CN106972949B - A kind of fractional order network system situation estimation method based on adaptive equalization technology - Google Patents
A kind of fractional order network system situation estimation method based on adaptive equalization technology Download PDFInfo
- Publication number
- CN106972949B CN106972949B CN201710082557.8A CN201710082557A CN106972949B CN 106972949 B CN106972949 B CN 106972949B CN 201710082557 A CN201710082557 A CN 201710082557A CN 106972949 B CN106972949 B CN 106972949B
- Authority
- CN
- China
- Prior art keywords
- moment
- follows
- network system
- fractional order
- packet loss
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The fractional order network system situation estimation method based on adaptive equalization technology that the invention discloses a kind of, for solving state estimation problem of the fractional order network system when random packet loss occurs for measurement signal data.The specific steps of the present invention are as follows: (1) based on binary system Bernoulli Jacob's distribution variable, in conjunction with the characteristics of metric data generation random packet loss, establishing meter and survey the fractional order network system model that data random packet loss occurs for signal.(2) based on innovation sequence, the adaptive equalization technology that dynamic compensation value surveys signal packet loss that can be used for is proposed.(3) on the basis of above-mentioned steps, in conjunction with traditional linear fractional rank kalman filter method, the fractional order network system situation estimation method in the case of can be used for measurement signal data random packet loss is given.Sample calculation analysis shows the method for the present invention validity and practicability.
Description
Technical field
The fractional order network system situation estimation method based on adaptive equalization technology that the present invention relates to a kind of, belongs to network
Network analysis and control technology field.
Background technique
The analysis of network system is with control for guaranteeing that network system security stable operation has great importance.In recent years
Come, with the development and progress of measurement technology, so that carrying out real time on-line monitoring and control to network system becomes possibility.?
In existing research, by means of real-time measurement information acquired in phasor measurement unit, by designing dynamic state estimator,
It is the main path realizing network system and being analyzed in real time with control.
Under normal circumstances, it is measured first by phasor measurement unit, obtains field measurement data, then pass through information
Transmission channel passes to control centre, it is however noted that, in the transmission process of measurement information, metric data can not be kept away
Exempt from there is a situation where data random loss, so, carry out network system dynamic estimator design when must count and measure
There is a situation where packet losses for signal.
Fractional order network system is widely used in each in recent years due to the structure of description system that can be more accurate
Field is such as used in power system network, can more accurate node voltage and electric current in electric system carry out it is pre-
It surveys and estimates.Currently, being directed to linear fractional rank network system metric data in existing research and asking for random packet loss occurring
Topic, related researcher proposes some metric data compensation techniques, but the validity of these methods, which is built upon measurement, makes an uproar
In condition known to sound and system noise covariance matrix, and in the practical application of engineering, these conditions are not often not
Know.Based on this, the present invention devises a kind of fractional order network system situation estimation method based on adaptive equalization technology, should
Method not only may be implemented to compensate packet loss metric data dynamic, but also can be expired with dynamic acquisition system noise and measurement noise
The covariance matrix of foot, therefore the method for the present invention has better engineering practicability.Finally, actual fractional order network system is calculated
Example test demonstrates the validity and practicability of the method for the present invention.
Summary of the invention
Goal of the invention: aiming at the problems existing in the prior art, the present invention provides a kind of based on adaptive equalization technology
Fractional order network system situation estimation method.
Technical solution: a kind of fractional order network system situation estimation method based on adaptive equalization technology, including it is as follows
Part:
1) meter and the linear fractional rank network system model of metric data random packet loss
Under the premise of assuming that each phasor measurement unit respectively works independently, with binary system Bernoulli Jacob distribution variable side
Method models the Discrete Linear fractional order network system in the case of meter and measurement signal data random packet loss, and model is
System EQUATION xk+1And output equationIt can be respectively depicted as:
Δγxk+1=Adxk+Buk+wk
In formula: ΔγFor fractional order operator, γ=[n1,n2…np]TFor fractional order order, xk+1Indicate the state at k+1 moment
Vector (dimension p), AdFor sytem matrix, B is control matrix, ukFor input variable,Indicate the output vector (dimension at k moment
Degree for m),It is the binary system scalar for meeting Bernoulli Jacob's distribution, value is 0 or 1, it is expected that being respectively with varianceC is output matrix, wkFor the system noise value at k moment,It is measured in respectively each phasor measurement unit
Noise figure is surveyed, and is hadBoth system noise and measurement noise are mutually indepedent unrelated, are met
Mean value is 0, and covariance matrix is respectively QkAnd Rk, and γ in formulajCalculation formula be defined as follows
N >=0 is fractional order order in formula, and j >=0 represents different moments.
2) the adaptive equalization technology of metric data packet loss
Under normal circumstances, for the output variable of m dimension, measuring the covariance matrix that noise is met be can be described as:
In formulaFor the constant value (and it is often assumed that for be known) at k moment, and when meter and measurement signal
When random packet loss, the k momentValue withThere are following relationships
By analyzing it is found that working asWhen packet loss occurs for metric data, σ → ∞ in formula measures what noise was met at this time
Covariance matrix RkIt can change, and then influence the meter of relevant to noise is measured filtering gain and evaluated error covariance
It calculates.
Based on this, the invention proposes system noise under metric data packet loss situation and noise covariance matrix Q is measuredk, Rk
Method for dynamic estimation, and then realize to the compensation of the dynamic of packet loss metric data, specific implementation step is as follows:
(1) innovation sequence is calculated, calculation formula is as follows
In formulaIt is the measuring value at k moment in the case of meter and data packetloss,It is the state estimation at k moment.
(2) when taking moving window size is L, innovation sequence s in calculation windowkAverage value, i.e., newly breath Matrix Cvk,
Calculation formula is as follows
(3) on the basis of previous step, noise covariance matrix Q is soughtk, RkDynamic estimation value, calculation formula is as follows
Qk=GkCvkGk T
G in formulakIt is the filtering gain at k moment,It is the evaluated error covariance matrix at k moment, ΞkIt is defined as follows
3) on the basis of above-mentioned, then letter to meter and can be measured by following Generalized fractional kalman filter method
The linear fractional rank network state of number packet loss is estimated that specific step is as follows, and this method is successively according to such as in a computer
What lower step was realized:
(1) setting filters relevant initial value, such as state estimation initial valueState estimation error covarianceSystem
Noise and the initial value for measuring covariance matrix are respectively Qk, Rk, dynamic estimation window value L and greatest iteration moment N.
(2) the status predication value at k+1 moment is calculatedCalculation formula is as follows
In formulaFor the state estimation at k moment.
(3) adaptive equalization technical method is utilized, the noise covariance matrix value Q at k+1 moment is calculatedk+1, Rk+1；It is (specific
Step is shown in the adaptive equalization technology segment of metric data packet loss)
(4) the status predication error covariance at k+1 moment is calculatedCalculation formula is as follows
In formula ()TFor the transposition for seeking matrix.
(5) the general Kalman filtering gain G at k+1 moment is calculatedk+1, calculation formula is as follows
C indicates output matrix, () in formula-1To ask inverse of a matrix operation.
(6) the evaluated error covariance at k+1 moment is calculatedCalculation formula is as follows
I is the unit matrix of corresponding dimension in formula.
(7) state estimation at k+1 moment is calculatedCalculation formula is as follows
(8) the then iteration stopping as k+1 > N, output estimation result；Conversely, our department (2)-(7) step by step are then repeated, into
The state estimation of row subsequent time.
Detailed description of the invention
Fig. 1 is the method flow diagram of the embodiment of the present invention；
Fig. 2 is state estimation result figure of the embodiment using traditional fractional order kalman filter method, and (a) is fractional order card
Kalman Filtering algorithm state estimated result (b) is fractional order Kalman filtering algorithm state estimation result；
Fig. 3 is evaluated error figure of the embodiment using traditional fractional order kalman filter method, and (a) is fractional order Kalman
Filtering algorithm state estimation error (b) is fractional order Kalman filtering algorithm state estimation error；
Fig. 4 is the state estimation result figure that embodiment uses the method for the present invention, and (a) is the method for the present invention state estimation knot
Fruit (b) is the method for the present invention state estimation result；
Fig. 5 is the state estimation Error Graph that embodiment uses the method for the present invention, and (a) is the method for the present invention state estimation mistake
Difference (b) is the method for the present invention state estimation error.
Specific embodiment
Combined with specific embodiments below, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate the present invention
Rather than limit the scope of the invention, after the present invention has been read, those skilled in the art are to various equivalences of the invention
The modification of form falls within the application range as defined in the appended claims.
As shown in Figure 1, the fractional order network system situation estimation method based on adaptive equalization technology, including such as lower part
Point:
1) meter and the linear fractional rank network system model of metric data random packet loss
Under the premise of assuming that each phasor measurement unit respectively works independently, with binary system Bernoulli Jacob distribution variable side
Method models the Discrete Linear fractional order network system in the case of meter and measurement signal data random packet loss, and model is
System EQUATION xk+1And output equationIt can be respectively depicted as:
Δγxk+1=Adxk+Buk+wk
In formula: ΔγFor fractional order operator, γ=[n1,n2…np]TFor fractional order order, xk+1Indicate the state at k+1 moment
Vector (dimension p), AdFor sytem matrix, B is control matrix, ukFor input variable,Indicate the output vector (dimension at k moment
Degree for m),It is the binary system scalar for meeting Bernoulli Jacob's distribution, value is 0 or 1, it is expected that being respectively with varianceC is output matrix, wkFor the system noise value at k moment,It is measured in respectively each phasor measurement unit
Noise figure is surveyed, and is hadBoth system noise and measurement noise are mutually indepedent unrelated, are met
Mean value is 0, and covariance matrix is respectively QkAnd Rk, and γ in formulajCalculation formula be defined as follows
N >=0 is fractional order order in formula, and j >=0 represents different moments.
2) the adaptive equalization technology of metric data packet loss
Under normal circumstances, for the output variable of m dimension, measuring the covariance matrix that noise is met be can be described as:
In formulaFor the constant value (and it is often assumed that for be known) at k moment, and when meter and measurement signal
When random packet loss, the k momentValue withThere are following relationships
By analyzing it is found that working asWhen packet loss occurs for metric data, σ → ∞ in formula measures what noise was met at this time
Covariance matrix RkIt can change, and then influence the meter of relevant to noise is measured filtering gain and evaluated error covariance
It calculates.
Based on this, the invention proposes system noise under metric data packet loss situation and noise covariance matrix Q is measuredk, Rk
Method for dynamic estimation, and then realize to the compensation of the dynamic of packet loss metric data, specific implementation step is as follows:
(2) innovation sequence is calculated, calculation formula is as follows
In formulaIt is the measuring value at k moment in the case of meter and data packetloss,It is the state estimation at k moment.
(2) when taking moving window size is L, innovation sequence s in calculation windowkAverage value, i.e., newly breath Matrix Cvk,
Calculation formula is as follows
(3) on the basis of previous step, noise covariance matrix Q is soughtk, RkDynamic estimation value, calculation formula is as follows
Qk=GkCvkGk T
G in formulakIt is the filtering gain at k moment,It is the evaluated error covariance matrix at k moment, ΞkIt is defined as follows
3) on the basis of above-mentioned, then letter to meter and can be measured by following Generalized fractional kalman filter method
The linear fractional rank network state of number packet loss is estimated that specific step is as follows
(1) setting filters relevant initial value, such as state estimation initial valueState estimation error covarianceSystem
Noise and the initial value for measuring covariance matrix are respectively Qk, Rk, dynamic estimation window value L and greatest iteration moment N.
(2) the status predication value at k+1 moment is calculatedCalculation formula is as follows
In formulaFor the state estimation at k moment.
(3) adaptive equalization technical method is utilized, the noise covariance matrix value Q at k+1 moment is calculatedk+1, Rk+1；
(4) the status predication error covariance at k+1 moment is calculatedCalculation formula is as follows
(5) the general Kalman filtering gain G at k+1 moment is calculatedk+1, calculation formula is as follows
In formula ()-1To ask inverse of a matrix operation.
(6) the evaluated error covariance at k+1 moment is calculatedCalculation formula is as follows
I is the unit matrix of corresponding dimension in formula.
(7) state estimation at k+1 moment is calculatedCalculation formula is as follows
(8) the then iteration stopping as k+1 > N, output estimation result；Conversely, our department (2)-(7) step by step are then repeated, into
The state estimation of row subsequent time.
In order to verify the validity and practicability of the method for the present invention, it is described below in fractional order network system research extensively
One fractional order network system of application, it is specific as follows
yk=[0.1 0.3] xk+vk
Formula mid-score rank order n1=0.7, n2=1.2, the random packet loss rate of phasor measurement unit metric data is 0.3, is
Unite noise wkWith measurement noise vkThe covariance matrix met is respectively
With the method for the present invention to the non-linear fractional order network of embodiment carry out state estimation when, state estimation it is initial
Value x0=[0 0]T；Greatest iteration estimates moment N=150, adaptive equalization technology moving window value L=10, Initial state estimation
Error co-variance matrixWith control input variable ukRespectively
Above-described embodiment measurement signal data with the linear fractional rank network system of packet loss occur, respectively with traditional point
Number rank Kalman filtering algorithm (related parameter values needed for it are identical with the initial parameter values of the method for the present invention), and base of the present invention
System state variables are estimated in the fractional order network system situation estimation method of adaptive equalization technology.Using traditional
Fractional order Kalman filtering algorithm state estimation result is as shown in Fig. 2, evaluated error is as shown in Figure 3；Using the bright method of this law
State estimation result is as shown in figure 4, evaluated error is as shown in Figure 5.
Complex chart 2 and test result shown in Fig. 3, it can be deduced that such as draw a conclusion: since metric data is in transmission channel
Data random packet loss can occur, so, traditional fractional order Kalman filtering method for estimating state cannot achieve in such cases
System mode accurately estimate.
Complex chart 4 and test result shown in fig. 5, it can be deduced that such as draw a conclusion: the present invention is based on adaptive equalization technologies
Fractional order network system situation estimation method may be implemented to compensate the packet loss of metric data dynamic, and then complete paired systems shape
The accurate tracking and estimation of state.
Claims (1)
1. a kind of fractional order network system situation estimation method based on adaptive equalization technology, which is characterized in that including as follows
Step:
(1) setting filters relevant initial value, such as state estimation initial valueState estimation error covarianceSystem noise and
The initial value for measuring covariance matrix is respectively Qk, Rk, dynamic estimation window value L and greatest iteration moment N；
(2) the status predication value at k+1 moment is calculatedCalculation formula is as follows
In formulaFor the state estimation at k moment；
(3) adaptive equalization technical method is utilized, the noise covariance matrix value Q at k+1 moment is calculatedk+1, Rk+1；
(4) the status predication error covariance at k+1 moment is calculatedCalculation formula is as follows
(5) the general Kalman filtering gain G at k+1 moment is calculatedk+1, calculation formula is as follows
In formula ()-1To ask inverse of a matrix operation；
(6) the evaluated error covariance at k+1 moment is calculatedCalculation formula is as follows
I is the unit matrix of corresponding dimension in formula；
(7) state estimation at k+1 moment is calculatedCalculation formula is as follows
(8) the then iteration stopping as k+1 > N, output estimation result；Conversely, then repeating our department (2)-(7) step by step, carry out down
The state estimation at one moment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710082557.8A CN106972949B (en) | 2017-02-16 | 2017-02-16 | A kind of fractional order network system situation estimation method based on adaptive equalization technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710082557.8A CN106972949B (en) | 2017-02-16 | 2017-02-16 | A kind of fractional order network system situation estimation method based on adaptive equalization technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106972949A CN106972949A (en) | 2017-07-21 |
| CN106972949B true CN106972949B (en) | 2019-10-18 |
Family
ID=59334652
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710082557.8A Active CN106972949B (en) | 2017-02-16 | 2017-02-16 | A kind of fractional order network system situation estimation method based on adaptive equalization technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106972949B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107977489A (en) * | 2017-11-08 | 2018-05-01 | 南京邮电大学 | A kind of design method of the guaranteed cost state estimator of complex network |
| CN109033017B (en) * | 2018-05-25 | 2022-05-13 | 浙江工业大学 | Vehicle roll angle and pitch angle estimation method under packet loss environment |
| CN109612738B (en) * | 2018-11-15 | 2020-02-21 | 南京航空航天大学 | Distributed filtering estimation method for improving gas path performance of turbofan engine |
| CN113507130B (en) * | 2021-08-06 | 2023-10-31 | 剑科云智(深圳)科技有限公司 | Power grid state estimation method and system of real-time data communication system based on DPMU |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104767656A (en) * | 2015-04-10 | 2015-07-08 | 中国电力科学研究院 | Network flow characteristic analysis method based on fractional order Fourier transformation |
| CN105449699A (en) * | 2016-01-11 | 2016-03-30 | 东北电力大学 | Nonlinear fractional order auto disturbance rejection damping control method of doubly fed induction generators |
| CN105931130A (en) * | 2016-04-11 | 2016-09-07 | 南京工业大学 | Improved ensemble Calman filter estimation method considering measurement signal loss |
-
2017
- 2017-02-16 CN CN201710082557.8A patent/CN106972949B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104767656A (en) * | 2015-04-10 | 2015-07-08 | 中国电力科学研究院 | Network flow characteristic analysis method based on fractional order Fourier transformation |
| CN105449699A (en) * | 2016-01-11 | 2016-03-30 | 东北电力大学 | Nonlinear fractional order auto disturbance rejection damping control method of doubly fed induction generators |
| CN105931130A (en) * | 2016-04-11 | 2016-09-07 | 南京工业大学 | Improved ensemble Calman filter estimation method considering measurement signal loss |
Non-Patent Citations (1)
| Title |
|---|
| 网络化最优预测状态估计;唐斌等;《控制理论与应用》;20110531;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106972949A (en) | 2017-07-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106972949B (en) | A kind of fractional order network system situation estimation method based on adaptive equalization technology | |
| CN107590317B (en) | Generator dynamic estimation method considering model parameter uncertainty | |
| CN107765347B (en) | Short-term wind speed prediction method based on Gaussian process regression and particle filtering | |
| Li et al. | A nonparametric assessment of properties of space–time covariance functions | |
| CN105929340B (en) | A method of battery SOC is estimated based on ARIMA | |
| CN110081923B (en) | Fault detection method and device for automatic acquisition system of field baseline environmental parameters | |
| Huang et al. | Robust event-triggered state estimation: A risk-sensitive approach | |
| CN112187528A (en) | Industrial control system communication flow online monitoring method based on SARIMA | |
| CN106971076A (en) | A kind of water quality of river Monitoring Data sequential encryption method | |
| CN114583767B (en) | Data-driven wind power plant frequency modulation response characteristic modeling method and system | |
| Kumar et al. | Power system dynamic state estimation using kalman filtering technique | |
| Wang et al. | Parameters estimation of electromechanical oscillation with incomplete measurement information | |
| Hernández et al. | Comparison between WLS and Kalman Filter method for power system static state estimation | |
| Wahlberg et al. | Identification of Wiener systems with process noise is a nonlinear errors-in-variables problem | |
| CN110501686A (en) | A kind of method for estimating state based on NEW ADAPTIVE high-order Unscented kalman filtering | |
| CN107656905B (en) | Air quality data real-time calibration method using error transfer | |
| Papadopoulos et al. | Online parameter identification and generic modeling derivation of a dynamic load model in distribution grids | |
| CN114792053B (en) | Reliability evaluation method based on initial value-rate related degradation model | |
| CN106936628B (en) | It is a kind of meter and sensor fault fractional order network system situation estimation method | |
| CN106878076B (en) | The fractional order network system situation estimation method of meter and data packetloss and gain disturbance | |
| Jiang et al. | A simulation analytics approach to dynamic risk monitoring | |
| Dingari et al. | Time series analysis for long memory process of air traffic using arfima | |
| CN103258144B (en) | Online static load modeling method based on data of fault recorder | |
| Madhag et al. | Online sensor noise covariance identification using a modified adaptive filter | |
| Tonchev et al. | Learning Graph Convolutional Neural Networks to Predict Radio Environment Maps |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 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 |