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Partial discharge (PD) is the most common sources of insulation failure in power transformers. The most important tools for quality assessment of power transformers are PD detection, measurement, and classification. As for the maintenance and repair of transformers, the major importance is the techniques for locating a PD source. The transfer function-based (TF) method for power transformers' winding in the high-frequency range is commonly used in power engineering applications, such as transient analysis, insulation coordination, and in transformer design. Although it is possible to localize PD in transformer winding using the transfer function (TF) method, this method cannot be used for transformers with no design data. Previous attempts toward finding a feature that localizes PD in transformers in general that lineate with PD location were found to be less successful. Therefore, in this paper, a neuro-fuzzy technique that uses unsupervised pattern recognition was proposed to localize PD source in power transformers. The proposed method was tested on a medium-voltage transformer winding in the laboratory. The results showed a significant improvement in localizing PD for major types of PD compared to currently available techniques, such as orthogonal transforms and the calibration line method.

Energy is vital to social and economic development. Increased energy demand and reduced fossil fuel resources led to use of renewable energy (RE) resources, whose intermittence and high investment cost spur research into optimal sizing of hybrid systems. Advancements in computer hardware and software enable solution of optimization problems through algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), etc. The recently introduced Imperialistic Competition Algorithm (ICA) has shown excellent capability in solving various optimization problems. This paper introduces it and shows its benefits to an optimal-sizing problem of a hybrid RE system. The results will be shown and the effect of changing optimization's parameters will be discussed. To test the potential of proposed algorithm for minimum cost solution finding, a comparison between ICA and PSO algorithm will be provided. Results show the advantage of using ICA algorithm to find a better optimum solution for hybrid power system.

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The uncertainties associated with load and wind power forecasting affect the system operation and planning decision. Ignoring the uncertainties in planning process leads to a high risk. In this paper a novel intelligent method is applied to the problem of sizing in a micro-grid power system for Ganje area in north-west Iran such that the electrical and heat demand of residential area is met. The micro-grid consists of fuel cells, some wind units, some electrolyzers, a reformer, an anaerobic reactor, some hydrogen tanks and also thermal storage tank. The heat requirements of systems consist of water heating load and Space heating load. The heat produced by fuel cells is used to supply heat required of system. When the heat produced by fuel cells is more than demand surplus heat is stored in thermal storage tank. In this study also waste and natural gas are used to produce heat for system. For supplying heating loads in micro grid , produced heat of fuel cell is used at first and then produced heat of waste and at the end the natural gas are used. Particle Swarm Optimization (PSO) algorithm is used for optimal sizing of system's components. The notions of reliability are considered in this microgrid.