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Refereed/Peer-reviewed This paper proposes two structures of dual-stator permanent-magnet vernier machines (VMs) for high-torque low-speed applications. The proposed structures consist of dual-sided rotor which is sandwiched by inner and outer stators. These topologies include 22 and 46 consequent-pole magnets in the rotor and 24 and 48 stator slots for Design A and Design B, respectively. Design A is an improved structure of dual stator VM and Design B is a novel topology of a dual stator VM that can deliver considerably higher back-EMF and torque than Design A. The only drawback in Design B is lower power factor. The characteristics and performance parameters of the proposed VMs are evaluated using FEA. Sensitivity analysis is used to figure out the optimum dimensions of the magnets. In fact, two combinations of slot/pole number of the vernier machines are investigated as Design A and Design B. The capabilities of the proposed vernier machines are presented in a fair comparison with other VMs namely as consequent pole dual rotor VM (CPDRVM), consequent pole dual stator VM (CPDSVM) and dual stator spoke array VM (DSSAVM). In this regard, the key machine features considered for comparison are back-EMF, torque, torque per magnet volume (TPMV), power factor, loss, and efficiency profiles. The proposed structure (Design B) generates higher torque and TPMV compared to the other studied machines while Design A can achieve a higher power factor.

The bond-graph (BG) enables to model multi-physics systems in the energy domains. This paper shows that BG models are an alternative, more intuitive and simpler to interpret, graphical tool as compared to existing dynamic models for performance prediction of the line-start permanent-magnet synchronous motors (LSPMSMs). It includes electrical, magnetic, and mechanical energy domains where the stator windings, rotor bars, and permanent magnets are the main components of the model. The dynamical equations of the proposed BG are extracted and solved using the MATLAB ordinary differential equation solver for a sample LSPMSM. The dynamics of the studied LSPMSM is investigated under two operating conditions (1) a healthy motor and (2) a motor with broken rotor bars. The simulation results are compared with the finite-element analysis (FEA) as the most accurate method to simulate the electric motor behaviour. It is shown that the proposed BG model accurately predicts the performance parameters of the motor such as current, torque, and speed under various operating conditions. In addition to the modelling validation, the transient behavior of the LSPMSM are discussed to describe the possible reasons for its behaviour during transient and steady-state operating conditions.

This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton-Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered. Refereed/Peer-reviewed