- 标题
- 摘要
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- 实验方案
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A Novel Modified Robust Load Frequency Control for Mass-Less Inertia Photovoltaics Penetrations via Hybrid PSO-WOA Approach
摘要： The high penetrations of distributed energy resources (DERs) leads to severe problems such as reducing system inertia and increasing the frequency deviations from the nominal value. The main target of this study is to enable modern photovoltaics (PVs) with large penetration amounts to participate effectively in the load frequency control in the interconnected power systems, in which frequency and tie-line power sharing deviations exist. In this research, a model for the solar PV is developed to help study the heavy penetration of the solar PVs within interconnected power systems. Secondly, a time domain objective function based on the norm of the area control error is formulated. Thirdly, in order to tune the PI controllers, a combined meta-heuristic algorithm based on particle swarm optimization and whale optimization algorithm (PSO-WOA) is developed and compared with the individual PSO and WOA controllers. From the studied scenarios, the developed combined scheme outperforms the other algorithms in terms of system performance indices. Therefore, the developed PSO-WOA approach is plausible and straightforward to solve many engineering problems as it benefits from the exploitation characteristics of the conventional PSO and the exploration features of the WOA.
关键词： robust control,frequency deviations (Df),virtual generator,mass-less inertia,load frequency control (LFC),whale optimization algorithm (WOA)
更新于2025-09-23 15:19:57
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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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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