CN109639736A - A kind of Power system state estimation malicious attack detection and localization method based on OPTICS - Google Patents
A kind of Power system state estimation malicious attack detection and localization method based on OPTICS Download PDFInfo
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- CN109639736A CN109639736A CN201910070408.9A CN201910070408A CN109639736A CN 109639736 A CN109639736 A CN 109639736A CN 201910070408 A CN201910070408 A CN 201910070408A CN 109639736 A CN109639736 A CN 109639736A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1416—Event detection, e.g. attack signature detection
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- 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
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- 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
- H04L41/147—Network analysis or design for predicting network behaviour
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Abstract
The invention discloses a kind of Power system state estimation malicious attack detection and localization method based on OPTICS, are related to the test problems of malicious attack in electric system.The present invention establishes the Power system state estimation mathematical model including nonlinear power system state equation and measurement equation, and the relationship between the quantity of state and measurement of each node is established according to Wide Area Measurement System, SCADA system；Construct the Power system state estimation model based on UKF；The Power system state estimation model based on UKF is constructed, mathematical model is obtained in each node and is based respectively on Wide Area Measurement System, the state estimation of SCADA system, using the deviation of state estimation twice of each node as detected sample data sequence；It is detected using OPTICS algorithm, obtains abnormal data, and the malicious attack data in SCADA are positioned according to abnormal data, complete malicious attack detection and positioning.Reliable basis is provided for the decision of Energy Management System, malicious attack influence, safeguards system safe and economical operation is effectively reduced.
Description
Technical field
The present invention relates to the test problems of malicious attack in electric system, more particularly to a kind of sorted based on point to identify cluster
The electric system shape of structure (Ordering Points to Identify the Clustering Structure, OPTICS)
State estimates malicious attack detection and localization method.
Background technique
The construction of smart grid is that China's power industry brings many opportunities and challenges, with the hair of Power System Intelligent
Exhibition, data acquisition with monitoring control (Supervisory Control And Data Acquisition, SCADA) system in by
Sophisticated equipment and the communication technology are gradually incorporated, power grid realizes the method for operation efficiently, economic, but exacerbates electric power system data simultaneously
Security risk, bring opportunity to hacker.Therefore, the false data note in reasonable manner detection SCADA system is taken
Enter attack, has great significance to the safe and stable operation for realizing electric system.
Summary of the invention
It is an object of the invention to propose a kind of Power system state estimation malicious attack detection based on OPTICS and fixed
Position method, it is intended to the attack data injected in SCADA are monitored, the decision for the safe and stable operation of electric system mentions
For reliable basis, built for China's smart grid, reducing malicious attack influences, safeguards system safe and stable operation provide science according to
According to.
In order to solve the above technical problems, technical solution provided by the present invention are as follows: a kind of electric system based on OPTICS
State estimation malicious attack detection and localization method, it is characterised in that: it includes non-that the method content, which includes the following steps: to establish,
The Power system state estimation mathematical model of linear electrical system state equation and measurement equation, foundation Wide Area Measurement System,
SCADA system establishes the relationship between the quantity of state and measurement of each node；Construct the Power system state estimation based on UKF
Model；The Power system state estimation model based on UKF is constructed, mathematical model is obtained in each node and is based respectively on wide area measurement
System, the state estimation of SCADA system, using the deviation of state estimation twice of each node as detected sample data sequence
Column；It is detected using OPTICS algorithm, obtains abnormal data, and the malicious attack number in SCADA is positioned according to abnormal data
According to completion malicious attack detection and positioning.
A further technical solution lies in: the Power system state estimation mathematical model is
Nonlinear power system state equation: xk=f (xk-1)+qk-1
Measurement equation: yk=h (x)+rk
In formula, xkIndicate that n ties up state vector, ykIndicate that m dimension measures vector；F () is the state transition function at k-1 moment
Vector, h () are to measure function vector；qk-1~N (0, Qk) it is systematic error, rk~N (0, Rk) it is error in measurement.
A further technical solution lies in: the relationship between the quantity of state and measurement of each node is as follows:
Quantity of state xi=[Vi,θi]TFor node voltage amplitude and phase angle, measurement yk=[Pk,Qk,Vk,θk]T∈RmFor node
Active power, reactive power, voltage magnitude and phase angle；When the measurement unit of node i is PMU, measuring value is corresponding section
Point voltage magnitude and phase angle, i.e. xi=[Vi,θi]T, yi=[Vi,θi]T；When using SCADA system measurement, h (x) is non-linear
Function ignores line power, measurement y for node ii=[Pi,Qi,Vi]TIt is expressed as follows with the relationship of quantity of state:
A further technical solution lies in: the Power system state estimation model based on UKF mainly includes that UKF carries out state
The key technology of estimation:
(1) UT process
It 1) is that mean value is for nonlinear transformation y=g (x), xVariance is PxStochastic variable, select suitable sampling
Strategy determines Sigma point set { χiAnd corresponding weight { Wi mAnd { Wi c, so that:
In formula, L is the number of Sigma point；
2) nonlinear transformation is carried out to all Sigma points, transformed point set is expressed as Yi=g (χi)；
3) to transformed point set { YiBy being weighted obtain statisticAnd PyAre as follows:
(2) it predicts
According to the quantity of state x at k-1 momentk-1With covariance Pk-1And selected sampling policy constructs Sigma point set
{χi,k-1, in conjunction with the state equation of electric system in step 1, obtain the statistic at k moment are as follows:
χi,k|k-1=f (χi,k-1)+qk-1
According toPk|k-1Construct Sigma point setIn conjunction with the measurement equation of electric system in step 1, obtain
Statistic are as follows:
The then measurement predictor at k momentAuto-covariance battle array SkWith cross covariance battle array CkAre as follows:
(3) it updates
Calculate gain Kk, k moment state estimation xkWith covariance estimated value PkAre as follows:
When carrying out UT transformation, the sampling policy of Sigma point is most important to state estimation result, and the present invention uses ratio
Symmetric sampling determines Sigma point set, and the principle is as follows:
λ=α2(n+κ)-n
The weight of mean value and variance are as follows:
Wherein, free parameter κ is used to reflect the high level matrix information of given distribution, and α is used to indicate ratio modifying factor, can
To reflect Sigma point with mean valueDistance, β indicate prior distribution High Order Moment knowledge,It is matrix square root
I-th column.
A further technical solution lies in: the OPTICS algorithm is as follows:
Input: sample data D, the radius of neighbourhood ε, minimum neighborhood number of objects MinPts
Step 1 establishes two queues: ordered queue Q (storage sample point sorts by reach distance), result queue M (output
The ordered queue of sample point)
If in step 2, D sample point all handled, algorithm terminates, otherwise selected from D one it is untreated and for
The point p of kernel object, which is put into result queue M, is obtained core neighborhood of a point object-point N and is put into ordered queue
In Q, and arranged according to reach distance ascending order；
If step 3, ordered queue Q are sky, step 2 is gone to, otherwise, first point q is taken out from ordered queue Q
(reach distance is minimum)；
3.1 judge whether the point is core point, are not to go to step 3, if the point is not in result queue M if being,
The point is stored in result queue；
If 3.2 points are core points, the neighborhood object N ' of q is obtained, these points are put into ordered queue Q, and will be orderly
Point in queue is resequenced according to reach distance, if the point in ordered queue and new reach distance is smaller,
Update the reach distance of the point；
3.3 repeat step 3, until ordered queue Q is sky；
Step 4, algorithm terminate, and export the ordered sample point in result queue M.
The present invention has the advantage that compared with existing electric system attack detection method
The present invention carries out state estimation to electric system using UKF, constructs the Power system state estimation mould based on UKF
Type completes state estimation twice by changing measurement system, avoids detecting residual error, obtains the inclined of state estimation twice
Difference provides sample data for malicious attack detection method.The present invention uses OPTICS method, right using MATLAB as programming language
Nominative testing system is programmed, and sample data obtained by input test system state estimation adjusts initial parameter, is clicked through to sample
Row is handled and is ranked up according to reach distance, according to the ordered queue of output, to detect whether SCADA measurement system is disliked
Meaning attack, and position injection malicious attack data specific location, for Energy Management System (EMS) decision provide reliably according to
According to malicious attack, which is effectively reduced, to be influenced, safeguards system safe and economical operation.
Detailed description of the invention
Fig. 1 is attack detecting flow chart of the invention；
Fig. 2 is two key concepts that the present invention uses OPTICS algorithm；
Wherein when MinPts=5, core distance and q1Distance r (q1, o), q2Reach distance r (q2,o)；
Fig. 3 is the flow chart that the present invention uses OPTICS method.
Specific embodiment
Illustrate to propose a kind of Power system state estimation malicious attack based on OPTICS to the present invention with reference to the accompanying drawing
Detection is described further with localization method:
A kind of Power system state estimation malicious attack detection and localization method based on OPTICS of the invention, such as Fig. 1
Shown, the content of this method includes the following steps:
Step 1: Power system state estimation mathematical model is established；
The Power system state estimation mathematical model mainly includes the expression formula and two of state equation and measurement equation
Relationship between person.
In conjunction with system running state, nonlinear power system state equation is xk=f (xk-1)+qk-1；Measurement equation are as follows: yk
=h (x)+rk。
In formula, xkIndicate that n ties up state vector, ykIndicate that m dimension measures vector；F () is the state transition function at k-1 moment
Vector, h () are to measure function vector；qk-1~N (0, Qk) it is systematic error, rk~N (0, Rk) it is error in measurement.
Wherein, the relationship between the quantity of state and measurement of each node is as follows: quantity of state xi=[Vi,θi]TFor node electricity
Pressure amplitude value and phase angle, measurement yk=[Pk,Qk,Vk,θk]T∈RmFor the active power of node, reactive power, voltage magnitude and phase
Angle.When the measurement unit of node i is synchronous phasor measurement unit (PhasorMeasurementUnite, PMU), measuring value is
For respective nodes voltage magnitude and phase angle, i.e. xi=[Vi,θi]T, yi=[Vi,θi]T；When using SCADA system measurement, h (x)
It is nonlinear function, for node i, ignores line power, measurement yi=[Pi,Qi,Vi]TIt is indicated with the relationship of quantity of state
It is as follows:
For the malicious attack detection problem of SCADA metric data, the present invention need to carry out state estimation twice, main region
Not Wei used measurement system it is different, it is a kind of using Wide Area Measurement System (Wide Area Measurement System,
WAMS it) is measured by PMU, realizes and the real time high-speed rate of the whole network synchronous phase angle and power grid key data is acquired；It is another
Metric data is only obtained from SCADA system.Due to being cut in PMU metric data comprising GPS time, it is believed that attacker is without normal direction
Therefore injection attacks in PMU can carry out school to the state estimation that SCADA system obtains by the state estimation result that PMU is obtained
It tests.
Step 2: Power system state estimation model of the construction based on UKF；
Power system state estimation model of the construction based on UKF of the present invention mainly includes that UKF carries out state estimation
Key technology.
UKF reaches propinquity effect by the probability density that non-loss transformation (UT) simulates nonlinear function.The basic principle is that
Centered on the average point of quantity of state, one group of the random distribution Sigma point with weight around average point carries out these points
Nonlinear transformation obtains quantity of state and variance.The key technology that UKF carries out state estimation is as follows:
(1) UT process
It 1) is that mean value is for nonlinear transformation y=g (x), xVariance is PxStochastic variable, select suitable sampling
Strategy determines Sigma point set { χiAnd corresponding weight { Wi mAnd { Wi c, so that:
In formula, L is the number of Sigma point.
2) nonlinear transformation is carried out to all Sigma points, transformed point set is expressed as Yi=g (χi)。
3) to transformed point set { YiBy being weighted obtain statisticAnd PyAre as follows:
(2) it predicts
According to the quantity of state x at k-1 momentk-1With covariance Pk-1And selected sampling policy constructs Sigma point set
{χi,k-1, in conjunction with the state equation of electric system in step 1, obtain the statistic at k moment are as follows:
χi,k|k-1=f (χi,k-1)+qk-1
According toPk|k-1Construct Sigma point setIn conjunction with the measurement equation of electric system in step 1, obtain
Statistic are as follows:
The then measurement predictor at k momentAuto-covariance battle array SkWith cross covariance battle array CkAre as follows:
(3) it updates
Calculate gain Kk, k moment state estimation xkWith covariance estimated value PkAre as follows:
When carrying out UT transformation, the sampling policy of Sigma point is most important to state estimation result, and the present invention uses ratio
Symmetric sampling determines Sigma point set, and the principle is as follows:
λ=α2(n+κ)-n
The weight of mean value and variance are as follows:
Wherein, free parameter κ is used to reflect the high level matrix information of given distribution, and α is used to indicate ratio modifying factor, can
To reflect Sigma point with mean valueDistance, β indicate prior distribution High Order Moment knowledge,It is matrix square root
I-th column.
Global viewable is realized to key node configuration PMU device in the measurement of PMU device, electric system is reached by UKF
State estimation xse；In conjunction with SCADA measurement system, the state estimation of electric system equally can be obtained by UKF, be denoted as
xse′。
Step 3: the deviation for using OPTICS method processing status to estimate is infused with detecting and positioning in SCADA measurement system
The malicious attack data entered.
Step 4: simulation analysis is carried out to example with MATLAB software
1, example and essential feature are determined
The initial data set that the present invention uses proposes false data detection method to project by IEEE30 standard test system
It is verified, in UKF state estimation procedure, load uses the daily load statistical data of certain network system, acquires within every ten minutes
Once, 24 hours one day totally 144 sampled points complete two respectively in connection with the measurement of Wide Area Measurement System, SCADA measurement system
Secondary mutually independent state estimation indicates same quantity of state with reach distance index using OPTICS detection and localization method
Deviation density, according to the action principle of false data, any node 8 chosen in SCADA measurement injects false data.Benefit
It is emulated with MATLAB programming software.
2, simulation analysis is carried out to example with MATLAB software programming function
It, can be to the false data malicious attack in SCADA measurement system by the mentioned detection method of this project known to emulation
It is effectively detected, and can be accurately positioned in SCADA measurement system while detecting false data and inject false data
Specific location.
The deviation that the present invention uses OPTICS method processing status to estimate is injected with detecting and positioning in SCADA measurement system
Malicious attack data, exactly the deviation of state estimation twice is detected using OPTICS method, based on electric system
Dynamic model and selected measurement mode, determine the relationship of quantity of state and measurement, complete state estimation twice in conjunction with UKF,
Consideration system ornamental carries out reasonable disposition to PMU device, and state estimation result in deriving step two calculates each quantity of state
Deviation xdev=| xse-xse′| as sample data D, to detect and position the malicious attack data injected in SCADA.
Fig. 2 is the key concept being related in OPTICS method, and the present invention detects malicious attack using this method
With positioning, flow chart is as shown in figure 3, specific implementation step is as follows:
Input: sample data D, the radius of neighbourhood ε, minimum neighborhood number of objects MinPts
Step 1 establishes two queues: ordered queue Q (storage sample point sorts by reach distance), result queue M (output
The ordered queue of sample point)
If in step 2, D sample point all handled, algorithm terminates, otherwise selected from D one it is untreated and for
The point p of kernel object, which is put into result queue M, is obtained core neighborhood of a point object-point N and is put into ordered queue
In Q, and arranged according to reach distance ascending order；
If step 3, ordered queue Q are sky, step 2 is gone to, otherwise, first point q is taken out from ordered queue Q
(reach distance is minimum)；
3.1 judge whether the point is core point, are not to go to step 3, if the point is not in result queue M if being,
The point is stored in result queue；
If 3.2 points are core points, the neighborhood object N ' of q is obtained, these points are put into ordered queue Q, and will be orderly
Point in queue is resequenced according to reach distance, if the point in ordered queue and new reach distance is smaller,
Update the reach distance of the point.
3.3 repeat step 3, until ordered queue Q is sky；
Step 4, algorithm terminate, and export the ordered sample point in result queue M.
Adjustment parameter radius of neighbourhood ε and minimum neighborhood number of objects MinPts is that sample points are clicked through according to could act as core
Row processing, can play booster action, but its slight change does not interfere with the opposite output sequence of sample point to algorithm.It is described
OPTICS detection and positioning link, are exactly handled state estimation result, are estimated based on the UKF POWER SYSTEM STATE
Meter, can accurate, real-time tracking system operating status nominative testing system is programmed using MATLAB as programming language；If
Quantitative examining system, establishes mathematical models of power system, in conjunction with UKF state estimation, provides sample data for detection method, adjusts defeated
Enter parameter, guarantee handles all sample points, finds out isolated point according to output queue and determines its specific location；In software
On the basis of auxiliary, scientific basis is provided for the development and construction of smart grid, the malicious attack in system is effectively examined
It surveys, reduces the probability that electric power accident occurs.
It should be pointed out that only listing property illustrates application method of the invention to this example, and is not intended to limit the present invention.It is any ripe
Such personnel using technology are known, can be modified without departing from the spirit and scope of the present invention to above-described embodiment.Cause
This, the scope of the present invention should be as listed in the claims.
Claims (5)
1. a kind of Power system state estimation malicious attack detection and localization method based on OPTICS, it is characterised in that: described
Method content includes the following steps: that establishing the POWER SYSTEM STATE including nonlinear power system state equation and measurement equation estimates
Mathematical model is counted, the relationship between the quantity of state and measurement of each node is established according to Wide Area Measurement System, SCADA system；
Construct the Power system state estimation model based on UKF；The Power system state estimation model based on UKF is constructed, in each section
Point obtains mathematical model and is based respectively on Wide Area Measurement System, the state estimation of SCADA system, and two next states of each node are estimated
Deviation is counted as detected sample data sequence；It is detected using OPTICS algorithm, obtains abnormal data, and according to exception
Data position the malicious attack data in SCADA, complete malicious attack detection and positioning.
2. a kind of Power system state estimation malicious attack detection based on OPTICS according to claim 1 and positioning side
Method, it is characterised in that: the Power system state estimation mathematical model is
Nonlinear power system state equation: xk=f (xk-1)+qk-1
Measurement equation: yk=h (x)+rk
In formula, xkIndicate that n ties up state vector, ykIndicate that m dimension measures vector；F () is the state transition function vector at k-1 moment,
H () is to measure function vector；qk-1~N (0, Qk) it is systematic error, rk~N (0, Rk) it is error in measurement.
3. a kind of Power system state estimation malicious attack detection based on OPTICS according to claim 1 and positioning side
Method, it is characterised in that: the relationship between the quantity of state and measurement of each node is as follows:
Quantity of state xi=[Vi,θi]TFor node voltage amplitude and phase angle, measurement yk=[Pk,Qk,Vk,θk]T∈RmFor having for node
Function power, reactive power, voltage magnitude and phase angle；When the measurement unit of node i is PMU, measuring value is respective nodes electricity
Pressure amplitude value and phase angle, i.e. xi=[Vi,θi]T, yi=[Vi,θi]T；When using SCADA system measurement, h (x) is nonlinear function,
For node i, ignore line power, measurement yi=[Pi,Qi,Vi]TIt is expressed as follows with the relationship of quantity of state:
4. a kind of Power system state estimation malicious attack detection based on OPTICS according to claim 1 and positioning side
Method, it is characterised in that: the Power system state estimation model based on UKF mainly includes the key technology that UKF carries out state estimation:
(1) UT process
1) for nonlinear transformation y=g (x), x be mean value be x, variance PxStochastic variable, select suitable sampling policy come
Determine Sigma point set { χiAnd corresponding weight { Wi mAnd { Wi c, so that:
In formula, L is the number of Sigma point；
2) nonlinear transformation is carried out to all Sigma points, transformed point set is expressed as Yi=g (χi)；
3) to transformed point set { YiBy being weighted obtain statisticAnd PyAre as follows:
(2) it predicts
According to the quantity of state x at k-1 momentk-1With covariance Pk-1And selected sampling policy constructs Sigma point set
{χi,k-1, in conjunction with the state equation of electric system in step 1, obtain the statistic at k moment are as follows:
χi,k|k-1=f (χi,k-1)+qk-1
According toPk|k-1Construct Sigma point setIn conjunction with the measurement equation of electric system in step 1, counted
Amount are as follows:
The then measurement predictor at k momentAuto-covariance battle array SkWith cross covariance battle array CkAre as follows:
(3) it updates
Calculate gain Kk, k moment state estimation xkWith covariance estimated value PkAre as follows:
When carrying out UT transformation, the sampling policy of Sigma point is most important to state estimation result, and the present invention is symmetrical using ratio
Sampling is to determine Sigma point set, and the principle is as follows:
λ=α2(n+κ)-n
The weight of mean value and variance are as follows:
Wherein, free parameter κ is used to reflect the high level matrix information of given distribution, and α is used to indicate ratio modifying factor, can be anti-
Sigma point is reflected with mean valueDistance, β indicate prior distribution High Order Moment knowledge,It is the i-th of matrix square root
Column.
5. a kind of Power system state estimation malicious attack detection based on OPTICS according to claim 1 and positioning side
Method, it is characterised in that: the OPTICS algorithm is as follows:
Input: sample data D, the radius of neighbourhood ε, minimum neighborhood number of objects MinPts
Step 1 establishes two queues: ordered queue Q (storage sample point sorts by reach distance), result queue M (output sample
The ordered queue of point)
If sample point has all been handled in step 2, D, algorithm terminates, otherwise selected from D one it is untreated and for core
The point p of object, which is put into result queue M, is obtained core neighborhood of a point object-point N and is put into ordered queue Q,
And it is arranged according to reach distance ascending order；
If step 3, ordered queue Q are sky, step 2 is gone to, otherwise, it is (reachable that first point q is taken out from ordered queue Q
Distance is minimum)；
3.1 judge whether the point is core point, are not to go to step 3, if the point, should not in result queue M if being
Point deposit result queue；
If 3.2 points are core points, the neighborhood object N ' of q is obtained, these points are put into ordered queue Q, and by ordered queue
In point resequence according to reach distance, if the point in ordered queue and new reach distance is smaller, updates
The reach distance of the point；
3.3 repeat step 3, until ordered queue Q is sky；
Step 4, algorithm terminate, and export the ordered sample point in result queue M.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112398840A (en) * | 2020-11-06 | 2021-02-23 | 国网山东省电力公司 | Electric power information physical system attack positioning method based on self-adaptive sliding mode observer |
| CN112804197A (en) * | 2020-12-29 | 2021-05-14 | 湖南大学 | Power network malicious attack detection method and system based on data recovery |
| CN113507430A (en) * | 2021-05-10 | 2021-10-15 | 湖南大学 | Method and system for detecting information physical cooperation attack of power system |
| CN113624219A (en) * | 2021-07-27 | 2021-11-09 | 北京理工大学 | Magnetic compass ellipse fitting error compensation method based on OPTICS algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615794A (en) * | 2009-08-05 | 2009-12-30 | 河海大学 | Electrical Power System Dynamic method for estimating state based on no mark transformation Kalman filter |
| CN102062841A (en) * | 2009-11-11 | 2011-05-18 | 北汽福田汽车股份有限公司 | Estimation method and system of state of charge (SOC) of power battery |
-
2019
- 2019-01-25 CN CN201910070408.9A patent/CN109639736A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615794A (en) * | 2009-08-05 | 2009-12-30 | 河海大学 | Electrical Power System Dynamic method for estimating state based on no mark transformation Kalman filter |
| CN102062841A (en) * | 2009-11-11 | 2011-05-18 | 北汽福田汽车股份有限公司 | Estimation method and system of state of charge (SOC) of power battery |
Non-Patent Citations (3)
| Title |
|---|
| 曲正伟等: "用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法", 《电力系统自动化》 * |
| 田甜: "基于无迹卡尔曼滤波的电力系统动态状态估计研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 邬启为: "基于向量空间的文本聚类方法与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112398840A (en) * | 2020-11-06 | 2021-02-23 | 国网山东省电力公司 | Electric power information physical system attack positioning method based on self-adaptive sliding mode observer |
| CN112398840B (en) * | 2020-11-06 | 2022-03-08 | 国网山东省电力公司 | Electric power information physical system attack positioning method based on self-adaptive sliding mode observer |
| CN112804197A (en) * | 2020-12-29 | 2021-05-14 | 湖南大学 | Power network malicious attack detection method and system based on data recovery |
| CN113507430A (en) * | 2021-05-10 | 2021-10-15 | 湖南大学 | Method and system for detecting information physical cooperation attack of power system |
| CN113624219A (en) * | 2021-07-27 | 2021-11-09 | 北京理工大学 | Magnetic compass ellipse fitting error compensation method based on OPTICS algorithm |
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