This paper presents current research trends and prospects of utilizing additive manufacturing (AM) techniques to manufacture electrical machines. Modern-day machine applications require extraordinary performance parameters such as high power-density, integrated functionalities, improved thermal, mechanical & electromagnetic properties. AM offers a higher degree of design flexibility to achieve these performance parameters, which is impossible to realize through conventional manufacturing techniques. AM has a lot to offer in every aspect of machine fabrication, such that from size/weight reduction to the realization of complex geometric designs. However, some practical limitations of existing AM techniques restrict their utilization in large scale production industry. The introduction of three-dimensional asymmetry in machine design is an aspect that can be exploited most with the prevalent level of research in AM. In order to take one step further towards the enablement of large-scale production of AM-built electrical machines, this paper also discusses some machine types which can best utilize existing developments in the field of AM.

This paper reviews recent progress in fault detection, reliability analysis, and predictive maintenance methods for grid-connected solar photovoltaic (PV) systems. With the rising adoption of solar power globally, maintaining system reliability and performance is vital for a sustainable energy supply. Common faults discussed include panel degradation, electrical issues, inverter failures, and grid disturbances, all of which affect system efficiency and safety. While traditional diagnostics like thermal imaging and V-I curve analysis offer valuable insights, they mostly detect issues reactively. New approaches using Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) enable real-time monitoring and predictive diagnostics, significantly enhancing accuracy and reliability. This study represents the introduction of a consolidated decision framework and taxonomy that systematically integrates and evaluates the fault types, symptoms, signals, diagnostics, and field-readiness across both plant types and voltage levels. Moreover, this study provides quantitative benchmarks of performance metrics, energy losses, and diagnostic accuracies of 95% confidence intervals. Adopting these advanced techniques promotes proactive management, reducing operational risks and downtime, thus reinforcing the resilience and sustainability of solar power infrastructure.

Over the past decade, additive manufacturing (AM) of functional electromagnetic components has grown into a promising new area of research. Not only does AM allow for unparalleled in-house prototyping speed and flexibility, but it also introduces a completely new set of design rules for obtaining optimized component shapes. Up to date, relatively few functional electromagnetic components or devices have actually been printed, and fewer still actually characterized or compared in terms of performance. This work describes the completion process of a prototype induction motor with fully 3D printed electrical steel cores, from design to printing, post-processing, assembling, and performance evaluation. This work aims to establish a baseline for further optimization and to map the design and production process of a fully functional electrical machine for future reference. The output of the finished motor was measured at 68 W (0.5 Nm) at 34% energy efficiency. This is roughly 2/3 of the efficiency of conventional machines of the same size and type. Further optimization steps for the 3D printed material and core design are proposed to obtain higher motor performance.

This paper presents an analytical modelling technique based on modified winding function and co‐energy analysis, for characterisation of synchronous reluctance machines, in conjunction with a hybrid algorithm for mapping of the machine's operating point on BH curve and the corresponding performance parameters in non‐linear magnetic condition. The model successfully provides the mean torque and the torque profiles of a synchronous reluctance motor as a function of input current and load‐angle in the magnetic linear and non‐linear conditions. The obtained results of this proposed model are compared with finite‐element‐analysis (FEA) based commercial design software Simcentre‐MAGNET. The comparison proves this model's capability to estimate the machine's performance parameters with considerable accuracy from an already established design analysis technique, FEA. The proposed modelling technique requires only a fraction of simulation runtime when compared to other design analysis techniques. Considering the accuracy of this modelling technique and the least requirement of computation resources and simulation time, this model can be used as an initial iterative‐design tool for mass personalised, additively manufactured electrical machines where rapidness of the iterative design analysis tool is of paramount importance.

Additive manufacturing (AM) enables the creation of parts with exceptional geometric versatility, creating new possibilities for developing non-symmetrical electrical machines (EM) with compact structures, reduced mass density, high torque, better power density, and minimal material waste during fabrication. This review highlights the current state of AM methods and their critical role in the production of EM. The impact of the additively built EM components concerning performance indices, including torque, power density, and efficiency is highlighted. Additionally, an overview of the constraints associated with the traditional production process of EM-specific components and the role of AM in addressing those limitations is emphasized. As per the current state of AM in the context of EM production, the non-conventional structures of the stator and rotor core of EMs are feasible to fabricate to accomplish improved utilization of magnetic flux. Moreover, AM enables the fabrication of windings/coils of any profiles with integrated functionality, including the design of thermal management mechanisms to enhance EM performance and achieve thermally controlled EM. However, the current state of AM technology is not very advanced and requires additional improvement, particularly in areas of EM production, which are minimizing eddy current losses, high-quality surface refinement, build volume restrictions, and simultaneous multi-materials processing.

This paper introduces a mesh-based magnetic equivalent circuit (MEC) modeling technique for induction machines (IMs) in healthy and broken rotor bars conditions. The MEC model is presented as a highly accurate and computationally efficient alternative to finite element (FE) models. By incorporating modifications to the air gap coupling method, including a new Lagrange interpolation function, and utilizing a harmonic MEC model, the accuracy of the solution is improved while reducing electrical and mechanical transients. Compared to experiments and 2D FE models, this model achieves precise results for electromagnetic torque, rotational speed, and forces across various conditions. The Lagrange interpolation function forms the basis for the air gap coupling between stator and rotor flux densities. The results demonstrate the MEC model’s exceptional accuracy in predicting speed oscillations, calculating forces, and analyzing current harmonics in faulty IMs. Furthermore, the MEC model performs over 30 times faster than the 2D FE models.