Ant-lion optimizer algorithm based FOPID controller for speed control and torque ripple minimization of SRM drive system

2016 ◽  
Author(s):  
E. Shiva Prasad ◽  
B. V. Sanker Ram
Keyword(s):  
Speed Control ◽  
Torque Ripple ◽  
Drive System ◽  
Ant Lion Optimizer ◽  
Ant Lion

scholarly journals Extended Permanent Magnet Synchronous Motors Speed Range Based on the Active and Reactive Power Control of Inverters

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3549
Author(s):  
Pham Quoc Khanh ◽  
Viet-Anh Truong ◽  
Ho Pham Huy Anh
Keyword(s):  
Power Control ◽  
Permanent Magnet ◽  
Reactive Power ◽  
Control Method ◽  
Speed Control ◽  
Torque Ripple ◽  
Current Control ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Speed Range

The paper proposes a new speed control method to improve control quality and expand the Permanent Magnet Synchronous Motors speed range. The Permanent Magnet Synchronous Motors (PMSM) speed range enlarging is based on the newly proposed power control principle between two voltage sources instead of winding current control as the conventional Field Oriented Control method. The power management between the inverter and PMSM motor allows the Flux-Weakening obstacle to be overcome entirely, leading to a significant extension of the motor speed to a constant power range. Based on motor power control, a new control method is proposed and allows for efficiently reducing current and torque ripple caused by the imbalance between the power supply of the inverter and the power required through the desired stator current. The proposed method permits for not only an enhanced PMSM speed range, but also a robust stability in PMSM speed control. The simulation results have demonstrated the efficiency and stability of the proposed control method.

scholarly journals Analysis of Torque Ripples of an Induction Motor Taking into Account a Inter-Turn Short-Circuit in a Stator Winding

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3626 ◽  
Author(s):  
Wojciech Pietrowski ◽  
Konrad Górny
Keyword(s):  
Early Stage ◽  
Short Circuit ◽  
Torque Ripple ◽  
Drive System ◽  
Electrical Machines ◽  
Stator Winding ◽  
Permanent Magnet Synchronous Machines ◽  
Noninvasive Diagnostics ◽  
Drive Systems ◽  
Commercial Applications

Despite the increasing popularity of permanent magnet synchronous machines, induction motors (IM) are still the most frequently used electrical machines in commercial applications. Ensuring a failure-free operation of IM motivates research aimed at the development of effective methods of monitoring and diagnostic of electrical machines. The presented paper deals with diagnostics of an IM with failure of an inter-turn short-circuit in a stator winding. As this type of failure commonly does not lead immediately to exclusion of a drive system, an early stage diagnosis of inter-turn short-circuit enables preventive maintenance and reduce the costs of a whole drive system failure. In the proposed approach, the early diagnostics of IM with the inter-turn short-circuit is based on the analysis of an electromagnetic torque waveform. The research is based on an elaborated numerical field–circuit model of IM. In the presented model, the inter-turn short-circuit in the selected winding has been accounted for. As the short-circuit between the turns can occur in different locations in coils of winding, computations were carried out for various quantity of shorted turns in the winding. The performed analysis of impact of inter-turn short-circuit on torque waveforms allowed to find the correlation between the quantity of shorted turns and torque ripple level. This correlation can be used as input into the first layer of an artificial neural network in early and noninvasive diagnostics of drive systems.

Preventive maintenance scheduling using analysis of variance-based ant lion optimizer

2018 ◽  
Vol 15 (2) ◽  
pp. 254-272 ◽  
Author(s):  
Umamaheswari Elango ◽  
Ganesan Sivarajan ◽  
Abirami Manoharan ◽  
Subramanian Srikrishna
Keyword(s):  
Analysis Of Variance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Statistical Tests ◽  
Population Based ◽  
Maintenance Scheduling ◽  
Content Type ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Selection Of

Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

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