Improving the reliability of photovoltaic and wind power storage systems using least squares support vector machine optimized by improved chicken swarm algorithm

• Main Authors: Liu, Zhi Feng, Li, Ling Ling, Tseng, Ming Lang, Tan, Raymond Girard R., Aviso, Kathleen B.
• Format: text
• Published: Animo Repository 2019
• Subjects: Storage batteries; Photovoltaic power systems; Lithium ion batteries; Chemical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2598
• Institution: De La Salle University

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. © 2019 by the authors.