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An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

  • In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.

    Citation: Foday Conteh, Shota Tobaru, Mohamed E. Lotfy, Atsushi Yona, Tomonobu Senjyu. An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system[J]. AIMS Energy, 2017, 5(5): 814-837. doi: 10.3934/energy.2017.5.814

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  • In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.


    Korteweg and de-Vries developed the classical KdV equation in 1895 as a nonlinear PDE to investigate the waves that occurs on the surfaces of shallow water. Many studies have been conducted on this exactly solvable model. Many scholars have proposed novel applications of the classical KdV equation, such as acoustic waves that produces in a plasma in ion form, and acoustic waves which are produces on a crystal lattice. In [1], the classical KdV equation is as follows

    Ut+β1Uxxx+β2UUx=0. (1.1)

    Different variations of Eq (1.1) have been published in the literature, including [2,3]. In literature [2], various efficient methods have been used to get solitons solutions of different kinds of KdV equations. Wang [3] used a quadratic function Ansatz to obtain lump solutions for the (2+1)-dimensional KdV equation. In [4,5,6], several systematic techniques were utiltized to investigate the different types of KdV equations. Wang and Kara introduced a new 2D-mKdV in 2019 [7]. The new (2+1)-dimensional mKdV equation is given by

    ft=6f2fx6f2fy+fxxxfyyy3fxxy+3fxyy. (1.2)

    After 1695, the non-integer order or fractional-order derivative (FOD) was described as a simple academic generalization of the classical derivative. A FOD is an operator that extends the order of differentiation from Natural numbers (N) to a set of real numbers (R) or even to a set of complex numbers (C). Fractional calculus has emerged as one of the most effective methods for describing long-memory processes over the last decade. Engineers and physicists, as well as pure mathematicians, are interested in such models. The models that are represented by differential equations with fractional-order derivatives are the most interesting [8,9,10]. Their evolutions are much more complicated than in the classical integer-order case, and deciphering the underlying principle is a difficult task. There are many fractional operators regarding to the kernels involved in the integration. The most popular fractional operator is Caputo-Liouville which is based on power-law kernel, but this kernel has issue about the singularity of the kernel. To tackle the limitation of derivative operators with power-law kernels, new types of nonlocal FOD have been introduced in recent literature. For example, in literature [11], Caputo and Fabrizio (CF) introduced a FOD that is focused on the exponential kernel. However, the CF derivative, on the other hand, has some issues with the kernel's locality. In 2016, Atangana and Baleanu constructed an updated version of FOD that is based on the Mittag-Leffler function [12]. This derivative solves the issues of locality and singularity. The FOD of Atangana-Baleanu in Caputo sense (ABC) accurately describes the memory. The ABC operator's most significant applications are available in [13,14,15,16,17].

    Certain important techniques have been used to solve fractional order differential equations (FODEs). Some of these includes the homotopy perturbation method (HPM), Laplace Adomian decomposition method (LADM) fractional operational matrix method (FOMM), homotopy analysis method (HAM) and many more [18,19,20,21,22]. In contrast to these techniques, the Laplace-Adomian decomposition method (LADM) is an important tool for solving non linear FODEs. The ADM and the Laplace transformation are two essential methods that are combined in LADM. Furthermore, unlike a Runge-Kutta process, LADM does not require a predefine size declaration. Every numerical or analytical approach has its own set of benefits and drawbacks. For instance, discretization of data is used in collocation techniques that require extra memory and a longer operation. Since the Laplace-Adomian approach has less parameters than all other methods, it is a useful tool that does not necessitate discretion or linearization [23]. With the aid of LADM, the smoke model was successfully solved in [24]. LADM was utilized by the authors to solve third order dispersive PDE defined by FOD in [22].

    Inspired by above literature, in this paper, we study Eq (1.2) under ABC-operator. We use LADM to solve the proposed equation. Consider Eq (1.2) under ABC-operator as

    ABCDΨtft=6f2fx6f2fy+fxxxfyyy3fxxy+3fxyy. (1.3)

    Definition 2.1. [12] Let 0<Ψ1 and f(t)H1. Then ABC FOD of order Ψ is expressed as

    ABCDΨtf(t)=c(Ψ)(1Ψ)t0f(η)EΨ((tη)ΨΨ(1Ψ))dη,

    where c(Ψ) is the normalization function such that c(0)=c(1)=1. The symbol EΨ denotes the Mittag-Leffler kernel which is defined as:

    EΨ(t)=k=0tkΓ(Ψk+1).

    Definition 2.2. [12] Let 0<Ψ1 and f(t)H1(0,T). Then AB fractional integral of order Ψ is defined as

    ABIΨtf(t)=(1Ψ)c(Ψ)f(t)+(Ψ)c(Ψ)Γ(Ψ)t0(tη)Ψ1f(η)dη.

    Definition 2.3. [12] The formula for the Laplace transform of ABC FOD of f(t) is defined by

    L[ABCDΨ0f(t)]=c(Ψ)sΨ(1Ψ)+Ψ[sΨL[f(t)]sΨ1u(0)].

    Theorem 2.4. [25] Let H be a Banach space and X:HHbe a mapping. Then X is said to be Picard's X-stable, if ξ,mH,

    XξXmaξXξ+bξm,

    where a0, and b[0,1]. Further, X has a fixedpoint.

    Theorem 3.1. [12] The ABC derivative holds the following Lipschitztype condition for 0<ϝ<.

    ABCDΨtf(t)ABCDΨtg(t)ϝf(t)g(t).

    Proof. By using definition of ABC, wehave

    ABCDΨtf(t)ABCDΨtg(t)=c(Ψ)(1Ψ)t0f(η)EΨ((tη)ΨΨ(1Ψ))dηc(Ψ)(1Ψ)t0g(η)EΨ((tη)ΨΨ(1Ψ))dη=c(Ψ)(1Ψ)[t0f(η)EΨ((tη)ΨΨ(1Ψ))dηt0g(η)EΨ((tη)ΨΨ(1Ψ))dη].

    Using the Lipschitz condition for the first order derivative, we can find a small positive constant ρ1 such that

    ABCDΨtf(t)ABCDΨtg(t)=c(Ψ)ρ1(1Ψ)EΨ(ΨtΨ(1Ψ))[t0f(η)dηt0g(η)dη]c(Ψ)ρ1(1Ψ)EΨ(ΨtΨ(1Ψ))t0f(η)dηt0g(η)dηϝt0f(η)dηt0g(η)dηϝf(t)g(t),

    where ϝ=c(Ψ)ρ1(1Ψ)EΨ(ΨtΨ(1Ψ)). Thus Lipschitz condition holds for ABC derivative.

    Let

    Φ(x,y,t;f)=6f2fx6f2fy+fxxxfyyy3fxxy+3fxyy, (4.1)

    Eq (1.3) can be written as

    ABCDΨtf(x,y,t)=Φ(x,y,t;f). (4.2)

    Applying the ABC FOI to Eq (4.2), we have

    f(x,y,t)f(x,y,0)=(1Ψ)c(Ψ)Φ(x,y,t;f)+(Ψ)c(Ψ)Γ(Ψ)t0(tη)Ψ1Φ(x,y,t;f)dη.

    First we have to verify that the Lipschitz condition holds for the kernel Φ(x,y,t;f). For this, let us take two bounded functions, f and g, i.e., fΔ1, and gΔ2 where Δ1,Δ2>0, and consider

    Φ(x,y,t;f)Φ(x,y,t;g)={(6f2fx6g2gx)(6f2fy6g2gy)+(fxxxgxxx)(fyyygyyy)(3fxxy3gxxy)+(3fxyy3gxyy)={2x(f3g3)2y(f3g3)+3x3(fg)3y3(fg)33yx2(fg)+33y2x(fg){2x(f3g3)+2y(f3g3)+3x3(fg)+3y3(fg)+33yx2(fg)+33y2x(fg).

    Since f and g are bounded functions. Therefore, their partial derivatives satisfy the Lipschitz conditions and there exists non-negative constants K,L,M,N,O,P such that

    Φ(x,y,t;f)Φ(x,y,t;g){2K(f3g3)+2L(f3g3)+M(fg)+N(fg)+3O(fg)+3P(fg){(2K+2L)(f2+fg+g2)(fg)+M(fg)+N(fg)+3O(fg)+3P(fg)=((2K+2L)(Δ21+Δ1Δ2+Δ22)+M+N+3O+3P)(fg)=κ(fg).

    Let

    κ=((2K+2L)(Δ21+Δ1Δ2+Δ22)+M+N+3O+3P),

    thus

    Φ(x,y,t;f)Φ(x,y,t;g)κfg.

    For further analysis, we make an iterative scheme as

    fξ+1(x,y,t)=(1Ψ)c(Ψ)Φ(x,y,t;fξ)+(Ψ)c(Ψ)Γ(Ψ)t0(tη)Ψ1Φ(x,y,η;fξ)dη,

    where f0(x,y,t)=f(x,y,0). Now the difference between two consecutive terms can be taken as

    eξ(x,y,t)=fξ(x,y,t)fξ1(x,y,t)=(1Ψ)c(Ψ)[Φ(x,y,t;fξ1)Φ(x,y,t;fξ2)]+(Ψ)c(Ψ)Γ(Ψ)t0(tη)Ψ1[Φ(x,y,η;fξ1)Φ(x,y,η;fξ2)]dη.

    Also, we have

    fξ(x,y,t)=ξk=0ek(x,y,t), (4.3)

    with f1=0.

    Theorem 4.1. Assume that f(x,y,t) is bounded a function. Then

    eξ(x,y,t)((1Ψ)c(Ψ)κ+κtΨc(Ψ)Γ(Ψ))ξf(x,y,0). (4.4)

    Proof. Consider

    eξ(x,y,t)=fξ(x,y,t)fξ1(x,y,t). (4.5)

    The Eq (4.5) gets the form under norm as

    eξ(x,y,t)=fξ(x,y,t)fξ1(x,y,t).

    To get the required result, we use the concept of mathematical induction. For ξ=1, one can get

    e1(x,y,t)=f1(x,y,t)f0(x,y,t)(1Ψ)c(Ψ)Φ(x,y,t;f0)Φ(x,y,t;f1)+Ψc(Ψ)Γ(Ψ)t0(tη)Ψ1Φ(x,y,η;f0)Φ(x,y,η;f1)dη(1Ψ)c(Ψ)κf0f1+Ψc(Ψ)Γ(Ψ)κt0(tη)Ψ1f0f1dη=(1Ψ)c(Ψ)κf(x,y,0)+Ψc(Ψ)Γ(Ψ)κf(x,y,0)t0(tη)Ψ1dη=(1Ψ)c(Ψ)κf(x,y,0)+Ψc(Ψ)Γ(Ψ)κf(x,y,0)t=((1Ψ)c(Ψ)κ+κtΨc(Ψ)Γ(Ψ))f(x,y,0).

    Now assume that the result is true for ξ1, i.e.,

    eξ1(x,y,t)((1Ψ)c(Ψ)κ+κtΨc(Ψ)Γ(Ψ))ξ1f(x,y,0). (4.6)

    Next, we have to show that

    eξ(x,y,t)((1Ψ)c(Ψ)κ+κtΨc(Ψ)Γ(Ψ))ξf(x,y,0). (4.7)

    To get the result (4.4), consider

    eξ(x,y,t)=fξ(x,y,t)fξ1(x,y,t)(1Ψ)c(Ψ)Φ(x,y,t;fξ1)Φ(x,y,t;fξ2)+Ψc(Ψ)Γ(Ψ)t0(tη)Ψ1Φ(x,y,η;fξ1)Φ(x,y,η;fξ2)dη(1Ψ)c(Ψ)κfξ1fξ2+Ψc(Ψ)Γ(Ψ)κt0(tη)Ψ1fξ1fξ2dη=(1Ψ)c(Ψ)κeξ1+κtΨ1c(Ψ)Γ(Ψ)eξ1=((1Ψ)c(Ψ)κ+κtΨ1c(Ψ)Γ(Ψ))eξ1=((1Ψ)c(Ψ)κ+κtΨ1c(Ψ)Γ(Ψ))((1Ψ)c(Ψ)κ+κtΨ1c(Ψ)Γ(Ψ))ξ1f(x,y,0)=((1Ψ)c(Ψ)κ+κtΨ1c(Ψ)Γ(Ψ))ξf(x,y,0).

    This ends the proof.

    Theorem 4.2. If the following relation holds at t=t00, where

    0((1Ψ)c(Ψ)κ+κtΨ10c(Ψ)Γ(Ψ))<1. (4.8)

    Then at least one solution of the new 2D KdV equation under the ABCfractional derivative exists.

    Proof. With the help of Eq (4.3), we have

    fξ(x,y,0)ξk=0ek(x,y,t)ξk=0(((1Ψ)c(Ψ)κ+κtΨ1c(Ψ)Γ(Ψ))kf(x,y,0)),

    for t=t0, we get

    fξ(x,y,0)f(x,y,0)ξk=0((1Ψ)c(Ψ)κ+κtΨ10c(Ψ)Γ(Ψ))k.

    From the above relation, we can say that

    limξfξ(x,y,0)f(x,y,0)limξξk=0((1Ψ)c(Ψ)κ+κtΨ10c(Ψ)Γ(Ψ))k.

    Since

    0((1Ψ)c(Ψ)κ+κtΨ10c(Ψ)Γ(Ψ))<1, (4.9)

    this implies that sequence fξ(x,y,t) is convergent and therefore the sequence is bounded for each ξ. Further, assume that

    Rξ(x,y,t)=f(x,y,t)fξ(x,y,t).

    Since fξ(x,y,t) is bounded. It follows that for λ>0, we have fξ(x,y,t)λ. After simple manipulation like we did in Theorems 4.1 and 4.2, we obtain

    Rξ(x,y,t)((1Ψ)c(Ψ)κ+κtΨ10c(Ψ)Γ(Ψ))ξ+1λ.

    Using Eq (4.9), one can get

    limξRξ(x,y,t)=0,

    it follows that limξfξ(x,y,t)=f(x,y,t). This finish the proof.

    Theorem 4.3. If the inequality (4.8) holds at t=t00.Then the unique solution of proposed equation exists.

    Proof. On contrary, suppose that there are two solutions f and g of the proposed equation such that fg. Now

    f(x,y,t)g(x,y,t)=(1Ψ)c(Ψ)[Φ(x,y,t;f)Φ(x,y,t;g)]+Ψc(Ψ)Γ(Ψ)×[t0(tη)Ψ1[Φ(x,y,η;f)Φ(x,y,η;g)]dη].

    Taking norm both side

    f(x,y,t)g(x,y,t)(1Ψ)c(Ψ)Φ(x,y,t;f)Φ(x,y,t;g)+Ψc(Ψ)Γ(Ψ)×[t0(tη)Ψ1Φ(x,y,η;f)Φ(x,y,η;g)dη](1Ψ)c(Ψ)κfg+Ψc(Ψ)Γ(Ψ)t0κ(tη)Ψ1fgdη((1Ψ)c(Ψ)κ+Ψc(Ψ)Γ(Ψ)κ)fgt0(tη)Ψ1dη=((1Ψ)c(Ψ)κ+κtΨc(Ψ)Γ(Ψ))fg,

    but

    0((1Ψ)c(Ψ)κ+κtΨc(Ψ)Γ(Ψ))<1.

    Using the above inequality, we achieve

    f(x,y,t)g(x,y,t)=0,

    thus, our supposition is wrong. Hence, the solution is unique.

    In this section, we briefly discuss the solution of the model by applying Ansatz method. For this purpose, we will consider a test function as

    f(x,y,t)=β0+β1sech(b1x+b2y+b3t). (5.1)

    By putting above equation into classical form of the model, we obtain

    6β20a16β20b2+b313b21b2+3b22b1b32b3=0,12β0β1b112β0b1b2=0,6β21b16β21b26b31+18b21b218b22b1+6b32=0,

    solution becomes as

    β0=0,β1=b1±b2,b3=b313b21b2+3b22b1b32,

    solution of classical model becomes as

    f1,2(x,y,t)=(b1±b2)sech(b1x+b2y+(b313b21b2+3b22b1b32)t).

    For b1=1 and b2=1, the above solution becomes

    f1,2(x,y,t)=4exp(xy+8t)1+exp(2x2y+16t). (5.2)

    In this section, to obtain analytic solution we applying Laplace transform (LT) on both sides of equations f(x,y,t) is the source term. Subject to the initial condition f(x,y,0)=f0(x,y,0). On utilizing LT, one can get

    L[ABCDΨtf]=L[6f2fx6f2fy+fxxxfyyy3fxxy+3fxyy],
    L[f(x,y,t)]=f(x,y,0)s+[s(1Ψ)+Ψc(Ψ)L[6f2fx6f2fy]+fxxxfyyy3fxxy+3fxyy]. (6.1)

    The approximate solution is represented by

    f(x,y,t)=ξ=0fξ(x,y,t), (6.2)

    and the nonlinear term is represented by Adomain polynomials, i.e., G(f)=f2=ξ=0Aξ, where Aξ is defined as follows for any ξ=0,1,2,

    Aξ=1Γ(ξ+1)dξdλξ[G(ξ=0(λξfξ))]λ=0.

    Using Eq (6.2), we obtain

    L[ξ=0fξ(x,y,t)]=f(x,y,0)s+s(1Ψ)+Ψc(Ψ)L[6ξ=0Aξξ=0xfξ6ξ=0Aξξ=0yfξ+ξ=0(3x3fξ3y3fξ33x2yfξ+33yx2fξ)].

    The following can be obtain by comparing terms

    L[f0(x,y,t)]=f(x,y,0)s,L[f1(x,y,t)]=sΨ(1Ψ)+Ψ)sΨc(Ψ)L[6A0f0x6A0f0y+f0xxx+f0yyy3f0xxy+3f0xyy],L[f2(x,y,t)]=sΨ(1Ψ)+Ψ)sΨc(Ψ)L[6A1f1x6A1f1y+f1xxx+f1yyy3f1xxy+3f1xyy],L[fξ+1(x,y,t)]=sΨ(1Ψ)+Ψ)sΨc(Ψ)L[6Aξfξx6Aξfξy+fξxxx+fξyyy3fξxxy+3fξxyy].

    Applying L1, we get

    f0(x,y,t)=L1[f(x,y,0)s],f1(x,y,t)=L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[6A0f0x6A0f0y+f0xxx+f0yyy3f0xxy+3f0xyy]]f2(x,y,t)=L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[6A1f1x6A1f1y+f1xxx+f1yyy3f1xxy+3f1xyy]]fξ+1(x,y,t)=L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[6Aξfξx6Aξfξy+fξxxx+fξyyy3fξxxy+3fξxyy]].

    The required series solution is given as

    f(x,y,t)=ξ=0fξ(x,y,t). (6.3)

    Here, we present two special cases of the proposed equation.

    For the first case, we take the initial condition as

    f(x,y,0)=4exp(x+y)1+exp(2(xy)).

    Using the detail procedure as discussed above, we achieve

    f0(x,y,t)=f(x,y,0)=4exp(x+y)1+exp(2(xy)),f1(x,y,t)=L1[sΨ(1Ψ)+ΨsΨc(Ψ)L[6f20xf06f20yf0+3x3f03y3f032x2(yf0)+3x(2y2f0)]],

    using Mathematica, we obtain

    f1(x,y,t)=(1Ψ+ΨtΨΓ(Ψ+1))(32texp(x+y)(exp(2x)+exp(2y))(exp(2x)+exp(2y))2c(Ψ)),

    similarly, other terms can be calculated with the help of Mathematica. The required series solution is given as

    {f(x,y,t)=4exp(x+y)1+exp(2(xy))+32t(1Ψ+ΨtΨΓ(Ψ+1))×(exp(x+y)(exp(2x)+exp(2y))(exp(2x)+exp(2y))2c(Ψ))+ (6.4)

    Remark 6.1. When we put Ψ=1 in Eq (6.4), the solution rapidly converges to the exact classical solution, i.e.,

    f(x,y,t)=4exp(xy+8t)1+exp(2(xy+8t)). (6.5)

    For the first case, we take the initial condition as

    f(x,y,0)=4exp(x+y)1+exp(2(xy)).

    Using the detail procedure as discussed above, we get

    f0(x,y,t)=f(x,y,0)=4exp(x+y)1+exp(2(xy)),f1(x,y,t)=L1[sΨ(1Ψ)+ΨsΨc(Ψ)L[6f20xf06f20yf0+3x3f03y3f032x2(yf0)+3x(2y2f0)]],

    using Mathematica, we obtain

    f1(x,y,t)=(1Ψ+ΨtΨΓ(Ψ+1))(32texp(x+y)(exp(2x)+exp(2y))(exp(2x)+exp(2y))2c(Ψ)),

    similarly, other terms can be calculated with the help of Mathematica. The required series solution is given as

    {f(x,y,t)=4exp(x+y)1+exp(2(xy))32t(1Ψ+ΨtΨΓ(Ψ+1))×(exp(x+y)(exp(2x)+exp(2y))(exp(2x)+exp(2y))2c(Ψ))+ (6.6)

    Remark 6.2. When we put Ψ=1 in Eq (6.6), the solution rapidly converges to the exact classical solution, i.e.,

    f(x,y,t)=4exp(xy+8t)1+exp(2(xy+8t)). (6.7)

    Here, we derive some results regarding to the convergence and stability of the proposed scheme with the help of functional analysis. The convergence of the proposed scheme of the is presented in the following theorem.

    Theorem 7.1. Let H be a Banach space and T:HHbe an operator. Suppose that f be the exact solution of the proposedequation. If ϖ such that 0ϖ<1 and fξ+1ϖfξ,ξN{0}, thenthe approximate solution ξ=0fξ convergesto the exact solution f.

    Proof. We construct a series as

    S0=f0,S1=f0+f1,S2=f0+f1+f2,Sξ=f0+f1+fξ.

    We want to prove that the sequence {Sξ}ξ=0 is a Cauchy sequence in H. Let us consider

    Sξ+1Sξ=fξϖfξϖ2fξ1ϖ3fξ2ϖξ+1f0.

    Now for every ξ,mN, we have

    SξSm=(SξSξ1)+(Sξ1Sξ2)++(Sm+1Sm)SξSξ1+Sξ1Sξ2++Sm+1Smϖξf0+ϖξ1f0++ϖm+1f0(ϖξ+1+ϖξ+2+)f0=ϖξ+11ϖf0.

    Now, limξ,mSξSm=0. This implies that {Sξ}ξ=0 is Cauchy sequence in H. So fH such that limξSξ=f. This ends the proof.

    Next, we present the Picard's X-stability of the proposed scheme in the following theorem.

    Theorem 7.2. Let X be a self-mapping which is definedas

    X(fξ(x,t))=fξ+1(x,t)=fξ(x,t)+L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[6f2ξfξx6f2ξfξy+fξxxx+fξyyy3fξxxy+3fξxyy]]. (7.1)

    The iteration is X-stable in L1(a,b), ifthe condition

    (2K+2L)(Φ21+Φ1Φ2+Φ2)1+M2N3+3O4+3P5<1, (7.2)

    is satisfied.

    Proof. With the help of Banach contraction theorem, first we show that the mapping X possesses a unique fixed point. For this, assume that the bounded iterations for (ξ,m)N×N. Let Φ1,Φ2>0 such that fξΦ1, and fmΦ2. Consider

    X(fξ(x,t))X(fm(x,t))=fξ(x,t)fm(x,t)+L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[2f2ξfξx2f2ξfξy+fξxxx+fξyyy3fξxxy+3fξxyy]]L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[2f2mfmx2f2mfmy+fmxxx+fmyyy3fmxxy+3fmxyy]]=fξ(x,t)fm(x,t)+L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[2K(f3ξf3m)6L(f3ξf3m)+M(fξfm)N(fξfm)3O(fξfm)+3P(fξfm)]].

    Now, using triangle inequality, we have

    X(fξ(x,t))X(fm(x,t))=fξ(x,t)fm(x,t)+L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[2K(f3ξf3m)2L(f3ξf3m)+M(fξfm)N(fξfm)3O(fξfm)+3P(fξfm)]].

    Using boundedness of fξ and fm, we have

    X(fξ(x,t))X(fm(x,t))[(2K+2L)(Φ21+Φ1Φ2+Φ2)1+M2N3+3O4+3P5]fξfm,

    where i,i=1,2,3,4,5, are functions obtained from L1[sΨ(1Ψ)+Ψ)sΨc(Ψ)L[]]. Using assumption (7.2), the mapping X fulfills the contraction condition. Hence by Banach fixed point result, X has a unique fixed point. Also, the mapping X satisfies the condition of Theorem 2.4 with

    a=0,b=(2K+2L)(Φ21+Φ1Φ2+Φ2)1+M2N3+3O4+3P5.

    Thus, the mapping X fulfills all conditions of Picard's X-stable. Hence our proposed scheme is Picard's X-stable.

    Thanks to the Mittag-Leffler kernel, which solves the singularity and locality problems with the Caputo and Caputo-Fabrizio FOD kernels. Since ABC-derivative is based on the Mittag-Leffler kernel, it has recently become popular for investigating the dynamics of a mathematical model that governs a physical process. We use ABC-derivative to investigate the soliton solution of the new modified KdV equation in (2+1) dimension in the current paper. Since the presence of a solution is essential for the study of a model, we have deduced some results using fixed point theory that guarantees at least one solution and the unique solution of the proposed equation. There are several techniques for solving FDEs, but among the analytical methods, LADM is the most effective and accurate. Under ABC-derivative, we used the LADM to obtain the solution of the proposed equation. Graphs of the solution are used to observe the method's convergence. The exact solution and the approximate solution obtained with the aid of LADM are in good agreement (See Figures 1 and 2). The dynamics of the solution under the ABC-derivative have been investigated. We can see from Figures 3 and 4 that the fractional-order solution curves are approaching the integer-order curve when fractional-order equals 1. Figure 5 shows that the ABC-derivative solution curve is much closer to the exact solution than the Caputo-Fabrizio derivative curve. As a result, the proposed model is better than Caputo-Fabrizio's. Figures 69 denote the graphical representation of solution obtained in the Case B. The considered equation will be studied under more generalized fractional operators in the next paper.

    Figure 1.  Two dimensional dynamics of exact and approximate solutions.
    Figure 2.  Dynamics of exact and approximate solutions in 3D.
    Figure 3.  Dynamics of approximate solution for different fractional orders in 2D.
    Figure 4.  Dynamics of approximate solution for different fractional orders in 3D.
    Figure 5.  Comparison between solution curves of exact solution, ABC-solution and Caputo-Fabrizio solution.
    Figure 6.  Dynamics of approximate solution in case B for different fractional orders in 2D.
    Figure 7.  Dynamics of approximate solution case B for different fractional orders in 3D.
    Figure 8.  Two dimensional dynamics of exact and approximate solutions in case B.
    Figure 9.  Dynamics of exact and approximate solutions in 3D in case B.

    The authors wish to express their sincere thanks to the honorable referees for their valuable comments and suggestions to improve the quality of the paper. In addition, authors would like to express their gratitude to the United Arab Emirates University, Al Ain, UAE for providing the financial support with Grant No. 12S005-UPAR 2020.

    In this research, there are no conflicts of interest.

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