Department of Electrical Engineering, Shambhunath Institute of Engineering and Technology, Prayagraj 211015, India.
Department of Electronics and Communication, J K Institute of Applied Physics and Technology, University of Allahabad, Prayagraj 211002, India.
Sensors (Basel). 2022 Mar 28;22(7):2594. doi: 10.3390/s22072594.
Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.
感应电动机在额定条件下效率往往更好，但在部分负载条件下，当这些电动机在额定磁通下运行时，效率会降低。在这种情况下，如果这些电动机长时间运行，电动机将消耗大量电能，从而增加工业工厂的计算成本和总运行成本。鼠笼式感应电动机由于成本低、坚固耐用、易于维护以及在整个生命周期内具有良好的功率/质量比，因此在工业中得到广泛应用。由于全球范围内有大量的运行单元，因此消耗了大量的电能；因此，即使效率略有提高，也可以在节省收入、全球电力消耗和其他环境因素方面做出重大贡献。为了提高感应电动机的效率，本研究论文提出了一种提高感应电动机效率的新方法。为此，采用感应电动机驱动模型，并对比例积分（PI）控制器进行了调整。对 PI 控制器的增益（如比例增益和积分增益）进行了最优调整。在控制器中进行调整的过程中，感应电动机的效率应达到最大值。此外，优化概念依赖于开发一种新的混合算法，即所谓的 Scrounger Strikes Levy 基于蜻蜓算法（SL-DA），它混合了蜻蜓算法（DA）和群体搜索优化（GSO）的概念。该算法与粒子群优化（PSO）进行了比较，以验证其性能。对效率、速度、转矩、节能和输出功率进行了分析，验证了所提出的方法优于所采用的比较算法的优越性能。
