Detection of emerging faults in power transformers using self-organising maps

Power transformers are a crucial part of the power system, one of the largest infrastructures in industrialised countries. In particular, wind turbine transformers are subjected to frequent thermal cycling as a function of varying turbine loads. Thus transformers are prone to developing faults and defects that can involve high repair costs for instance due to the repeated thermal stress on the winding. Faults develop mainly when the insulation produces small leakage currents between turns, which if not detected early, might become short circuits that can result in interruptions in electricity supply, and difficult and costly repairs. An optimum overhaul of damaged transformers is not accomplished often because of lack of appropriate inspection tools. Detailed assessment and preventive maintenance work, which will allow the detection and repair of failures at early stages, is believed to be the only suitable way to cope with power transformer degradation at low cost. This paper presents a methodology based on the analysis of current signals converted by the S transform for the detection of incipient faults in transformers. The procedure is based on calculating the energy of the zones of the time-frequency spectrum. Its main advantage is its possible real time implementation that can be applied while the transformer is in use. Experimental results with PSCAD are presented.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio