Power electronic devices have a wide range of uses because of their supremely efficient capacity to transform electrical energy into usable forms like heat, light, motion, and sound, among others. Multiple industry applications make use of motor drives, which serve as a good example of this technology. Motor loads, the majority of which are induction motors, account for about 70% of industrial load. Perfect management of these motors is essential for industry because it might save companies a ton of money and resources. Each of these high frequency switching devices requires precise control to function properly. Under continuously shifting environmental conditions, standard control methods cannot keep power electronic equipment working at BEP (Best Efficiency Point). In this case, experts use artificial intelligence-gathering methods. Artificial neural networks have had the greatest impact on power electronics and motor drives of all artificial intelligence subfields.
Neural networks’ role in core power electronics
The application of ANN represents the next step in the evolution of power electronics design, control, and application. It is applicable to many renewable energy applications, including grid-connected inverters, solar PV inverters, electric charging stations, and others. Grid-connected PV inverters are one such application that has seen significant progress in recent days. ANN networks are being used to improve PV cell design, operation, and maintenance. Traditional PV controllers employ PI controllers or PR algorithms, which can be slow to respond to sudden disturbances. Even though disturbances are common in grid-connected operations, these controllers reduce efficiency and precision.
One of the primary reasons why people do not switch to EVs is the lengthy charging time required to fully charge the vehicle. Smart EV charging systems powered by AI optimize charging by efficiently monitoring charging current, battery type, and other charging parameters in order to charge the battery faster. Continuous monitoring while charging will also aid in the prediction of battery life and the prevention of faults.
The Function of Neural Networks in Power Induction Motor Drives
Losses in an induction motor can be minimized to locate the Best Efficiency Point (BEP) under lower load situations by keeping precisely the proper quantity of flux at a specific value of speed and torque. However, determining the appropriate amount of flux in a fully dynamic environment is a significant challenge for engineers.
Traditional mathematical tools cannot solve complex nonlinear functions with variable parameters, but neural networks can. Induction motors’ optimal rotor flux for maximum efficiency is a nonlinear function of both rotor shaft speed and load torque, and machine parameters change as machine temperature rises. As a result, a NN model can be used for rotor magnetizing current prediction and field control.
Future potential
Neural networks are a broad discipline in AI, and there has been significant progress in the field in recent years. Considering neural networks are a new and unreliable technology, their use in power electronics and motor drives is currently limited. Although the technique has a lot of potential in the coming days. The main challenge that these technologies face today is a lack of good training sets and data. Since it is not cost-effective to create data expressly for this use, the industry can investigate and put into practice low-data, high-accuracy models.
References
[1] K. M. Hasan, Li Zhang and B. Singh, "Neural network control of induction motor drives for energy efficiency and high dynamic performance," Proceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066), 1997, pp. 488-493 vol.2, doi: 10.1109/IECON.1997.671782.
