Article, 2023

A Self-Data-Driven Method for Lifetime Prediction of PV Arrays Considering the Uncertainty and Volatility

IEEE Transactions on Power Electronics, ISSN 1941-0107, 0885-8993, Volume 39, 3, Pages 3668-3682, 10.1109/tpel.2023.3337713

Contributors

Liu, Yongjie 0000-0003-3125-8760 [1] Ding, Kun 0000-0002-6077-1064 (Corresponding author) [1] Zhang, Jingwei [1] Sangwongwanich, Ariya 0000-0002-2587-0024 [2] Wang, Huai 0000-0002-5404-3140 [2]

Affiliations

  1. [1] Hohai University
    2. [NORA names: China; Asia, East];
  2. [2] Aalborg University
    3. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

This article proposes a self-data-driven method for remaining useful life prediction of PV arrays based on self-condition monitoring data considering the uncertainty and volatility. First, a health indicator reconstruction method is presented to eliminate the uncertainty and volatility of condition monitoring data. Second, a nonlinear Gamma stochastic process model is established to describe the probability distribution of the degradation trend. Then, the model parameter solution is transformed into an optimization problem, and a hybrid particle swarm and gray wolf optimization algorithm is developed to estimate the model parameters avoiding trapping in local optimization and divergence. Finally, two case studies are demonstrated to verify the effectiveness of the proposed method based on the Desert Knowledge Australia Solar Center and NREL datasets, and the performance is further evaluated in comparisons with the empirical models, statistical models, and long short-term memory network. Experimental results demonstrate that the proposed method has excellent RUL prediction accuracy.

Keywords

NREL, NREL dataset, PV array, RUL, RUL prediction accuracy, accuracy, algorithm, array, case study, cases, center, comparison, condition monitoring data, data, dataset, degradation, degradation trend, desert, distribution, divergence, effect, empirical model, experimental results, grey wolf optimization algorithm, health, life, life prediction, lifetime, lifetime prediction, local optimization, memory network, method, model, model parameters, monitoring data, network, optimization, optimization algorithm, optimization problem, parameter solutions, parameters, particle swarm, particles, performance, prediction accuracy, probability, probability distribution, problem, process model, reconstruction method, results, short-term memory network, solar center, solution, statistical model, stochastic process model, study, swarm, trends, uncertainty, volatility, wolf optimization algorithm

Funders

  • National Natural Science Foundation of China
  • Changzhou Science and Technology Bureau

Data Provider: Digital Science

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