CN114977192B - Wind farm grid-connected point voltage optimal control method for resisting random false data injection - Google Patents
Wind farm grid-connected point voltage optimal control method for resisting random false data injection Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention particularly provides a grid-connected point voltage optimization control method of a wind power plant for resisting random false data injection, aiming at the malicious information attack risks caused by wide access of fans, wide use of sensors and mass information transmission in the grid optimization operation process.
Description
Technical Field
The invention relates to a wind farm grid-connected point voltage optimal control method, in particular to a wind farm grid-connected point voltage optimal control method for resisting random false data injection, which solves the problem of malicious data injection by the voltage optimal control method in a grid-connected wind farm, reserves the traditional voltage optimal control function, performs reactive power output control on SVC and each DFIG, and reduces the attack influence while keeping the grid-connected point voltage stable.
Background
Along with the adjustment of the energy structure, the development and the utilization of new energy represented by wind energy are rapidly developed, and the wind power installation capacity and the wind generation grid-connected speed increase of China are in the front of the world at present. However, the instability of wind energy also has a certain influence on the system along with the increase of the scale of wind power grid connection, such as difficult prediction of wind power generation, reactive unbalance, reduced voltage stability and the like. The wind power grid connection of China generally adopts a form of centralized grid connection of large-scale wind farms, and from the perspective of electrical connection, the structure among wind farms is characterized by grouping and sheeting, and strong electrical connection and voltage reactive power coupling can exist among wind farms in the same area. After each wind field group passes through the grid-connected point access system, each wind field group meets the reactive power demand of the wind field group, and meanwhile, reactive power support is needed to be carried out on the grid-connected point through each reactive power regulating device so as to maintain the stability of the voltage of the grid-connected point.
The wind farm in China is generally provided with reactive compensation equipment such as a capacitor bank, a Static Var Generator (SVG) and the like, the operation characteristics of various different compensation equipment are different, and various devices do not have unified coordination control during operation, so that repeated adjustment is easily caused among different reactive compensation equipment connected with the same grid-connected point, a double-fed fan with reactive compensation function and dynamic reactive compensation equipment, the high-efficiency control of reactive voltage cannot be realized, and the equipment loss is increased. The large-scale wind power receiving power grid can have a certain influence on the voltage stability of the grid-connected point of the system, the coordination control strategy among the reactive compensation devices is researched by using the operation characteristics of different reactive compensation devices, and different reactive compensation measures are adopted under different voltage disturbance conditions to maintain the voltage stability of the grid-connected point of the wind field.
To address this challenge, monitoring and data acquisition (SCADA) systems and large-scale sensors/actuators are widely used in the power grid. The development and application of advanced information and communication technology and related equipment make the power grid more vulnerable to network attack, and pose a serious threat to the safe operation of the power grid. Heretofore, network attacks have caused numerous power incidents worldwide, and sufficiently complex attacks can lead to catastrophic consequences such as over/under voltage, equipment damage, cascading failures, renewable energy outages, and the like.
Wind power has received a great deal of attention as a representative of renewable energy sources. Meanwhile, the information security of the grid-connected wind power plant is also widely focused by academia and industry. And in the presence of renewable energy sources, grid-connected point voltage control is an important component for maintaining the voltage stability of the power grid. The influence of information attack on grid-connected point voltage control is reduced or even eliminated, and the method becomes a difficult problem to be solved.
Therefore, it is highly desirable to provide a wind farm grid-connected point voltage optimization control method for resisting random false data injection to solve the above problems.
Disclosure of Invention
In order to solve the problems, the invention provides a grid-connected point voltage optimization control method of a wind power plant for resisting random false data injection, which considers a plurality of optimization targets such as stable grid-connected point voltage of the wind power plant, reactive power compensation devices (SVCs) and reactive power output of fans, and the like, divides each fan unit in the wind power plant to support grid-connected point voltage in cooperation with the SVCs, and based on tubular model predictive control, provides the grid-connected point voltage optimization control method for resisting random false data injection attack, so that reactive power output of each fan and the SVCs is adjusted according to reference quantity given by optimization, and further reduces the adjustment quantity of each fan in each control period while stabilizing the grid-connected point voltage so as to meet the requirement of fan regulation rate. The invention fully considers the attack risk of malicious information, and has the attack resistance capability while realizing the voltage optimization control of the grid-connected point.
The voltage optimization control method in the grid-connected wind power plant solves the problem of malicious data injection, reserves the traditional voltage optimization control function, performs reactive power output control on SVC and each DFIG, and reduces attack influence while keeping the voltage of the grid-connected point stable.
The invention provides a wind farm grid-connected point voltage optimal control method for resisting random false data injection, which comprises the following steps:
s1: according to the structure of the wind power plant, a first-order dynamic control model of the wind power plant containing a fan and SVC is established;
s2: according to the attack scene analysis, two attack scene models are established: sensor attacks and actuator attacks;
s3: establishing a grid-connected point voltage optimization target according to control requirements;
s4: and establishing an inner layer ideal controller and an outer layer tracking controller for grid-connected point voltage optimization control according to a tubular model predictive control theory, and calculating to obtain a reactive power output plan of each fan and SVC.
Preferably, the fan and the SVC first-order dynamic control model in S1 are shown in formulas (1) and (2), respectively:
wherein ΔQW The reactive output variable quantity of the fan in the current control period is used,is delta Q W Reference value of DeltaQ S For the reactive output variable quantity of SVC in the current control period, T W and TS For the time constants in the fan and SVC control models,is delta Q W Reference value of DeltaV int Integration of the deviation between the actual value of the voltage of SVC and the reference value,/>Is a reference value for the voltage variation value of the SVC in the current control period.
Preferably, said A s 、E S and BS For the control matrix, it is specifically defined as follows:
in the formula ,KP and KI Is a PI controller parameter of the SVC,
thus, the first order dynamic control model of a wind farm can be expressed as:
wherein ：
the matrices a and B are defined as:
preferably, the attack scene model in S2 is:
assume that the attack vector is
Sensor attack
When an attack vector is injected into the sensor, a wind farm state vector x is transmitted to the control center a =x+α. The attacked wind farm control model is:
preferably, the attack scene model in S2 is:
assume that the attack vector is
Actuator attack
When an attack vector is injected into the actuator, a wind farm control vector u is transmitted to the control center a =u+α. The attacked wind farm control model is:
preferably, the optimization objective function in S3 is as shown in equation 11,
wherein ,ΔVPOC Is the deviation between the reference value and the actual value of the voltage of the grid-connected point.
The optimization function contains two optimization objectives:
i) The deviation between the actual value of the grid-connected point voltage and the reference value is minimum, namely the grid-connected point voltage is as close to the reference value as possible;
ii) the reactive power output change of the SVC and the fan is as small as possible, namely the grid-connected point voltage stabilization is realized on the premise of keeping the output as stable as possible, and the aim is mainly to consider the problem of the SVC and the fan regulation rate.
Preferably, the inner layer ideal controller in S4 is as shown in formula (12):
wherein ,is a system state quantity which is not considered for attack (i.e. under ideal conditions), is +.>Is the control quantity of the system in the ideal scene.
Preferably, the design of the outer layer tracking controller in S4 is shown in formula (13):
μ x and μu And the weight coefficients of the tracking state reference value and the control quantity reference value in the outer layer tracking controller are obtained.
Compared with the prior art, the invention has the following beneficial effects:
according to the wind farm with the fans and the SVC, when the system is attacked by malicious information, the reactive power output of the single fan and the SVC in the wind farm is optimized based on the tubular model prediction control method, so that the voltage of the grid-connected point can be maintained at a relatively stable level and is not influenced by the malicious attack.
The invention provides a grid-connected point voltage optimization control method for resisting random false data injection, which considers a plurality of optimization targets such as stable grid-connected point voltage of a wind power plant, reactive power compensation (SVC) and reactive power output of a fan, divides each fan unit in the wind power plant to support grid-connected point voltage in cooperation with the SVC, and provides the grid-connected point voltage optimization control method for resisting random false data injection attack based on tubular model predictive control, so that reactive power output of each fan and the SVC is adjusted according to the reference quantity given by optimization, and further, the adjustment quantity of each fan in each control period is reduced while the grid-connected point voltage is stable, and the requirement of fan regulation rate is met. The invention fully considers the attack risk of malicious information, and has the attack resistance capability while realizing the voltage optimization control of the grid-connected point.
Drawings
FIG. 1 is a schematic diagram of a malicious attack injection location;
FIG. 2 is a schematic diagram of a tubular model predictive control effect;
FIG. 3 is a flow chart of a voltage optimization control based on a tubular model predictive control;
FIG. 4 is a schematic diagram of a test system;
FIG. 5 is a graph of the daily output of a wind farm;
FIG. 6 is a graph of experimental results of reactive output of a wind farm when a sensor is attacked by random false data injection;
FIG. 7 is a graph of experimental results of grid-tie point voltage when a sensor is attacked by random dummy data injection;
FIG. 8 is a graph of experimental results of reactive output of a wind farm when an actuator is attacked by random false data injection;
fig. 9 is a graph of experimental results of grid-tie voltage when the actuator is attacked by random dummy data injection.
Detailed Description
The invention is further illustrated below in connection with specific embodiments.
The present invention will be more fully understood by those skilled in the art by the following examples, which are not intended to limit the scope of the present invention in any way.
The present invention will be more fully understood by those skilled in the art by the following examples, which are not intended to limit the scope of the present invention in any way. In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
A wind farm grid-connected point voltage optimal control method for resisting random false data injection comprises the following steps:
s1: according to the structure of the wind power plant, a first-order dynamic control model of the wind power plant containing a fan and SVC is established;
the fan and SVC first-order dynamic control model are shown in formulas (1) and (2) respectively:
wherein ΔQW The reactive output variable quantity of the fan in the current control period is used,is delta Q W Reference value of DeltaQ S For the reactive output variable quantity of SVC in the current control period, T W and TS For the time constants in the fan and SVC control models,is delta Q W Reference value of DeltaV int Integration of the deviation between the actual value of the voltage of SVC and the reference value,/>A is the reference value of the voltage change value of SVC in the current control period s 、E S and BS For the control matrix, it is specifically defined as follows:
in the formula ,KP and KI Is a PI controller parameter of the SVC,
thus, the first order dynamic control model of a wind farm can be expressed as:
wherein ：
the matrices a and B are defined as:
s2: according to the attack scene analysis, two attack scene models are established: sensor attacks and actuator attacks; the attack scene model is as follows:
assume that the attack vector is
Sensor attack
When an attack vector is injected into the sensor, a wind farm state vector x is transmitted to the control center a =x+α. The attacked wind farm control model is:
actuator attack
When an attack vector is injected into the actuator, a wind farm control vector u is transmitted to the control center a =u+α. The attacked wind farm control model is:
s3: establishing a grid-connected point voltage optimization target according to control requirements;
the optimization objective function is shown in equation 11,
wherein ,ΔVPOC Is the deviation between the reference value and the actual value of the voltage of the grid-connected point.
The optimization function contains two optimization objectives:
i) The deviation between the actual value of the grid-connected point voltage and the reference value is minimum, namely the grid-connected point voltage is as close to the reference value as possible;
ii) the reactive power output change of the SVC and the fan is as small as possible, namely the grid-connected point voltage stabilization is realized on the premise of keeping the output as stable as possible, and the aim is mainly to consider the problem of the SVC and the fan regulation rate.
S4: according to a tubular model predictive control theory, an inner layer ideal controller and an outer layer tracking controller for grid-connected point voltage optimal control are established, reactive output plans of each fan and SVC are calculated, and the inner layer ideal controller and the outer layer tracking controller are respectively designed as shown in a formula (12) and a formula (13):
wherein ,is a system state quantity which is not considered for attack (i.e. under ideal conditions), is +.>Is the control quantity of the system in the ideal scene. Mu (mu) x and μu And the weight coefficients of the tracking state reference value and the control quantity reference value in the outer layer tracking controller are obtained.
Specific examples of the invention:
experiments were performed on a grid-connected model of a wind farm consisting of 20 5MW fans and 1 10MW SVC, the model structure is shown in FIG. 4, and the daily output curve of the wind farm is shown in FIG. 5. The invention is tested for the following two scenes respectively:
1) Attacks occur on the sensor;
2) Attacks occur on the executor.
Wherein, attacks are all launched at t=50s, and the whole test duration is 600s.
The experimental screenshot is as follows:
(1) As can be seen from FIG. 6, before the attack occurs (0-50 s), the reactive output condition of the algorithm provided by the invention is the same as that of the conventional model predictive control algorithm, and the capability of realizing the same target optimal performance under the condition of no interference is proved to be the same. After attack (50-600 s), reactive output fluctuation by adopting a traditional model predictive control algorithm is severe, and deviation from a reference value is large, as shown by a blue solid line in fig. 6. The reactive power output can be regulated to be near the reference value under the control of the algorithm provided by the invention.
(2) Fig. 7 shows the voltage situation when the wind power grid-connected power system is under malicious attack and not under malicious attack. At 0-50 s, the traditional model predictive control algorithm and the algorithm provided by the invention can be stabilized to the rated value (1.00 p.u). The traditional model predictive control algorithm has larger voltage deviation and severe fluctuation within 50-600 s after attack. However, in the algorithm provided by the invention, the voltage fluctuation is smaller near the rated value, so that the influence of malicious attack on the grid-connected point voltage in the wind power grid-connected power system is basically eliminated. Therefore, the algorithm provided by the invention plays an important role in maintaining the voltage stability of the grid-connected point which can cope with network attack.
(3) FIG. 8 is a graph of experimental results of reactive output of a wind farm when an actuator is attacked by random dummy data injection. Fig. 9 is a graph of experimental results of grid-tie voltage when the actuator is attacked by random dummy data injection. The comparison result is similar to the situation when the sensor is attacked by random false data injection, and further analysis and explanation are omitted.
It should be understood that parts of the specification not specifically set forth herein are all prior art.
While particular embodiments of the present invention have been described above with reference to the accompanying drawings, it will be understood by those skilled in the art that these are by way of example only, and that various changes and modifications may be made to these embodiments without departing from the principles and spirit of the invention. The scope of the invention is limited only by the appended claims.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (4)
1. The wind farm grid-connected point voltage optimal control method for resisting random false data injection is characterized by comprising the following steps of: the method comprises the following steps:
s1: according to the structure of the wind power plant, a first-order dynamic control model of the wind power plant containing a fan and SVC is established;
s2: according to the attack scene analysis, two attack scene models are established: sensor attacks and actuator attacks, respectively;
the attack scene model in the S2 is as follows: assume that the attack vector is
The sensor attacks are:
when an attack vector is injected into the sensor, a wind farm state vector x is transmitted to the control center a =x+α, then the attacked wind farm control model is:
the attack of the executor is as follows:
when an attack vector is injected into the actuator, a wind farm control vector u is transmitted to the control center a =u+α, then the attacked wind farm control model is:
s3: establishing a grid-connected point voltage optimization target according to control requirements;
s4: according to a tubular model predictive control theory, an inner layer ideal controller and an outer layer tracking controller for grid-connected point voltage optimal control are established, and reactive output plans of each fan and SVC are calculated;
the inner layer ideal controller in the S4 is shown in a formula (12):
wherein ,is a system state quantity in ideal scene without considering attack, +.>Is the system control quantity in ideal scene;
the design of the outer layer tracking controller in the S4 is shown in the formula (13):
μ x and μu And the weight coefficients of the tracking state reference value and the control quantity reference value in the outer layer tracking controller are obtained.
2. The wind farm grid-connected point voltage optimization control method resistant to random false data injection according to claim 1, wherein the method comprises the following steps: the fan and the SVC first-order dynamic control model in the S1 are respectively shown in formulas (1) and (2):
A s 、E S and BS For controlling the matrix, where DeltaQ W The reactive output variable quantity of the fan in the current control period is used,is delta Q W Reference value of DeltaQ S For the reactive output variable quantity of SVC in the current control period, T W and TS For time constant in fan and SVC control model, deltaV int Integration of the deviation between the actual value of the voltage of SVC and the reference value,/>Is a reference value for the voltage variation value of the SVC in the current control period.
3. The wind farm grid-connected point voltage optimization control method resistant to random false data injection according to claim 2, wherein the method comprises the following steps: the said
in the formula ,KP and KI Is a PI controller parameter of the SVC,
thus, the first order dynamic control model of a wind farm can be expressed as:
wherein ：
the matrices a and B are defined as:
4. the wind farm grid-connected point voltage optimization control method resistant to random false data injection according to claim 1, wherein the method comprises the following steps: the optimization objective function in S3 is shown in formula (11),
wherein ,ΔVPOC Is the deviation between the reference value and the actual value of the voltage of the grid-connected point,
the optimization function contains two optimization objectives:
i) The deviation between the actual value of the grid-connected point voltage and the reference value is minimum, namely the grid-connected point voltage is as close to the reference value as possible;
ii) the reactive power output change of the SVC and the fan is as small as possible, namely the grid-connected point voltage stabilization is realized on the premise of keeping the output as stable as possible, and the aim is mainly to consider the problem of the SVC and the fan regulation rate.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107659554A (en) * | 2016-07-25 | 2018-02-02 | 通用电气公司 | Method and system for detection of false Data Injection Attacks |
| CN110942109A (en) * | 2019-12-17 | 2020-03-31 | 浙江大学 | PMU false data injection attack prevention method based on machine learning |
| CN112701723A (en) * | 2020-12-22 | 2021-04-23 | 华南理工大学 | Micro-grid economic control system and method for resisting data tampering attack |
| CN112804197A (en) * | 2020-12-29 | 2021-05-14 | 湖南大学 | Power network malicious attack detection method and system based on data recovery |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9507367B2 (en) * | 2012-04-09 | 2016-11-29 | Clemson University | Method and system for dynamic stochastic optimal electric power flow control |
-
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- 2022-05-27 CN CN202210588521.8A patent/CN114977192B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107659554A (en) * | 2016-07-25 | 2018-02-02 | 通用电气公司 | Method and system for detection of false Data Injection Attacks |
| CN110942109A (en) * | 2019-12-17 | 2020-03-31 | 浙江大学 | PMU false data injection attack prevention method based on machine learning |
| CN112701723A (en) * | 2020-12-22 | 2021-04-23 | 华南理工大学 | Micro-grid economic control system and method for resisting data tampering attack |
| CN112804197A (en) * | 2020-12-29 | 2021-05-14 | 湖南大学 | Power network malicious attack detection method and system based on data recovery |
Non-Patent Citations (1)
| Title |
|---|
| 赵俊华.乌克兰事件的启示:防范针对电网的虚假数据注入攻击.《电力系统自动化》.2016,全文. * |
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