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This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA's robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes the use of two recent optimization techniques, called the Local Unimodal Sampling (LUS) and the Spotted Hyena Optimizer (SHO), to optimally tune cascaded PID controller designed while controlling the speed and reducing the torque ripples of the switched reluctance motor. The proposed implementation of these algorithms has the advantage of better convergence and lower computational efforts when compared to other optimization techniques. To demonstrate the effectiveness of the proposed cascaded PID controller using LUS and SHO, they are compared to other controller designs present in previous literature, specifically the cascaded fractional-order PID (FOPID) controller. Results show that the PID controller is more superior to the FOPID controller and that the SHO-based cascaded PID controller leads to less current and torque ripples with better speed response. The study is implemented at different loading conditions. The results are presented in the form of a time-domain simulation conducted via MATLAB/SIMULINK. [ABSTRACT FROM AUTHOR]

Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system's stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes' law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system's performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and particle swarm optimization (PSO) were compared to ensure the efficacy of the introduced optimization algorithm. The simulation results showed the effectiveness of the optimally controlled SMES system, using the AOA in smoothing the output power variations and increasing the stability of the system under various operating conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters' uncertainty compared with other techniques. [ABSTRACT FROM AUTHOR]

This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search (CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systems' constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systems—IEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function. [ABSTRACT FROM AUTHOR]

This paper introduces a novel enhancement to the Transient Search Optimization (TSO) algorithm to estimate an accurate electrical model of the proton exchange membrane fuel cell (PEMFC). The PEMEFC model is a non-linear model that includes seven unknown variables which cannot be calculated analytically. The TSO is enhanced by inserting two new factors, the Levy function and the Weibull distribution function. The proposed enhanced Transient Search Optimization (ETSO) and TSO algorithms are applied to estimate the seven variables by minimizing the sum of the squared errors (SSEs) between the measured and calculated voltages. The error is defined as the difference between the measured and the calculated output voltage of the PEMFC. Three different commercial types of PEMFCs are modeled: i) Ballard, Mark V 5 kW, ii) Horizon H-12, and iii) 6 kW Nedstack PS6 stacks PEMFC. The estimated seven variables and the minimum SSE of electrical PEMFCs using ETSO and TSO algorithms are compared with the results obtained by using other optimization algorithms like whale optimization algorithm, genetic algorithm, neural network algorithm and others. The results obtained by the ETSO are better than that obtained by TSO by more than 10% and this percentage increases with other algorithms. The accuracy of the proposed PEMFC model is verified by comparing the estimated V – I and P – I characteristics with the measured data. The effectiveness of the proposed ETSO based model is verified by an investigation of sensitivity analysis for design variables and the robustness of the ETSO algorithm via the statistical analysis and the parametric t -test. • This paper presents a novel improved TSO algorithm to extract PEMFC model parameters. • Three commercial PEMFC models are implemented in this paper. • The parameters of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. • The sensitivity and robustness analyses of the proposed method are tested. [ABSTRACT FROM AUTHOR]

This paper introduces an enhancement to the transient stability of a wave energy conversion system (WECS) by using the coot optimization algorithm (COA) combined with an anti-windup method. This combination helps in elimination of the windup issue in the integral term of the proportional-integral (PI) controller during power system faults leading to a significant enhancement for the transient stability of the WECS connected to the grid. The COA is utilized to select the PI controller parameters and the anti-windup method back-calculation coefficients. The WECS converts the linear vertical motion into electrical energy by using an Archimedes wave swing device connected to a linear synchronous generator. Minimization of the generator's stator losses and maximization of the generator's real power is accomplished by the utilization of a generator-side converter (GSC). Also, both the point of common coupling voltage and the capacitor link voltage are set at their reference values by utilizing a grid-side inverter (GSI). An optimal design for the PI controllers in both converters is achieved by direct application of the COA to the MATLAB/Simulink model. A comparison is made among the results obtained by the COA and those obtained by other recent optimization algorithms under different grid fault conditions. [Display omitted] • A detailed model of the WECS is presented comprehensively. • Enhancement of the transient performance of grid-connected WECS is the main goal. • A new optimization algorithm is applied to select the parameters of the PI controllers and the anti-windup coefficients. • Different scenarios have been investigated under normal and abnormal operating conditions. • The results obtained validate the robustness of the proposed controllers. [ABSTRACT FROM AUTHOR]