CN116094769A - Port micro-grid control method for resisting false data injection attack - Google Patents

Port micro-grid control method for resisting false data injection attack Download PDF

Info

Publication number
CN116094769A
CN116094769A CN202211658152.1A CN202211658152A CN116094769A CN 116094769 A CN116094769 A CN 116094769A CN 202211658152 A CN202211658152 A CN 202211658152A CN 116094769 A CN116094769 A CN 116094769A
Authority
CN
China
Prior art keywords
control
voltage
frequency
attack
false data
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.)
Granted
Application number
CN202211658152.1A
Other languages
Chinese (zh)
Other versions
CN116094769B (en
Inventor
马锴
董玉飞
杨婕
郭士亮
王彩璐
袁亚洲
赵朋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yanshan University
Original Assignee
Yanshan University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Yanshan University filed Critical Yanshan University
Priority to CN202211658152.1A priority Critical patent/CN116094769B/en
Publication of CN116094769A publication Critical patent/CN116094769A/en
Application granted granted Critical
Publication of CN116094769B publication Critical patent/CN116094769B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • 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
    • H04L63/1441—Countermeasures against malicious traffic
    • H04L63/1466—Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computing Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Power Engineering (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention provides a port micro-grid control method for resisting false data injection attack, which belongs to the technical field of micro-grid safety and comprises the steps of constructing a port micro-grid model based on distributed secondary control under FDI attack, constructing a secondary controller based on an extended state observer, expanding an attack signal into a system state to obtain an augmentation system, estimating the attack signal in real time by using the proposed extended state observer, and compensating the attack by using the proposed secondary controller when the FDI attack occurs, so as to realize the frequency, voltage recovery and power distribution of the port micro-grid. The method can estimate the FDI attack of the system in real time, can resist the negative influence of the attack, and ensures the stable operation of the port micro-grid system.

Description

Port micro-grid control method for resisting false data injection attack
Technical Field
The invention belongs to the technical field of micro-grid security, and particularly relates to a port micro-grid control method for resisting false data injection attack.
Background
As the share of maritime in global maritime trade increases, port hubs become more complex and port micro-grids (seaport microgrid, SMG) are a promising technology aimed at shaping future green maritime traffic. SMG is a self-controlling power system that uses micro-grid control technology to interconnect devices such as distributed power generation units, energy storage units, electric vehicles, service loads, all-electric ships, and communications, and the like, and the existence of SMG enables the integration of more and more distributed energy sources (distributed energy resources, debs) that can improve the energy utilization and economic benefits of the port power system, and improve the port operation efficiency.
SMGs have the same components and framework as land-based micro-grids, and they can all operate in grid-connected and island modes. In this regard, their basic control and operating frameworks may be similar. SMGs typically implement energy management in island mode of operation using a hierarchical control architecture consisting of discrete primary and distributed secondary control stages. The primary control is typically power distribution and primary settling control based on droop principles, and since the primary droop control is a differential control, the secondary control is typically required to adjust the primary set point to achieve frequency and voltage recovery.
Distributed secondary control often requires a combination of communication networks, making the SMG a informative physical system where network systems and physical processes are tightly coupled, and the operation of the SMG depends on efficient and reliable data flows in the network system. While the distributed control architecture provides a superior control concept for SMGs, the measurement and control units (communication links, sensors and actuators) are exposed to potential cyber attack threats due to the lack of a central controller capable of acquiring global information. False data injection (false data injection, FDI) attacks, which are a type of fraudulent network attacks, can lead to instability of the power system and even serious accidents by accessing and tampering with the real data of sensors and actuators. Therefore, designing a distributed control strategy capable of resisting the influence of FDI attack has important significance for SMG stable and safe operation.
Disclosure of Invention
The invention aims to provide a port micro-grid (seaport microgrid, SMG) control method for resisting false data injection (false data injection, FDI) attacks, which is based on an elastic control strategy of an extended state observer (extended state observers, ESO) and realizes frequency and voltage recovery and power distribution of island SMG under the two conditions of FDI attacks and no attacks.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a port micro-grid control method for resisting false data injection attack comprises a hierarchical control model of a port micro-grid, a false data injection attack model based on the hierarchical control model and a distributed elastic control strategy based on an extended state observer, so that the port micro-grid bus frequency, voltage recovery and output power proportional distribution under the false data injection attack are realized.
The invention further improves a layered control model of the port micro-grid, a false data injection attack model based on the layered control model and a distributed elastic control strategy based on an extended state observer, so as to realize the frequency, voltage recovery and output power proportional distribution of the port micro-grid bus under the false data injection attack, wherein the method comprises the following steps:
1) Calculating the output power of each distributed power supply according to the voltage and current components obtained through dq conversion;
2) Controlling output power through a droop controller of a secondary control stage to obtain a given input value of an inner loop voltage current controller;
3) The inner loop voltage current controller generates PWM modulation signals through the PI controller to control the inverter output frequency and the voltage amplitude to track a given input value;
4) The output power, the frequency and the voltage value of the distributed power inverter are obtained through sensor measurement, the secondary controller based on a communication network generates frequency and voltage auxiliary control input, the auxiliary control input issues an executor to generate frequency and voltage control compensation items, and the given input value output by the droop controller is changed to be a rated value;
5) Considering that false data injection attacks respectively occur on the sensor and the actuator, respectively destroying frequency, voltage and power measurement results and auxiliary control input, and finally destroying the output value of the inverter;
6) Designing an extended state observer to estimate the output frequency, voltage and power of the distributed power inverter and an attack signal;
7) Introducing a virtual variable to estimate reference information of each distributed power supply, and designing a distributed elastic control protocol based on estimated attack signals, state signals and virtual variables so as to realize port micro-grid bus frequency, voltage recovery and output power proportional distribution under false data injection attack.
The invention further improves the hierarchical control model of the port micro-grid, which comprises the following steps:
based on the droop control strategy, the frequency and voltage expression of DERs are obtained as shown in a formula (1):
Figure BDA0004012415740000031
wherein ,
Figure BDA0004012415740000032
and />
Figure BDA0004012415740000033
As frequency and voltage references for inner loop control, respectively, real-time frequency and voltage may be tracked by dual loop control and Pulse Width Modulation (PWM) tracking references, ω ref and vref Rated frequency and rated voltage, m, respectively, of micro-grid system operation i and ni Droop control coefficients p-f and q-v, respectively, for the ith DER, delta 1i and δ2i Correction values for frequency and voltage control of the ith DER, respectively, are generated by the secondary controller in accordance with the control target. To achieve frequency, voltage recovery, and power allocation, the frequency and voltage control correction values for the ith DER may be morphed to:
Figure BDA0004012415740000034
Figure BDA0004012415740000035
wherein ,uωi The rate of change of the DER frequency is represented as a frequency recovery control rate; u (u) pi The control rate for the active power allocation represents the rate of change of the active power allocation. Delta ωi Represented by u ωi The generated correction value component; delta pi Is composed of u pi The generated correction value component. Similarly, the variables in the expression (3) mean control rate and correction value components corresponding to the voltage control.
The control rate is distributed secondary control rate based on a sparse communication network, and is respectively as follows:
Figure BDA0004012415740000041
Figure BDA0004012415740000042
Figure BDA0004012415740000043
/>
Figure BDA0004012415740000044
wherein ,aij Indicating the connection between the ith DER and the jth DER, the former can obtain the latterIn the case of information, a ij > 0, otherwise a ij ＝0；b i0 Representing the case where the ith DER obtains the reference value information, b if and only if the ith DER is selected as a fixed DER to access the reference information i0 > 0, otherwise, b i0 ＝0；ω ref ,
Figure BDA0004012415740000045
v ref ,/>
Figure BDA0004012415740000046
The reference values are allocated for the frequency, active power allocation, voltage and reactive power of the SMG system, respectively.
Since the inverter response is fast, it is assumed that the frequency and voltage of the SMG can track its reference value in real time. To simplify the analysis, the dynamics of the inner ring are ignored, i.e
Figure BDA0004012415740000047
and />
Figure BDA0004012415740000048
The invention is further improved in that the false data is injected into an attack model, and two positions are considered to be attacked by FDI, and the process is as follows:
when the i-th DER sensor is attacked, the attacker's goal is to destroy the measurements of frequency, voltage and power information obtained by the sensor, propagate the measurements injected with false data through the communication network, and model the destroyed information as:
Figure BDA0004012415740000049
Figure BDA0004012415740000051
Figure BDA0004012415740000052
Figure BDA0004012415740000053
wherein ,
Figure BDA0004012415740000054
and />
Figure BDA0004012415740000055
The frequency recovery control and active power distribution control attacker inject false data of the sensor, which is a derivative bounded signal; />
Figure BDA0004012415740000056
and />
Figure BDA0004012415740000057
The voltage recovery control and active power distribution control attackers inject false data of the sensor, which is a derivative bounded signal.
When the i-th DER's actuator is attacked, the attacker's goal is to destroy the feedback control signal, which is modeled as:
Figure BDA0004012415740000058
Figure BDA0004012415740000059
Figure BDA00040124157400000510
Figure BDA00040124157400000511
wherein ,
Figure BDA00040124157400000512
and />
Figure BDA00040124157400000513
The frequency recovery control and active power distribution control attacker inject false data of the actuator, which is a derivative bounded signal; />
Figure BDA00040124157400000514
and />
Figure BDA00040124157400000515
The voltage recovery control and active power distribution control attacks inject false data into the actuator, which is a derivative bounded signal.
The invention further improves the distributed elastic control strategy based on the extended state observer, which comprises the following steps:
definition of intermediate variables
Figure BDA00040124157400000516
The distributed extended state observer is: />
Figure BDA0004012415740000061
wherein ,
Figure BDA0004012415740000062
and />
Figure BDA0004012415740000063
Respectively the frequency omega i And active power standard value->
Figure BDA0004012415740000064
Is>
Figure BDA0004012415740000065
and />
Figure BDA0004012415740000066
Correction value component +.>
Figure BDA0004012415740000067
and />
Figure BDA0004012415740000068
Is>
Figure BDA0004012415740000069
and />
Figure BDA00040124157400000610
Attack signals respectively injected into the sensor +.>
Figure BDA00040124157400000611
and />
Figure BDA00040124157400000612
Is>
Figure BDA00040124157400000613
And
Figure BDA00040124157400000614
attack signals respectively injected into the actuator>
Figure BDA00040124157400000615
and />
Figure BDA00040124157400000616
The correction value equation form of the frequency recovery control and the active power distribution control is still the form in (2). F (F) ωi ,G ωi ,G pi ,H ωi ,H pi Is the gain of ESO. Zeta type toy ωi and ξpi Is an introduced virtual variable, is an internal variable of the controller, is an estimate of the reference information from the perspective of each DER, is modeled as:
Figure BDA00040124157400000617
by estimating the attack signal, the elasticity control rate designed by using the estimated value is as follows:
Figure BDA00040124157400000618
wherein Kωi ,K pi Is the controller gain.
Similarly defined intermediate variables
Figure BDA00040124157400000619
Figure BDA00040124157400000620
The voltage controlled distributed elastic control strategy is: />
Figure BDA0004012415740000071
Figure BDA0004012415740000072
Figure BDA0004012415740000073
By adopting the technical scheme, the invention has the following technical progress:
compared with a control strategy based on detection, the elastic control method does not need a detection mechanism, false detection and missing detection caused by detection errors are avoided, and the method is suitable for SMG control under a non-attack scene and can offset adverse effects of attacks on a system under an FDI attack scene. The adopted ESO introduces a correction value component, the attack signal of the actuator is estimated in real time, the observer does not need any information of the attack signal, and the required parameters can be measured by the sensor. The control method introduces virtual variables to realize the estimation of each DER on the reference information, changes the information such as the attacked unreliable frequency and the like into local variables, and does not overflow the attack influence through a communication channel.
Drawings
FIG. 1 is a topological structure diagram of an SMG;
FIG. 2 is a hierarchical control structure of SMG and FDI attack location graph;
FIG. 3 is a block diagram of a proposed distributed elastic control strategy;
FIG. 4 is a graph of the frequency of each DER in the primary and secondary control strategy after addition of an attack signal in the experiment of the present invention;
FIG. 5 is a frequency chart of DERs in the distributed elastic control strategy proposed by the present invention after adding an attack signal in the experiment of the present invention;
FIG. 6 is a graph of the active power of each DER in the primary and secondary control strategy after addition of an attack signal in the experiment of the present invention;
FIG. 7 is a graph of the active power of each DER in the distributed elastic control strategy proposed by the present invention after addition of an attack signal in the experiment of the present invention;
FIG. 8 is a graph of voltage across DERs in the primary and secondary control strategy after addition of an attack signal in the experiment of the present invention;
FIG. 9 is a voltage plot of each DER under the distributed elastic control strategy proposed by the present invention after addition of an attack signal in the present invention experiment;
FIG. 10 is a graph of reactive power of each DER under the primary and secondary control strategy after addition of an attack signal in the experiment of the present invention;
fig. 11 is a reactive power diagram of each DER in the distributed elastic control strategy proposed by the present invention after adding an attack signal in the present invention experiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention are further described below with reference to the accompanying drawings and experiments.
Referring to fig. 1 to 11, the present invention proposes a port micro-grid control method for resisting a false data injection attack:
for an inverter-type island SMG consisting of a plurality of distributed power supplies (distributed energy resources, deres), a hierarchical control structure is adopted, and the ESO-based elastic control strategy for resisting FDI attacks comprises the following steps:
(1) The SMG model under FDI attack is established as follows:
(1.1) consider a typical SMG with n DER's as shown in fig. 1, each DER accessing the bus through an inverter and LC filter, with the control objective of maintaining the frequency, voltage stability, and power sharing of the SMG. To achieve this control goal, as the distributed hierarchical control architecture in fig. 2 is employed, one-time control of each DER includes droop control that generates a given input to the internal control loop and an internal voltage-current control loop that generates pulse width modulation (pulse width modulation, PWM) that controls the output frequency, voltage, and power of the inverter. In order to coordinate control of multiple DERs, droop control is applied with the following characteristics:
Figure BDA0004012415740000091
wherein ,
Figure BDA0004012415740000092
and />
Figure BDA0004012415740000093
Omega as frequency and voltage reference for inner loop control ref and vref Rated frequency and rated voltage, m, respectively, of SMG system operation i and ni Droop control coefficients p-f and q-v, respectively, for the ith DER, delta 1i and δ2i Correction values for frequency and voltage control, respectively, for the ith DER are generated by the secondary controller according to the following control objectives:
1) Synchronizing the output frequency and voltage of DER at nominal values, namely:
Figure BDA0004012415740000094
2) DER power scaling to maintain power limits of individual DER in balance with microgrid power, namely:
Figure BDA0004012415740000095
wherein the sagging coefficient m i ,n i Is arranged as
Figure BDA0004012415740000096
The output power limit of the DER is shown.
To achieve frequency, voltage recovery, and power allocation, the frequency and voltage control correction values for the ith DER may be morphed to:
Figure BDA0004012415740000101
Figure BDA0004012415740000102
wherein ,uωi The rate of change of the DER frequency is represented as a frequency recovery control rate; u (u) pi The control rate for the active power allocation represents the rate of change of the active power allocation. Delta ωi Represented by u ωi The generated correction value component; delta pi Is composed of u pi The generated correction value component. Similarly, the variables in the expression (6) mean control rate and correction value components corresponding to the voltage control.
Since the inverter response is fast, to simplify the analysis, the dynamics of the internal control loop are ignored, assuming that the frequency and voltage of the SMG can track its reference value in real time, i.e
Figure BDA0004012415740000103
and />
Figure BDA0004012415740000104
(1.2) secondary control objective of SMG is to restore the frequency and voltage of debs to rated values while achieving power distribution, and distributed secondary control rates based on sparse communication network are respectively:
Figure BDA0004012415740000105
Figure BDA0004012415740000106
Figure BDA0004012415740000107
Figure BDA0004012415740000108
wherein ,aij Indicating the connection between the ith DER and the jth DER, a when the former can acquire the information of the latter ij > 0, otherwise a ij ＝0；b i0 Representing the case where the ith DER obtains the reference value information, b if and only if the ith DER is selected as a fixed DER to access the reference information i0 > 0, otherwise, b i0 ＝0；
Figure BDA0004012415740000109
Respectively standardized active power output and reactive power output; omega ref ,/>
Figure BDA00040124157400001010
v ref ,/>
Figure BDA00040124157400001011
The reference values of frequency, standardized active power, voltage and standardized reactive power of the SMG system are respectively shown, wherein the two standardized power reference values are as follows:
Figure BDA0004012415740000111
Figure BDA0004012415740000112
wherein ,PL ,Q L As a function of the total power demand of the load,
Figure BDA0004012415740000113
is the maximum output power of the ith DER.
(1.3) as shown in fig. 2 where the SMG is attacked by the FDI, consider two possible locations of attack by the FDI, namely, a sensor and an actuator, assuming that the information measured by the sensor is attacked by the FDI as follows:
Figure BDA0004012415740000114
Figure BDA0004012415740000115
Figure BDA0004012415740000116
Figure BDA0004012415740000117
wherein ,
Figure BDA0004012415740000118
and />
Figure BDA0004012415740000119
The frequency recovery control and active power distribution control attacker inject false data of the sensor, which is a derivative bounded signal; />
Figure BDA00040124157400001110
and />
Figure BDA00040124157400001111
The voltage recovery control and active power distribution control attackers inject false data of the sensor, which is a derivative bounded signal.
When the i-th DER's actuator is attacked, the attacker's goal is to destroy the feedback control signal, which is modeled as:
Figure BDA00040124157400001112
Figure BDA00040124157400001113
Figure BDA00040124157400001114
Figure BDA00040124157400001115
wherein ,
Figure BDA0004012415740000121
and />
Figure BDA0004012415740000122
The frequency recovery control and active power distribution control attacker inject false data of the actuator, which is a derivative bounded signal; />
Figure BDA0004012415740000123
and />
Figure BDA0004012415740000124
Dummy data injected into the actuator by the voltage recovery control and active power distribution control attackers respectively,is a derivative bounded signal.
The secondary control process of the i-th DER after being attacked becomes:
Figure BDA0004012415740000125
Figure BDA0004012415740000126
Figure BDA0004012415740000127
(2) Establishing an ESO-based distributed elastic control strategy, wherein the control structure is shown in fig. 3, and the process is as follows:
(2.1) taking frequency control as an example, define intermediate variables
Figure BDA0004012415740000128
Figure BDA0004012415740000129
The dispersed ESO is:
Figure BDA00040124157400001210
wherein ,
Figure BDA00040124157400001211
and />
Figure BDA00040124157400001212
Respectively the frequency omega i And active power standard value->
Figure BDA00040124157400001213
Is>
Figure BDA00040124157400001214
and />
Figure BDA00040124157400001215
Respectively the correction value component delta ωi and />
Figure BDA00040124157400001216
Is>
Figure BDA00040124157400001217
and />
Figure BDA00040124157400001218
Attack signals respectively injected into the sensor +.>
Figure BDA00040124157400001219
and />
Figure BDA00040124157400001220
Is>
Figure BDA00040124157400001221
And
Figure BDA00040124157400001222
attack signals respectively injected into the actuator>
Figure BDA00040124157400001223
and />
Figure BDA00040124157400001224
The correction value equation form of the frequency recovery control and the active power distribution control is still equation (4). F (F) ωi ，G ωi ，G pi ，H ωi ，H pi Is the gain of ESO. Zeta type toy ωi and ξpi Is an introduced virtual variable, is an internal variable of the controller, is an estimate of the reference information from the perspective of each DER, is modeled as: />
Figure BDA0004012415740000131
S3.2, by estimating attack signals, the elasticity control rate designed by using the estimated value is as follows:
Figure BDA0004012415740000132
wherein ,Kωi ,K pi Is the controller gain.
(2.2) definition of intermediate variables by the same theory
Figure BDA0004012415740000133
Figure BDA0004012415740000134
The voltage controlled distributed elastic control strategy is:
Figure BDA0004012415740000135
Figure BDA0004012415740000136
Figure BDA0004012415740000141
wherein ,
Figure BDA0004012415740000142
and />
Figure BDA0004012415740000143
Respectively the voltage v i And reactive power standard value->
Figure BDA0004012415740000144
Is>
Figure BDA0004012415740000145
and />
Figure BDA0004012415740000146
Respectively the correction value component delta vi and />
Figure BDA0004012415740000147
Is>
Figure BDA0004012415740000148
and />
Figure BDA0004012415740000149
Attack signals respectively injected into the sensor +.>
Figure BDA00040124157400001410
and />
Figure BDA00040124157400001411
Is>
Figure BDA00040124157400001412
and />
Figure BDA00040124157400001413
Attack signals respectively injected into the actuator>
Figure BDA00040124157400001414
and />
Figure BDA00040124157400001415
The correction value equation form of the voltage recovery control and the reactive power distribution control is still equation (5). F (F) vi ，G vi ，G qi ，H vi ，H qi Is the gain of ESO. Zeta type toy vi and ξqi Is an introduced virtual variable, is an internal variable of the controller, and is an estimate of the reference information from the perspective of each DER. K (K) vi ,K qi Is the controller gain.
(3) In order to intuitively verify the effect of the control strategy provided by the present invention, the following description is given by way of specific examples:
1) Parameter setting: the experiment uses matlab/simulink software to build an SMG simulation model consisting of 4 DERs. The main parameters and controller parameters are shown in table 1:
table 1 system parameters and observer and controller parameters
Figure BDA00040124157400001416
Figure BDA0004012415740000151
2) The experimental scenario and results are as follows:
1. before t=1 s, all DER uses one droop control;
2. at t=1s, the secondary control strategy is activated;
3. at t=2s, the sensor of der3 is
Figure BDA0004012415740000152
Attack signal attack by (a); at t=2.5s, the sensor of der4 is +.>
Figure BDA0004012415740000153
Attack signal attack by (a);
4. at t=3 s, the sensor attacks on DER3 and DER4 are reduced, at t=3.5 s, at
Figure BDA0004012415740000154
Figure BDA0004012415740000155
In the case of DER1 and DER4, attacks on the actuators;
5. at t=4s, the actuator attacks on DER1 and DER4 are mitigated, but the actuator of DER2 is signaled by a new attack
Figure BDA0004012415740000156
Attack;
6. at t=5 s, FDI attacks by all DER are mitigated.
Fig. 4 and fig. 5 are frequency diagrams of the above scenario using the primary secondary control strategy and the proposed elastic control strategy, respectively, and comparing the two diagrams can show that the frequency under the primary secondary control strategy oscillates and is unstable when the FDI attack exists, and the frequency of the DER that is not attacked also oscillates, which is the result of the propagation of the FDI attack signal through the communication network. Figure 5 shows that only the frequency of the attacked DER fluctuates before and after the onset of the FDI attack and then quickly stabilizes at 50Hz, with no fluctuations in the frequency of the non-attacked DER.
Fig. 6 and fig. 7 are respectively normalized active power diagrams using the primary and secondary control strategies and the proposed elastic control strategy in the above scenario, and comparing the two diagrams can show that the active power under the primary and secondary control strategies oscillates and is unstable even exceeds the DER output power limit value in the presence of an FDI attack. Fig. 7 shows that only the active power of the attacked DER fluctuates before and after the FDI attack is initiated and then stabilizes rapidly, and the active power of the non-attacked DER does not fluctuate. In the period of no attack, the power standard value under the original secondary control strategy and the proposed elastic control strategy both meet the control target shown in the formula (3). The proposed elastic control strategy is shown to be capable of achieving similar control effects as conventional control strategies when no attack is present.
Fig. 8 and fig. 9 are voltage diagrams of the above scenario using the primary secondary control strategy and the proposed elastic control strategy, respectively, and comparing the two diagrams can show that the voltage under the primary secondary control strategy oscillates and is unstable when the FDI attack exists. Fig. 9 shows that only the voltage of the attacked DER fluctuates before and after the FDI attack is initiated and then stabilizes rapidly, and the voltage of the non-attacked DER does not fluctuate.
Fig. 10 and 11 are respectively standardized reactive power diagrams using the primary and secondary control strategies and the proposed elastic control strategy under the above-mentioned scenario, and it can be seen by comparing the two diagrams that the reactive power under the primary and secondary control strategies oscillates and is unstable even exceeds the DER output power limit value when the FDI attack exists. Fig. 11 shows that only the reactive power of the attacked DER fluctuates before and after the onset of the FDI attack and then stabilizes rapidly, and the reactive power of the non-attacked DER does not fluctuate. In the period of no attack, the power standard value under the original secondary control strategy and the proposed elastic control strategy both meet the control target shown in the formula (3).
The working principle of the invention is as follows: firstly modeling FDI attack in an SMG system, then regarding an attack signal as disturbance expansion to be in an augmentation system state, constructing ESO real-time estimation attack signals according to the output of the SMG system, and finally designing a distributed secondary elasticity controller by utilizing the attack signal estimation value to realize frequency and voltage recovery and power distribution.
In summary, the present invention proposes a distributed elastic control strategy based on ESO, where the control strategy can achieve the same control effect as the primary and secondary control strategies when no attack occurs, that is, the objectives shown in formulas (2) and (3) are satisfied. In the FDI attack period, the proposed control strategy can resist the adverse effect brought by the attack and still meet the control target.
The above examples are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the present invention.

Claims (5)

1. A port micro-grid control method for resisting false data injection attack is characterized by comprising a hierarchical control model of a port micro-grid, a false data injection attack model based on the hierarchical control model and a distributed elastic control strategy based on an extended state observer, so that the port micro-grid bus frequency, voltage recovery and output power proportional distribution under the false data injection attack are realized.
2. The port micro-grid control method for resisting false data injection attack according to claim 1, wherein the port micro-grid hierarchical control model, the false data injection attack model based on the hierarchical control model and the distributed elastic control strategy based on the extended state observer are used for realizing the port micro-grid bus frequency, voltage recovery and output power proportional distribution under the false data injection attack, and the port micro-grid bus frequency, voltage recovery and output power proportional distribution are as follows:
1) Calculating the output power of each distributed power supply according to the voltage and current components obtained through dq conversion;
2) Controlling output power through a droop controller of a secondary control stage to obtain a given input value of an inner loop voltage current controller;
3) The inner loop voltage current controller generates PWM modulation signals through the PI controller to control the inverter output frequency and the voltage amplitude to track a given input value;
4) The output power, the frequency and the voltage value of the distributed power inverter are obtained through sensor measurement, the secondary controller based on a communication network generates frequency and voltage auxiliary control input, the auxiliary control input issues an executor to generate frequency and voltage control compensation items, and the given input value output by the droop controller is changed to be a rated value;
5) Considering that false data injection attacks respectively occur on the sensor and the actuator, respectively destroying frequency, voltage and power measurement results and auxiliary control input, and finally destroying the output value of the inverter;
6) Designing an extended state observer to estimate the output frequency, voltage and power of the distributed power inverter and an attack signal;
7) Introducing a virtual variable to estimate reference information of each distributed power supply, and designing a distributed elastic control protocol based on estimated attack signals, state signals and virtual variables so as to realize port micro-grid bus frequency, voltage recovery and output power proportional distribution under false data injection attack.
3. The port microgrid control method for combating false data injection attacks according to claim 1, wherein said port microgrid hierarchical control model comprises:
the frequency and voltage expression for obtaining the debs based on droop control and consistency algorithms is shown in formula (1):
Figure FDA0004012415730000021
wherein ,
Figure FDA0004012415730000022
and />
Figure FDA0004012415730000023
As frequency and voltage references for inner loop control, respectively, real-time frequency and voltage tracking references, ω, by dual loop control and Pulse Width Modulation (PWM) ref and vref Rated frequency and rated voltage, m, respectively, of port micro-grid system operation i and ni Droop control coefficients p-f and q-v, respectively, for the ith DER, delta 1i and δ2i Correction values for frequency and voltage control for the ith DER, respectively, are the outputs of the secondary controller:
Figure FDA0004012415730000024
Figure FDA0004012415730000025
wherein ,uωi The rate of change of the DER frequency is represented as a frequency recovery control rate; u (u) pi The control rate for the active power allocation represents the rate of change of the active power allocation; delta ωi Represented by u ωi The generated correction value component; delta pi Is composed of u pi The generated correction value component; similarly, each variable in the formula (3) means a control rate and a correction value component corresponding to voltage control; the control rate is distributed secondary control rate based on a sparse communication network, and is respectively as follows:
Figure FDA0004012415730000026
Figure FDA0004012415730000027
Figure FDA0004012415730000028
Figure FDA0004012415730000031
wherein ,aij Indicating the connection between the ith DER and the jth DER, a when the former obtains the information of the latter ij > 0, otherwise a ij ＝0；b i0 Representing the case where the ith DER obtains the reference value information, b if and only if the ith DER is selected as a fixed DER to access the reference information i0 > 0, otherwise, b i0 ＝0；
Figure FDA0004012415730000032
And respectively distributing reference values for the frequency, the active power distribution, the voltage and the reactive power of the micro-grid system.
4. The port microgrid control method for combating false data injection attacks according to claim 1, wherein said false data injection attack model comprises:
when the i-th DER's sensor is attacked, the attacker's goal is to destroy the measurements of frequency, voltage, and power information obtained by the sensor, propagate the measurements injected with false data through the communication network, and the sensor attack is modeled as:
Figure FDA0004012415730000033
Figure FDA0004012415730000034
Figure FDA0004012415730000035
Figure FDA0004012415730000036
wherein ,
Figure FDA0004012415730000037
and />
Figure FDA0004012415730000038
The frequency recovery control and active power distribution control attacker inject false data of the sensor, which is a derivative bounded signal; />
Figure FDA0004012415730000039
and />
Figure FDA00040124157300000310
The false data of the sensor is injected by an attacker of voltage recovery control and active power distribution control respectively, and the false data is a derivative bounded signal;
when the i-th DER's actuator is attacked, the attacker's goal is to destroy the feedback control signal, the actuator attack is modeled as:
Figure FDA00040124157300000311
Figure FDA00040124157300000312
Figure FDA0004012415730000041
Figure FDA0004012415730000042
wherein ,
Figure FDA0004012415730000043
and />
Figure FDA0004012415730000044
The frequency recovery control and active power distribution control attacker inject false data of the actuator, which is a derivative bounded signal; />
Figure FDA0004012415730000045
and />
Figure FDA0004012415730000046
The voltage recovery control and active power distribution control attacks inject false data into the actuator, which is a derivative bounded signal.
5. The port micro-grid control method for resisting false data injection attack according to claim 1, wherein the distributed elastic control strategy based on the extended state observer comprises the following steps:
first, virtual variable xi is introduced ωi and ξpi As an internal variable of the controller, the estimation of the reference information from the per DER point of view, a virtual variable ζ as part of the frequency distributed elastic control strategy ωi and ξpi The dynamics of (2) are:
Figure FDA0004012415730000047
definition of intermediate variables
Figure FDA0004012415730000048
The distributed extended state observer is:
Figure FDA0004012415730000049
wherein ,
Figure FDA00040124157300000410
and />
Figure FDA00040124157300000411
Respectively the frequency omega i And active power standard value->
Figure FDA00040124157300000412
Is>
Figure FDA00040124157300000413
and />
Figure FDA00040124157300000414
Respectively the correction value component delta ωi and />
Figure FDA00040124157300000415
Is>
Figure FDA00040124157300000416
and />
Figure FDA00040124157300000417
Attack signals respectively injected into the sensor +.>
Figure FDA00040124157300000418
and />
Figure FDA0004012415730000051
Is>
Figure FDA0004012415730000052
and />
Figure FDA0004012415730000053
Attack signals respectively injected into the actuator>
Figure FDA0004012415730000054
and />
Figure FDA0004012415730000055
The form of the correction value equation of the frequency recovery control and the active power allocation control is still the form in (2); f (F) ωi ,G ωi ,G pi ,H ωi ,H pi Is the gain of ESO;
by estimating the attack signal, the elasticity control rate designed by using the estimated value is as follows:
Figure FDA0004012415730000056
wherein Kωi ,K pi Is the controller gain;
similarly defined intermediate variables
Figure FDA0004012415730000057
The voltage controlled distributed elastic control strategy is: />
Figure FDA0004012415730000058
wherein ,
Figure FDA0004012415730000059
and />
Figure FDA00040124157300000510
Respectively the voltage v i And reactive power standard value->
Figure FDA00040124157300000511
Is>
Figure FDA00040124157300000512
and />
Figure FDA00040124157300000513
Respectively the correction value component delta vi and />
Figure FDA00040124157300000514
Is>
Figure FDA00040124157300000515
and />
Figure FDA00040124157300000516
Attack signals respectively injected into the sensor +.>
Figure FDA00040124157300000517
and />
Figure FDA00040124157300000518
Is>
Figure FDA00040124157300000519
and />
Figure FDA00040124157300000520
Attack signals respectively injected into the actuator>
Figure FDA00040124157300000521
and />
Figure FDA00040124157300000522
The form of the correction value equation of the voltage recovery control and the reactive power distribution control is still the form in (3); f (F) vi ,G vi ,G qi ,H vi ,H qi Is the gain of ESO; zeta type toy vi and ξqi Is an introduced virtual variable, is an internal variable of the controller, is an estimate of the reference information from the perspective of each DER, is a virtual variable ζ that is part of the voltage distributed elastic control strategy vi and ξqi The dynamics of (2) are:
Figure FDA0004012415730000061
by estimating the attack signal, the elasticity control rate designed by using the estimated value is as follows:
Figure FDA0004012415730000062
wherein ,Kvi ,K qi Is the controller gain.
CN202211658152.1A 2022-12-22 2022-12-22 Port micro-grid control method for resisting false data injection attack Active CN116094769B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211658152.1A CN116094769B (en) 2022-12-22 2022-12-22 Port micro-grid control method for resisting false data injection attack

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211658152.1A CN116094769B (en) 2022-12-22 2022-12-22 Port micro-grid control method for resisting false data injection attack

Publications (2)

Publication Number Publication Date
CN116094769A true CN116094769A (en) 2023-05-09
CN116094769B CN116094769B (en) 2024-03-01

Family

ID=86186035

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211658152.1A Active CN116094769B (en) 2022-12-22 2022-12-22 Port micro-grid control method for resisting false data injection attack

Country Status (1)

Country Link
CN (1) CN116094769B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109194623A (en) * 2018-08-02 2019-01-11 谢聪敏 Security server based on cloud computing
AU2019100008A4 (en) * 2019-01-05 2019-02-14 Feng Chen Secure Distributed Estimation against False Data Injection Attack
CN110830514A (en) * 2019-12-12 2020-02-21 四川大学 Detection method for collusion-based false data injection attack of smart power grid
CN111384717A (en) * 2020-01-15 2020-07-07 华中科技大学 Adaptive damping control method and system for resisting false data injection attack
CN111817286A (en) * 2020-07-20 2020-10-23 安徽工业大学 Detection method for false data injection attack of direct current micro-grid cluster
CN114157460A (en) * 2021-11-15 2022-03-08 道和邦(广州)电子信息科技有限公司 SMG-VME-aDDoS attack defense system based on VME-TCP-IP anti-DDoS
US20220076141A1 (en) * 2019-02-15 2022-03-10 AGROROBOTICA S.r.l. Insect attack risk prediction system and method
CN114492083A (en) * 2022-03-18 2022-05-13 浙江工业大学 Direct-current microgrid attack detection and recovery method for FDI attack

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109194623A (en) * 2018-08-02 2019-01-11 谢聪敏 Security server based on cloud computing
AU2019100008A4 (en) * 2019-01-05 2019-02-14 Feng Chen Secure Distributed Estimation against False Data Injection Attack
US20220076141A1 (en) * 2019-02-15 2022-03-10 AGROROBOTICA S.r.l. Insect attack risk prediction system and method
CN110830514A (en) * 2019-12-12 2020-02-21 四川大学 Detection method for collusion-based false data injection attack of smart power grid
CN111384717A (en) * 2020-01-15 2020-07-07 华中科技大学 Adaptive damping control method and system for resisting false data injection attack
CN111817286A (en) * 2020-07-20 2020-10-23 安徽工业大学 Detection method for false data injection attack of direct current micro-grid cluster
CN114157460A (en) * 2021-11-15 2022-03-08 道和邦(广州)电子信息科技有限公司 SMG-VME-aDDoS attack defense system based on VME-TCP-IP anti-DDoS
CN114492083A (en) * 2022-03-18 2022-05-13 浙江工业大学 Direct-current microgrid attack detection and recovery method for FDI attack

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
HABIBI M 等: ""Detection of False Data Injection Cyber Attacks in DC Microgrids Based on Recurrent Neural Networks"", 《IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS》, 31 December 2021 (2021-12-31), pages 20 - 40 *
HUA C 等: ""Evaluation and Governance of Green Development Practice of Port"", 《A SEA PORT CASE OF CHINA》, 31 December 2020 (2020-12-31) *
田继伟;王布宏;李腾耀;尚福特;曹?锐;: "智能电网虚假数据注入攻击研究进展与展望", 网络空间安全, no. 09, 25 September 2019 (2019-09-25) *
舒隽;郭志锋;韩冰;: "电网虚假数据注入攻击的双层优化模型", 电力系统自动化, no. 10, 26 February 2019 (2019-02-26) *
陈雅琳;刘薇;徐安;李巧平;何进;陈雷霆;: "面向健康云的定制化网络安全服务", 计算机系统应用, no. 08, 15 August 2018 (2018-08-15) *

Also Published As

Publication number Publication date
CN116094769B (en) 2024-03-01

Similar Documents

Publication Publication Date Title
NL2024333B1 (en) Microgrid distributed controller parameter determination method based on linear quadratic optimization
CN107294085B (en) Micro-grid delay margin calculation method based on critical feature root tracking
Duan et al. Q-learning-based damping control of wide-area power systems under cyber uncertainties
Park et al. When adversary encounters uncertain cyber-physical systems: Robust zero-dynamics attack with disclosure resources
CN108075488B (en) Island microgrid layered control method considering communication data disturbance under CPS concept
CN111817286A (en) Detection method for false data injection attack of direct current micro-grid cluster
CN114336674B (en) Distributed toughness frequency control method for alternating-current micro-grid
CN113471955B (en) Island direct current micro-grid distributed dynamic event trigger control method
Hu et al. Privacy Preserving Consensus Strategy for Secondary Control in Microgrids Against Multi-link False Data Injection Attacks
Liu et al. Active disturbance rejection control based distributed secondary control for a low-voltage DC microgrid
CN116094769B (en) Port micro-grid control method for resisting false data injection attack
CN110768301B (en) Micro-grid frequency synchronization anti-attack cooperative control method
CN113110344A (en) Multi-wheeled robot cooperative control method for DoS attack
Su et al. Distributed adaptive secondary control of AC microgrid under false data injection attack
Aluko et al. Observer-based detection and mitigation scheme for isolated microgrid under false data injection attack
Zhan et al. Resilient distributed control of islanded microgrids under false data injection attacks
Wooding et al. Formal Control of New England 39-Bus Test System: An Assume-Guarantee Approach
CN115036935A (en) Micro-grid frequency distributed cooperative control method
Ghanbari et al. Design of switched controllers using an enhanced passivation method
Zhang et al. Distributed event‐triggered secondary control for microgrids applicable to directed communication graph
Lin et al. Distributed Optimal Consensus-Based Secondary Frequency and Voltage Control of Isolated AC Microgrids
Tran et al. Closed-loop development for dissipativity constraint
Girard et al. Approximate hierarchies of linear control systems
Geng et al. Distributed Fixed-Time Transient Stability Control Scheme for Power Systems With Heterogeneous Dynamics
Zou et al. Security Defense of AC Microgrids Against False Data Injection Attacks based on Distributed Disturbance Observers

Legal Events

Date Code Title Description
PB01 Publication
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