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Semi-supervised Learning Based Parametric Study of PV Array Output Characteristics for the Diagnosis and Quantification of Concurrent Faults

   School of Electronics and Computer Science

   Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

Project details:

The equivalent circuit models of the photovoltaic (PV) modules provide parametric information which grant direct insights into the electrical properties and physical phenomena that transpire within the PV cells. However, the models are rarely used for the online fault diagnosis of PV array due to their inherent complexities i.e., the presence of multiple unknown parameters and exponential terms, and the transcendental nature of the model equations. Solving for the model entails large number of operating data point information. The limited amount of information provided by on-site sensors meant that the existing PV fault diagnosis methods: (1) lacks the ability to distinguish concurrent faults; and are (2) unable to quantify the extents of detected faults. Both aspects are critical for accurate yield prediction and timely deployment of maintenance efforts. In recent years, the advent of smart inverters with I-V curve tracing capabilities has opened the opportunity to approach the analysis and identification of PV array faults from a new perspective. This work proposes to exploit wealth of information provided by the real-time PV array output characteristics, their corresponding model parameters, and the onsite sensor measurements to mitigate the abovementioned shortcomings. Through extensive investigation of the PV array faults and their effects on these key aspects, it is envisaged that a computationally efficient script-based PV model can be developed for the conception of a semi-supervised learning based fault detection and diagnosis (FD) scheme. The successful candidate will work in University of Southampton Malaysia (UoSM) to develop a robust solution for accurate identification and quantification of concurrent array DC fault instances. 


First class or upper second class honours degree, or a master’s degree (or equivalent qualification), in electrical engineering, electronic engineering, E&E engineering, mechatronics, controls, or computer science (M/BEng). Evidence of good spoken and written English is essential. The candidate should have a minimum IELTS score of 6.5 (or equivalent). This position is open to all qualified candidates irrespective of nationality. Candidates with good academic results and experience in machine learning, solar PV system modelling, or soft computing methods are encouraged to apply.

Full tuition fee waiver and competitive stipend for eligible candidate. Stipend amount: RM2500 per month (3 years). Interested candidates please contact Dr. Chin Vun Jack at .

Funding Notes

Funding agency: Ministry of Higher Education Malaysia

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