Minimization of Starting Energy Loss of Three Phase Induction Motors based on Particle Swarm Optimization and Neuro Fuzzy Network

Elkholy, Mahmoud M.; El‐Hameed, Mohamed A.

doi:10.11591/ijpeds.v7.i4.pp1038-1048

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…The energy optimization of a brush-phase asynchronous motor at different load levels is presented in Ref. [3]. To minimize the energy loss at start-up, it is necessary to control the start voltage and frequency as a function of the load torque within a given start time interval.…”

Section: Introductionmentioning

confidence: 99%

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

et al. 2023

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In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage.

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Section: Introductionmentioning

confidence: 99%

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

et al. 2023

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“…During the recent years, the induction motors (IM) are utilized, instead of the DC motors, in many different applications including domestic and industrial uses such as heat pumps, wind generator, textile mills, machine tools, transposition, air conditioners, and robotics [1]- [4]. In order to make the induction motors operate, there is no need for sliding contacts or permanent magnets, which make them too simple and have low manufacturing cost [5]- [8]. With little exceptions, the electric motors are considered the major source of the mechanical energy supplies in industry.…”

Section: Introductionmentioning

confidence: 99%

Comparison and assessment of a different steel materials based on core losses reduction for three-phase induction motor

Yassin

Hussain

Ahmed

2021

IJPEDS

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This paper presents a core losses and performance calculation with different type of steel materials in the core design for three-phase induction motor by using "ANSYS Maxwell" program in order to identify the core material that provides the most effective performance by iron losses reduction. The coefficients of core losses are calculated from the magnetization curve and core Loss curve based on the on steel material databases. Although the difficult to obtain because of the little of existing information. Results show the capability of the proposed Cobalt steel (Hiperco 50) to achieve the significant losses reduction in comparison to the Electrical Steel NGO–AK Steel’s M-19 and Low Carbon Steel-SAE1020.

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“…Working from any place and any time is one among the advantages. The clients do not need to purchase any resource for their data storage [1,2]. The WSN has to increase its battery power so as to increase node's efficiency.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of energy based optimized dynamic source multipath routing protocol in WSNs

Ameen

Jamal

Raj

2019

IJEECS

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<p>Wireless Sensor Network (WSN) has been considered to be a formula for overcoming various requirements like surveillance, health care, traffic surveillance and defense systems. As an aid to the demands for Wireless sensor network, research has been going on the efficient consumption of energy and secure communication areas for WSN. Here introducing Ant colony optimization based Optimized dynamic Secure multipath routing protocol (ODMRP) have improvised WSN in terms of energy efficiency, performance, strength and scalability. This paper proposed mechanism of Ant Colony Optimization based on the optimized dynamic secure multipath routing protocol (ODMRP-ACO) for data transmission in the WSNs. This eases the data transmission via WSNs. The shortest path between the source node and the destination node is analyzed by the ant colony cooperation which is based on the ACO. There are several routing protocols like Energy-aware and Secure Routing with Trust (ESRT-ACO), Split multipath routing protocol-Ant colony Optimization (SMR-ACO), Light Weight Trust Based routing protocol (TLB-AODV) and AODV routing protocols that are built with the ACO based Routing Protocol. They are concerned over the End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. The data analytic results have proven to be effective compared to the other routing protocols designed for WSN. This is because; the overall ODMRP-ACO was calculated to be 96.98% better than the other existing. For the newly developed ODMRP-ACO protocol, the simulation platform used is NS2 simulation software.</p>

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Minimization of Starting Energy Loss of Three Phase Induction Motors based on Particle Swarm Optimization and Neuro Fuzzy Network

Cited by 9 publications

References 16 publications

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

Comparison and assessment of a different steel materials based on core losses reduction for three-phase induction motor

Comparative analysis of energy based optimized dynamic source multipath routing protocol in WSNs

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