Multilevel inverters have gained increasing interest for advanced energy-conversion systems due to their features of high-quality produced waveforms, modularity, transformerless operation, voltage and current scalability, and fault-tolerant operation. However, these merits usually come with the cost of a high number of components. Over the past few years, proposing new multilevel inverters with a lower component count has been one of the most active topics in power electronics. The first aim of this work is to update and summarize the recently developed multilevel topologies with a reduced component count, based on their advantages, disadvantages, construction, and specific applications. Within the framework, both single-phase and three-phase topologies with symmetrical, and asymmetrical operations are taken into consideration via a detailed comparison in terms of the used component count and type. The second objective is to propose a comparative method with novel factors to take component ratings into account. The effectiveness of the proposed method is verified by a comparative study.

Detection and isolation of single and mixed-faults in a gearbox are very important to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning algorithm, consisting of Multilayer Perceptron (MLP)- and Convolutional Neural Network (CNN)-based classifiers, for diagnosis of gearbox mixed faults. Domain knowledge features are required to train the MLP classifier, while the CNN classifier can learn features itself, allowing to reduce the required knowledge features for the counterpart. Vibration data from an experimental setup with gearbox mixed faults is used to validate the effectiveness of the algorithms and compare them with conventional methods. The comparative study shows that accuracies and robustness of the individual MLP- and CNN-algorithms are better than those of the compared methods and can be significantly improved using data fusion at the feature level. Furthermore, the robustness of the algorithm is secured under noises by combining the results of individual classifiers.

Bipolar dc grid systems are fast gaining attraction for renewable energy source integration, because of their merits of higher reliability, efficiency, and robustness as compared to the unipolar dc grids. However, the progress in multiport converters, resulting into lower cost and more compact design for bipolar microgrid systems, is fairly slow. Therefore, this article proposes a novel family of five nonisolated multiport dc–dc converter topologies with bipolar symmetric outputs. The performance and key operational features of the proposed converters under varying input voltages, duty cycles and loads are numerically verified and experimentally on an in-house test setup to prove the concept of the proposed converters. In the experimental validation, the operation of the converter under simultaneous and arbitrary individual power transfer from two input ports is tested. Furthermore, the easy integration of the proposed converters with a multilevel inverter to achieve high-quality ac voltages is demonstrated. As compared to the few existing counterparts, the proposed converters have a competitive edge in terms of higher number of input ports and voltage gains. Alongside the possibility of arbitrary independent power flow from the input ports, inherently symmetrical outputs require a simple balance control for asymmetrical members of the family.

Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine operators have time to take preventive action before a large-scale failure. The usefulness of the algorithm is validated by using a run-to-failure experimental test data.

Temperature rise of the form-wound multi-conductor stator winding of a 1250-kW cage induction motor was analyzed. Eddy currents in the winding significantly affect the temperature rise. The eddy current was modeled using time-discretized finite-element analysis (FEA). The resistive losses in each of the bars obtained from the FEA are used as input for thermal modeling. The distance from the air gap to the topmost bar in the stator has a significant effect on the eddy-current loss as well as temperature rise in the winding. An acceptable distance for winding design was recommended. By using magnetic slot wedges the eddy-current losses can be reduced.

Nowadays, electrical machines and drive systems are playing an essential role in different applications. Eventually, various failures occur in long-term continuous operation. Due to the increased influence of such devices on industry, industrial branches, as well as ordinary human life, condition monitoring and timely fault diagnostics have gained a reasonable importance. In this review article, there are studied different diagnostic techniques that can be used for algorithms’ training and realization of predictive maintenance. Benefits and drawbacks of intelligent diagnostic techniques are highlighted. The most widespread faults of electrical machines are discussed as well as techniques for parameters’ monitoring are introduced.