
A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability.
Citation: Nishui Cai, Guofeng He. Multi-cloud resource scheduling intelligent system with endogenous security[J]. Electronic Research Archive, 2024, 32(2): 1380-1405. doi: 10.3934/era.2024064
[1] | Zongying Feng, Guoqiang Tan . Dynamic event-triggered H∞ control for neural networks with sensor saturations and stochastic deception attacks. Electronic Research Archive, 2025, 33(3): 1267-1284. doi: 10.3934/era.2025056 |
[2] | Yawei Liu, Guangyin Cui, Chen Gao . Event-triggered synchronization control for neural networks against DoS attacks. Electronic Research Archive, 2025, 33(1): 121-141. doi: 10.3934/era.2025007 |
[3] | Xingyue Liu, Kaibo Shi, Yiqian Tang, Lin Tang, Youhua Wei, Yingjun Han . A novel adaptive event-triggered reliable H∞ control approach for networked control systems with actuator faults. Electronic Research Archive, 2023, 31(4): 1840-1862. doi: 10.3934/era.2023095 |
[4] | Chao Ma, Hang Gao, Wei Wu . Adaptive learning nonsynchronous control of nonlinear hidden Markov jump systems with limited mode information. Electronic Research Archive, 2023, 31(11): 6746-6762. doi: 10.3934/era.2023340 |
[5] | Hangyu Hu, Fan Wu, Xiaowei Xie, Qiang Wei, Xuemeng Zhai, Guangmin Hu . Critical node identification in network cascading failure based on load percolation. Electronic Research Archive, 2023, 31(3): 1524-1542. doi: 10.3934/era.2023077 |
[6] | Chengbo Yi, Jiayi Cai, Rui Guo . Synchronization of a class of nonlinear multiple neural networks with delays via a dynamic event-triggered impulsive control strategy. Electronic Research Archive, 2024, 32(7): 4581-4603. doi: 10.3934/era.2024208 |
[7] | Liping Fan, Pengju Yang . Load forecasting of microgrid based on an adaptive cuckoo search optimization improved neural network. Electronic Research Archive, 2024, 32(11): 6364-6378. doi: 10.3934/era.2024296 |
[8] | Ramalingam Sakthivel, Palanisamy Selvaraj, Oh-Min Kwon, Seong-Gon Choi, Rathinasamy Sakthivel . Robust memory control design for semi-Markovian jump systems with cyber attacks. Electronic Research Archive, 2023, 31(12): 7496-7510. doi: 10.3934/era.2023378 |
[9] | Ahmed Bengermikh, Abdelhamid Midoun, Nacera Mazouz . Dynamic design and optimization of a power system DC/DC converter using peak current mode control. Electronic Research Archive, 2025, 33(4): 1968-1997. doi: 10.3934/era.2025088 |
[10] | Yejin Yang, Miao Ye, Qiuxiang Jiang, Peng Wen . A novel node selection method for wireless distributed edge storage based on SDN and a maldistributed decision model. Electronic Research Archive, 2024, 32(2): 1160-1190. doi: 10.3934/era.2024056 |
A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability.
Load frequency control (LFC) is significant for the safe and stable work of the power grids [1,2,3]. With the continuous breakthrough of new energy technology, new energy power generation methods are gradually combined with traditional power generation methods and the penetration rate of modern smart grids continues to increase. Wind power and photovoltaic power are the most widely used forms of new energy generation. However, wind energy and photovoltaic energy are characterized by dispersion, randomness, fluctuation and intermittency [4], which can affect the power grid, especially the frequency.
Usually, when the frequency of the power system fluctuates, the aggregator load changes its working state according to the scheduling instruction to prevent frequency fluctuation [5,6]. Most dynamic demand control models have been with thermostatically controlled devices, with air conditioning loads (ACs) accounting for a huge percentage of them [7,8]. However, most literature focused on how ACs can be controlled based on optimized thought, such as the method based on the load frequency modulation model [9]. Only a few studies have applied ACs to resolve external perturbations [10,11].
With the progress of ultra-high voltage technology and the increasing proportion of new energy in power generation [12,13,14], the communication data between power grid systems has become more complex, which also increases the communication pressure between power grids. In recent years, there has been a growing interest in event-triggered mechanisms (ETM) due to their potential in reducing communication and computation resources while maintaining stability and performance in complex systems [15,16,17,18,19]. ETM is designed to trigger actions and update processes in a system only when certain predefined conditions, or events, are met rather than at fixed time intervals. This smart adaptive strategy is particularly beneficial for systems in which resources are constrained or in dynamic environments where unnecessary updates could be costly. Kazemy et al. [15] have addressed synchronization of master-slave neural networks under deception attacks using event-triggered output feedback control to enhance stability and resilience.
While considering the limited network resources, we should also consider the security problems faced by networked systems [20]. Especially in recent years, industrial open networks have been attacked frequently. At present, network attacks are mainly divided into denial-of-service (DoS) attacks and spoofing attacks. DoS attacks can cause the loss of transmitted data [21], while spoofing attacks are launched by attackers that attempt to disrupt the availability of data [22]. Foroush and Martinez [23] showed that the general security problem of networked is DoS attack, which attempts to transmit a large amount of invalid data and intentionally interfere with network communication resources. Persis and Tesi [24] proved that on the premise of ensuring the asymptotic stability of the system, and the average active time of DoS attacks cannot exceed a certain percentage. In recent years, the research on the security of event-triggered networked systems has made some progress [15].
Inspired by the analysis presented above, this paper explores the application of an observer-based ETM for secure LFC in power systems, with the aim of ensuring the safety and stability of the power system. Considering the inherent instability caused by the increasing penetration of renewable energy in the grid, we treat it as a perturbation. In addition, this study proposes the integration of AC demand response as a control strategy to effectively regulate system frequency fluctuations. Additionally, this paper addresses key challenges related to network bandwidth limitations and potential DoS attacks by incorporating the ETM. A novel aspect of this work is the application of ETM to the observer rather than directly to the system state to mitigate the complexity and impracticalities associated with direct state measurement in real-world applications. The contribution of this paper is as follows:
ⅰ) The volatility associated with renewable energy generation is modeled as a perturbation, accounting for its unpredictable nature in the power system. To enhance the LFC, ACs demand response is incorporated as an effective tool to dynamically adjust the system's frequency in response to fluctuating generation conditions, thereby improving overall system resilience.
ⅱ) In contrast to conventional ETM that rely on direct measurement of system states, this paper introduces an observer-based ETM. By using an observer to estimate system states, this approach avoids the challenges and limitations of directly measuring these values, offering a practical solution for real-time control in large-scale, decentralized power networks.
Notations. U>0 means the matrix U is positive definite. ∗ stands for the corresponding symmetric term in the matrix. diag{⋯} stands for diagonal matrix.
First, considering the random disturbance of wind power and photovoltaic power generation, a multi-area smart grid control model is established with ACs participating in grid frequency regulation, as shown in Figure 1 [25]. System parameters and meanings are shown in Table 1, where kaci=micpikEER.
Symbol | Name |
Δfi | frequency deviation |
ΔPmi | generation mechanical output deviation |
ΔPtie−i | tie-line active power deviation |
ΔPwind,i | wind power deviation |
ΔPWi | output of wind turbine generator deviation |
ΔPsolar,i | solar power deviation |
ΔPSi | the deviations of output of photovoltaic |
ΔPaci | the deviations of output of ACs |
ΔPvi | valve position deviation |
ACEi | area control error |
Tgi | governor time constant |
Tti | turbine time constant |
Di | the generator unit damping coefficient |
TWi | the wind turbine generator time constant |
TPVi | the photovoltaic time constant |
Tij | lie-line synchronizing coefficient between the ith and jth control area |
daci | the damping coefficient |
kaci | the combined integral gain |
βi | frequency bias factor |
EER | energy efficiency ratio |
cpi | specific heat capacity of the air |
Ri | the speed drop |
k | gain factor in the smart thermostat |
Mi | the moment of inertia of the generator unit |
mi | the mass of air flow |
According to Figure 1, we can obtain
{Δ˙fi=1Mi(ΔPmi+ΔPvi−ΔPtie−i−ΔPaci−ΔPLi−DiΔfi)Δ˙Ptie−i(t)=2π(TiiΔfi−n∑j=1,j≠iTijΔfj)Δ˙Pmi=1Tti(ΔPvi−ΔPmi)Δ˙Pvi=1Tgi(u−ΔPvi−ΔfiRi)Δ˙PWi=1TWi(ΔPwind,i−ΔPWi)Δ˙PSi=1TPCi(ΔPsolar,i−ΔPSi)Δ˙Paci=(0.5kaci−DaciDi)Δfi+Daci(ΔPmi+ΔPWi−ΔPtie−i−ΔPaci−ΔPLi)ACEi=βiΔfi+ΔPtie−i, | (2.1) |
where Daci=2πdaciMi, Tii=∑nj=1,j≠iTij,i,j=1,2,…,n. Define
xi(t)=[ΔfiΔPtie−iΔPmiΔPviΔPWiΔPSiΔPaci]T, |
ωi(t)=[ΔPLiΔPwind,iΔPsolar,i]T,y(t)=ACEi. |
Then, we can obtain that
{˙xi(t)=Aiixi(t)+Biu(t)+Fiωi(t)−n∑j=1,j≠iAijxj(t)yi(t)=Cixi(t), | (2.2) |
where
Aii=[−DiMi−1Mi1Mi01Mi1Mi−1Mi2πN∑j=1,j≠iTij00000000−1Tii1Tii000−1RiTgi00−1Tgi0000000−1TWi0000000−1TPCi00.5kaci−DaciDi−DaciDaci0DaciDaci−Daci],Bi=[0001Tgi000], |
Fi=[−1Mi0000000000001TWi0001TPCi−Daci00],Ci=[βi100000]T,Aij=[(2,1)=−2πTij]. |
From (2.2), the state-space form of multi-area ACs LFC model can be described as
{˙x(t)=Ax(t)+Bu(t)+Fω(t)y(t)=Cx(t), | (2.3) |
with
A=[A11A12…A1nA21A22…A2n⋮⋮⋱⋮An1An2…Ann], |
C=diag{C1,C2,…,Cn},B=diag{B1,B2,…,Bn},F=diag{F1,F2,…,Fn}, |
x(t)=[x1(t)x2(t)…xn(t)]T,y(t)=[y1(t)y2(t)…yn(t)]T, |
ω(t)=[ωT1(t)ωT2(t)…ωTn(t)]T,u(t)=[u1(t)u2(t)…un(t)]T. |
The full-order state observer is constructed as
{˙ˆx(t)=Aˆx(t)+Bu(t)+L(y(t)−ˆy(t))ˆy(t)=Cˆx(t), | (2.4) |
where ˆy(t) is the observer output; ˆx(t) is the observer state and L is the observer gain to be solved.
In this paper, inspired by [26,27], and periodic event-triggered control schemes [28], we select the ETM activations:
tk+1=tk+min{lh|f[ˆx(tk),ζ(t)]≤0},f[ˆx(tk),ζ(t)]=ζT(t)Vζ(t)−σˆxT(tk)Vˆx(tk),ikh=tk+lh, | (2.5) |
where the difference between the most recent data sample and the current one is referred to as ζ(t), i.e., ζ(t)=ˆx(tk+lh)−ˆx(tk), lh is the intervals between tk and tk+1, l∈N,ikh∈(tk,tk+1], and σ is a threshold parameter.
Remark 1. Unlike traditional ETM that rely on state controllers, this paper introduces a novel approach using observer packets for triggering. This approach leverages the observer pattern, which offers significant advantages in terms of modularity and flexibility. Specifically, the observer pattern enables the seamless addition of new observers to the system without disrupting or modifying other components, ensuring a high degree of decoupling. Similarly, the observed object itself is free to evolve or modify its internal implementation without causing any adverse effects on the observers.
In addition, consider the time-varying delay dk caused by the network between the observer and the event generator [29]. Define d(t) as follows:
d(t)={t−tk,t∈I1t−tk−lh,t∈Il2,l=1,2,…,m−1t−tk−mh,t∈I3,dm≤d(t)≤dM, | (2.6) |
where I1=[tk+dk,tk+dk+h), Il2=[tk+dk+lh,tk+dk+lh+h), I2=∪l=m−1l=1Il2, I3=[tk+dk+mh,tk+1+dk+1), [tk+dk,tk+1+dk+1)=I1∪I2∪I3. Moreover, ˆx(tk) and ˆx(tk+ιh) with ι=1,2,…,m satisfy (2.5); dm (dM) denotes the lower (upper) bound on the time delay.
Remark 2. In a multi-area power system, data transmission is routine, but network delays are inevitable. These delays can arise from factors like communication latency or network congestion, and if ignored, they may compromise the accuracy of the measured data. In this study, we define the delay as dk and focus on the case where dk is shorter than the sampling interval. This ensures that the integrity of the data sequence is preserved, with each measurement arriving and being processed within its designated sampling period.
For t∈[tk+dk,tk+1+dk+1), we can define
ζ(t)={0,t∈I1ˆx(tk+lh)−ˆx(tk),t∈Il2ˆx(tk+mh)−ˆx(tk),t∈I3. | (2.7) |
In what follows, inspired by [30], the observer-based ETM u(t) can be written as
u(t)=Kˆx(tk), |
where K is the control gain matrix.
Based on (2.6) and (2.7), the observer-based ETM u(t) can be rewritten as
u(t)=Kˆx(tk)=K(ˆx(t−d(t))−ζ(t)). | (2.8) |
For a DOS attack in a network, define τ(ikh) as the attack state, τ(ikh)=1 as the attack occurred, and τ(ikh)=0 as the attack did not occur. In a typical power system, most DoS attacks fail to achieve their purpose, and only a fraction of these attacks cause notable disruptions. We divide DoS attacks into the following four types:
{{τ(ikh)=0,t∈(tk,tk+1):noDOSattackτ(ikh)=0,t∈(tk+1,tDOSk+1):noDOSattack{τ(ikh)=1,t∈(tk,tk+1):ineffectiveDosattackτ(ikh)=1,t∈(tk+1,tDOSk+1):effectiveDoSattacks. | (2.9) |
In addition, the energy of general DoS attacks is limited. Facing such attacks, we introduce elastic variable ζDoS(t) on the event triggering mechanism to represent the packet loss and delay caused by DoS attacks on the system [31]. We can obtain
tDoSk+1=tk+min{lh|f[ˆx(tk),ζ(t)]−τ(ikh)ζTDoS(t)VζDoS(t)≥0}, | (2.10) |
tDoSk+1−tk+1=ΔDoStk+1≤ΔDoS, | (2.11) |
where ΔDoS denotes the longest duration of a DoS attack and tDoSk+1 refers to an upcoming sampling moment affected by DoS.
Substituting (2.8) into sysetm (2.3) yields
{˙x(t)=Ax(t)+BKˆx(t−d(t))−BKζ(t)+Fω(t)y(t)=Cx(t). | (2.12) |
Substituting (2.8) into system (2.4) yields
˙ˆx(t)=Aˆx(t)+BKˆx(t−d(t))−BKζ(t)+LC(x(t)−ˆx(t)). | (2.13) |
Define the state error as δ(t)=x(t)−ˆx(t), then
˙δ(t)=(A−LC)δ(t)+Fω(t). | (2.14) |
Define the augmented state as ˜xT(t)=[xT(t)δT(t)], the augmented system is
{˙˜x(t)=A1˜x(t)+A2˜x(t−d(t))+B1ζ(t)+B2ω(t)y(t)=CE˜x(t), | (2.15) |
where
A1=[A00A−LC],A2=[0BK00],B1=[−BK0],B2=[FF],E=[I0]T. |
Definition 1 [32,33] For an arbitrary initial condition, given γ>0, the augmented system (2.15) is thought to be stable with H∞ performance if the following two conditions are met:
1) for ω(t)=0, the system is asymptotically stable;
2) for ω(t)∈ℓ2[0,∞), the following condition is satisfied:
∥y(t)∥2≤γ∥ω(t)∥2. |
Lemma 1. [34] For a given time delays constant dm and dM satisfying d(t)∈[dm,dM], for dM>dm>0, there exist matrices R2 and H when
[R2H∗R2]>0 |
for which the following inequality holds:
−(dM−dm)∫t−dmt−dM˙˜xT(s)R2˙˜x(s)ds≤[˜xT(t−dm)˜xT(t−d(t))˜xT(t−dM)]T[−R2R2−HH∗−2R2+H+HTR2−H∗∗−R2][˜x(t−dm)˜x(t−d(t))˜x(t−dM)]. |
Two theorems are given in this section. Theorem 1 illustrates the stability and H∞ performance of the augmented system, and Theorem 2 gives the design of an observer-based controller.
Theorem 1. For some given positive constants σ, dm and dM, the augmented system (2.15) is asymptotically stable with an H∞ performance index γ, if there exist positive definite matrices P, Q1, Q2, R1, R2, H and V with appropriate dimensions such that
Ω=[Ω11Ω12∗Ω22]<0, | (3.1) |
[R2H∗R2]>0, | (3.2) |
where
Ω11=[Π11R1PA20PB1PB2∗Π22R2−HH00∗∗Π33R2−H00∗∗∗−Q2−R200∗∗∗∗−V0∗∗∗∗∗−γ2I], |
Ω12=[dmAT1(dM−dm)AT1(CE)T00000dmAT2(dM−dm)AT20σΠT40000dmBT1(dM−dm)BT10−σIdmBT2(dM−dm)BT200], |
Ω22=diag{−R−11,−R−12,−I,−σV−1}, |
Π11=AT1P+PAT1+Q1+Q2−R1, |
Π22=−Q1−R1−R2, |
Π33=−2R2+H+HT, |
Π4=[I−I]. |
Proof. Select Lyapunov function as
V(t)=˜xT(t)P˜x(t)+∫tt−dm˜xT(s)Q1˜x(s)ds+∫tt−dM˜xT(s)Q2˜x(s)ds+dm∫tt−dm∫ts˙˜xT(v)R1˙˜x(v)dvds+(dM−dm)∫−dm−dM∫t−dmt−dM˙˜xT(v)R2˙˜x(v)dvds. |
From the augmented system (2.15), we have
˙V(t)=˙˜xT(t)P˜x(t)+˜xT(t)P˙˜x(t)+˜xT(t)Q1˜x(t)−˜xT(t−dm)Q1˜x(t−dm)+˜xT(t)Q2˜x(t)−˜xT(t−dM)Q2˜x(t−dM)+˙˜xT(t)[d2mR1+(dM−dm)2R2]˙˜x(t)−dm∫tt−dm˙˜xT(s)R1˙˜x(s)ds−(dM−dm)∫t−dmt−dM˙˜xT(s)R2˙˜x(s)ds. | (3.3) |
Using Jessen inequality, Lemma 1 and the Schur complement theorem, at the same time to join the event trigger condition, it can be concluded that
˙V(t)+yT(t)y(t)−γ2ωT(t)ω(t)≤ξT(t){Ω11+ΓT1[d2mR1+(dM−dm)2R2]Γ1+ΓT2Γ2+ΓT3Γ3}ξ(t)≤ξT(t)Ωξ(t), | (3.4) |
where
ξT(t)=[˜x(t),˜x(t−dm),˜x(t−d(t)),˜x(t−dM),ζ(t),ω(t)], |
Γ1=[A1,0,A2,0,B1,B2], |
Γ2=[CE,0,0,0,0,0], |
Γ3=[0,0,σΠ4,0,−σI,0]. |
Further, we achieve
yT(t)y(t)−γ2ωT(t)ω(t)≤−˙V(t). | (3.5) |
According to (3.4) and (3.5), the presence of λ>0 ensures that ˙V(t)<0 is satisfied when ω(t)=0, and we can achieve limt→∞V(t)=0.
By Definition 1, the augmented system (2.15) is asymptotically stable.
When ω(t)≠0, both sides of (3.5) integrate from 0 to +∞,
∫+∞0(yT(t)y(t)−γ2ωT(t)ω(t))dt≤V(0)−V(+∞). |
At zero original condition, we obtain
∥y(t)∥2≤γ∥ω(t)∥2. |
Since Ω<0, there exists a proper positive ε so that
ξT(t)Ωξ(t)≤−εV(t), |
˙V(t)+yT(t)y(t)−γ2ωT(t)ω(t)≤ξT(t)Ωξ(t)≤−εV(t)+τ(ikh)ζTDoS(t)VζDoS(t). |
Multiply the upper expression by eεt and integrate the result. Subsequently,
V(t)≤eεtV(0)+(1−e−εt)τ(ikh)ζTDoS(t)VζDoS(t)ε≤V(0)+τ(ikh)ζTDoS(t)VζDoS(t)ε. |
It is evident that
˜xT(t)P˜x(t)≤V(0)+τ(ikh)ζTDoS(t)VζDoS(t)ε, |
∥˜x(t)∥≤√V(0)+τ(ikh)ζTDoS(t)VζDoS(t)ελ(P)=Δ. |
In the presence of an attack, the system's security performance bounds uniformly, with a degradation no greater than Δ. This finishes the proof.
Theorem 2. For some given positive constants σ, dm and dM, the augmented system (2.15) is asymptotical stable with an H∞ performance index γ, if there exist positive definite matrices Xi, Y, S, ¯Qi, ¯Ri, ¯H and ¯V (i=1,2) with appropriate dimensions such that
¯Ω=[¯Ω11¯Ω12∗¯Ω22]<0, | (3.6) |
[¯R2¯H∗¯R2]>0, | (3.7) |
where
¯Ω11=[¯Π11¯R1Ψ10Ψ2B2∗¯Π22¯R2−¯H¯H00∗∗¯Π33¯R2−¯H00∗∗∗−¯Q2−¯R200∗∗∗∗−¯V0∗∗∗∗∗−γ2I], |
¯Ω12=[dmΨT0(dM−dm)ΨT0ΨT300000dmΨT1(dM−dm)ΨT10σ¯ΠT40000dmΨT2(dM−dm)ΨT20−σX2dmBT2(dM−dm)BT200], |
¯Ω22=diag{¯R1−2X,¯R2−2X,−I,σ(¯V−2X2)}, |
Ψ0=[AX100AX2−SC],Ψ1=[0BY00],Ψ2=[−BY0],Ψ3=[CX10], |
¯Π11=Ψ0+ΨT0+¯Q1+¯Q2−¯R1,¯Π22=−¯Q1−¯R1−¯R2, |
¯Π33=−2¯R2+¯H+¯HT,¯Π4=[X1−X2], |
X=[X10∗X2]. |
The control coefficient matrix of the observer-based controller is shown by: K=YX2−1, L=S¯X−12, with CX2=¯X2C.
Proof. Set P=[P10∗P2] and define X=P−1=[X10∗X2], ¯Qi=XTQiX, ¯Ri=XTRiX (i=1,2), ¯H=XTHX, ¯V=X2TVX2, Y=KX2, S=L¯X2. For X2=W[X210∗X22]WT, on the basis of [35], there exist ¯X2=MNX22N−1MT such that CX2=¯X2C.
Define Φ=diag{X,X,X,X,X2,I,I,I,I,I}, then pre- and postmultiply Φ and its transpose on both sides of (3.1). It is easy to know that −X¯R−1iX≤¯Ri−2X,(i=1,2), −X2¯V−1X2≤¯V−2X2. Finally, we can conclude that (3.1) is a sufficient condition to guarantee (3.6) holds. The proof is accomplished.
Remark 3. The power system under investigation is a complex continuous system, whose stabilization is further complicated by disturbances such as wind power generation, making the calculation of controller parameters a challenging task. Lyapunov theory is well used to facilitate analysis and predict system behavior, especially for complex systems.
In this part, we consider a simulation example for a two-area power system and demonstrate the usefulness of this method. The parameter values of system (2.15) are shown in Table 2 [25].
Tt | Tg | R | D | β | M | cp | m | k | dac | EER | T12 | TW | TPC | |
Area1 | 0.30 | 0.10 | 0.05 | 1.00 | 21.0 | 10 | 1.01 | 0.25 | 8 | 0.025 | 3.75 | 0.1968 | 0.30 | 0.30 |
Area2 | 0.40 | 0.17 | 1.50 | 21.5 | 12 | 15 | 0.015 |
Set σ=0.01, dM=0.1, dm=0.02 and the sampling period is h=0.05. The initial condition, in this simulation analysis, is given by ˜x(0)=[0.0010.001…0.001]. Next, by solving Theorem 2, the control gain K and the observer gain L are given by
K=[0.0003−0.00740000−0.00050.0003−0.008200000.0011−0.00010.00160000−0.0001−0.00010.00170000−0.0002], |
L=[0.04370.05470.0004−9.42840.00300.00300.0096−0.0012−0.05200.00030.0017000.0061−0.0007−0.05160.00080.0981000.00180.03650.0535−0.0129−0.11270.00210.00210.0186]T. |
Meanwhile, H∞ performance index γ=15.4285. Here we set the disturbance ω(t) occurs at every sampling instant as ω(t)=√0.161+t2, and the probability that the system suffers from an effective DoS is 0.3.
In this simulation, we analyze the effects of a DoS attack on system dynamics and control responses.
Figure 2 illustrates DoS attack signals from 0−10s denoted by τ(ikh), highlighting the attack's temporal characteristics and intervals of disrupted transmission.
The frequency deviation Δfi in the affected region, as depicted in Figure 3, shows fluctuations due to the DoS attack. However, it gradually stabilizes and tends toward zero, indicating a system tendency to regain frequency balance over time. This response underscores the system's capacity for frequency recovery even under compromised conditions.
Figure 4 presents the output power deviation ΔPaci of the ACs in the region under DoS attack. Notably, the fluctuations are contained within a narrow range, remaining below 0.1, demonstrating a limited impact on the output power deviation of the ACs. This outcome suggests that the control strategy maintains a relatively stable response in the face of attack-induced disturbances.
The area control error ACEi in Figure 5 provides insights into area control performance under the DoS attack.
Meanwhile, Figure 6 depicts the error between the observer state and the system state, revealing a similar pattern between them. While exhibiting oscillatory behavior, the rapid reduction of the error, however, is not completely zero due to the presence of perturbations. This suggests that the observer maintains some accuracy despite the attacks.
The timing of event triggers, illustrated in Figure 7, reveals that the maximum trigger interval significantly exceeds the sampling period. This long trigger interval shows the efficacy of the proposed ETM in maintaining system stability and efficiency under DoS conditions. The extended trigger interval also highlights the ETM's capability to manage communication load, triggering transmissions only when essential and thus enhancing safety and stability in the presence of network-based attacks.
In this paper, we have studied the problem of LFC with ACs under DoS attack considering ETM in network environment. When constructing the system model, we have considered the randomness of wind power generation and photovoltaic power generation and have also added the ACs to participate in the frequency regulation. In order to improve the utilization efficiency of network resources while resisting DoS attacks, an ETM has been proposed. During DoS attacks, system stability criteria and control design methods have been derived using H∞ stability theory and LMI techniques for co-designing controller and observer gains. Finally, the proposed control strategy has been applied to the two-area smart grid, and the results have shown that the control method is effective.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
This work was supported by Science and Technology Project of State Grid Anhui Electric Power Co., Ltd. (Grant No. 521205240016).
The authors declare there are no conflicts of interest.
[1] |
P. Kühn, D. N. Relke, C. Reuter, Common vulnerability scoring system prediction based on open source intelligence information sources, Comput. Secur., 131 (2023), 1103286. https://doi.org/10.1016/j.cose.2023.103286 doi: 10.1016/j.cose.2023.103286
![]() |
[2] |
S. Nazir, S. Patel, D. Patel, Assessing and augmenting SCADA cyber security: A survey of techniques, Comput. Secur., 70 (2017), 436–454. https://doi.org/10.1016/j.cose.2017.06.010 doi: 10.1016/j.cose.2017.06.010
![]() |
[3] |
J. Wu, Cyberspace endogenous safety, security, Engineering, 15 (2021), 179–185. https://doi.org/10.1016/j.eng.2021.05.015 doi: 10.1016/j.eng.2021.05.015
![]() |
[4] |
B. Yang, S. Wang, Q. Cheng, T. Jin, Scheduling of field service resources in cloud manufacturing based on multi-population competitive-cooperative GWO, Comput. Ind. Eng., 154 (2021), 107104. https://doi.org/10.1016/j.cie.2021.107104 doi: 10.1016/j.cie.2021.107104
![]() |
[5] |
Z. X. Sun, H. Huang, Z. Li, C. Gu, R. Xie, B. Qian, Efficient, economical and energy-saving multi-workflow scheduling in hybrid cloud, Expert Syst. Appl., 228 (2023), 120401. https://doi.org/10.1016/j.eswa.2023.120401 doi: 10.1016/j.eswa.2023.120401
![]() |
[6] |
G. Zhou, W. Tian, R. Buyya, K. Wu, Growable Genetic Algorithm with Heuristic-based Local Search for multi-dimensional resources scheduling of cloud computing, Appl. Soft Comput., 136 (2023), 110027. https://doi.org/10.1016/j.asoc.2023.110027 doi: 10.1016/j.asoc.2023.110027
![]() |
[7] |
G. Agarwal, S. Gupta, R. Ahuja, A. K. Rai, Multiprocessor task scheduling using multi-objective hybrid genetic Algorithm in Fog–cloud computing, Knowledge-Based Syst., 272 (2023), 110563. https://doi.org/10.1016/j.knosys.2023.110563 doi: 10.1016/j.knosys.2023.110563
![]() |
[8] |
W. Zhang, J. Xiao, W. Liu, Y. Sui, Y. Li, S. Zhang, Individualized requirement-driven multi-task scheduling in cloud manufacturing using an extended multifactorial evolutionary algorithm, Comput. Ind. Eng., 179 (2023), 109178. https://doi.org/10.1016/j.cie.2023.109178 doi: 10.1016/j.cie.2023.109178
![]() |
[9] |
W. Xiong, M. K. Lim, M. L. Tseng, Y. Wang, An effective adaptive adjustment model of task scheduling and resource allocation based on multi-stakeholder interests in cloud manufacturing, Adv. Eng. Inf., 56 (2023), 101937. https://doi.org/10.1016/j.aei.2023.101937 doi: 10.1016/j.aei.2023.101937
![]() |
[10] | W. Zhang, Y. Zheng, W. Ma, R. Ahmad, Multi-task scheduling in cloud remanufacturing system integrating reuse, reprocessing, and replacement under quality uncertainty, J. Manuf. Syst., 68 (2023), 176–195. https://doi.org/10.1016/j.jmsy.2023.03.008 |
[11] |
X. Wang, H. Lou, Z. Dong, C. Yu, R. Lu, Decomposition-based multi-objective evolutionary algorithm for virtual machine and task joint scheduling of cloud computing in data space, Swarm Evol. Comput., 77 (2023), 101230. https://doi.org/10.1016/j.swevo.2023.101230 doi: 10.1016/j.swevo.2023.101230
![]() |
[12] |
Y. H. Wu, H. B. Li, RNNCTPs: A neural symbolic reasoning method using dynamic knowledge partitioning technology, Knowledge-Based Syst., 268 (2023), 110481. https://doi.org/10.1016/j.knosys.2023.110481 doi: 10.1016/j.knosys.2023.110481
![]() |
[13] |
G. Wang, Y. Zhang, F. Zhang, Z. Wu, An ensemble method with DenseNet and evidential reasoning rule for machinery fault diagnosis under imbalanced condition, Measurement, 214 (2023), 112806. https://doi.org/10.1016/j.measurement.2023.112806 doi: 10.1016/j.measurement.2023.112806
![]() |
[14] |
Y. Gao, R. Bao, Z. Pan, G. Ma, J. Li, X. Cai, Q. Peng, Mechanical equipment health management method based on improved intuitionistic fuzzy entropy and case reasoning technology, Eng. Appl. Artif. Intell., 116 (2022), 105372. https://doi.org/10.1016/j.engappai.2022.105372 doi: 10.1016/j.engappai.2022.105372
![]() |
[15] |
M. B. Fard, A. Hamedani, M. Ebadi, D. Hamidi, K. Motlaghzadeh, M. Emarati, et al., Sustainable waste-to-energy plant site selection by a hybrid method of geographic information system and evidential reasoning: A case study Guilan province, Process Saf. Environ. Prot., 176 (2023), 316–331. https://doi.org/10.1016/j.psep.2023.05.063 doi: 10.1016/j.psep.2023.05.063
![]() |
[16] |
W. Xu, Y. Huang, S. Song, Y. Chen, G. Cao, M. Yu, et al., A new online optimization method for boiler combustion system based on the data-driven technique and the case-based reasoning principle, Energy, 263 (2023), 125508. https://doi.org/10.1016/j.energy.2022.125508 doi: 10.1016/j.energy.2022.125508
![]() |
[17] |
M. R. N. Kalhori, M. H. FazelZarandi, A new interval type-2 fuzzy reasoning method for classification systems based on normal forms of a possibility-based fuzzy measure, Inf. Sci., 581 (2021), 567–586. https://doi.org/10.1016/j.ins.2021.09.060 doi: 10.1016/j.ins.2021.09.060
![]() |
[18] |
J. Wang, Z. Zhang, G. Zhao, Task recommendation method for fusion of multi-view social relationship learning and reasoning in the mobile crowd sensing system, Comput. Commun., 206 (2023), 60–72. https://doi.org/10.1016/j.comcom.2023.04.028 doi: 10.1016/j.comcom.2023.04.028
![]() |
[19] | W. Xu, Y. Huang, S. Song, B. Chen, X. Qi, A novel online combustion optimization method for boiler combining dynamic modeling, multi-objective optimization and improved case-based reasoning, Fuel, 337 (2023), 126854. https://doi.org/10.1016/j.fuel.2022.126854 |
[20] |
R. Yadav, A. Giri, S. Chatterjee, Understanding the users' motivation and barriers in adopting healthcare apps: A mixed-method approach using behavioral reasoning theory, Technol. Forecasting Social Change, 183 (2022), 121932. https://doi.org/10.1016/j.techfore.2022.121932 doi: 10.1016/j.techfore.2022.121932
![]() |
[21] |
Z. Zhang, L. Wang, J. Duan, Y. M. Wang, An early warning method based on fuzzy evidential reasoning considering heterogeneous information, Int. J. Disaster Risk Reduct., 82 (2022), 103356. https://doi.org/10.1016/j.ijdrr.2022.103356 doi: 10.1016/j.ijdrr.2022.103356
![]() |
[22] |
Z. Zhao, J. Chen, K. Xu, H. Xie, X. Gan, H. Xu, A spatial case-based reasoning method for regional landslide risk assessment, Int. J. Appl. Earth Obs. Geoinf., 102 (2021), 102381. https://doi.org/10.1016/j.jag.2021.102381 doi: 10.1016/j.jag.2021.102381
![]() |
[23] |
X. Long, H. Li, W. Ren, Y. Du, E. Mao, N. Ding, A parameter-extended case-based reasoning method based on a functional basis for automated experiential reasoning in mechanical product designs, Adv. Eng. Inf., 50 (2021), 101409. https://doi.org/10.1016/j.aei.2021.101409 doi: 10.1016/j.aei.2021.101409
![]() |
[24] |
S. Chen, J. Liu, Y. Xu, A logical reasoning based decision making method for handling qualitative knowledge, Int. J. Approximate Reasoning, 129 (2021), 49–63. https://doi.org/10.1016/j.ijar.2020.11.003 doi: 10.1016/j.ijar.2020.11.003
![]() |
[25] |
A. Wang, X. Gao, A variable scale case-based reasoning method for evidence location in digital forensics, Future Gener. Comput. Syst., 122 (2021), 209–219. https://doi.org/10.1016/j.future.2021.03.019 doi: 10.1016/j.future.2021.03.019
![]() |
[26] |
N. Cercone, A. An, C. Chan, Rule-induction and case-based reasoning: Hybrid architectures appear advantageous, IEEE Trans. Knowl. Data Eng., 11 (1999), 166–174. https://doi.org/10.1109/69.755625 doi: 10.1109/69.755625
![]() |
[27] |
D. Sottara, P. Mello, M. Proctor, A configurable rete-oo engine for reasoning with different types of imperfect information, IEEE Trans. Knowl. Data Eng., 22 (2010), 1535–1548. https://doi.org/10.1109/TKDE.2010.125 doi: 10.1109/TKDE.2010.125
![]() |
[28] |
Y. Cao, Z. Zhou, C. Hu, W. He, S. Tang, On the interpretability of belief rule-based expert systems, IEEE Trans. Fuzzy Syst., 29 (2021), 3489–3503. https://doi.org/10.1109/TFUZZ.2020.3024024 doi: 10.1109/TFUZZ.2020.3024024
![]() |
[29] | S. Guo, W. Zhou, K. Li, Multi-layer Case-based Reasoning Approach of Complex Product System, in 2012 Third World Congress on Software Engineering, (2012), 107–110. https://doi.org/10.1109/WCSE.2012.27 |
[30] |
R. Ouache, E. Bakhtavar, G. Hu, K. Hewage, R. Sadiq, Evidential reasoning and machine learning-based framework for assessment and prediction of human error factors-induced fire incidents, J. Build. Eng., 49 (2022), 104000. https://doi.org/10.1016/j.jobe.2022.104000 doi: 10.1016/j.jobe.2022.104000
![]() |
[31] |
S. Kierner, J. Kucharski, Z. Kierner, Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review, J. Biomed. Inf., 144 (2023), 104428. https://doi.org/10.1016/j.jbi.2023.104428 doi: 10.1016/j.jbi.2023.104428
![]() |
[32] |
H. Bride, C. H. Cai, J. Dong, J. S. Dong, Z. Hóu, S. Mirjalili, et al., Silas: A high-performance machine learning foundation for logical reasoning and verification, Expert Syst. Appl., 176 (2021), 114806. https://doi.org/10.1016/j.eswa.2021.114806 doi: 10.1016/j.eswa.2021.114806
![]() |
[33] |
Y. Chen, Z. Dai, H. Yu, B. K. H. Low, T. H. Ho, Recursive reasoning-based training-time adversarial machine learning, Artif. Intell., 315 (2023), 103837. https://doi.org/10.1016/j.artint.2022.103837 doi: 10.1016/j.artint.2022.103837
![]() |
[34] |
L. Bellomarini, R. R. Fayzrakhmanov, G. Gottlob, A. Kravchenko, E. Laurenza, Y. Nenov, et al., Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice, Future Gener. Comput. Syst., 129 (2022), 407–422. https://doi.org/10.1016/j.future.2021.10.021 doi: 10.1016/j.future.2021.10.021
![]() |
[35] |
M. Namvar, A. Intezari, S. Akhlaghpour, J. P. Brienza, Beyond effective use: Integrating wise reasoning in machine learning development, Int. J. Inf. Manage., 69 (2023), 102566. https://doi.org/10.1016/j.ijinfomgt.2022.102566 doi: 10.1016/j.ijinfomgt.2022.102566
![]() |
[36] |
J. G. C. Krüger, A. de Souza Britto Jr, J. P. Barddal, An explainable machine learning approach for student dropout prediction, Expert Syst. Appl., 233 (2023), 120933. https://doi.org/10.1016/j.eswa.2023.120933 doi: 10.1016/j.eswa.2023.120933
![]() |
[37] |
R. Gao, S. Cui, H. Xiao, W. Fan, H. Zhang, Y. Wang, Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule, Inf. Sci., 615 (2022), 529–556. https://doi.org/10.1016/j.ins.2022.10.029 doi: 10.1016/j.ins.2022.10.029
![]() |
[38] |
J. Liu, Q. Qian, Reinforcement learning-based knowledge graph reasoning for aluminum alloy applications, Comput. Mater. Sci, 221 (2023), 112075. https://doi.org/10.1016/j.commatsci.2023.112075 doi: 10.1016/j.commatsci.2023.112075
![]() |
[39] |
N. Muslim, S. Islam, J. C. Grégoire, Reinforcement learning based offloading framework for computation service in the edge cloud and core cloud, J. Adv. Inf. Technol., 13 (2022), 139–114. https://doi.org/10.12720/jait.13.2.139-146 doi: 10.12720/jait.13.2.139-146
![]() |
[40] |
I. Ahmad, T. Kumar, M. Liyanage, J. Okwuibe, M. Ylianttila, A. Gurtov, Overview of 5g security challenges and solutions, IEEE Commun. Stand. Mag., 2 (2018), 36–43. https://doi.org/10.1109/MCOMSTD.2018.1700063 doi: 10.1109/MCOMSTD.2018.1700063
![]() |
[41] |
H. Hu, J. Wu, Z. Wang, G. Cheng, Mimic defense: a designed-in cybersecurity defense framework, IET Inf. Secur., 12 (2018), 226–237. https://doi.org/10.1049/iet-ifs.2017.0086 doi: 10.1049/iet-ifs.2017.0086
![]() |
Symbol | Name |
Δfi | frequency deviation |
ΔPmi | generation mechanical output deviation |
ΔPtie−i | tie-line active power deviation |
ΔPwind,i | wind power deviation |
ΔPWi | output of wind turbine generator deviation |
ΔPsolar,i | solar power deviation |
ΔPSi | the deviations of output of photovoltaic |
ΔPaci | the deviations of output of ACs |
ΔPvi | valve position deviation |
ACEi | area control error |
Tgi | governor time constant |
Tti | turbine time constant |
Di | the generator unit damping coefficient |
TWi | the wind turbine generator time constant |
TPVi | the photovoltaic time constant |
Tij | lie-line synchronizing coefficient between the ith and jth control area |
daci | the damping coefficient |
kaci | the combined integral gain |
βi | frequency bias factor |
EER | energy efficiency ratio |
cpi | specific heat capacity of the air |
Ri | the speed drop |
k | gain factor in the smart thermostat |
Mi | the moment of inertia of the generator unit |
mi | the mass of air flow |
Tt | Tg | R | D | β | M | cp | m | k | dac | EER | T12 | TW | TPC | |
Area1 | 0.30 | 0.10 | 0.05 | 1.00 | 21.0 | 10 | 1.01 | 0.25 | 8 | 0.025 | 3.75 | 0.1968 | 0.30 | 0.30 |
Area2 | 0.40 | 0.17 | 1.50 | 21.5 | 12 | 15 | 0.015 |
Symbol | Name |
Δfi | frequency deviation |
ΔPmi | generation mechanical output deviation |
ΔPtie−i | tie-line active power deviation |
ΔPwind,i | wind power deviation |
ΔPWi | output of wind turbine generator deviation |
ΔPsolar,i | solar power deviation |
ΔPSi | the deviations of output of photovoltaic |
ΔPaci | the deviations of output of ACs |
ΔPvi | valve position deviation |
ACEi | area control error |
Tgi | governor time constant |
Tti | turbine time constant |
Di | the generator unit damping coefficient |
TWi | the wind turbine generator time constant |
TPVi | the photovoltaic time constant |
Tij | lie-line synchronizing coefficient between the ith and jth control area |
daci | the damping coefficient |
kaci | the combined integral gain |
βi | frequency bias factor |
EER | energy efficiency ratio |
cpi | specific heat capacity of the air |
Ri | the speed drop |
k | gain factor in the smart thermostat |
Mi | the moment of inertia of the generator unit |
mi | the mass of air flow |
Tt | Tg | R | D | β | M | cp | m | k | dac | EER | T12 | TW | TPC | |
Area1 | 0.30 | 0.10 | 0.05 | 1.00 | 21.0 | 10 | 1.01 | 0.25 | 8 | 0.025 | 3.75 | 0.1968 | 0.30 | 0.30 |
Area2 | 0.40 | 0.17 | 1.50 | 21.5 | 12 | 15 | 0.015 |