Design of Novel Converter using ANN Algorithm for Industrial Loads

The proposed project deals with the integrated Renewable energy system that uses wind (AC) and solar (DC) power sources will be designed and put into operation that is applicated to the industrial loads in the intension to optimize its operation. The proposed model involves in the design of the novel converter that converters DC source into AC source and supply it to the load. To control power generation, transform energy, and store it in a battery for later use, the system makes use of a controller. Pulse Width Modulation (PWM) is used to control the battery's energy so that to control the voltage that is given to the Industrial load. The control of the project model is managed by feeding ANN Algorithm to the PIC Micro Controller. In order to power an external load, the system additionally has an inverter that transforms the DC energy that has been stored into AC. By integrating an LCD display, real-time monitoring is made easier, guaranteeing defect identification and operating efficiency. The project's goal is to an intelligent and flexible control mechanism is provided by Artificial Neural Networks (ANNs), which present a promising answer to these problems. Because ANN-based algorithms can learn and adapt to changing load circumstances in real time, unlike traditional controllers, they are especially well-suited for converters operating in complex and varied industrial environments. Because of their nonlinearity and flexibility, Artificial Neural Networks (ANNs) can effectively simulate the behavior of industrial loads and optimize power converter control to minimize losses, lower harmonic distortion, and improve response time, reliance on conventional power sources and it is achieved through the design of the novel converter.