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This paper presents a new multi objective optimization algorithm with the aim of complete coverage, faster global convergence and higher solution quality. In this technique, the high-speed characteristic of particle swarm optimization (PSO) is combined with non-dominated differential evolutionary (NSDE) and an efficient multi objective optimization algorithm is created. This method posses high convergence characteristic in quite less execution times. Generating fewer populations to find the Pareto front also makes the proposed algorithm use less memory. For the purpose of performance evaluation, the algorithm is verified with four benchmarking functions on its global optimal search ability and compared with two recognized algorithm to assess its diversity. The capability of the suggested algorithm in solving practical engineering problems such as power system protection is also studied and the results are discussed in detail.

Among the most noticeable root causes of improper performance in power transformers, internal short circuit faults can be noted and if not quickly be identified and addressed in the accepted time interval, irrecoverable damages such as interruption or even collapse of the network connected to the power transformer would happen. In this contribution, three-phase transformer behaviors under magnetizing inrush, internal short circuit condition and their current values determination have been surveyed using electromagnetic coupling model approach and structural finite element method. Utilizing the definition of transformer in the form of multi-coil and their electromagnetic and electric couple, a three dimensional geometric model of transformer is developed which includes nonlinear characteristics of the transformer, different states of normal and under internal short circuit occurrence and the moment of magnetizing inrush creation are investigated. The comparison between obtained results of presented model simulation with the consequences of practical studies on a typical three phase transformer reveals that the proposed model has a reliable accuracy in detection and modelling the transformer behavior in normal conditions, magnetizing inrush and different types of internal faults. The proposed approach represents an accurate model of a three-phase transformer for protection aims.

A new distributed generation placement method based on biogeography-based optimization (BBO) is investigated in this paper. A significant novelty of this study lies in considering fuzzy load uncertainty. For this purpose a fuzzy backward- forward sweep load flow is proposed. The main objectives of this study is minimizing power losses and improving voltage profile. A comparative study between optimal location and sizing under typical load condition and fuzzy load uncertainty is presented. To verify the efficiency of proposed BBO method, it is conducted on IEEE 33 bus distribution system, also a comparative study between proposed BBO approach and particle swarm optimization (PSO), Technical-learning based optimization (TLBO), Artificial bee colony (ABC), Imperialist competitive algorithm (ICA) is investigated. The simulation results show the excellent and superior performance of proposed BBO approach in comparison with the other intelligent methods.

Among the most noticeable root causes of improper performance in power transformers

Abstract

The duty of protective systems is the timely detection of fault and removing it from the power network. The accuracy of the results and reducing the execution time of the optimizing algorithm are two crucial elements in selecting optimizing algorithms in protective functions. The most important protective elements that are used in power networks are distance and overcurrent relays. In this article, a new algorithm is presented to solve the optimization problem of coordination of overcurrent and distance relays by using Cuckoo Optimization Algorithm which considers the non-linear model overcurrent relays at all stages of setting. The proposed method is tested on a standard 8-bus power system network. Also the results obtained have been compared with other evolutionary algorithms. The results show that the proposed approach can be provide more effective and practical solutions to minimize the time function of the relays and achieving optimal coordination in comparison with previous studies on optimal coordination of overcurrent and distance relays in power system networks.