Fuzzy based DG allocation for Loss Minimization in a Radial Distribution System

Abstract

Due to the restructuring in electricity market and environmental concerns penetration level of DG unit has been increased rapidly. It is also playing a significant role in minimization of line losses of a power system network. So it is very important to define the size and location of distributed generation unit to be allocated in a power system network. On the other hand due to radial distribution systems basic inherent features such as radial structure, a wide range of ⁄ ratios, and a large number of nodes; the optimal sizing and siting problem of a DG unit cannot be determined by the conventional techniques, which are used for transmission systems. A novel method for the estimation as well as minimization of losses in a radial distribution system with DG unit has been presented in this paper. It also improves the voltage profile of the existing power systems and in addition a combined and optimal power factor is also calculated. An algorithm is presented in this paper to obtain the optimum position of DG units in the distribution network based on the available amount of DG using Fuzzy logic. Forward backward sweep method makes the load flow solution faster. Test results on a 33-bus system reveal that the superiority and simplicity of the proposed algorithm.

Coal Spontaneous Combustion Temperature Prediction Based on Fuzzy Combined Kernel Relevance Vector Machine

“…On the other hand, in the process of machine learning model construction, it is generally considered that each sample point has the same contribution to the model, but the actual situation is not the case. When there is noise or outliers in the data, it may have a serious impact on the model construction and prediction, especially for sparse machine learning methods such as SVM and RVM, they only rely on a small part of data, and they may be very sensitive to noise or outliers [21]. e main methods of data processing based on fuzzy theory are as follows: first, define the upper and lower bounds of fuzzy membership, then select the appropriate calculation method of fuzzy membership according to the main attributes of the data set, finally assign each fuzzy membership to its corresponding sample points, and construct a machine learning model to make each sample point have different contribution to the model.…”

Fuzzy Logic Controller Based Distributed Generation Integration Strategy for Stochastic Performance Improvement

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