CN111384717A - Adaptive damping control method and system for resisting false data injection attack - Google Patents
Adaptive damping control method and system for resisting false data injection attack Download PDFInfo
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Abstract
The invention discloses a self-adaptive damping control method and a system for resisting false data injection attack, which comprises the steps of collecting wide area measurement signals possibly suffering from false data injection attack, and performing attack detection, attack source confirmation and data recovery on the wide area measurement signals by utilizing a linear state estimation algorithm to generate estimation input signals; and amplifying and phase-shifting the estimated input signal, and generating a control signal after the estimated input signal passes through a GrHDP neural network so as to realize the suppression of the low-frequency oscillation of the power system. The linear state estimator ensures that the measured signal can still maintain the integrity and authenticity of the measured signal when facing various types of false data injection attacks by executing distributed state estimation on the local power system, and ensures the trueness and the availability of a remote signal; on the other hand, the wide-area damping controller designed by the GrHDP algorithm can play a good role in inhibiting low-frequency oscillation aiming at double weak damping modes on the premise of not needing a data model of a power system, and is suitable for different operating conditions and measurement noise.
Description
Technical Field
The invention belongs to the field of power system control, and particularly relates to a self-adaptive damping control method and a self-adaptive damping control system for resisting false data injection attack.
Background
The wide-area damping controller is designed by adopting a remote signal acquired by a wide-area measurement system, and is a very effective measure for inhibiting inter-area low-frequency oscillation. With the increasingly obvious information physical fusion characteristics of the power system and the increasingly complex interaction of various information, network attacks become key factors threatening the effect of the wide-area damping controller. An attacker triggers a controller to make a wrong decision through a phase angle measuring unit, a communication line and the like in invasion, so that the effect same as that of destroying primary equipment on a physical side is achieved, the stable operation of a power system is seriously influenced, and even a large-scale power failure accident is caused. Therefore, it is necessary to take measures to enhance the attack resistance of the wide-area damping controller.
Signal uncertainty factors in the field of wide area damping control have long been studied. In terms of communication skew processing, a lot of research has been conducted and good results have been obtained. The network attack is a problem which is highlighted in the context of physical fusion of power information, and related researches are relatively rare. In the only research, the wide-area damping controller is still designed by utilizing a linear mathematical model of the system under a certain typical operation condition. The method is not only difficult to adapt to the change of the operation condition, but also difficult to obtain the mathematical model of the actual power system. Therefore, model-less damping controllers with adaptive capabilities should be considered.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a self-adaptive damping control method and a self-adaptive damping control system for resisting false data injection attacks, and aims to solve the problem that a wide area damping controller makes a wrong decision due to a polluted wide area measurement system measurement signal under different false data injection attacks.
To achieve the above object, according to an aspect of the present invention, there is provided an adaptive damping control method for resisting a dummy data injection attack, comprising the steps of:
collecting wide area measurement signals which are likely to suffer from false data injection attack, and performing attack detection, attack source confirmation and data recovery on the wide area measurement signals by using a linear state estimation algorithm to generate estimation input signals;
amplifying and phase-shifting the estimated input signal, and generating a control signal after passing through a GrHDP (Global representation dynamic programming) neural network so as to realize the suppression of the low-frequency oscillation of the power system.
Further, the linear state estimation algorithm includes:
attack detection; performing initial state estimation on the wide area measurement signal, expanding the obtained residual error to obtain an expanded residual error vector CNE, and performing chi-square hypothesis test on the expanded residual error vector CNE; if the chi-square hypothesis passes the checking, the wide area measurement signal is considered not to be attacked; if the chi-square test fails, the wide area measurement signal is considered to be attacked;
confirming an attack source; when the wide-area measurement signal is attacked, the quantity corresponding to the numerical maximum element in the extended residual error vector is measured as an attack source, and the j-th quantity measurement is assumed;
recovering data; for confirmed sources of attack, by yj，new＝yj，old-CNEj·σjMaking a correction in which yj，newFor the measurement of the quantity after the current recovery, yj，oldTo confirm the attacked quantity measurement, CNEjTo extend the jth element in the residual, σ, corresponding to the confirmed attack sourcejAnd the j element in the standard deviation is corrected to obtain an estimated input signal.
Further, amplifying and phase-shifting the estimated input signal, and generating a control signal after passing through a GrHDP neural network specifically includes:
amplifying and phase-shifting the estimated input signal to generate a parallel phase shift signal;
and obtaining an output control signal adaptive to the current operation environment of the power grid by utilizing the GrHDP neural network according to the parallel phase offset signal so as to realize the suppression of the low-frequency oscillation of the power grid.
According to another aspect of the present invention, there is provided an adaptive damping control system for resisting false data injection attacks, comprising a wide area measurement system, a linear state estimator, and an adaptive damping controller;
the wide area measurement system is used for collecting wide area measurement signals which are possibly attacked by false data injection; selecting a connecting line with the highest observability in the power system, extracting a plurality of nodes and connecting branches thereof in the connecting line, and acquiring a wide area measurement signal by adopting a phase measurement unit;
the wide area measurement signal is input into a linear state estimator, and the linear state estimator is used for carrying out attack detection, attack source confirmation and data recovery on the wide area measurement signal by using a linear state estimation algorithm to generate an estimation input signal;
the estimated input signal is input into the adaptive damping controller, the adaptive damping controller amplifies and shifts the phase, and the control signal is generated after the signal passes through the GrHDP neural network, so that the low-frequency oscillation of the power system is inhibited.
Furthermore, the transmission power of the tie line with a considerably higher degree is taken as a distant signal, so that only the measurement information of 2 nodes at both ends of the tie line and one node connected with each node, namely 4 nodes in total, is extracted. The measurement information includes the amplitude and phase angle of the 4 node voltages and the 6 branch currents on the branches connected to the node voltages. The linear state estimator performs the linear state estimation algorithm only for the subsystem consisting of the 4 nodes and their connected branches. The linear state estimator performs state estimation once in each sampling period, and outputs an estimated input signal with high real-time performance.
Further, the linear state estimator comprises an attack detection module, an attack source confirmation module and a data recovery module;
the attack detection module is used for carrying out preliminary state estimation on the wide area measurement signal, expanding the obtained residual error to obtain an expanded residual error vector CNE and carrying out chi-square hypothesis test on the expanded residual error vector CNE; if the chi-square hypothesis passes the checking, the wide area measurement signal is considered not to be attacked; if the chi-square test fails, the wide area measurement signal is considered to be attacked;
an attack source confirmation module, configured to, when the wide-area measurement signal is attacked, measure a quantity corresponding to a maximum-value element in the extended residual vector as an attack source, assuming that the quantity is a jth quantity measurement;
a data recovery module for the confirmed attack source according to yj，new＝yj，old-CNEj·σjMaking a correction in which yj，newFor the measurement of the quantity after the current recovery, yj，oldTo confirm the attacked quantity measurement, CNEjTo extend the jth element in the residual, σ, corresponding to the confirmed attack sourcejAnd the j element in the standard deviation is corrected to obtain an estimated input signal.
Further, the adaptive damping controller comprises a phase shift module and a GrHDP module;
the phase shift module is used for receiving the estimated input signal output by the linear state estimator, amplifying and performing phase shift processing on the estimated input signal to obtain a parallel phase shift signal; in the invention, the phase shift module is a dual-channel link, a parallel phase shift channel is added on the basis of the original channel, and the phase of the original signal is shifted by 90 degrees through a differential link. Along with the change of the execution network weight value, the original signal and the signal after the deviation thereof can be synthesized into an output signal with any phase, thereby effectively improving the control effect.
The GrHDP module is connected to the output end of the phase shift module and used for obtaining a control signal adaptive to the current operation condition of the power system according to the parallel phase shift signal so as to realize the suppression of the low-frequency oscillation of the power system.
Compared with the prior art, the elastic self-adaptive wide area damping control method comprises two parts. Firstly, the linear state estimator ensures that the measured signal can still maintain the integrity and authenticity of the measured signal when facing various types of false data injection attacks by executing distributed state estimation on a local power system, and ensures the trueness and the availability of a remote signal; on the other hand, the wide-area damping controller designed by the GrHDP algorithm can play a good role in inhibiting low-frequency oscillation aiming at double weak damping modes on the premise of not needing a data model of a power system, and is suitable for different operating conditions and measurement noise.
Drawings
FIG. 1 is a schematic diagram of an elastic adaptive wide-area damping control structure according to an embodiment of the present invention;
fig. 2 is a schematic diagram of distributed state estimation for a specific tie line according to an embodiment of the present invention;
fig. 3 is a flowchart of a linear state estimation attack resistance provided by the embodiment of the present invention;
FIG. 4 is a schematic diagram of a principle that a GrHDP controller controls VSC-HVDC according to an embodiment of the present invention;
FIG. 5 shows the node 38 voltage U under different types of dummy data injection attacks provided by embodiments of the present invention38A plot of response versus time;
FIG. 6 shows the relative rotation angle δ of the generator using three different control methods according to the embodiment of the present invention14-15And delta14-16A graph of time;
FIG. 7 shows three different control methods adopted in different operation conditions J according to embodiments of the present inventionITAECompare the figures.
FIG. 8 shows the relative rotation angle δ of the generator under different input signals of the controller according to the embodiment of the present invention14-15And delta14-16Graph over time.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present invention provides an elastic adaptive wide area damping control method for resisting false data injection attack, which includes the following steps:
collecting wide area measurement signals which are likely to suffer from false data injection attack, and performing attack detection, attack source confirmation and data recovery on the wide area measurement signals by using a linear state estimation algorithm to generate estimation input signals;
and amplifying and phase-shifting the estimated input signal, and generating a control signal after the estimated input signal passes through a GrHDP neural network so as to realize the suppression of the low-frequency oscillation of the power system.
Where efficient implementation of the linearity state estimation algorithm relies on redundancy of the measurement of the quantities, in an embodiment of the invention the redundancy is set to 2.5. I.e. the quantity measurement comprises 4 voltage vectors, 6 current vectors, the estimated state vector is 4 voltage vectors.
A remote signal with high observability is selected for each weak damping mode by aiming at a self-adaptive wide area damping controller with double weak damping modes, the remote signals are respectively corrected by a linear state estimator and then enter a phase shifting link to generate parallel phase offset signals, then the two signals enter a three-layer neural network in GrHDP together, control signals are output after training, and direct current power transmitted by a flexible direct current system is adjusted, so that system oscillation is inhibited.
The core part of the control method is a linear state estimator and a GrHDP controller, the linear state estimator and the GrHDP controller are intensively configured in a VSC-HVDC control system, a control signal is generated by the controller and then directly sent to a constant active power control outer ring of the VSC-HVDC, a long-distance transmission process similar to a measurement signal of a wide area measurement system is not involved, and therefore the network attack threat in the process is not considered.
As shown in fig. 2, a diagram of distributed state estimation for a particular tie line. The linear state estimator only extracts PMU (phase measurement Unit) measurement information of 2 nodes at two ends of the connecting line and one connected node at each end of the connecting line, namely 4 nodes. The measurement information includes the amplitude and phase angle of the 4 node voltages and the 6 branch currents on the branches connected to the node voltages. The state of the local system is reflected in the voltage amplitude and phase angle of the 4 nodes. The linear state estimator also executes a linear state estimation algorithm only for the subsystem consisting of the 4 nodes and the connected branches thereof.
As shown in fig. 3, a linear state estimation method for protecting against a spurious data injection attack, the method comprising the steps of:
s1, setting an initial Jacobian matrix, a chi-square test threshold value lambda, a measurement standard deviation and an iteration upper limit N according to a network topological structure.
Specifically, the Jacobian matrix is mainly determined with the parameters of the net rack and is a constant matrix; the chi-squared test threshold λ is related to the measurement vector degree of freedom, which in this embodiment of the invention is 18.307; the standard deviation of measurement was set to 1% of the measured value; the iteration upper limit N is set to 20.
And S2, carrying out state estimation according to a weighted least square method, calculating a joint error CNE of the state estimation, and carrying out chi-square hypothesis test on the joint error CNE. If the chi-square hypothesis passes the check, the chi-square hypothesis determines that no attack data exists, and the step S5 is entered; if the chi-square hypothesis test fails, it is determined that attack data exists, and the process proceeds to step S3.
From the analysis of a geometric view, the linear state estimation forms an n-dimensional state estimation vector after mapping an m-dimensional measurement vector on a Jacobian matrix H, and the error calculated by the n-dimensional state estimation vector is composed of two parts. Are respectively located at
Detectable error epsilon with upper degree of freedom in m-n dimensionDI.e. the residual p, and lies in
With an upper degree of freedom of m-dimensional undetectable error epsilonU. Introducing a spreading factor
Deriving joint error from residual extension
And S3, regarding the quantity corresponding to the element with the maximum value in the CNE as an attack data source.
The residual pollution phenomenon causes that errors after state estimation cannot reflect real attack sources, and the maximum numerical element is regarded as an attack data source.
S4, assuming that the jth element in the CNE is the maximum value, passing through yj，new＝yj，old-CNEj·σjA correction is made to the jth quantity measurement.
And S5, judging whether the iteration number exceeds an upper limit value, if so, entering the step S6, and if not, returning to the step S1 to carry out the next state estimation.
And S6, outputting the estimated state quantity to finish the linear state estimation process.
As shown in fig. 4, the GrHDP wide area damping controller includes: a dual-channel phase-shifting link and a three-layer neural network.
Specifically, a phase shift link is additionally provided with a parallel phase shift channel on the basis of an original signal channel, and the phase shift channel shifts the phase of an original signal by 90 degrees through a differential link. The original signal and its shifted signal can be combined into an output signal of arbitrary phase as the change of the network weights is performed. The two far-distance signals respectively correspond to a group of double channels.
Specifically, the three neural networks are respectively an execution network, a target network and an evaluation network, and each neural network adopts a feed-forward single hidden layer structure. The target network outputs a self-adaptive internal strengthening signal S (t) and optimizes the mapping relation between the input state quantity and the output control quantity; and (3) evaluating a Bellman equation cost function J (t) of the network fitting whole three-layer network, and outputting an optimal control command u (t) by the target network when the J (t) is minimum.
As shown in FIG. 5, the node 38 voltage U is attacked by different types of spurious data injections38Graph of response curve over time. It can be seen that after the dummy data is injected, the real part and the imaginary part of the voltage signal have larger offsets compared with the real value, and the signal processed by the linear state estimator is nearly consistent with the real value, which shows that the linear state estimator exerts a better effect of resisting the dummy data attack. In addition to this, the present invention is,under three different types of false data injection attacks, namely pulse, step and ramp, the linear state estimator shows good adaptability.
As shown in fig. 6, the relative rotation angle δ of the generator is controlled by three different control methods14-15And delta14-16Graph over time. It can be seen that, in the absence of any wide-area control measure, the system is in a destabilized state, and after the flexible adaptive control method consisting of the linear state estimator and the GrHDP control module is adopted, the system can still be rapidly restored to a stable state under the attack of false data injection. In addition, the comparison result of the control method consisting of the linear state estimator and the conventional LL-WADC shows that the wide-area damping controller designed by the GrHDP algorithm has more excellent control performance.
As shown in FIG. 7, three different control methods are adopted to operate under different operating conditions JITAECompare the figures. J. the design is a squareITAEThe method is a common index reflecting control performance in the field of wide-area stable control. It can be seen that from the working condition 1 to the working conditions 2 and 3, the working conditions of the system become more severe, the control performance of the conventional LL-WADC becomes worse under the severe working conditions, and the GrHDP wide-area damping controller still maintains better damping control performance under the severe working conditions, which indicates that the GrHDP controller has better adaptability to the operating working conditions.
As shown in FIG. 8, the relative rotation angle delta of the generator under different controller input signals14-15And delta14-16Graph over time. It can be seen that, when the signal of the output controller is an unprocessed noise-containing signal, the GrHDP loses the damping suppression effect due to learning the rule of the noise-containing signal, and even causes the system to fall into irregular oscillation, and when the estimated signal obtained after the noise-containing signal is processed by the linear state estimator enters the controller, the performance of the controller is nearly consistent with that of the input real signal, which shows that the embodiment of the present invention has a better anti-noise capability.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. An adaptive damping control method for resisting false data injection attack is characterized by comprising the following steps:
collecting wide area measurement signals which are likely to suffer from false data injection attack, and performing attack detection, attack source confirmation and data recovery on the wide area measurement signals by using a linear state estimation algorithm to generate estimation input signals;
and amplifying and phase-shifting the estimated input signal, and generating a control signal after the estimated input signal passes through a GrHDP neural network so as to realize the low-frequency oscillation suppression of the power system.
2. The control method of claim 1, wherein the linear state estimation algorithm comprises:
attack detection: performing preliminary state estimation on the wide area measurement signal, expanding the obtained residual error to obtain an expanded residual error vector CNE, and performing chi-square hypothesis test on the expanded residual error vector CNE; if the chi-square hypothesis passes the checking, the wide area measurement signal is considered not to be attacked; if the chi-square test fails, the wide area measurement signal is considered to be attacked;
confirming an attack source: when a wide-area measurement signal is attacked, measuring a quantity corresponding to a numerical maximum element in the extended residual vector as an attack source, and assuming that the quantity is a jth quantity measurement;
and (3) data recovery: for confirmed sources of attack, by yj，new＝yj，old-CNEj·σjMaking a correction in which yj，newFor the measurement of the quantity after the current recovery, yj，oldTo confirm the attacked quantity measurement, CNEjTo extend the jth element in the residual, σ, corresponding to the confirmed attack sourcejAnd the j element in the standard deviation is corrected to obtain an estimated input signal.
3. The control method according to claim 1, wherein the amplifying and phase-shifting the estimated input signal, and generating the control signal after passing through the GrHDP neural network specifically comprises:
amplifying and phase-shifting the estimated input signal to generate a parallel phase shift signal;
and obtaining an output control signal adaptive to the current operation environment of the power grid by utilizing the GrHDP neural network according to the parallel phase offset signal so as to realize the suppression of the low-frequency oscillation of the power grid.
4. An adaptive damping control system for resisting false data injection attack is characterized by comprising a wide area measurement system, a linear state estimator and an adaptive damping controller;
the wide area measurement system is used for collecting wide area measurement signals which are possibly attacked by false data injection;
the linear state estimator is used for carrying out attack detection, attack source confirmation and data recovery on the wide area measurement signal by using a linear state estimation algorithm to generate an estimation input signal;
the self-adaptive damping controller is used for amplifying and phase-shifting the estimated input signal, and generating a control signal after passing through a GrHDP neural network so as to realize the low-frequency oscillation suppression of the power system.
5. The control system of claim 4, wherein the wide-area measurement system selects a tie line with the highest observability in the power system, extracts a plurality of nodes and their connected branches in the tie line, and obtains the wide-area measurement signal.
6. The control system of claim 4, wherein the linear state estimator performs state estimation once per sampling period thereof, outputting an estimated input signal with high real-time.
7. The control system of claim 4, wherein the linear state estimator comprises an attack detection module, an attack source validation module, and a data recovery module;
the attack detection module is used for carrying out preliminary state estimation on the wide area measurement signal, expanding the obtained residual error to obtain an expanded residual error vector CNE and carrying out chi-square hypothesis test on the expanded residual error vector CNE; if the chi-square hypothesis passes the checking, the wide area measurement signal is considered not to be attacked; if the chi-square test fails, the wide area measurement signal is considered to be attacked;
an attack source confirmation module, configured to, when the wide-area measurement signal is attacked, measure a quantity corresponding to a maximum-value element in the extended residual vector as an attack source, assuming that the quantity is a jth quantity measurement;
a data recovery module for the confirmed attack source according to yj，new＝yj，old-CNEj·σjMaking a correction in which yj，newFor the measurement of the quantity after the current recovery, yj，oldTo confirm the attacked quantity measurement, CNEjTo extend the jth element in the residual, σ, corresponding to the confirmed attack sourcejAnd the j element in the standard deviation is corrected to obtain an estimated input signal.
8. The control system of claim 4, wherein the adaptive damping controller comprises a phase shift module and a GrHDP module;
the phase shift module is used for receiving an estimated input signal output by the linear state estimator, and amplifying and phase-shifting the estimated input signal to obtain a parallel phase shift signal;
and the GrHDP module is connected to the output end of the phase shift module and is used for obtaining a control signal adaptive to the current operation condition of the power system according to the parallel phase shift signal so as to realize the suppression of the low-frequency oscillation of the power system.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114362245A (en) * | 2022-01-12 | 2022-04-15 | 全球能源互联网研究院有限公司 | Wind power plant attack method and system |
| CN114362245B (en) * | 2022-01-12 | 2023-09-08 | 全球能源互联网研究院有限公司 | Wind farm attack method and system |
| CN116094769A (en) * | 2022-12-22 | 2023-05-09 | 燕山大学 | Port micro-grid control method for resisting false data injection attack |
| CN116094769B (en) * | 2022-12-22 | 2024-03-01 | 燕山大学 | Port micro-grid control method for resisting false data injection attack |
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