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3D surface compression is a selected method for reduction of required memory and effective triangular mesh data transfer in networks with low bandwidth. In this paper, an improved technique for compression of triangular surfaces is introduced. It is based on centroidal Voronoi tessellation with density function. By choosing appropriate density function with curvature feature and Lloyd relaxation, vertex density in compressed mesh tends toward details in rough surfaces and prevents vertex redundancy and non-necessary vertex concentration in smoother surface areas. In post-processing step, non-linear Nelder-Mead optimization is applied for better vertex localization and reducing compression error in the simplified mesh. Our proposed method is compared with classic and modern techniques in recent studies. Implementation results show improvements in compression and accuracy of our method compared with available techniques.

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Robust algorithm is known as the one of high potential models for strengthening in optimization of large and complex distribution networks considering uncertainty. In this research we are looking for some suitable template and appropriate clustering pattern in order to analyze the operation of distribution network. The analysis of optimization process in both individual and adaptive cases of distributed generation sources applied to some standard distribution network shows that the adaptive case of DGs determines less partial and total operation cost. except in islanded case of DGs. The results of clustering approach based on choosing the candidate points which includes DGs and feeders are used in medium voltage (MV) network level. By investigating the software data in both single cluster with first up to fifth and tenth repeatition, one to five clusters and finally ten clusters case shows the superiority of mean data besed clustering. because of having the three following properties such as being sensitive to all weighted candidate points, the speed of acceptable operation and feeding the clusters in islanded case in multiplicity of clusters, it is suitable to designate the robust optimization method using pattern random clustering.

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Extracting maximum power, especially with partial shading conditions, is one of the most critical issues in using a photovoltaic system. Under partial shading conditions, the power-voltage characteristic of photovoltaic arrays has several local maximum points. A maximum power point tracking method for photovoltaic systems should enable fast and accurate tracking of the global maximum during partial shading conditions to minimize power losses and steady-state fluctuations. This research presents an algorithm for tracking the maximum power point in a photovoltaic system under partial shading conditions using the gray wolf optimization technique. The gray wolf algorithm is a new optimization method that overcomes limitations such as poor tracking, steady-state fluctuations, and undesirable transients in perturb and observe and particle swarm optimization techniques. The proposed algorithm based on the gray wolf optimization algorithm is implemented on a photovoltaic system in MATLAB software to prove its efficiency. The performance of the proposed design is compared with two maximum power point tracking techniques based on cuckoo search and particle swarm optimization. The simulation results show that the performance of the proposed maximum power point tracking technique is superior to the compared designs in terms of speed and steady-state stability of the response, so that it reduces the values of maximum overshoot, settling time, and sustained fluctuations up to 40.91%, 66.67% and 59.1% respectively.