用等价输入干扰法控制TORA系统的稳定性;李依霖;《吉林工程技术师范学院学报》;20131226;第29卷(第12期);94-96 * |
| 基于二维混合模型和状态观测器的重复控制设计;吴敏等;《自动化学报》;20090715;第35卷(第7期);945-952 * |
| 基于等价输入干扰补偿的直流电机定位控制;方明星等;《仪器仪表学报》;20090915;第30卷(第9期);1801-1806 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106124933A (en) | 2016-11-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106124933B (en) | A kind of fault diagnosis method of electric power system based on input nonlinearities method of equal value | |
| CN105158725B (en) | A kind of electric energy meter metering accuracy appraisal procedure based on multidimensional influence amount | |
| CN107145707B (en) | Distribution network transformer planning method considering photovoltaic output uncertainty and life cycle cost | |
| CN108680244A (en) | A kind of rotating machinery vibrating wireless monitoring device and method | |
| CN110070282A (en) | A kind of low-voltage platform area line loss analysis of Influential Factors method based on Synthesis Relational Grade | |
| CN110289613B (en) | Sensitivity matrix-based power distribution network topology identification and line parameter identification method | |
| Oluic et al. | Methodology for rotor angle transient stability assessment in parameter space | |
| CN107565571B (en) | A kind of method and device judging power system steady state voltage stability | |
| Liu et al. | Neuro-fuzzy networks for voltage security monitoring based on synchronized phasor measurements | |
| CN104819107A (en) | Diagnostic method and system for abnormal shift of wind turbine generator power curve | |
| CN105223512A (en) | Based on the method for the dynamic rectification dump energy of battery behavior | |
| CN103544542A (en) | Power system transient stability margin predicting method | |
| CN107831448A (en) | A kind of state-of-charge method of estimation of parallel connection type battery system | |
| CN104407273A (en) | Electric energy quality disturbance source positioning method considering monitoring reliability | |
| CN106483393B (en) | Configuration method and system are unified in harmonic wave and voltage dip monitoring point | |
| CN104899353A (en) | PQDS positioning method based on evidence theory | |
| CN115128395A (en) | Voltage sag source positioning method and device | |
| CN108090244A (en) | A kind of parallel type lithium ion battery system modeling method | |
| Xiong et al. | A novel joint estimation method of state of charge and state of health based on the strong tracking-dual adaptive extended Kalman filter algorithm for the electric vehicle lithium-ion batteries | |
| CN112083299B (en) | DC system insulation fault prediction method based on Kalman filtering | |
| CN103972889B (en) | A kind of distribution line impedance on-line identification method | |
| CN110632521B (en) | Fusion estimation method for lithium ion battery capacity | |
| CN109193639B (en) | Robust estimation method for power system | |
| CN109376990B (en) | Method and system for determining critical removal time of power system based on Simese network model | |
| CN105203865A (en) | Working method of on-line test system of distributed power-supply grid-connected inverter and capacitor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |