Award

The SolarSurveillance project addresses security and reliability issues of string inverters in solar photovoltaic systems. Existing intrusion detection systems have limitations, hindering their effectiveness in detecting potential cyber threats and system failures. The project aims to develop an effective and adaptive intrusion detection system for string inverters, contributing to the development and deployment of renewable energy systems while ensuring their safety, reliability, and security, and benefiting both the environment and society. The overall objective of the Phase I project is to develop a transfer learning-based intrusion detection system for string inverters that addresses the limitations of existing intrusion detection systems. The SolarSurveillance system will leverage the latest advances in machine learning and artificial intelligence to improve the accuracy and effectiveness of intrusion detection in string inverters, providing a more reliable and effective solution for security in the solar energy sector. The project approach includes research and development of the SolarSurveillance system, prototype testing and validation, commercialization planning, and effective management and control of the project. In Phase I, the SolarSurveillance project will conduct a feasibility study to determine the technical and commercial viability of the proposed transfer learning-based intrusion detection system. The study will involve developing a tangible prototype using the resources of the Google Cloud Platform and conducting comprehensive testing to validate the system's performance and reliability. The project team will also develop a commercialization plan and explore potential partnerships and collaborations to ensure successful product development and market launch. If the SolarSurveillance project is carried over into Phase II and III, it has the potential to deliver significant public benefits, including improved security and reliability of renewable energy systems, reduced risks of cyberattacks and system failures, and enhanced overall performance of string inverters. The development of a more effective and adaptive intrusion detection system for string inverters will benefit both the solar energy industry and consumers by improving the safety and security of renewable energy systems, reducing downtime and maintenance costs, and increasing access to clean and affordable energy. Additionally, the transfer learningbased approach developed in this project can be applied to other critical infrastructure systems, improving overall resilience and preparedness for potential threats or adverse events. Ultimately, the SolarSurveillance system has the potential to play a vital role in advancing the development and deployment of renewable energy systems and supporting the transition to a cleaner and more sustainable energy future.