This paper proposes a topology of flux-switching machine with dc excitation to improve the field-weakening capability of flux-switching machines. This dc-excited flux-switching machine preserves the structure of typical flux-switching permanent-magnet machines while replacing the permanent magnets with dc field windings. The combined advantages of this type of machine in torque production and field weakening are exhibited and validated by finite element analysis.

This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.

The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.

Weight limitations in electric/hybrid cars demand the highest possible power-to-weight ratio from the traction motor, as in double salient permanent magnet (PM) machines. Their high flux densities in the air gap result in nonlinear analytical models, which need to be time optimized. The incorporated reluctance networks are sensitive to the correctness of air gap permeances. Conventionally, in these networks, air gap permeances are calculated by approximating flux paths; however, it is time inefficient. For an improved simulation time, spatial discretization techniques are presented to calculate air gap permeances in double salient PM machines. The spatial techniques discussed here cover the tooth contour method and Schwarz-Christoffel (SC) mapping as semidiscrete methods, which are used to discretize only the air gap region. Their results are verified by the spatial discrete method and finite element method, which discretizes the whole machine geometry. For consistency in this paper, all methods are explained on a three-phase 12/10 flux-switching PM motor. Obtained air gap permeances show a very good agreement with only 0.8% difference. Further on, machine characteristics such as phase flux linkage and cogging torque are also compared to show the impact of the modeling techniques. The total machine simulation time is improved by 20% using the SC method. Although methods are explained particularly for double salient PM machines, formulas are generalizable for other machine types as well.

Based on investigation of the reluctance network method, this paper proposes a methodology for fast adapting, and solving the global reluctance network of flux-switching machine. In this methodology, magnetic equivalent circuit (MEC) models of different parts of flux switching machines (FSM) are defined and adapted in synchronism with the rotor position. Using the incidence matrix method, a variable global reluctance network can be easily assembled the adaptive MEC modules. This methodology is validated finite element analysis.