Optimal reactive power dispatch using an improved slime mould algorithm

In this work, an enhanced slime mould algorithm (ESMA) based on neighborhood dimension learning (NDL) search strategy is proposed for solving the optimal power flow (OPF) problem. Before using the proposed ESMA for solving the OPF problem, its validity is verified by an experiment using 23 benchmark functions and compared with the original SMA, and three other recent optimization algorithms. Consequently, the ESMA is used to solve a modified power flow model including both conventional energy, represented by thermal power generators (TPGs), and renewable energy represented by wind power generators (WPGs) and solar photovoltaic generators (SPGs). Despite the important role of WPGs and SPGs in reducing CO2 emissions, they represent a big challenge for the OPF problem due to their intermittent output powers. To forecast the intermittent output powers from SPGs and WPGs, Lognormal and Weibull probability density functions (PDFs) are used, respectively. The objective function of the OPF has two extra costs, penalty cost and reserve cost. The penalty cost is added to formulate the underestimation of the produced power from the WPGs and SPGs, while the reserve cost is added to formulate the case of overestimation. Moreover, to decrease CO2 emissions from TPGs, a direct carbon tax is added to the objective function in some cases. The uncertainty of load demand represents also another challenge for the OPF that must be taken into consideration while solving it. In this study, the uncertainty of load demand is represented by the normal PDF. Simulation results of ESMA for solving the OPF are compared with the results of the conventional SMA and two further optimization methods. The simulation results obtained in this research show that ESMA is more effective in finding the optimal solution of the OPF problem with regard to minimizing the total power cost and the convergence of solution.

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“…Reactive power limits of all generators are showed by (18) to (20) with the same arrangement in the previous active power limits equations.…”

Section: P Minmentioning

confidence: 99%

“…Like many metaheuristic optimization algorithms, SMA suffers from premature convergence during solving complex nonlinear problems. It also suffers from the insufficient precision of optimization and slow optimization speed, which may lead to easily falling into a local optima [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

et al. 2022

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“…Therefore, the CACO algorithm takes on a global optimization ability. In addition, some new optimization algorithms have been proposed in recent years [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

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Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

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“…Liu et al [22], Mostafa et al [23] and Yousri et al [24] used hybrid and improved SMA to estimate parameters of solar photovoltaic cells, respectively; Agarwal and Bharti [25] applied improved SMA to the collision-free shortest time path planning of mobile robots; Rizk-Allah et al [26] proposed a chaos-opposition-enhanced SMA (CO-SMA) to minimize the energy costs of wind turbines at highaltitude sites; Hassan et al [27] applied improved SMA (ISMA) to efficiently solve economic and emission dispatch (EED) problem with single and dual objectives; Abdollahzadeh et al [28] proposed a binary SMA to solve the 0-1 knapsack problem; Zubaidi et al [29] combined SMA and artificial neural network (ANN) for urban water demand prediction; Chen and Liu [30] combined Kmeans clustering and chaotic SMA with support vector regression to obtain higher prediction accuracy; Ekinci et al [31] applied SMA to the power system stabilizer design (PSSD); Wazery et al [32] combined SMA and K-nearest neighbor for disease classification and diagnosis system; Wei et al [33] developed a simpler SMA for the problem of wireless sensor network coverage; Wei et al [34] proposed an enhanced SMA in power systems for optimal reactive power dispatch; Premkumar et al [35] and Houssein et al [36] developed multi-objective SMA (MOSMA) for solving complicated multi-objective engineering design problems in the real world; Yu et al [37] proposed an improved SMA (WQSMA) that enhanced the original SMA's robustness by using a quantum rotation gate (QRG) and a water cycle operator. Houssein et al [38] proposed a hybrid SMA and adaptive guided differential evolution (AGDE) algorithm, which makes a good combination of SMA's exploitation ability and AGDE's exploration ability.…”

Section: Introductionmentioning

confidence: 99%

An Equilibrium Optimizer Slime Mould Algorithm For Inverse Kinematics Of The 7-DOF Robotic Manipulator

Yin

Luo

Zhou

et al. 2022

Preprint

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In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective version of EOSMA is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7-DOF manipulator in two scenarios and compared with 15 single-objective and 3 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e-17 and 10e-18, and the average solution time is 0.05s and 0.36s, respectively.

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Optimal reactive power dispatch using an improved slime mould algorithm

Cited by 78 publications

References 41 publications

ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

An Equilibrium Optimizer Slime Mould Algorithm For Inverse Kinematics Of The 7-DOF Robotic Manipulator

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