Daytime Inspection Solutions for Advanced Operation & Solar Farm Maintenance

Daytime Inspection Solutions for Advanced Operation & Solar Farm Maintenance

Solar farms are growing fast in size and number. Some now have more than a million photovoltaic (PV) modules. Solutions are needed to automate the inspection of solar farms, to detect faulty modules and determine their cause. In 2023 the Australian Renewable Energy Agency awarded a project to bring new inspection technologies to market - “Daytime Inspection Solutions for Advanced Operation & Solar Farm Maintenance”. Murdoch University’s role in the project is to develop a package of a robotic ground vehicle, multiple cameras, and software to analyze the images and guide the robot. The images captured will be used to identify faults and defects within the solar farm through machine learning and image-based identification.

The project involves several key aspects including:

  • Robotic Ground Vehicle: Design and implement a robotic ground vehicle equipped with multiple cameras.
  • Image Analysis Software: Develop software to analyze images captured by the robot.
  • Machine Learning for Fault Detection: Leverage machine learning algorithms to identify problematic PV modules, including issues like cracks, dirt, or malfunctioning cells.
  • Navigation with Machine Vision: Enable the robot to navigate the solar farm autonomously using machine vision techniques. Interpret surroundings, avoid obstacles, and follow optimal paths.
  • Data Visualization: Present findings through effective data visualization techniques.
Murdoch University
Duration of Award:
3 years
Level of study:
  • Research
Study Area:
  • Engineering


We seek a highly motivated PhD candidate with strong machine learning, image identification and machine vision background. Experience working with renewable energy or a passion for renewable energy is desirable as you will be working with Engineers and stakeholders in the energy industry.

How to Apply

Email Associate Professor David Parlevliet with

1. A cover letter

2. CV

3. Academic transcripts

4. Language testing results (if applicable)

Associate Professor David Parlevliet

School of Engineering & Energy, Murdoch University 

Required Documents

  1. Cover letter detailing your interest and relevant background in strong machine learning, image identification, machine vision and renewable energy; research background; relevant training/experience research methods and analysis; demonstrated track record in project management; demonstrated capacity to work independently and as a member of a team.
  2. CV outlining academic qualifications and achievements 
  3. Academic transcripts 
  4. Language testing results (if applicable)

Payment Method

Fortnightly stipend payments