A novel hybrid algorithm based on rat swarm optimization and pattern search for parameter extraction of solar photovoltaic models

Abstract

Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed.

Tourist Attraction Recommendation Method and Data Management Based on Big Data Analysis

“…e tourist industry is now thriving, and the government is increasingly appreciating its contribution to economic growth and job creation. Many challenges in the growth of tourism can be solved with the advancement of computer arti cial intelligence technologies [2][3][4][5][6][7]. When planning a trip, people have a lot of choices to make, including where they want to go, how long they want to spend there, and what form of transportation they want to use.…”

“…Researchers and people in charge of making business decisions are becoming increasingly interested in big data. To name a few, the tourism sector has shown a keen interest in topics like tourist destination (TD) tactical thinking, tourism management, relationship management, and even destination marketing [5][6][7]14]. In despite the fact that digital networks have indeed been acknowledged as a helpful and dependable resource of information for travelers [8], it is still in its infancy in the analysis of large data created specifically via social media, especially in the domain of tourism management.…”

A Hybrid-Strategy-Improved Dragonfly Algorithm for the Parameter Identification of an SDM

“…There are two articles [71,72] applying the improved SSA and ARSO to the parameter identification of the SDM, DDM, and Photowatt-PWP201 modules. Using the DDM as an example, the RMSEs of the improved methods in these two publications can reach 9.83 × 10 −4 and 9.82 × 10 −4 , respectively, while the improved method in this paper can reach 3.20 × 10 −3 and the standard DA reaches 4.37 × 10 −3 .…”

Optimizing transmission line parameter estimation with hybrid evolutionary techniques

“…Given these limitations, there is a need to employ straightforward concepts and easily implementable optimization techniques that do not require gradient definitions. Benchmark methods such as Salp Swarm and Sine Cosine Optimization have been verified in different power system applications, including power flow analysis [21,22], parameter estimation [23,24], parameter extraction of solar photovoltaic models using rat swarm optimization [25], power system stabilizer [26], stability improvement [27], and optimization of retaining walls [28,29]. Popular optimization techniques such as Whale Optimization Algorithm (WOA) [30], Grey Wolf Optimization (GWO) [31], Student Psychology-Based Optimization (SPBO) [32], Symbiotic Organisms Search (SOS) [33], and Firefly algorithm (FFO) [34] have been introduced, offering alternative approaches to address these challenges.…”