Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: a particle swarm optimization technique

 Partial shading is one of the unavoidable complications in the field of solar power generation. Although the most common approach in increasing a photovoltaic (PV) array’s efficiency has always been to introduce a bypass diode to the said array, this poses another problem in the form of multi-peaks curves whenever the modules are partially shaded. To further complicate matters, most conventional Maximum Power Point Tracking methods develop errors under certain circumstances (for example, they detect the local Maximum Power Point (MPP) instead of the global MPP) and reduce the efficiency of PV systems even further. Presently, much research has been undertaken to improve upon them. This study aims to employ an evolutionary algorithm technique, also known as particle swarm optimization, in MPP detection. VC 2014 Author(s).

Lingo: linearized grassmannian optimization for nuclear norm minimization

As a popular heuristic to the matrix rank minimization problem, nuclear norm minimization attracts intensive research attentions. Matrix factorization based algorithms can reduce the expensive computation cost of SVD for nuclear norm minimization. However, most matrix factorization based algorithms fail to provide the theoretical guarantee for convergence caused by their non-unique factorizations. This paper proposes an efficient and accurate Linearized Grass-mannian Optimization (Lingo) algorithm, which adopts matrix factorization and Grassmann manifold structure to alternatively minimize the subproblems. More specially, linearization strategy makes the auxiliary variables unnecessary and guarantees the close-form solution for low periteration complexity. Lingo then converts linearized objective function into a nuclear norm minimization over Grass-mannian manifold, which could remedy the non-unique of solution for the low-rank matrix factorization. Extensive comparison experiments demonstrate the accuracy and efficiency of Lingo algorithm. The global convergence of Lingo is guaranteed with theoretical proof, which also verifies the effectiveness of Lingo.

Cluster analysis and optimization in color-based clustering for image abstract

Cluster analysis has been identified as a core task in data mining. What constitutes a cluster, or a good clustering, may depend on the background of researchers and applications. This paper proposes two optimization criteria of abstract degree and fidelity in the field of image abstract. To satisfy the fidelity criteria, a novel clustering algorithm named Global Optimized Color-based DBSCAN Clustering (GOC-DBSCAN) is provided. Also, non-optimized local color information based version of GOC-DBSCAN, called HSV-DBSCAN, is given. Both of them are based on HSV color space. Clusters of GOC-DBSCAN are analyzed to find the factors that impact on the performance of both abstract degree and fidelity. Examples show generally the greater the abstract degree is, the less is the fidelity. It also shows GOC-DBSCAN outperforms HSV-DBSCAN when they are evaluated by the two optimization criteria.

A simulated annealing global maximum power point tracking approach for PV modules under partial shading conditions

This paper proposes a simulated annealing (SA)-based global maximum power point tracking (GMPPT) technique designed for photovoltaic (PV) systems which experience partial shading conditions (PSC). The proposed technique is compared with the common perturb and observe MPPT technique and the particle swarm optimization method for GMPPT. The performance is assessed by considering the time taken to converge and the number of sample cases where the technique converges to the GMPP. Simulation results indicate the improved performance of the SA-based GMPPT algorithm, with arbitrarily selected parameters, in tracking to the global maxima in a multiple module PV system which experiences PSC. Experimental validation of the technique is presented based on PV modules that experience nonuniform environmental conditions. Additionally, studies regarding the influence of the key parameters of the SA-based algorithm are described. Simulation and experimental results verify the effectiveness of the proposed GMPPT method.