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Peak Detection Based on FPGA Using Quasi-Newton Optimization Method for Femtosecond Laser Ranging
摘要： In this paper, an identi?cation problem for nonlinear models is explored and a novel fuzzy identi?cation method based on the ant colony optimization algorithm is proposed. First, a modi?ed cluster validity criterion with a fuzzy c-regression model is adopted to ?nd appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy c-regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.
关键词： ant colony optimization algorithm (ACO),Takagi-Sugeno fuzzy model,fuzzy c-regression model,Fuzzy system identi?cation
更新于2025-09-19 17:13:59
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Multielectron Effect for High-Order Harmonic Generation From Molecule Irradiated by Bichromatic Counter-Rotating Circularly Polarized Laser Pulses
摘要： In this paper, an identi?cation problem for nonlinear models is explored and a novel fuzzy identi?cation method based on the ant colony optimization algorithm is proposed. First, a modi?ed cluster validity criterion with a fuzzy c-regression model is adopted to ?nd appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy c-regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.
关键词： fuzzy c-regression model,Fuzzy system identi?cation,ant colony optimization algorithm (ACO),Takagi-Sugeno fuzzy model
更新于2025-09-19 17:13:59
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Coyote optimization algorithm for the parameter extraction of photovoltaic cells
摘要： In this paper, a new and powerful metaheuristic optimization technique known as the Coyote Optimization Algorithm (COA) is proposed for the parameter extraction of the PV cell/module. It is utilized to identify the parameters of the single diode and two-diode models. Inspired by the social norms adopted by the coyotes to ensure the survivability of their species, the COA possesses several outstanding merits such as low number of control parameters, ease of implementation and diverse mechanisms for balancing exploration and exploitation. For physically meaningful solutions, a set of parametric constraints is introduced to prevent the coyotes from straying outside of the predefined boundaries of the search space. Extensive tests indicate that the proposed optimizer exhibits superior accuracy compared to other state-of-the-art EA-based parameter extraction methods. It achieved root-mean-square error (RSME) as low as 7.7301E-04 A and 7.3265E-04 A, for the single-diode and two-diode models, respectively. Moreover, the algorithm maintains outstanding performance when tested on an assortment of modules of different technologies (i.e. mono-crystalline, poly-crystalline, and thin film) at varying irradiance and temperature. The standard deviations (STDs) of the fitness values over 35 runs are measured to be less than 1 × 10?5 for both models. This suggests that the results produced by the algorithm are highly consistent. With these outstanding merits, the COA is envisaged to be a competitive option for the parameter extraction problem of PV cell/module.
关键词： Equivalent circuit model,Solar photovoltaic,Evolutionary algorithm,Coyote optimization algorithm,Parameter extraction
更新于2025-09-16 10:30:52
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Optimal VMD-based Signal Denoising for Laser Radar via Hausdorff Distance and Wavelet Transform
摘要： Laser radar echo signals are easily contaminated by noise, such as background light and electronic noise, and this noise is an obstacle for the subsequent signal detection. However, the conventional denoising methods cannot achieve satisfactory effects when the signal-to-noise-ratio (SNR) is ultralow. In this paper, a novel denoising method for laser radar echo signals based on the parameter-optimal variational mode decomposition (VMD) combined with the Hausdorff distance (HD) and wavelet transform (WT) is proposed. Compared with conventional VMD-based methods, the proposed method utilizes a newly developed grasshopper optimization algorithm (GOA) to obtain the optimal combination of parameters for the VMD. Then, the HD is applied to select the relevant modes and then uses the basis function to reconstruct the signal. In addition, the relevant modes are further processed by the WT denoising method, which allows the reconstructed signal to obtain a higher SNR. The simulation and experimental results show the feasibility, effectiveness and robustness of the proposed method compared to three other available denoising techniques. The proposed method could promote the distance measurement performance of laser radars in harsh environments.
关键词： grasshopper optimization algorithm,wavelet transform,variational mode decomposition,Laser radar echo signal denoising,Hausdorff distance
更新于2025-09-16 10:30:52
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Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm
摘要： In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems.
关键词： chaotic search,least squares support vector machine,chicken swarm optimization algorithm,storage system,sustainable lithium-ion battery
更新于2025-09-16 10:30:52
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[IEEE 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) - Vellore, India (2019.3.22-2019.3.23)] 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) - A Novel Optimization Method for Parameter Extraction of Industrial Solar Cells
摘要： Triple diode model is used in the present work for making a lumped parameter equivalent circuit for solar photovoltaic (PV) model. Flower pollination optimization algorithm (FPOA) is used for finding out various parameters of the solar cell. The theoretical values considering the estimation are not the same as the industrial samples of double diode model. Many current components of solar cells were not found by the two diode model correctly whereas a triple diode model can extract the data accurately. The FPOA is being used to extract different parameters of the given triple diode model. The success of the FPOA optimization process is that it performs the local and global search within the single stage to extract the parameters. Simulation is carried out and the performance of the FPOA is compared with two other optimization techniques such as differential evolution (DE) method and particle swarm optimization (PSO) method. Comparison result shows that the triple diode model including FPOA provides superior performance than the double and single diode model. Moreover, huge silicon solar cells could be explained easily by the triple diode model.
关键词： parameter extraction,diode model,solar cell,photovoltaic array,optimization algorithm
更新于2025-09-16 10:30:52
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Optimization for Hydro-Photovoltaic-Wind Power Generation System Based on Modified Version of Multi-Objective Whale Optimization Algorithm
摘要： The intractable problem of the solar energy and wind energy are their discontinuity and instability cause by the environmental change. Hydropower was often chosen as the compensation of electric energy system for its celerity and low cost of adjustment and respond. This paper presents a long term multi-objective optimization model of hydro-photovoltaic(PV)-wind power system, in which, cascade hydropower station acts as the compensation of the power system. One objective of the model is maximizing the annual total power generation of the power system, and another objective is to smoothen the fluctuation of the power output of the system. This model calculates all the PV power and wind power at first, and then inputs the calculated results to the power grid as the boundary condition of the hydropower optimization. A modified version of non-dominated sorting whale optimization algorithm (modified NSWOA) is proposed to get a solution set of the proposed model. The results demonstrate that the modified NSWOA can provide decision maker a series of solutions for optimal selection and the hydropower can well compensate the PV power and wind power by its great adjusting ability.
关键词： Hydro-photovoltaic-wind power system,Multi-objective optimization,Cascade hydropower station,Modified non-dominated sorting whale optimization algorithm
更新于2025-09-12 10:27:22
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Whale inspired algorithm based MPPT controllers for grid-connected solar photovoltaic system
摘要： Over the past decades, meta-heuristic optimization techniques have become surprisingly very popular due to their flexibility and local optima avoidance capability. This paper uses the Whale Optimization Algorithm (WOA), a swarm-based technique to tune the Proportional-Integral (PI) based Maximum Power Point Tracking (MPPT) controllers of a grid-connected solar Photovoltaic (PV) system. The results of the PI-based Incremental Conductance (IC) MPPT technique are compared with both the conventional incremental conductance and the Perturb & Observe (P&O) MPPT techniques. Various modes of the PI controller are used. I, PI and Fractional order PI (FOPI) gain parameters are determined using WOA. Performance indices are applied to estimate the best parameters of the PI controller. This paper aims to show the effect of using PI-based MPPT controllers on enhancing the performance of a 400-kW grid-connected PV system. Simulation results show the capability of PI-based MPPT controllers on improving the performance of the PV system. It demonstrates the superiority of FOPI controllers over the other modes in enhancing system performance. The proposed work is simulated using MATLAB SIMULINK.
关键词： Photovoltaic,Perturb and Observe,Whale Optimization Algorithm,Proportional-Integral controller,Maximum Power Point Tracking,Incremental conductance
更新于2025-09-12 10:27:22
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Combined pulsed laser drilling of metal by continuous wave laser and nanosecond pulse train
摘要： The purpose of this paper is to propose a new hybrid metaheuristic to solve the problem of feature selection. Feature selection problem is the process of finding the most relevant subset based on some criteria. A hybrid metaheuristic is a new trend in the development of optimization algorithms. In this paper, two different hybrid models are designed based on spotted hyena optimization (SHO) for feature selection problem. The SHO algorithm can find the optimal or nearly optimal feature subset in the feature space to minimize the given fitness function. In the first model, the simulated annealing algorithm (SA) is embedded in the SHO algorithm (called SHOSA-1) to enhance the optimal solution found by the SHO algorithm after each iteration. In the second model, SA enhances the final solution obtained by the SHO algorithm (called SHOSA-2). The performance of these methods is evaluated in 20 datasets in the UCI repository. The experiments show that SHOSA-1 performs better than the native algorithm and SHOSA-2. And then, SHOSA-1 is compared with six state-of-the-art optimization algorithms. The experimental results confirm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimization algorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.
关键词： simulated annealing,Hybrid optimization,classification,spotted hyena optimization algorithm,SHO optimization
更新于2025-09-11 14:15:04
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Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models
摘要： Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their e?cacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modi?ed search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by di?erential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modi?ed search strategies is introduced to meet the search-oriented requirements of di?erent dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to ?ve PV types. Both single diode and double diode models are used to model these ?ve PV types. The good performance of MCSWOA is veri?ed by various algorithms.
关键词： metaheuristic,solar photovoltaic,whale optimization algorithm,parameter extraction
更新于2025-09-11 14:15:04