Localization of the underwater magnetic sensor arrays plays a pivotal role in the magnetic silencing facility. A localization approach is proposed for underwater sensors based on the optimization of magnetic field gradients in the inverse problem of localization. In the localization system, a solenoid coil carrying direct current serves as the magnetic source. By measuring the magnetic field generated by the magnetic source in different positions, an objective function is established. The position vector of the sensor is determined by a novel multi-swarm particle swarm optimization with dynamic learning strategy. Without the optimization of the magnetic source’s positions, the sensors’ positions, especially in the z-axis direction, struggle to meet the requested localization. A strategy is proposed to optimize the positions of the magnetic source based on magnetic field gradients in the three directions of x, y and z axes. Compared with the former method, the model experiments show that the proposed method could achieve a 10 cm location error for the position type 2 sensor and meet the request of localization.
Due to its simple construction, the linear induction motor (LIM) provides a linear driving force without any intermediate motion translation system. LIMs are widely used in various industrial applications, including maglev rail transit and the national defense industry. However, LIMs are affected by the end effect and suffer from problems such as low efficiencies and low power factors. To make improvements, in this paper, an ensemble multi‐objective optimal design method for a short primary double‐sided linear induction motor (SP‐DLIM) is proposed. First, a simplified Quasi‐3D equivalent circuit model (ECM) for an SP‐DLIM applicable to the model in this paper is derived. The 3‐D transient finite element method and an experimental prototype are utilised to prove that the derived ECM is accurate enough to solve the SP‐DLIM optimisation problem. Second, an ensemble multi‐objective optimal design method of SP‐DLIM is presented, with proposed design constraints and four different optimisation problems. Then, an improved differential evolutionary (IDE) algorithm is proposed to optimise the efficiency, power factor, and tooth weight of the motor. The three‐dimensional time‐stepping finite element method is utilised to verify the validity of the optimisation method. Further, a comparison of the results suggests that the IDE yields the best performance to those of other advanced heuristic algorithms.
Linear induction motors (LIMs) have been widely used in rail transit. However, Due to the breaking of the primary core and the large air gap, the efficiency and power factor of LIMs are seriously damaged, causing a large amount of energy waste. To improve the efficiency and power factor of LIMs for urban rail transit, we present a new optimization method for the design of a short primary double-sided linear induction motor (SP-DLIM) with a rated speed of 45 km/h and small thrust. The method is based on a steady state equivalent circuit model and the differential evolutionary algorithm (DEA). Moreover, the design constraints and the objective functions are proposed for the optimization problem. Finally, the optimized SP-DLIM is simulated by 2D transient finite element method (FEM). The 2-D transient FEM results verify the accuracy of the optimization method proposed in this paper.
Linear phase shifting transformer is a new type of phase shifting transformer with the advantages of simple winding structure, easy modularity, and the ability to shift phase at any angle. In order to improve the efficiency and power factor of linear phase shift transformer and reduce the weight of the body, based on the equivalent circuit, this paper first, selects the main design parameters of the linear phase shift transformer model as the optimization object, secondly, selects the constraints and objective function applicable to this model, and then, establishes the linear phase shift transformer optimization design model. Finally, a differential evolutionary algorithm with strong global search performance is used to optimize the design parameters. Taking the 1 kW linear phase shifting transformer as an example, the efficiency, power factor, and weight before and after optimization are compared using finite element simulation to verify the effectiveness of the proposed optimization method. The test results of the experimental prototype prove that the proposed method can effectively solve the optimization design problem of linear phase shifting transformers. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
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