This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

This paper investigates the issues of ensuring global power optimization for cascaded dc-dc converter architectures of photovoltaic (PV) generators irrespective of the irradiance conditions. The global optimum of such connections of PV modules is generally equivalent with performing the maximum power point tracking (MPPT) on all the modules. The most important disturbance occurs when the irradiance levels of modules happen to be sensibly different from a module to another - in this case, voltage-limitation requirements may be broken. The proposed supervisory algorithm then attempts to establish the best suboptimal power regime. Validation has been achieved by MATLAB/Simulink numerical simulation in the case of a single-phase grid-connected PV system, where individual MPPTs have been implemented by an extremum-seeking control, a robust and less-knowledge-demanding perturb-and-observe method.

This paper deals with distribution network (DN) reconfiguration for loss minimization. To solve this combinatorial problem, a genetic algorithm (GA) is considered. In order to enhance its ability to explore the solution space, efficient genetic operators are developed. After a survey of the existing DN topology description methods, a theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered. These concepts are used in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem. All resulting individuals after GA operators are claimed to be feasible (radial) configurations. Moreover, the presented approach is valid for planar or nonplanar DN graph topologies and avoids tedious mesh checks for the topology constraint validation. The proposed method is finally compared to some previous topology coding techniques used by other authors. The results show smaller or at least equal power losses with considerably less computation effort.

This paper studies a photovoltaic (PV)-module-embedded power-electronics topology derived from a battery equalizer. The proposed topology eliminates the multiple maximum power point peaks common to partial shading in PV modules. The topology does so by equalizing the overall energy of the PV module through the use of only one inductive storage element. A theoretical study is carried out to describe the physical equations of the topology. Based on it, a prototype is designed and built. It is tested with a partially shaded PV module, raising its power output by nearly 40% at best. The results are compared to similar topologies dedicated to mitigate partial shading in PV applications.