The whale optimization algorithm is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum (...) and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model, double diode model, and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules. (shrink)

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- optimized kernel extreme learning machine model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions using the complete (...) ensemble empirical mode decomposition based on adaptive noise. Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings. (shrink)