This paper proposes a novel and efficient algorithm to obtain the optimal power flow in power system operation and planning phases by solving a multiobjective optimization problem. In deciding the optimal system operation, various objectives, such as economy, reliability and minimum influence on environments, should simultaneously be attained. However, these objectives are contradictory to each other and are in trade-off relations, thus making it difficult to handle this class of problem by conventional approaches which optimizes a single performance index.the optimal load flow problem is first formulated as a multi-objective optimization problem. €-constrained technique is used to obtain a set of non-inferiority solutions. Then, we introduce the idea of a preference index to decide the optimum solution. In this particular study, security index is chosen as the preference index.The proposed algorithm has made it possible to treat the optimal dispatch problems with multiple performance indices and to grasp trade-off relations between selected indices. The effect of uncertain factors pertaining to power systems can also be taken into account.The validity and effectiveness of the proposed approach is verified through numerical examples on the IEEE 30 node, 6 generator system.In the proposed algorithm,

No abstract

Absorption coefficients of colored dissolved organic matter (CDOM) [ag(λ)] were measured and relationship with salinity was derived in the East China Sea (ECS) during summer when amount of the Changjiang River discharge is large. Low salinity Changjiang Diluted Water (CDW) was observed widely in the shelf region and was considered to be the main origin of CDOM, resulting in a strong relationship between salinity and ag(λ). Error of satellite ag(λ) estimated by the present ocean color algorithm could be corrected by satellite‐retrieved chlorophyll data. Satellite‐retrieved salinity could be predicted with about ±1.0 accuracy from satellite ag(λ) and the relation between salinity and ag(λ). Our study suggests that satellite‐derived ag(λ) can be an indicator of the low salinity CDW during summer.

Mean shift clustering finds the modes of the data probability density by identifying the zero points of the density gradient. Since it does not require to fix the number of clusters in advance, the mean shift has been a popular clustering algorithm in various application fields. A typical implementation of the mean shift is to first estimate the density by kernel density estimation and then compute its gradient. However, since good density estimation does not necessarily imply accurate estimation of the density gradient, such an indirect two-step approach is not reliable. In this paper, we propose a method to directly estimate the gradient of the logdensity without going through density estimation. The proposed method gives the global solution analytically and thus is computationally efficient. We then develop a mean-shift-like fixed-point algorithm to find the modes of the density for clustering. As in the mean shift, one does not need to set the number of clusters in advance. We empirically show that the proposed clustering method works much better than the mean shift especially for high-dimensional data. Experimental results further indicate that the proposed method outperforms existing clustering methods.