Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models

In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e.; (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM-RF ensemble (or FMM-RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM-RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM-RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM-RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM-RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments. © 2014 Elsevier Ltd. All rights reserved.

Analytical assessment of optimum magnet pole-arc for PMSM under influence of different magnetization patterns

Permanent magnet synchronous machines (PMSMs) are popular in both industrial and domestic applications because of its high efficiency, power density, and reliability as compared with the conventional types of electrical machines. Generally, the analytical models and their field solutions are preferable to provide an accurate insight of the PMSM performances, instead of using the finite element models, because the former takes a considerably shorter computational time. PMSM design could have different properties of either slotted or slotless, or varieties of magnet placement on the rotor. By focusing on semi-closed surface-mounted PMSMs, the 2D analytical subdomain model in [1] demonstrates an accurate prediction of the magnetic fields that can facilitate the evaluation of the global quantities of PMSMs, such as cogging torque (Tcog), back-EMF, and total harmonic distortion (THDv). Previously, researchers investigated the influences of the machine performance by a single factor, e.g., the variation of Tcog during changes of magnet pole-arc (αP) [2, 3], or slot-opening [2, 3]. These investigations normally considered two types of magnetization patterns, i.e., parallel (PaM) and radial magnetization (RM). Therefore, the motivation of our work hinges on predicting the optimum value of αP in designing a surface-mounted PMSM under influence of four different magnetization patterns, using the analytical subdomain model.

Design of electrical infrastructure at container terminal and 'net metering'

Net metering is generally a consumer-based incentive for renewable sources such as wind or solar power systems also referred to as dasiacogenerationdasia. It is still a grey area for container terminals with large electric machines, such as quay cranes, automatic stacking cranes, that can operate in the regenerative mode and export electric energy to the grid. With actual measured electrical data presented for discussion, this paper provides information for the readers to provide a better understanding of their access to net metering, ultilizing their electrical equipment capabilities and be informed for their next negotiation with the power supply company.