Power distribution network energy storage system active false data attack defense method based on elastic controller

Abstract

The application discloses a power distribution network energy storage system active false data attack defense method based on an elastic controller, belongs to the technical field of power distribution network energy storage system information detection, and comprises the following steps: S1, a physical dynamic model of a power distribution network battery energy storage system under false data attack is established; S2, a self-adaptive robust observer is designed to estimate the injected false data attack in real time; S3, based on the estimated false data attack, a detection criterion of the false data attack is given; and S4, when the false data attack is detected, the elastic controller is triggered to dynamically adjust the control instruction, attack compensation and system function maintenance are realized. The application firstly designs a self-adaptive observer to estimate the injected false data attack, and then by embedding security situation awareness into the controller design, active dynamic defense can be realized. That is, the control parameters are dynamically adjusted based on real-time attack detection, and attack fault tolerance is used to maintain the operation of key functions when part of the subsystems are damaged.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network energy storage system information detection, and particularly relates to a power distribution network energy storage system active false data attack defense method based on an elastic controller. BACKGROUND

[0002] With the increasing penetration of renewable energy and the rapid development of distributed energy, energy storage systems (ESS) in power distribution networks have become a key infrastructure to support voltage / frequency regulation, peak shaving, and new energy accommodation. However, energy storage systems highly rely on the collaborative control of cyber-physical systems (CPS), and their deep coupling with the power grid also expands the network attack surface, making network security issues increasingly prominent. In recent years, attack incidents targeting energy storage systems have occurred frequently, such as inducing battery overcharging / overdischarging by tampering with SOC (State of Charge) data, causing equipment damage, and causing energy storage system oscillation by forging frequency modulation instructions, leading to power distribution network frequency collapse. Such attacks can cause millions of dollars in economic losses and even threaten the physical security of the power grid.

[0003] Current security protection for energy storage systems mainly focuses on traditional network security measures (such as firewalls, intrusion detection systems), or anomaly detection algorithms based on fixed thresholds. These methods have the following defects:

[0004] Static defense: unable to adapt to new attack variants (such as false data attacks, delay attacks);

[0005] Lack of resilience: when the attack breaks through the first line of defense, the system often lacks dynamic recovery capability, leading to cascading failures;

[0006] Control-security separation: traditional control strategies do not incorporate network security status into real-time control loops, leaving control blind spots. SUMMARY

[0007] To solve the problem of the deficiency of the existing attack defense method, the application provides a power distribution network energy storage system active false data attack defense method based on an elastic controller. This method considers the actual physical dynamic changes of the battery energy storage system in the new power system distribution network under false data attacks. First, an adaptive observer is designed to estimate the injected false data attacks, and then security situation awareness is embedded into the controller design to achieve active dynamic defense. That is, based on real-time attack detection, the control parameters are dynamically adjusted, and attack tolerance is used to maintain key functions when part of the subsystem is damaged.

[0008] The technical scheme of the power distribution network energy storage system active false data attack defense method based on the elastic controller provided by the application is as follows:

[0009] A power distribution network energy storage system active false data attack defense method based on an elastic controller, comprising the following steps,

[0010] S1, a physical dynamic model of a battery energy storage system of a power distribution network under a false data attack is established;

[0011] S2, an adaptive robust observer is designed to estimate the injected false data attack in real time;

[0012] S3, based on the estimated false data attack, a detection measure of the false data attack is given;

[0013] S4, when the false data attack is detected, the elastic controller is triggered to dynamically adjust the control instruction, and attack compensation and system function maintenance are realized.

[0014] Further improvement of the technical scheme of the application is that in step S1, considering that the power distribution network is composed of N battery energy storage systems, the physical dynamics equation of the Ith (I=1,…,N) battery energy storage system is established as,

[0015]

[0016] In the formula,

[0017] Wherein, y (I) (t) is the sensor output measurement value of the Ith energy storage system, Δt is the control period, SOC (I) (t) is the state of charge of the Ith energy storage system, T (I) (t) represents the battery temperature of the Ith energy storage system, SOH (I) (t) is the health state of the Ith energy storage system, is the actual output power of the Ith energy storage system, represents the charge / discharge power instruction of the Ith energy storage system, is the cooling system power of the Ith energy storage system, ε (I) is the self-discharge rate of the Ith energy storage system, χ (I) and β (I) are the SOC-temperature coupling coefficient and temperature attenuation rate of the Ith energy storage system; ι (I) and γ (I) are the temperature-SOH coupling coefficient and aging rate of the Ith energy storage system, ο (I) is the power response time constant of the Ith energy storage system, is the heat capacity of the Ith energy storage system, is the charge / discharge efficiency of the Ith energy storage system, is the total capacity of the Ith energy storage system, and is the influence coefficient of the SOC / temperature of the Ith energy storage system on the terminal voltage, R (I) is the internal resistance of the Ith energy storage system, (I) (t) is the uncertainty and disturbance existing in the establishment of the physical model of the Ith energy storage system, f (I) (t) is the false data attack injected into the Ith energy storage system, C (I) is the observation matrix of the Ith energy storage system.

[0018] Further improvement of the technical scheme of the present application is that the adaptive robust observer in step S2 is specifically designed as follows,

[0019]

[0020] wherein, is the estimated value of the false data attack of the Ith energy storage system, L (1) is the observer gain of the Ith energy storage system, is the output estimation of the observer of the Ith energy storage system, f (I) (t) = C (I) θ (I) , θ (I) represents the residual change amount of the Ith energy storage system caused by the false data attack, which has the following deception characteristics for the current detection mechanism based on Kalman state estimation,

[0021]

[0022] wherein, R (I) (t) and Z (I) (t) represent the measurement residual and the output estimation of the Ith energy storage system, and represent the measurement residual of the Ith energy storage system under attack, the sensor output measurement value and the output estimation, μ (I) is the prior threshold of the Ith energy storage system.

[0023] Further improvement of the technical scheme of the present application is that the detection criterion in step S3 is as follows,

[0024]

[0025] wherein, FDIA is the false data attack.

[0026] Further improvement of the technical scheme of the present application is that the elastic controller in step S4 when the false data attack is designed as follows,

[0027]

[0028] wherein, and is the control gain of the Ith energy storage system elastic controller, u (I) (t) is the control rate of the Ith energy storage system elastic controller, a (I) is the triggering condition parameter of the Ith energy storage system elastic controller.

[0029] The technical progress achieved by the present application includes:

[0030] The present application designs an active false data attack defense method for power distribution network energy storage system based on elastic controller to solve the problem of cascade failure caused by the lack of system dynamic recovery ability under attack in the existing security defense method. The method first designs an adaptive observer to estimate the injected false data attack, and then triggers the elastic controller to realize the fast recovery and active dynamic defense of the system under attack by embedding the security situation awareness into the elastic controller. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 is the energy storage system structure diagram in the active false data attack defense method for power distribution network energy storage system based on elastic controller of the present application;

[0032] Figure 2 is the flow chart of the active false data attack defense method for power distribution network energy storage system based on elastic controller of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application is further described in detail below in combination with specific embodiments and with reference to the drawings. In the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present application.

[0034] The present application provides an active false data attack defense method for power distribution network energy storage system based on elastic controller, which comprises the following steps:

[0035] S1, a physical dynamic model of battery energy storage system of power distribution network under false data attack is established.

[0036] In the above step S1, considering that the power distribution network is composed of N battery energy storage systems, the physical dynamics equation of the Ith (I=1,…,N) battery energy storage system is established as

[0037]

[0038] In the formula,

[0039] Wherein, y (I) (t) is the sensor output measurement value of the Ith energy storage system, and Δt is the control period, SOC(I) (t) is the state of charge of the Ith energy storage system, T (I) (t) is the temperature of the Ith energy storage system battery, SOH (I) (t) is the state of health of the Ith energy storage system, is the actual output power of the Ith energy storage system, is the charge / discharge power command of the Ith energy storage system, is the cooling system power of the Ith energy storage system, ε (I) is the self-discharge rate of the Ith energy storage system, χ (I) and β (I) is the SOC-temperature coupling coefficient of the Ith energy storage system, temperature decay rate; ι (I) and γ (I) is the temperature-SOH coupling coefficient of the Ith energy storage system, aging rate, ο (I) is the power response time constant of the Ith energy storage system, is the thermal capacity of the Ith energy storage system, is the charge-discharge efficiency of the Ith energy storage system, is the total capacity of the Ith energy storage system, and is the impact coefficient of the Ith energy storage system SOC / temperature on terminal voltage, R (I) is the internal resistance of the Ith energy storage system (related to temperature, SOH), Ο (I) (t) is the uncertainty and disturbance existing in the establishment of the physical model of the Ith energy storage system, f (I) (t) is the false data attack injected into the Ith energy storage system, C (I) is the observation matrix of the Ith energy storage system.

[0040] S2, design an adaptive robust observer to estimate the injected false data attack in real time.

[0041] The adaptive robust observer in the above step S2 is specifically designed as follows,

[0042]

[0043] wherein, is the estimated value of the false data attack of the Ith energy storage system, L (1) is the observer gain of the Ith energy storage system, is the observer output estimate of the Ith energy storage system, f (I) (t) = C (I) θ (I) , θ (I) represents the residual change amount of the Ith energy storage system caused by the false data attack, which has the following deception characteristics to the current detection mechanism based on Kalman state estimation,

[0044]

[0045] where R (I) (t) and Z (I) (t) represent the first energy storage system measurement residual and output estimate, and represents the first energy storage system measurement residual under attack, sensor output measurement and output estimate, μ (I) is the first energy storage system prior threshold (its value depends on the system noise).

[0046] S3, based on the estimated false data attack, the detection criterion of the false data attack is given.

[0047] The detection criterion in the above step S3 is as follows,

[0048]

[0049] where FDIA is the false data attack.

[0050] S4, when the false data attack is detected, the dynamic adjustment of the control command of the resilience controller is triggered to realize attack compensation and system function maintenance.

[0051] The above step S4 is specifically based on the detection of the injected false data attack in step S3, and then the resilience controller is designed to realize the active false data attack defense of the power grid energy storage system.

[0052] The resilience controller under FDIA is designed as follows:

[0053]

[0054] where, and is the control gain of the first energy storage system resilience controller, u (I) (t) is the control rate of the first energy storage system resilience controller, α (I) is the resilience controller triggering condition parameter of the first energy storage system.

[0055] Embodiment one

[0056] In this embodiment, the parameters of the first battery energy storage system are selected as follows, ε (1) = 0.001; χ (1) = 0.05 and β (1) = 0.01; ι (1) = 5 × 10 -5 and γ (1) = 1 × 10 -6 ; o (1) = 0.5; and R (1) =0.1Ω, and then a comparison between the active false data attack defense method and the passive defense method of the power distribution network energy storage system based on the elastic controller is designed as follows:

[0057] Method / indicator Method of the invention Passive defense method Improved defense performance Attack recovery time (s) 1.2 3 60％

[0058] According to the above table, the method can improve the defense performance by 60% compared with the traditional passive defense method.

[0059] In the above embodiment, in view of the lack of system dynamic recovery capability under attack in the existing security defense method, leading to the problem of cascading failure, an active false data attack defense method of the power distribution network energy storage system based on the elastic controller is designed. The method first designs an adaptive observer to estimate the injected false data attack, and then triggers the elastic controller, and realizes the fast recovery and active dynamic defense of the system under attack by embedding the security situation awareness into the elastic controller.

[0060] The above-described embodiments are merely preferred embodiments of the present application and do not limit the concept and scope of the present application. Various modifications and improvements to the technical solutions of the present application made by those of ordinary skill in the art without departing from the design concept of the present application shall fall within the protection scope of the present application, and the technical content claimed by the present application has been fully recorded in the claims.

Claims

1. A method for defending against active spoofing attacks on distribution network energy storage systems based on resilient controllers, characterized in that: Includes the following steps, S1. Establish a physical dynamic model of the distribution network battery energy storage system under false data attack; In step S1, the distribution network is considered to consist of N battery energy storage systems, and the first... The physical dynamics equations of the battery energy storage system are as follows: In the formula, , , , , , in, This is the measured value output by the I-th energy storage system sensor. It is a control cycle. For the I-th energy storage system, This represents the battery temperature of the I-th energy storage system. For the I-th energy storage system in its healthy state, This is the actual output power of the I-th energy storage system. This represents the charging / discharging power command for the I-th energy storage system. The power of the cooling system of the I-th energy storage system. Let I be the self-discharge rate of the I-th energy storage system. and The SOC-temperature coupling coefficient and temperature decay rate of the I-th energy storage system; and It is the temperature-SOH coupling coefficient and aging rate of the I-th energy storage system. It is the power response time constant of the I-th energy storage system. It is the heat capacity of the I-th energy storage system. This is the charge / discharge efficiency of the I-th energy storage system. It is the maximum total capacity of the I-th energy storage system. and It is the influence coefficient of the SOC / temperature on the terminal voltage of the I-th energy storage system. It is the internal resistance of the I-th energy storage system. The uncertainty and disturbance in establishing the physical model of the I-th energy storage system This is an attack that injects false data into the I-th energy storage system. This is the observation matrix of the I-th energy storage system; It is the control rate of the I-th energy storage system resilient controller; S2. Design an adaptive robust observer to estimate the injected fake data attack in real time; S3. Based on the estimated fake data attack, a detection criterion for fake data attacks is given; S4. When a false data attack is detected, the elastic controller is triggered to dynamically adjust the control command to achieve attack compensation and maintain system function.

2. The active spoofing attack defense method for distribution network energy storage systems based on resilient controllers according to claim 1, characterized in that: The adaptive robust observer in step S2 is specifically designed as follows. in, This is an estimate of the spoofed data attack on the I-th energy storage system. It is the gain of the I-th energy storage system observer. This is the output estimate of the I-th energy storage system observer. , Let represent the change in the residual of the i-th energy storage system caused by a false data attack. This change exhibits the following deceptive characteristics against current detection mechanisms based on Kalman state estimation: In the formula, and This represents the measurement residual and output estimate of the i-th energy storage system. , and This represents the measurement residual of the i-th energy storage system under attack, along with the sensor output measurement and output estimate. It is the prior threshold of the I-th energy storage system.

3. The method for defending against active spoofing attacks in a distribution network energy storage system based on a resilient controller as described in claim 1, characterized in that: The detection criteria in step S3 are as follows. In the formula, FDIA stands for Fake Data Attack.

4. The method for defending against active spoofing attacks in a distribution network energy storage system based on a resilient controller as described in claim 1, characterized in that: The elastic controller design for the fake data attack in step S4 is as follows. In the formula, and It is the control gain of the I-th energy storage system resilient controller. It is the trigger condition parameter for the elastic controller of the I-th energy storage system.

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