Electrical Engineering Personal Statementfor Oxford & Cambridge

In May 2019 Great Britain went a full week without coal-fired electricity for the first time since 1882. What held my attention was not the headline alone but the engineering consequence behind it. A system with fewer large synchronous generators has less inherited inertia, so stability depends more heavily on control, timing and fast correction when supply or demand shifts. Reading National Grid material on balancing made electrical engineering feel broader than circuits on a worksheet. It was about designing networks that keep working when conditions are imperfect and at national scale. Since then, the part of the subject that has interested me most is the point where mathematical models meet physical behaviour. Working on renewable-energy control later made me realise that decarbonisation is not only about building enough solar, wind and storage. It is also about how inverter-based systems regulate, synchronise and remain stable in a network that no longer receives support from heavy rotating machines in the same way. That combination of theory, design and reliability is why I want to study electrical engineering at university, particularly power electronics and control.

My school studies have given me the concepts that made those questions precise. In Physics, alternating currents, transformers and electromagnetic induction showed me that elegant equations only take you so far unless you can explain losses, phase relationships and distortion in a real system. Rectification and smoothing interested me for the same reason: a circuit that looks settled on paper can still contain ripple, delay and compromise in practice. Because my sixth form does not offer electronics as a separate subject, I used those topics as a starting point for more independent academic study rather than leaving them at syllabus level. Following MIT OpenCourseWare's 6.002 Circuits and Electronics helped me understand the lumped circuit abstraction and, more importantly, when that simplification stops working. Reading sections of Horowitz and Hill's The Art of Electronics reinforced the same lesson. I came away thinking less about neat answers and more about trade-offs between efficiency, stability, noise and thermal limits. That way of thinking feels like strong preparation for degree-level engineering because it requires both mathematical reasoning and judgement about physical systems. It has also made me more attentive to what assumptions a model is hiding.

Outside formal education, I tried to test those trade-offs in hardware by building a maximum power point tracking charger for a small photovoltaic panel as a CREST Gold project. I chose a buck converter because it turned an abstract control problem into something measurable. I first modelled it in LTspice, then used an Arduino to vary PWM duty cycle with a perturb-and-observe algorithm based on voltage and current readings. The simulation suggested a clean route to the maximum power point, but my prototype behaved much less neatly: ripple and sensor noise made the controller hunt around the optimum rather than settle near it. Averaging successive readings and reducing the duty-cycle step size improved the output, but the tracker still lagged when light levels changed quickly. That taught me that a control system is only as reliable as the measurements feeding it. I also tutor younger pupils in maths at a community centre, and explaining algebra step by step has made me more careful about what I assume other people can infer. Both experiences have been useful because they have made me more precise, more patient with imperfect results, and more interested in how technical ideas hold up outside ideal conditions.