CN111384717A - An adaptive damping control method and system for resisting false data injection attacks - Google Patents
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Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明属于电力系统控制领域，更具体地，涉及一种抵御虚假数据注入攻击的自适应阻尼控制方法及系统。The invention belongs to the field of power system control, and more particularly, relates to an adaptive damping control method and system for resisting false data injection attacks.
背景技术Background technique
采用广域测量系统采集的远方信号设计广域阻尼控制器是抑制区域间低频振荡十分有效的措施。随着电力系统的信息物理融合特征愈加明显，各类信息交互日益复杂，网络攻击成为威胁广域阻尼控制器效果的关键因素。攻击者通过入侵中的相角测量单元、通信线路等，引发控制器错误决策，从而达到与破坏物理侧一次设备相同的效果，严重影响电力系统的稳定运行，甚至造成大规模的停电事故。因此，十分有必要采取措施增强广域阻尼控制器的攻击抵御能力。Designing a wide-area damping controller using the distant signals collected by the wide-area measurement system is a very effective measure to suppress low-frequency oscillations between areas. With the increasingly obvious features of cyber-physical fusion of power systems, various information interactions are becoming more and more complex, and network attacks have become a key factor that threatens the effect of wide-area damping controllers. The attacker can cause the controller to make wrong decisions through the intrusion of the phase angle measurement unit, communication line, etc., so as to achieve the same effect as destroying the primary equipment on the physical side, seriously affecting the stable operation of the power system, and even causing large-scale power outages. Therefore, it is very necessary to take measures to enhance the attack resistance of the wide-area damping controller.
有关广域阻尼控制领域中信号不确定性因素的研究由来已久。在通信时滞处理方面，已有大量研究并取得了不错的效果。网络攻击作为电力信息物理融合背景下凸显出的问题，相关的研究还比较少见。在仅有的研究中，广域阻尼控制器依然为在某一典型运行工况下，利用系统的线性化数学模型设计得来的。该方法不仅难以适应运行工况变化，且实际电力系统的数学模型也很难获得。因此，具有自适应能力的无模型阻尼控制器应该被考虑。There is a long history of research on signal uncertainty factors in the field of wide-area damping control. In the aspect of communication delay processing, a lot of research has been done and good results have been achieved. As a prominent problem under the background of power-information-physical fusion, related research is still relatively rare. In the only study, the wide-area damping controller is still designed using the linearized mathematical model of the system under a typical operating condition. This method is not only difficult to adapt to changes in operating conditions, but also difficult to obtain the mathematical model of the actual power system. Therefore, a model-free damping controller with adaptive capability should be considered.
发明内容SUMMARY OF THE INVENTION
针对现有技术的缺陷，本发明的目的在于提供一种抵御虚假数据注入攻击的自适应阻尼控制方法及系统，旨在解决不同虚假数据注入攻击下，被污染的广域测量系统量测信号引发广域阻尼控制器错误决策的问题。Aiming at the defects of the prior art, the purpose of the present invention is to provide an adaptive damping control method and system for resisting false data injection attacks, aiming to solve the problem caused by the polluted wide-area measurement system measurement signal caused by different false data injection attacks. The problem of wrong decision-making in a wide-area damped controller.
为实现上述目的，按照本发明的一方面，提供了一种抵御虚假数据注入攻击的自适应阻尼控制方法，包括以下步骤：In order to achieve the above object, according to an aspect of the present invention, an adaptive damping control method for resisting false data injection attacks is provided, comprising the following steps:
采集可能遭受虚假数据注入攻击的广域量测信号，利用线性状态估计算法对广域量测信号进行攻击检测、攻击源确认和数据恢复，生成估计输入信号；Collect wide-area measurement signals that may be attacked by false data injection, and use linear state estimation algorithm to perform attack detection, attack source confirmation, and data recovery on the wide-area measurement signals to generate estimated input signals;
对估计输入信号进行放大与移相，经过GrHDP(Goal representation HeuristicDynamic Programming)神经网络后生成控制信号，以实现对电力系统的低频振荡的抑制。The estimated input signal is amplified and phase-shifted, and a control signal is generated after passing through the GrHDP (Goal representation HeuristicDynamic Programming) neural network to suppress the low-frequency oscillation of the power system.
进一步地，线性状态估计算法包括：Further, the linear state estimation algorithm includes:
攻击检测；对广域量测信号进行初步的状态估计，并将所得残差扩展，得到扩展残差向量CNE，对扩展残差向量进行卡方假设检验；若卡方假设检验通过，则认为广域量测信号未被攻击；若卡方检验未能通过，则认为广域量测信号被攻击；Attack detection: perform preliminary state estimation on the wide-area measurement signal, and expand the obtained residual to obtain the extended residual vector CNE, and perform the chi-square hypothesis test on the extended residual vector; if the chi-square hypothesis test passes, it is considered that the The domain measurement signal is not attacked; if the chi-square test fails, the wide-area measurement signal is considered to be attacked;
攻击源确认；当广域量测信号被攻击时，扩展残差向量中数值最大元素对应的量测量为攻击源，假设为第j个量测量；Confirmation of the attack source; when the wide-area measurement signal is attacked, the quantity measurement corresponding to the largest element in the extended residual vector is the attack source, which is assumed to be the jth measurement;
数据恢复；对确认的攻击源，依yj，new＝yj，old-CNEj·σj进行修正，其中yj，new为当前次恢复后的量测量，yj，old为确认被攻击的量测量，CNEj为扩展残差中第j个元素，与确认的攻击源相对应，σj为标准差中第j个元素，修正后得到估计输入信号。Data recovery; correct the confirmed attack source according to yj, new = yj, old -CNEj ·σj , where yj, new is the quantity measurement after the current recovery, yj, old is the confirmed attack CNEj is the j-th element in the extended residual, corresponding to the confirmed attack source, σj is the j-th element in the standard deviation, and the estimated input signal is obtained after correction.
进一步地，对估计输入信号进行放大与移相，经过GrHDP神经网络后生成控制信号具体包括：Further, the estimated input signal is amplified and phase-shifted, and the control signal generated after passing through the GrHDP neural network specifically includes:
对估计输入信号进行放大与移相后生成并行的相位偏移信号；Amplify and phase shift the estimated input signal to generate a parallel phase offset signal;
利用GrHDP神经网络根据并行的相位偏移信号得到与电网当前运行环境相适应的输出控制信号，以实现对电网低频振荡的的抑制。The GrHDP neural network is used to obtain the output control signal suitable for the current operating environment of the power grid according to the parallel phase offset signal, so as to realize the suppression of the low frequency oscillation of the power grid.
按照本发明的另一方面，提供了一种抵御虚假数据注入攻击的自适应阻尼控制系统，包括广域测量系统、线性状态估计器和自适应阻尼控制器；According to another aspect of the present invention, an adaptive damping control system for resisting spurious data injection attacks is provided, including a wide-area measurement system, a linear state estimator and an adaptive damping controller;
广域测量系统用于采集可能遭受虚假数据注入攻击的广域量测信号；具体包括选取电力系统中可观度最高的联络线，抽取联络线中的若干节点及其相连支路，采用相位量测单元获取广域量测信号；The wide-area measurement system is used to collect wide-area measurement signals that may be attacked by false data injection; specifically, it includes selecting the tie line with the highest observability in the power system, extracting several nodes in the tie line and their connected branches, and using phase measurement The unit acquires wide-area measurement signals;
广域量测信号输入到线性状态估计器，线性状态估计器用于利用线性状态估计算法对所述广域量测信号进行攻击检测、攻击源确认和数据恢复，生成估计输入信号；The wide-area measurement signal is input to the linear state estimator, and the linear state estimator is used to perform attack detection, attack source confirmation and data recovery on the wide-area measurement signal by using a linear state estimation algorithm to generate an estimated input signal;
估计输入信号输入到自适应阻尼控制器，自适应阻尼控制器进行放大与移相，经过GrHDP神经网络后生成控制信号，以实现对电力系统的低频振荡的抑制。The estimated input signal is input to the adaptive damping controller, which amplifies and shifts the phase, and generates a control signal after passing through the GrHDP neural network to suppress the low-frequency oscillation of the power system.
进一步地，以可观度较高的联络线传输功率为远方信号，因此仅抽取联络线两端2个节点及其各自相连的一个节点，即共4个节点处的量测信息。量测信息包括4个节点电压及其相连支路上6个支路电流的幅值与相角。线性状态估计器便仅针对此4个节点及其相连支路组成的子系统执行线性状态估计算法。线性状态估计器在其每一个采样周期均进行一次状态估计，输出实时性较高的估计输入信号。Further, the transmission power of the tie line with higher observability is used as the remote signal, so only the measurement information of two nodes at both ends of the tie line and one node connected to each other, that is, a total of 4 nodes is extracted. The measurement information includes 4 node voltages and the amplitudes and phase angles of the 6 branch currents on the connected branches. The linear state estimator executes the linear state estimation algorithm only for the subsystem composed of the four 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.
进一步地，线性状态估计器包括攻击检测模块、攻击源确认模块和数据恢复模块；Further, the linear state estimator includes an attack detection module, an attack source confirmation module and a data recovery module;
攻击检测模块，用于对广域量测信号进行初步的状态估计，并将所得残差扩展，得到扩展残差向量CNE，对扩展残差向量进行卡方假设检验；若卡方假设检验通过，则认为广域量测信号未被攻击；若卡方检验未能通过，则认为广域量测信号被攻击；The attack detection module is used to perform preliminary state estimation on the wide-area measurement signal, and expand the obtained residual to obtain an expanded residual vector CNE, and perform a chi-square hypothesis test on the expanded residual vector; if the chi-square hypothesis test passes, It is considered that the wide-area measurement signal is not attacked; if the chi-square test fails, the wide-area measurement signal is considered to be attacked;
攻击源确认模块，用于当广域量测信号被攻击时，扩展残差向量中数值最大元素对应的量测量为攻击源，假设为第j个量测量；The attack source confirmation module is used to measure the quantity corresponding to the largest element in the extended residual vector as the attack source when the wide-area measurement signal is attacked, assuming that it is the jth quantity measurement;
数据恢复模块，用于对确认的攻击源，依yj，new＝yj，old-CNEj·σj进行修正，其中yj，new为当前次恢复后的量测量，yj，old为确认被攻击的量测量，CNEj为扩展残差中第j个元素，与确认的攻击源相对应，σj为标准差中第j个元素，修正后得到估计输入信号。The data recovery module is used to correct the confirmed attack source according to yj, new = yj, old -CNEj ·σj , where yj, new is the quantity measurement after the current recovery, yj, old is Confirm the attacked quantity measurement, CNEj is the j-th element in the extended residual, corresponding to the confirmed attack source, σj is the j-th element in the standard deviation, and the estimated input signal is obtained after correction.
进一步地，自适应阻尼控制器包括移相模块以及GrHDP模块；Further, the adaptive damping controller includes a phase shifting module and a GrHDP module;
移相模块用于接收线性状态估计器输出的估计输入信号，对估计输入信号进行放大和移相处理，得到并行的相位偏移信号；在本发明中，移相模块为一个双通道的环节，在原通道基础上新增一个并联相位偏移通道，该通道通过一个微分环节将原信号的相位偏移90°。随着执行网络权值的变化，原信号及其偏移后信号可以合成任意相位的输出信号，有效改善控制效果。The phase-shifting module is used to receive the estimated input signal output by the linear state estimator, amplify and phase-shift the estimated input signal, and obtain parallel phase-shifted signals; in the present invention, the phase-shifting module is a two-channel link, On the basis of the original channel, a parallel phase shift channel is added, which shifts the phase of the original signal by 90° through a differential link. With the change of the weights of the execution network, the original signal and its offset signal can be synthesized into an output signal of any phase, which effectively improves the control effect.
GrHDP模块连接至移相模块的输出端，用于根据并行的相位偏移信号得到与电力系统当前运行工况相适应的控制信号，以实现对电力系统的低频振荡的抑制。The GrHDP module is connected to the output end of the phase shifting module, and is used for obtaining a control signal adapted to the current operating condition of the power system according to the parallel phase shift signal, so as to suppress the low frequency oscillation of the power system.
通过本发明所构思的以上技术方案，与现有技术相比，本发明中弹性自适应广域阻尼控制方法由两部分组成。首先线性状态估计器通过对局部电力系统执行分布式状态估计保证了量测信号在面对各类型虚假数据注入攻击时依然能够维持其完整性和真实性，确保了远方信号的真实可用；另一方面，采用GrHDP算法设计的广域阻尼控制器，在不需要电力系统数据模型的前提下针对双弱阻尼模态可以发挥良好的低频振荡抑制性能，并适应不同运行工况和测量噪声。Through the above technical solutions conceived by the present invention, compared with the prior art, the elastic adaptive wide-area damping control method in the present invention consists of two parts. First, the linear state estimator ensures that the integrity and authenticity of the measurement signal can still maintain its integrity and authenticity in the face of various types of false data injection attacks by performing distributed state estimation on the local power system, ensuring the real availability of remote signals; On the one hand, the wide-area damping controller designed by the GrHDP algorithm can exert good low-frequency oscillation suppression performance for double weak damping modes without the need for a power system data model, and can adapt to different operating conditions and measurement noise.
附图说明Description of drawings
图1为本发明实施例提供的弹性自适应广域阻尼控制结构示意图；FIG. 1 is a schematic structural diagram of an elastic adaptive wide-area damping control provided by an embodiment of the present invention;
图2为本发明实施例提供的针对特定联络线的分布式状态估计示意图；2 is a schematic diagram of distributed state estimation for a specific tie line provided by an embodiment of the present invention;
图3为本发明实施例提供的线性状态估计抵御攻击流程图；3 is a flowchart of a linear state estimation defense attack provided by an embodiment of the present invention;
图4为本发明实施例提供的GrHDP控制器控制VSC-HVDC原理示意图；4 is a schematic diagram of a GrHDP controller controlling a VSC-HVDC according to an embodiment of the present invention;
图5为本发明实施例提供的不同类型虚假数据注入攻击下节点38电压U38响应曲线随时间变化的曲线图；FIG. 5 is a graph showing the variation of the response curve of the voltage U38 of the
图6为本发明实施例提供的采用三种不同的控制方法的发电机相对转角δ14-15与δ14-16随时间变化的曲线图；FIG. 6 is a graph showing the variation of relative rotation angles δ14-15 and δ14-16 of generators with time using three different control methods according to an embodiment of the present invention;
图7为本发明实施例提供的采用三种不同的控制方法在不同运行工况JITAE对比图。FIG. 7 is a JITAE comparison diagram under different operating conditions using three different control methods according to an embodiment of the present invention.
图8为本发明实施例提供的不同控制器输入信号下发电机相对转角δ14-15与δ14-16随时间变化的曲线图。FIG. 8 is a graph showing the variation of the relative rotation angles δ14-15 and δ14-16 of the generator with time under different input signals of the controller according to the embodiment of the present invention.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间不构成冲突就可以相互组合。In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
如图1所示，本发明提出了一种抵御虚假数据注入攻击的弹性自适应广域阻尼控制方法，包括以下步骤：As shown in FIG. 1 , the present invention proposes an elastic adaptive wide-area damping control method for resisting false data injection attacks, including the following steps:
采集可能遭受虚假数据注入攻击的广域量测信号，利用线性状态估计算法对广域量测信号进行攻击检测、攻击源确认和数据恢复，生成估计输入信号；Collect wide-area measurement signals that may be attacked by false data injection, and use linear state estimation algorithm to perform attack detection, attack source confirmation, and data recovery on the wide-area measurement signals to generate estimated input signals;
对所述估计输入信号进行放大与移相，经过GrHDP神经网络后生成控制信号，以实现对电力系统低频振荡的抑制。The estimated input signal is amplified and phase-shifted, and a control signal is generated after passing through the GrHDP neural network, so as to suppress the low-frequency oscillation of the power system.
其中线性状态估计算法的有效实施依赖于量测量的冗余性，在本发明实施例中，冗余度的设置为2.5。即量测量包括为4个电压向量、6个电流向量，所估计的状态向量为4个电压向量。The effective implementation of the linear state estimation algorithm depends on the redundancy of the quantity measurement. In the embodiment of the present invention, the redundancy is set to 2.5. That is, the quantity measurement includes 4 voltage vectors and 6 current vectors, and the estimated state vector is 4 voltage vectors.
针对双弱阻尼模态的自适应广域阻尼控制器为每一个弱阻尼模态选定一个可观度高的远方信号，各自分别经过线性状态估计器纠正后进入移相环节生成并行的相位偏移信号，之后两个信号一起进入GrHDP内部的三层神经网络，经过训练后输出控制信号，调节柔性直流系统输送的直流功率，从而抑制系统振荡。The adaptive wide-area damping controller for dual weakly damped modes selects a distant signal with high observability for each weakly damped mode, and each is corrected by the linear state estimator and then enters the phase shift link to generate parallel phase shifts signal, and then the two signals enter the three-layer neural network inside GrHDP together. After training, a control signal is output to adjust the DC power delivered by the flexible DC system, thereby suppressing the system oscillation.
控制方法的核心部分是线性状态估计器与GrHDP控制器，将两者集中配置在VSC-HVDC的控制系统中，控制信号由控制器生成后直接送给VSC-HVDC的定有功控制外环，不涉及与广域测量系统量测信号相似的远距离传输过程，故不考虑此环节过程中的网络攻击威胁。The core part of the control method is the linear state estimator and the GrHDP controller, which are centrally configured in the VSC-HVDC control system. It involves a long-distance transmission process similar to the measurement signal of the wide-area measurement system, so the threat of network attacks in this process is not considered.
如图2所示，为针对特定联络线的分布式状态估计示意图。线性状态估计器仅抽取联络线两端2个节点及其各自相连的一个节点，即共4个节点处的PMU(Phase MeasurementUnit)量测信息。量测信息包括4个节点电压及其相连支路上6个支路电流的幅值与相角。该局部系统的状态用4个节点的电压幅值与相角来反映。线性状态估计器也仅针对此4个节点及其相连支路组成的子系统执行线性状态估计算法。As shown in FIG. 2 , it is a schematic diagram of distributed state estimation for a specific tie line. The linear state estimator only extracts the measurement information of the PMU (Phase Measurement Unit) at the two nodes at both ends of the tie line and one node connected to each other, that is, a total of four nodes. The measurement information includes 4 node voltages and the amplitudes and phase angles of the 6 branch currents on the connected branches. The state of the local system is reflected by the voltage amplitudes and phase angles of the four nodes. The linear state estimator also executes the linear state estimation algorithm only for the subsystem composed of the 4 nodes and their connected branches.
如图3所示，抵御虚假数据注入攻击的线性状态估计方法，该方法包括以下步骤：As shown in Figure 3, a linear state estimation method to resist fake data injection attacks, the method includes the following steps:
步骤S1.根据网络拓扑结构设置初始雅克比矩阵、卡方检验门槛值λ、测量标准差、迭代上限N。Step S1. Set the initial Jacobian matrix, the chi-square test threshold value λ, the measurement standard deviation, and the iteration upper limit N according to the network topology.
具体地，雅克比矩阵主要与网架参数决定，为常数矩阵；卡方检验门槛值λ与量测向量自由度相关，本发明实施例中为18.307；测量标准差设定为量测值的1％；迭代上限N设定为20。Specifically, the Jacobian matrix is mainly determined by the network frame parameters and is a constant matrix; the chi-square test threshold λ is related to the degree of freedom of the measurement vector, which is 18.307 in the embodiment of the present invention; the measurement standard deviation is set to 1 of the measurement value %; the upper limit of iteration N is set to 20.
步骤S2.根据加权最小二乘法进行状态估计，计算状态估计的的联合误差CNE，并对其进行卡方假设检验。若卡方假设检验通过，则认为不存在攻击数据，进入步骤S5；若卡方假设检验不通过，则认为存在攻击数据，进入步骤S3。Step S2. Perform state estimation according to the weighted least squares method, calculate the joint error CNE of the state estimation, and perform a chi-square hypothesis test on it. If the chi-square hypothesis test is passed, it is considered that there is no attack data, and the process proceeds to step S5; if the chi-square hypothesis test fails, it is considered that there is attack data, and the process proceeds to step S3.
从几何视角分析，线性状态估计将m维的量测向量在雅克比矩阵H上映射后形成了n维的状态估计向量，由此计算出的误差有两部分组成。分别为位于
步骤S3.将CNE中数值最大元素对应的量测量认定为攻击数据来源。Step S3. Identify the quantity measurement corresponding to the element with the largest numerical value in the CNE as the source of the attack data.
残差污染现象导致经过状态估计后的误差并不能反映真实的攻击源，此处便认定数值最大元素为攻击数据来源。The residual pollution phenomenon causes the error after state estimation to not reflect the real attack source. Here, the element with the largest value is identified as the attack data source.
步骤S4.假设CNE中第j个元素为最大值，则通过yj，new＝yj，old-CNEj·σj对第j个量测量进行修正。Step S4. Assuming that the jth element in the CNE is the maximum value, correct the jth quantity measurement by yj, new =yj, old -CNEj ·σj .
步骤S5.判断迭代次数是否超过上限值，若超过上限值，则进入步骤S6，若未超过上限值，则回到步骤S1，进行下一次的状态估计。Step S5. Determine whether the number of iterations exceeds the upper limit. If it exceeds the upper limit, go to step S6. If it does not exceed the upper limit, return to step S1 to perform the next state estimation.
步骤S6.输出估计的状态量，完成线性状态估计过程。Step S6. Output the estimated state quantity to complete the linear state estimation process.
如图4所示，GrHDP广域阻尼控制器包括：双通道的移相环节、三层神经网络。As shown in Figure 4, the GrHDP wide-area damping controller includes a two-channel phase-shifting link and a three-layer neural network.
具体地，移相环节在原信号通道基础上新增一个并联相位偏移通道，该通道通过一个微分环节将原信号的相位偏移90°。随着执行网络权值的变化，原信号及其偏移后信号可以合成任意相位的输出信号。两个远方信号分别对应一组双通道。Specifically, the phase shift link adds a parallel phase shift channel based on the original signal channel, and this channel shifts the phase of the original signal by 90° through a differential link. With the change of the weights of the execution network, the original signal and its offset signal can be synthesized into an output signal of any phase. The two remote signals correspond to a group of dual channels respectively.
具体地，三层神经网络分别为执行网络、目标网络、评价网络，每层神经网络都采用前馈式单隐含层结构。其中，目标网络输出自适应的内部强化信号S(t)，优化输入状态量与输出控制量之间的映射关系；评价网络拟合整个三层网络的贝尔曼方程代价函数J(t)，当J(t)最小时，目标网络输出最优控制指令u(t)。Specifically, the three-layer neural network is an execution network, a target network, and an evaluation network, and each layer of the neural network adopts a feedforward single-hidden layer structure. Among them, the target network outputs an adaptive internal reinforcement signal S(t) to optimize the mapping relationship between the input state quantity and the output control quantity; the evaluation network fits the Bellman equation cost function J(t) of the entire three-layer network, when When J(t) is the smallest, the target network outputs the optimal control instruction u(t).
如图5所示，不同类型虚假数据注入攻击下节点38电压U38响应曲线随时间变化的曲线图。可以看出，虚假数据注入后，电压信号的实部和虚部与真实值相比都有较大的偏移，而经过线性状态估计器处理后的信号则与真实值近乎一致，这说明线性状态估计器发挥了较好的虚假数据攻击抵御效果。此外，在脉冲、阶跃、斜坡这三种不同类型的虚假数据注入攻击下，线性状态估计器表现出很好的适应性。As shown in FIG. 5 , the response curve of the voltage U38 of the
如图6所示，采用三种不同的控制方法的发电机相对转角δ14-15与δ14-16随时间变化的曲线图。可以看出，在无任何广域控制措施时，系统处于失稳的状态，而采用了与线性状态估计器与GrHDP控制模块组成的弹性自适应控制方法后，系统在虚假数据注入攻击下依然能够迅速恢复到稳定状态。另外，由线性状态估计器与常规的LL-WADC组成的控制方法对比结果表明，采用GrHDP算法设计的广域阻尼控制器具有更加优越的控制性能。As shown in FIG. 6 , the relative rotation angles δ14-15 and δ14-16 of the generators vary with time using three different control methods. It can be seen that without any wide-area control measures, the system is in an unstable state, but after using the elastic adaptive control method composed of the linear state estimator and the GrHDP control module, the system can still be attacked by false data injection. quickly returned to a steady state. In addition, the comparison results of the control method composed of the linear state estimator and the conventional LL-WADC show that the wide-area damping controller designed by the GrHDP algorithm has better control performance.
如图7所示，采用三种不同的控制方法在不同运行工况JITAE对比图。JITAE是广域稳定控制领域一种常用的反映控制性能的指标。可以看出，从工况1到工况2、3，系统的工况愈加严重，常规的LL-WADC控制性能在严重工况下变差，而GrHDP广域阻尼控制器则在严重工况下依然维持了较好的阻尼控制性能，这表明GrHDP控制器对于运行工况具有较好的适应能力。As shown in Figure 7, three different control methods are used in the JITAE comparison chart under different operating conditions.JITAE is a commonly used indicator reflecting control performance in the field of wide-area stability control. It can be seen that from working
如图8所示，不同控制器输入信号下发电机相对转角δ14-15与δ14-16随时间变化的曲线图。可以看出，当输出控制器的信号为未经处理的含噪声信号时，GrHDP由于学习到了噪声信号的规律而失去阻尼抑制作用，甚至引起了系统陷入无规则的振荡之中，而当含噪声信号经过线性状态估计器处理后得到的估计信号进入控制器时，控制器性能与输入真实信号时近乎一致，这说明了本发明实施例具有较好的抗噪声能力。As shown in FIG. 8 , the curves of the relative rotation angles δ14-15 and δ14-16 of the generators vary with time under different controller input signals. It can be seen that when the signal of the output controller is an unprocessed signal containing noise, GrHDP loses its damping and suppressing effect due to learning the law of the noise signal, and even causes the system to fall into irregular oscillation. When the estimated signal obtained after the signal is processed by the linear state estimator enters the controller, the performance of the controller is almost the same as when the real signal is input, which shows that the embodiment of the present invention has better anti-noise capability.
本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。Those skilled in the art can easily understand that the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, etc., All should be included within the protection scope of the present invention.
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