On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks.

We describe initial attempts to model the dynamic thermal behavior of electrical machines by evaluating the ability of linear and non-linear (regression) modeling techniques to replicate the performance of simulations carried out using a lumped parameter thermal network (LPTN) and two different test scenarios. Our focus falls on creating highly accurate simple models that are well-suited for the real-time computational demands of an envisioned symbiotic interaction paradigm. Preliminary results are quite encouraging and highlight the very positive impact of integrating synthetic features based on exponential moving averages

Approaches for Improving Lumped Parameter Thermal Networks for Outer Rotor SPM Machines

This work is about the transient modeling of the thermal characteristics of outer rotor SPM machines by considering a lumped parameter thermal network based approach. The machine considered here poses particular challenges for the modeling, e.g., due to the semi-closed stator surrounded by a rotor bell that provides a speed-dependent cooling of the stator coils. Starting from a simpler basic network configuration, model extensions and refinements are presented and discussed. The subsequent parameter identification is done by means of an initial design of experiments based sampling, and a subsequent single-objective and also a multi-objective optimization of error functions for the components' temperatures. Analyzing the therefrom derived Pareto fronts and the consequent tradeoff regarding achievable minimum modeling errors for different system's components gives insights into where and how the modeling can be further improved. All the investigations are based on experimental results obtained through operating a particularly developed test setup

Measurement-Based Identification of Lumped Parameter Thermal Networks for sub-Kw Outer Rotor PM Machines

This work is on deriving precise lumped parameter thermal networks for modeling the transient thermal characteristics of electric machines under variable load conditions. The goal is to facilitate an accurate estimation of the temperatures of critical machines' components and to allow for running the derived model in real time to adapt the motor control based on the load history and maximum permissible temperatures. Consequently, the machine's capabilities can be exhausted at best considering a highly-utilized drive. The model shall be as simple as possible without sacrificing the exactness of the predicted temperatures. Accordingly, a specific lumped parameter thermal network topology was selected and its characteristics are explained in detail. The measurement data based optimization of its critical parameters through an evolutionary optimization strategy, and the therefore utilized experimental setup will be described in detail here. Measurement cycles were recorded for modeling and verification purposes including both static and dynamic test cycles with changing load torque and speed requirements. Applying the proposed hybrid approach for determining the model's parameters through involving physics-based equations as well as numerical optimization followed a significant improvement of the preciseness of the predicted motor temperatures compared to solely determining the networks's coefficients based on expert knowledge. Thereby, the validation included both the original measurement data as well as extra measurement runs. The proposed and applied strategy provides an excellent basis for future thermal modeling of electric machines

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices

A Thermographic Method to Evaluate Different Processes and Assembly Effects on Magnetic Steels

Ferromagnetic materials may be affected by the presence of local losses due to defects or magnetic anomalies caused by machining processes. To highlight such anomalies is not easy; a noninvasive thermographic method has been refined to allow a proper comparison of different machining processes' impact on the iron losses. Specimens obtained with punching, wire erosion, and laser cut have been analyzed using a high-speed IR camera when subjected to alternate magnetization at different frequencies. Also, the same technique has been adopted to the assembled stacks to investigate more simultaneous phenomena. The possibility to point out localized anomalies should be exploited to foresee and avoid electrical machines core faults

Robust Design Optimization of Electrical Machines: Multi-Objective Approach

This article presents a new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques. First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization. Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30%. A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems. To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines. Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost. For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach. The proposed method can be applied to many-objective (robust) design optimization of electrical machines

A Thermographic Method to Evaluate Different Processes Effects on Magnetic Steels

Ferromagnetic materials may be affected by the presence of local losses due to defects or magnetic anomalies caused by machining processes. To highlight such anomalies is absolutely not easy; a non invasive thermographic method has been refined to allow a proper comparison of different machining processes impact on the iron losses. Specimens obtained with punching, wire erosion and laser cut have been analyzed by means of a high speed IR camera when subjected to alternate magnetization at different frequencies. The possibility to point out localized anomalies should be exploited to foresee and avoid electrical machines core faults

Multi-Objective Optimization of Medium-Scale Wound-Field Electric Generators

This work deals with the optimization of electric generators for multiple performance measures, e.g., the simultaneous maximization of the fundamental harmonic and the minimization of the total harmonic distortion of the Back-EMF. For a given slot/pole-configuration and pole span, the goal is to find the optimal rotor outer contour along the air gap. Different approaches for modeling the rotor contour are followed here, i.e., a Fourier-series based approach and a piecewise discretization of the circumference. The work is about performing optimizations for both approaches and to compare the achieved results (i) when considering similar computational effort, (ii) regarding modeling complexity versus design flexibility, and (iii) the possibility for acquiring additional information, as for instance the sensitivity of the contour regarding manufacturing tolerances

Robust Design Optimization of Electrical Machines: A Comparative Study and Space Reduction Strategy

This article presents a comparative study on different types of robust design optimization methods for electrical machines. Three robust design approaches, Taguchi parameter design, worst-case design and design for six-sigma, are compared for low-dimensional and high-dimensional design optimization scenarios, respectively. For the high-dimensional scenario, the computational burden is normally massive due to the robustness evaluation of a huge number of design candidates. To attempt this challenge, as the second aim of this paper, a space reduction optimization (SRO) strategy is proposed for these robust design approaches, yielding three new robust optimization methods. To illustrate and compare the performance of different robust design optimization methods, a permanent magnet motor with soft magnetic composite cores is investigated with the consideration of material diversities and manufacturing tolerances. 3-D finite element model and thermal network model are employed in the optimization process and the accuracy of both models has been verified by experimental results. Based on the theoretical analysis and optimization results, a detailed comparison is provided for all investigated and proposed robust design optimization methods in terms of different aspects. It shows that the proposed SRO strategy can greatly improve the design optimization effectiveness and efficiency of those three conventional robust design methods