e-Newsletter-June 2024

Department Events

Celebrating IEEE Power Electronics Day on 20/06/2024

On June 20th, 2024, the Department of Electrical Engineering of K.K.Wagh Institute of Engineering Education & Research marked a significant milestone by hosting the IEEE Power Electronics Day Celebration. Our talented students showcased their innovative projects in Power Electronics, demonstrating their ingenuity and technical prowess.

The event was graced by esteemed industry experts who evaluated the projects and provided invaluable feedback. This interaction was a golden opportunity for our students, bridging the gap between academic knowledge and industry practice.

Admission Awareness seminar on 27/06/2024

The First Year Engineering Admission Awareness seminar was conducted on 27th June 2024 at Buldhana. Dr. Ravindra Munje provided the complete overview of the institute and various courses run by the institute and Dr Vilas Patil provided information on FY Admissions and addressed all the queries of parents and aspiring students.

Student Corner

Student Placement

The following students are placed in various multinational companies. Congratulations to all the students!

Placed Students Details (June - 2024)

Sr. No.	Name of the Student	Package	Placement Date
1.	Tejas Kautik Wagh	3	13/06/2024
2.	Gawande Uday	4.2	17/06/2024
3.	Krutika Salve	4.2	17/06/2024
4.	Mayuresh Kulkarni	4.2	17/06/2024
5.	Sonawane Mangesh Kishor	4.2	17/06/2024
6.	Mehul Ravindra chaudhari	4.2	17/06/2024
7.	Siddhesh Dilip Suryavanshi	4.2	17/06/2024
8.	Yash Pundlik cheke	4.2	17/06/2024
9.	Aniket Sanjay Gulve	4.2	17/06/2024
10.	Apurva Anil Kothawade	4.2	17/06/2024
11.	Katkade Pratik Sanjay	4.2	17/06/2024
12.	Shaikh Shahrukh Khayyum	4.2	17/06/2024

 

We are thrilled to announce the felicitation ceremony honoring the proud parents of Kalpesh Patil, Adinath Lahane,Priyanka Gosavi, and Sakshi Ohol. Rajashree Kushare, Sarthak Jadhav, Om Paner, Prasad Mandlik. Rahul Avhad, Ashish Khatalon May 27th, 2024, at the prestigious Aurangabadkar Hall, Nashik. Students have achieved a remarkable milestone by securing a coveted position in a multinational company, and we couldn't be prouder.

The ceremony was graced by esteemed personalities Dr. Keshav Nandurkar and Dr. Sunil Kute, Dr. Preeti Bhamre Dr. Ravindra Munje Dr. Pramod Shahabadkar Dr. Vandana Bagal, Dr. P. D. Dhake, Dr. Suyog Jain, Dr. Padmakar Pawar, Dr. Sanjay D. Barahate, Dr. Saroj Dhake whose presence added immense value to the event.

This achievement underscores the student's dedication, hard work, and determination. We extend our heartfelt congratulations to his family on this significant accomplishment. Here's to many more successes on her journey ahead!

Faculty Corner

Congratulations Prof. Nayana N Jangle

Congratulations Prof. Ganesh Jadhav

IET CCSA Meeting on 15th June 2024

Dr. Ravindra Munje attended the IET CCSA Meeting on 15th June 2024 at Courtyard by Marriott as a CCSA Member of the IET Nashik Local Network. CCSA stands for Communities Commitee South Asia. The objectives of CCSA are to support regional activities, drive communication between communities and share best practice methods among the IET Local Networks. 

Felicitation for  IET Members

Dr. Keshav Nandurkar, Dr. Ravindra Munje, Prof. S. K. Shinde, Dr. Prashant Kushare, Prof. Nayana Jangle and other members from K.K.Wagh Institute of Engineering Education & Research attended the Networking Dinner organized by the IET CCSA Meeting on 14th June 2024 at Courtyard by Marriott. It was a great opportunity to interact with the IET Volunteers from other Local Networks. During this interaction, Dr. Keshav Nandurkar felicitated Mr. Shekar Sanyal, Head of IET India, Dr. Ravindra Munje felicitated Mr. R. N. Rajpoot, Past Chairman of IET CCSA and Dr Prashant Kushare felicitated Mr. Ajay Kulshreshta, Chairman of IET CCSA and Prof. Nayana Jangle felicitated Ms. Varsha Kothari, IET Bangalore office staff.

Student Articles

Real-Time Implementation of Adaptive Neuro Backstepping Controller for Maximum Power Point Tracking in Photovoltaic Systems

AkshayNarayanKakade,BEDiv-B(Electrical) akshaykakade1213@gmail.com

Introduction

Photovoltaic(PV) systems are widely utilized for harnessing solar energy, and maximizing their efficiency is crucial for sustainable energy production. One of the key challenges in PV systems is tracking the Maximum Power Point (MPP) to ensure optimal energy conversion. In recent years, advanced control techniques have gained attention, and the Adaptive Neuro Backstepping Controller (ANBC) has emerged as a promising solution for MPP tracking.

1. Understanding Adaptive Neuro Backstepping Control

The Adaptive Neuro Backstepping Controller combines adaptive control strategies with neural networks to enhance the tracking performance of nonlinear systems. This controller is particularly well-suited for PV systems, which exhibit complex and time-varying characteristics. The backstepping approach allows the controller to handle uncertainties in the system dynamics, while the adaptive component ensures continuous adjustment to changing conditions.

2. Real-Time Implementation Challenges

Implementing ANBC in real-time for MPP tracking involves addressing various challenges. One key aspect is the integration of neural networks to approximate the nonlinearities of the PV system. Training the neural network in real time and ensuring its accuracy under different operating conditions is crucial for the success of the controller. Additionally, the adaptive nature of the controller requires continuous parameter updates to adapt to variations in the PV array.

 3. Benefits of ANBC for MPP Tracking

(i) Improved Tracking Accuracy: NBC excels in providing accurate MPP tracking, even in the presence of system uncertainties and variations.

(ii) Robustness: The adaptive nature of the controller enhances robustness, making it well-suited for real-world PV systems with dynamic operating conditions.

(iii) Reduced Oscillations: Backstepping control minimizes oscillations, leading to smoother operation and increased energy harvesting efficiency.

(iv) Adaptability: The controller can adapt to changes in environmental conditions, such as variations in solar radiation and temperature, ensuring optimal performance.

 4. Experimental Validation

Extensive experimental testing is essential to validate the real-time implementation of the ANBC for MPP tracking. Utilizing a hardware-in-the-loop (HIL) setup with a realistic PV emulator, researchers can simulate various operating scenarios and assess the controller’s performance under dynamic conditions. These experiments provide valuable insights into the controller’s adaptability and robustness in a controlled environment before deployment in actual PV systems.

 5. Future Scope

The future scope of real-time implementation of the Adaptive Neuro Backstepping Controller (ANBC) for Maximum Power Point Tracking (MPPT) in Photovoltaic (PV) systems holds significant promise, with potential advancements in several key areas.

(i) Integration of Machine Learning Techniques

Future developments may involve further integration of machine learning techniques toenhance the adaptive capabilities of the ANBC. Deep learning approaches, such as deep neural networks or reinforcement learning, could be explored to improve the controller’s ability to adapt to complex and dynamic operating conditions.

(ii) Enhanced Neural Network Architectures

Research can focus on developing more sophisticated neural network architectures to better model the nonlinearities inherent in PV systems. This could include the exploration of recurrent neural networks or attention mechanisms to capture temporal dependencies and improve the accuracy of the neural network component.

(iii) Hybrid Control Strategies

Investigating hybrid control strategies that combine the strengths of ANBC with other control methodologies could be an exciting avenue. This might involve integrating predictive control methods or combining ANBC with traditional proportional-integral-derivative (PID) controllers to achieve a synergistic effect, optimizing stability and adaptability.

(iv) Real-Time Parameter Estimation Techniques

Continuous improvement in real-time parameter estimation techniques is essential for the success of adaptive controllers. Future research might focus on developing more robust and computationally efficient methods for estimating and updating the parameters of the ANBC in real time, ensuring accurate adaptation to changing environmental conditions.

(v) Cyber-Physical Systems and Internet of Things (IoT) Integration

Integrating ANBC with Cyber-Physical Systems (CPS) and IoT technologies could open new possibilities. This includes the use of sensor data from the PV system, weather predictions, and other relevant parameters to enhance the controller’s decision-making process and improve overall system efficiency.

(vi) Experimental Validation in Real-World Settings

As the technology matures, there will be a growing need for extensive experimental validation in real-world PV installations. Field trials and long-term studies can provide valuable insights into the controller’s performance under diverse climatic conditions and varying system configurations.

(vii) Implementation in Microgrid and Smart Grid Systems

Extending the application of ANBC beyond standalone PV systems to microgrid and smart grid environments is a logical progression. The controller’s adaptability and robustness make it potentials candidate for managing distributed energy resources in complex grid structures.

(viii) Standardization and Commercialization

Standardizing the implementation of ANBC for MPPT in PV systems will be crucial for widespread adoption. As the technology matures, efforts to establish industry standards and protocols can accelerate its integration into commercial photovoltaic solutions.

 6. Conclusion

The real-time implementation of the Adaptive Neuro Backstepping Controller presents a promising approach for enhancing MPP tracking in Photovoltaic Systems. Its ability to handle nonlinearities, adapt to changing conditions, and reduce oscillations makes it a robust solution for improving the efficiency and reliability of solar energy harvesting. Further research and practical implementation will contribute to the advancement of adaptive control strategies in renewable energy systems.