FDI attack detection and mitigation method in DC microgrid

Abstract

The invention relates to the technical field of DC / DC converter control equipment of a DC micro-grid, in particular to an FDI attack detection and mitigation method in the DC micro-grid, and aims to solve the problems of rapidly detecting and mitigating the FDI attack, the method comprises the following steps: S1, establishing a DC micro-grid model, and obtaining a power flow equation; s2, establishing a direct-current micro-grid control target model, and verifying whether the voltage of a bus is equal to a rated voltage and whether each output current realizes expected proportional distribution through a solution of a power flow equation; s3, establishing a distributed hierarchical controller model based on the DC micro-grid model and the DC micro-grid control target model, wherein hierarchical control comprises primary control and secondary control; s4, establishing a model of the influence of the FDI attack on the DC micro-grid, wherein the model detects the existence of the FDIA; the FDI attack injected into the sampling data of the sampler is detected by using the artificial neural network, and the influence of the attack on the hierarchical controller is reduced, so that the safe and stable operation of the micro-grid is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current microgrid DC / DC converter control equipment, and in particular to a FDI attack detection and mitigation method in a direct current microgrid. Background Art

[0002] DC microgrids integrate the advantages of photovoltaic, energy storage, fuel cells and other new energy sources, and have broad application prospects in terms of high efficiency, high reliability and low loss. The combination of controllers and electronic components in DC microgrids can be achieved through hierarchical control of power converters. This control method can reduce the computational burden and has the advantages of improved scalability and reliability.

[0003] Hierarchical control in DC microgrids is divided into distributed control and centralized control. The distributed hierarchical control structure includes primary control and secondary control. A key aspect of DC microgrid operation is to ensure stable and accurate voltage control and equivalent current distribution between system agents, i.e. voltage regulation and current sharing. The local stability of distributed generators and the distribution of current between DC power sources are controlled by the primary controller; the voltage regulation is controlled by the secondary controller.

[0004] Control systems require secure communication networks to transmit data between distributed power sources, and these communication networks are vulnerable to malicious network attacks. FDI attacks are a very common and highly destructive attack in communication networks. They disrupt the normal operation of controllers by injecting false information into communication information, thereby causing microgrid system instability, which has a significant impact on economic growth and the reliability of power systems. Therefore, detecting FDI attacks is crucial for the reliable and efficient operation of DC microgrids. Summary of the invention

[0005] In view of this, the purpose of the present invention is to propose a FDI attack detection and mitigation method in a DC microgrid to solve the problem of quickly detecting and mitigating FDI attacks.

[0006] A method for detecting and mitigating FDI attacks in a DC microgrid, the method comprising:

[0007] S1: Establish a DC microgrid model and obtain the power flow equation;

[0008] S2: Establish the DC microgrid control target model and verify the bus voltage through the solution of the power flow equation.

[0009] Whether it is equal to the rated voltage and whether each output current is distributed in the expected proportion;

[0010] S3: Establish distributed hierarchical control based on DC microgrid model and DC microgrid control target model

[0011] The hierarchical control includes a primary control and a secondary control;

[0012] S4: Establish a model of the impact of FDI attacks on DC microgrids, which detects the presence of FDIA; use artificial neural networks to detect FDI attacks injected into sampler sampled data and mitigate the impact of attacks on hierarchical controllers to maintain safe and stable operation of the microgrid.

[0013] As a further improvement of the present application, the establishment of the DC microgrid model includes:

[0014] The resistance of the common DC bus is set to zero. The DC microgrid includes N power sources and M loads. Each power source is connected to the common bus via a boost DC / DC converter.

[0015] The power flow equation is

[0016]

[0017] Among them, V i is the output voltage of the i-th converter, I i , R i are the corresponding output current and line impedance, R Li is the impedance of the ith load, V b is the bus voltage.

[0018] As a further improvement of the present application, the establishment of the DC microgrid control target model includes maintaining the bus voltage at a rated value and driving the output current to achieve a desired proportional distribution;

[0019] Verify whether the bus voltage is equal to the rated voltage by solving the power flow equation:

[0020] V b =V * , V * is the rated voltage of the DC bus;

[0021] And whether the output currents are distributed in the expected proportion:

[0022] di is the desired scale parameter.

[0023] As a further improvement of the present application, the primary control is:

[0024] The voltage and current dual PI control method is adopted. The dynamics of primary control are mainly determined by droop control. The physical model of each Yichang control is

[0025]

[0026] RD,i is the droop coefficient, is the reference voltage, V * is the rated voltage of the DC bus, I i is the output current.

[0027] As a further improvement of the present application, the secondary control is:

[0028] Output voltage V i (t) Tracking reference voltage Assuming a distributed generator as a voltage-controlled source, then:

[0029]

[0030] Reference voltage The form is:

[0031]

[0032] u i (t) is the secondary control signal;

[0033] The bus voltage error is

[0034] e V (t) = V b (t)-V * , V b is the bus voltage;

[0035] The current error of the i-th controller is

[0036]

[0037] The secondary controller is

[0038]

[0039] δ i is the internal state of the controller; α and β are positive parameters that need to be designed.

[0040] As a further improvement of the present application, the model of the impact of FDI attacks on DC microgrids is established, and the model detects the existence of FDIA; an artificial neural network is used to detect the FDI attacks injected into the sampler sampled data, and the impact of the attacks on the hierarchical controller is mitigated to maintain the safe and stable operation of the microgrid;

[0041] FDI attack is to sample data V b ,I i Up, V b is the bus voltage, I i is the output current;

[0042] The mathematical model of FDI attack is: To inject in I i False data value on

[0043] By using BP neural network to estimate I i The estimated value is used as the reference value in the reference tracking application, which consists of a neural network, a PI controller, and an output, which is added to I i In the collected values, in the case of FDI attack, I i 'Replace I i , γ i is the output of the PI controller, then:

[0044]

[0045] Similarly, To inject V b The false data on V b 'replace

[0046] If the neural network is functioning properly, assuming:

[0047]

[0048] The output of the PI controller converges to To eliminate false data in the system,

[0049] By adding γ i It converges to a non-zero value, and the existence of FDI attack is detected;

[0050] Based on the proposed attack mitigation strategy, the neural network accurately estimates I i and V b .

[0051] The bus voltage error and the current error of the i-th controller are rewritten as:

[0052] e' V (t) = V' b (t-τ)-V *

[0053]

[0054] Among them, τ is the communication delay.

[0055] The beneficial effects of the present invention are as follows: by establishing a model for the impact of FDI attacks on DC microgrids, the model detects the presence of FDIA; an artificial neural network is used to detect FDI attacks injected into sampler sampled data, and the impact of attacks on hierarchical controllers is mitigated to maintain safe and stable operation of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 This is a schematic diagram of the structure of a DC microgrid according to an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of the structure of a secondary controller of a method for detecting and mitigating FDI attacks in a DC microgrid according to an embodiment of the present invention;

[0059] Figure 3 The present invention is a schematic diagram of the structure of a BP neural network of a method for detecting and mitigating FDI attacks in a DC microgrid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0061] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0062] like Figure 1-3 As shown, a FDI attack detection and mitigation method in a DC microgrid includes:

[0063] S1: Establish a DC microgrid model and obtain the power flow equation;

[0064] include

[0065] The resistance of the common DC bus is set to zero. The DC microgrid includes N power sources and M loads. Each power source is connected to the common bus via a boost DC / DC converter.

[0066] The power flow equation is

[0067]

[0068] Among them, V i is the output voltage of the i-th converter, I i , R i are the corresponding output current and line impedance, R Li is the impedance of the ith load, V b is the bus voltage.

[0069] S2: Establish a DC microgrid control target model and verify whether the bus voltage is equal to the rated voltage and whether each output current is distributed in the expected proportion by solving the power flow equation;

[0070] include

[0071] Keep the bus voltage at the rated value and drive the output current to achieve the expected proportional distribution;

[0072] Verify whether the bus voltage is equal to the rated voltage by solving the power flow equation:

[0073] V b =V * , V * is the rated voltage of the DC bus;

[0074] And whether the output currents are distributed in the expected proportion:

[0075] di is the desired scale parameter.

[0076] S3: establishing a distributed hierarchical controller model based on the DC microgrid model and the DC microgrid control target model, wherein the hierarchical control includes primary control and secondary control;

[0077] The primary control is:

[0078] The voltage and current dual PI control method is adopted. The dynamics of primary control are mainly determined by droop control. The physical model of each Yichang control is

[0079]

[0080] RD,i is the droop coefficient, is the reference voltage, V * is the rated voltage of the DC bus, I i is the output current.

[0081] The secondary control is:

[0082] Output voltage V i (t) Tracking reference voltage Assuming a distributed generator as a voltage-controlled source, then:

[0083]

[0084] Reference voltage The form is:

[0085]

[0086] u i (t) is the secondary control signal;

[0087] The bus voltage error is

[0088] e V (t) = V b (t)-V * , V b is the bus voltage;

[0089] The current error of the i-th controller is

[0090]

[0091] The secondary controller is

[0092]

[0093] δ i is the internal state of the controller; α and β are positive parameters that need to be designed.

[0094] S4: Establish a model of the impact of FDI attacks on DC microgrids, which detects the presence of FDIA; use artificial neural networks to detect FDI attacks injected into sampler sampled data and mitigate the impact of attacks on hierarchical controllers to maintain safe and stable operation of the microgrid.

[0095] FDI attack is to sample data V b ,I i Up, V b is the bus voltage, I i is the output current;

[0096] The mathematical model of FDI attack is: To inject in I i False data value on

[0097] By using BP neural network to estimate I i The estimated value is used as the reference value in the reference tracking application, which consists of a neural network, a PI controller, and an output, which is added to I i In the collected values, in the case of FDI attack, I i 'Replace I i , γ i is the output of the PI controller, then:

[0098]

[0099] Similarly, To inject V b The false data on V b 'replace

[0100] If the neural network is functioning properly, assuming:

[0101]

[0102] The output of the PI controller converges to To eliminate false data in the system,

[0103] By adding γ i It converges to a non-zero value, and the existence of FDI attack is detected;

[0104] Based on the proposed attack mitigation strategy, the neural network accurately estimates I i and V b .

[0105] The bus voltage error and the current error of the i-th controller are rewritten as:

[0106] e' V (t) = V' b (t-τ)-V *

[0107]

[0108] Among them, τ is the communication delay.

[0109] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0110] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting and mitigating FDI attacks in a DC microgrid, characterized in that: The method includes S1: Establish a DC microgrid model and obtain the power flow equation; S2: Establish a DC microgrid control target model and verify whether the bus voltage is equal to the rated voltage and whether each output current is distributed in the expected proportion by solving the power flow equation; S3: establishing a distributed hierarchical controller model based on the DC microgrid model and the DC microgrid control target model, wherein the hierarchical control includes primary control and secondary control; S4: Establish a model of the impact of FDI attacks on DC microgrids, which detects the presence of FDIA; use artificial neural networks to detect FDI attacks injected into sampler sampled data and mitigate the impact of attacks on hierarchical controllers to maintain safe and stable operation of the microgrid.

2. The FDI attack detection and mitigation method in a DC microgrid according to claim 1 is characterized in that: The DC microgrid model is established, including The resistance of the common DC bus is set to zero. The DC microgrid includes N power sources and M loads. Each power source is connected to the common bus via a boost DC / DC converter. The power flow equation is Among them, V i is the output voltage of the i-th converter, I i , R i are the corresponding output current and line impedance, R Li is the impedance of the ith load, V b is the bus voltage.

3. The FDI attack detection and mitigation method in a DC microgrid according to claim 1, characterized in that: The DC microgrid control target model is established, including Keep the bus voltage at the rated value and drive the output current to achieve the expected proportional distribution; Verify whether the bus voltage is equal to the rated voltage by solving the power flow equation: V b =V * , V * is the rated voltage of the DC bus; And whether the output currents are distributed in the expected proportion: di is the desired scale parameter.

4. The FDI attack detection and mitigation method in a DC microgrid according to claim 1, characterized in that: The primary control is: The voltage and current dual PI control method is adopted. The dynamics of primary control are mainly determined by droop control. The physical model of each Yichang control is V i ref (t)=V * -R D,i I i (t) R D,i is the droop coefficient, V i ref (t) is the reference voltage, V * is the rated voltage of the DC bus, I i is the output current.

5. The FDI attack detection and mitigation method in a DC microgrid according to claim 4 is characterized in that: The secondary control is: Output voltage V i (t) Tracking reference voltage V i ref (t), assuming a distributed generator as a voltage-controlled source, then: V i (t)=V i ref (t); Reference voltage V i ref (t) is of the form: V i ref (t)=V * -R D,i I i (t)+u i (t) u i (t) is the secondary control signal; The bus voltage error is e V (t) = V b (t)-V * , V b is the bus voltage; The current error of the i-th controller is The secondary controller is δ i is the internal state of the controller; α and β are positive parameters that need to be designed.

6. The FDI attack detection and mitigation method in a DC microgrid according to claim 1, characterized in that: The method establishes a model of the impact of FDI attacks on DC microgrids, which detects the presence of FDIA; uses an artificial neural network to detect the FDI attacks injected into the sampled data of the sampler, and mitigates the impact of the attacks on the hierarchical controller to maintain the safe and stable operation of the microgrid; FDI attack is to sample data V b ,I i Up, V b is the bus voltage, I i is the output current; The mathematical model of FDI attack is: i a (t) = I i (t)+ω i z (t),ω i z (t) is the injection i on False data value, By using BP neural network to estimate I i The estimated value is used as the reference value in the reference tracking application, which consists of a neural network, a PI controller, and an output, which is added to I i In the collected values, in the case of FDI attack, I i 'Replace I i , γ i is the output of the PI controller, then: Similarly, To inject V b The false data on V b 'Replace V b； If the neural network is functioning properly, assuming: The output of the PI controller converges to ω i z (t) to eliminate false data in the system, By adding γ i It converges to a non-zero value, and the existence of FDI attack is detected; Based on the proposed attack mitigation strategy, the neural network accurately estimates I i and V b .

7. The FDI attack detection and mitigation method in a DC microgrid according to claim 5, characterized in that: The bus voltage error and the current error of the i-th controller are rewritten as: e' V (t)=V′ b (t-τ)-V * τ is the communication delay.

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