Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient.

A Self-Adaptive FCM for the Optimal Fuzzy Weighting Exponent

Fuzzy weighting exponent [Formula: see text] is an important parameter of fuzzy [Formula: see text]-means (FCM), closely related to the performance of the algorithm. First, an improved fuzzy correlation degree was put forward to measure the relevance between the clusters, based on which a new cluster validity function was defined to evaluate the quality of the fuzzy partition. Then a self-adaptive FCM for the optimal value of [Formula: see text] was proposed with the aid of the global search ability of improved particle swarm algorithm to find out both the final clustering centroids and the optimal value of fuzzy weighting exponent automatically. The improved particle swarm algorithm updated the speed and the position based on dynamic inertia weight and learning factors, and introduced mutation of genetic algorithm to keep the diversity of the particles, preventing premature convergence. The experimental results showed that the proposed algorithm automatically calculated the optimal value of [Formula: see text] and meanwhile achieved better clustering results.