A Comparative Assessment of Genetic and Golden Search Algorithm for Loss Minimization of Induction Motor Drive

Despite activities to introduce low-carbon energy sources worldwide, the share of conventional facilities burning organic fuels remains high. One approach to address this problem is to look for solutions to reduce energy consumption. There are various research projects in the area of energy efficiency that lead to diverse results—such as models, methodologies, new data and theories. On the other hand, induction motor drives are becoming a major consumer of electric power because of their wide range of applications. In this paper, after careful selection and systematization of 151 literature sources, an extensive study and criteria analysis of the existing state of affairs in the area of energy efficiency improvement of induction motor drives has been carried out. Five major and 48 minor research areas in this field have been identified. The results show that issues related to the adaptation of scientific results and the conditions for their effective and wide-ranging application in practice have not been discussed and investigated so far. Adaptation should take into account the possibilities of data acquisition, including data from measurements; the competences of energy managers; and the type of information provided to them. Based on the seven conclusions formulated below, summary recommendations are made to direct future research towards the justification of models for increasing the power efficiency of induction drives, adapted for use by energy managers.

Section: Energy-efficient Operating Modes Of Drivesmentioning

confidence: 99%

Energy Efficiency of Induction Motor Drives: State of the Art, Analysis and Recommendations

Dinolova,

Ruseva,

Dinolov

2023

“…Therefore, in this paper, an energy-saving method for a three-phase IM is proposed. In previous works, energy-saving methods such as frequency control [4,5], flux control [6][7][8][9], optimal balance of the stator current on the d-axis (i ds ) and the q-axis (i qs ) [10][11][12][13] and i ds control [14] were reported. In the present work, the minimum power loss (P loss ) is computed using the partial derivative of P loss with respect to i ds that is equal to 0.…”

Section: Introductionmentioning

confidence: 99%

Online Estimation of Three-Phase Induction Motor Parameters Using an Extended Kalman Filter for Energy Saving

Udomsuk,

Areerak,

Areerak

et al. 2024

In this paper, the online estimation of three-phase induction motor parameters using an extended Kalman filter for energy saving is proposed. The optimal value of the stator current on the d-axis is calculated to obtain the minimum power loss. Accurate motor parameters are required to calculate the optimal stator current value for energy saving. Hence, to estimate motor parameters in real time, an online estimator known as the extended Kalman filter is applied. The energy consumption results for the motor using the proposed approach (estimated parameters with extended Kalman filter) are compared with those obtained using the conventional approach and energy saving (fixed parameters without parameter estimation) approach. As revealed by the comparison results from implementation in a laboratory, the proposed approach can provide minimum power losses for the three-phase induction motor drive, and the maximum energy-saving percentage is 60.18% compared with using the conventional drive approach.

Optimized Network Reconfiguration with Integrated Generation Using Tangent Golden Flower Algorithm

Swaminathan

Rajagopalan

2022

The importance of integrating distributed generation (DG) units into the distribution network (DN) recently developed. To decrease power losses (PL), this article presents a meta-heuristic population-based tangent golden flower pollination algorithm (TGFPA) as an optimization technique for selecting the ideal site for DG. Furthermore, the proposed algorithm also finds the optimal routing configuration for power flow. TGFPA requires very few tuning parameters and is comprised of a golden section and a tangent flight algorithm (TFA). Hence, it is easy to update these parameters to obtain the best values, which provide highly reliable results compared to other existing techniques. In different case studies, the TGFPA’s performance was assessed on four test bus systems: IEEE 33-bus, IEEE 69-bus, IEEE 119-bus, and Indian-52 bus. According to simulation results, TGFPA computes the optimal reconfigured DN embedded along with DG, achieving the goal of minimal power loss.