Efficiency Estimation of the Induction Machine by Particle Swarm Optimization Using Rapid Test Data With Range Constraints

Bijan, Mahmud Ghasemi; Pillay, Pragasen

doi:10.1109/tie.2018.2873121

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…Where; w denotes the inertia weight, it's used to provide a balance between global and local search, thus requiring less iteration on average to find a sufficiently optimal solution, it's set according to (10), and decreases linearly from about 0.9 to 0.4 [23]. c1 is the cognitive and c2 is the social parameter.…”

Section: Basic Concepts Of Particle Sswarm Optimizationmentioning

confidence: 99%

Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive

Rezgui

Benalla

Bouhebel

2021

IJEECS

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<p dir="ltr"><span>This paper presents a hybrid algorithm that combines the particle swarm optimization method with the bacteria foraging technique, named: BF-PSO. The aim is to achieve more efficient and precise parameters determination of the regulators that leads to performance improvement in the speed-loop control of an induction motor (IM) implemented in a direct torque control (DTC). The approach consists of tuning the proportional-integral (PI) parameters that meet high dynamics and tracking behavior using the hybrid BF-PSO algorithm. </span>Investigations have been completed with Matlab/Simulink and several performance tests are conducted. The comparison results are exposed with the most used indices in the controllers' tuning with optimization techniques. It will be shown that the presented technique presents better quality results compared to the conventional method of calculated PI.</p><div><span><br /></span></div>

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Section: Basic Concepts Of Particle Sswarm Optimizationmentioning

confidence: 99%

Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive

Rezgui

Benalla

Bouhebel

2021

IJEECS

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“…The inertia weight ω controls the impacts of the previous velocity of particles on the current one [26], [27], which plays a crucial role in searching for the optimal value. In general, individuals in the swarm are expected to have strong exploration capabilities at the beginning iterations of PSO algorithm.…”

Section: Nonlinear Time-varying Inertia Weightmentioning

confidence: 99%

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

et al. 2019

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Because of its excellent small sample learning abilities and simple network structure, support vector machine (SVM) is widely applied in various pattern recognition fields, e.g., face recognition, scene classification, fault diagnosis, etc. Due to the complexity and diversity of analog circuit faults, the diagnosis accuracy and stability of SVM classifier optimized by traditional particle swarm optimization (PSO) are unsatisfactory. Therefore, this paper proposes an improved hybrid particle swarm optimization (IH-PSO) algorithm to optimize SVM, which is applied in the fault diagnosis of analog circuits. Compared with the traditional PSO algorithm, the proposed IH-PSO mainly has three improvements, namely, the oppositionbased learning population initialization, the nonlinear time-varying inertia weight, and the new position updating strategy with a spiral convergence mechanism. The performance of the proposed IH-PSO algorithm is verified by 12 commonly used benchmark functions and experimental results show that the proposed IH-PSO algorithm overcomes the deficiencies of the traditional PSO algorithm, such as slow convergence speed and trapping into local optimums. In addition, to further verify the performance of IH-PSO algorithm, the IH-PSO optimized SVM is applied to solve analog circuits fault diagnosis problems. Extensive experiments are carried out and results indicate that the proposed method has better performances both in diagnosis accuracy and stability compared with that of the traditional method.

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“…Generally speaking, PSO algorithm represents an optimization tool that finds its application in the investigations of solar cells, electrical machines, electronic systems etc. [20][21][22][23]. The PSO algorithm is established on the population (swarm) of candidate solutions.…”

Section: Pso Algorithmmentioning

confidence: 99%

“…Based on the previous analyses, the aim of this paper is to improve the PID controller design from the perspective of better response quality with respect to limitations dictated by the components of the regulation loop (for example, hydraulic pumps, rudder angle etc.). This improvement was achieved by using the optimization technique called Particle Swarm Optimization (PSO), which is a very powerful technique, whose application can be found in a number of areas such as power converters [20], solar cells [21], electrical machines [22], power network [23], etc. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Pso-Based Pid Controller Design for Ship Course-Keeping Autopilot

Dlabač

Ćalasan

Krčum

et al. 2019

brod

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This paper deals with autopilot proportional-integral-derivative (PID) controller design. In the literature, various available methods for PID controller have been presented. Based on the fact that the existing methods do not guarantee the optimal response of the system on the input step change, in this paper we used a valuable technique called Particle Swarm Optimization (PSO) in order to optimally design PID controller taking into account the system limitation such as the value of the rudder angle saturation. Furthermore, we have compared system response on input step and ramp change of input signal for a few PID controller parameters values obtained by using different methods known in literature. It has been proven that by applying the PSO method, it is possible to determine the optimal PID controller parameters which guarantee fast and proper response from the aspect of the minimal overshoot and the minimal settling time. The obtained results confirm the applicability and efficiency of using PSO method for optimal autopilot PID controller design.

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Efficiency Estimation of the Induction Machine by Particle Swarm Optimization Using Rapid Test Data With Range Constraints

Cited by 19 publications

References 27 publications

Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive

Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

Pso-Based Pid Controller Design for Ship Course-Keeping Autopilot

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