Experience: Daimler-Benz · Siemens · DHL · Deutsche Bahn · Infineon Technologies · BMW · VW · Swisscom
Languages: Swiss German · German · English
Nationality: Switzerland
Education: Ph.D. in Data Science · MBA
Tools:
Microsoft Fabric: Python · PySpark · SQL · T-SQL · KQL · Data Pipelines · Dataflow Gen2 · Medallion Architecture · Lakehouse · Warehouse · Real-Time Intelligence · Semantic Models · Power BI · DAX · Maps · Event Streams· CI/CD · Data Science · AI Agents
Portfolio:
Switzerland's photovoltaic buildout is accelerating rapidly — with over 7 GW of installed capacity today and a federal target of 34 TWh of solar generation by 2035, grid operators, industrial asset owners, and energy utilities face a growing challenge: keeping large and often hard-to-reach PV installations performing at their peak.
Traditional inspection methods — rope teams, manual thermal checks, or periodic aerial surveys — are costly, infrequent, and slow to surface faults. In alpine environments where snow loads, soiling, and physical access compound the problem, a single undetected defect can quietly erode yield for months.
This solution brings together drone-based thermal and visual imaging, automated defect detection powered by computer vision, and real-time asset intelligence — all built on a unified Microsoft Fabric data platform. Drone-mounted sensors are capable of identifying four critical fault categories at scale:
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Localized cell failure — individual cells with thermal anomalies (hotspots) that reduce string output and risk cascading damage
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String-level failure — entire strings dropping off-line due to inverter, bypass diode, or wiring faults
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Dirt and dust accumulation — soiling patterns detected via visual imaging and correlated with yield loss data
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Physical panel degradation — micro-cracks, delamination, and glass damage identified before they cause irreversible yield loss
Imagery flows directly into a Fabric Data (real-time, batch, or both), where AI models classify panel-level faults and results surface in Real-Time Dashboards or Power BI dashboards tailored for asset managers and O&M teams. Maintenance tickets are generated automatically, closing the loop from detection to resolution.
The business case is concrete:
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Inspection costs reduced by up to 60–70% compared to rope access or manual thermal surveys
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Faults detected 4–8 weeks earlier, recovering an estimated 1–3% of annual energy yield
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Inspection cycles shortened from yearly to quarterly or on-demand
Dataset
Mockup dataset simulating a drone inspection of a solar farm. GPS coordinates clustered around St. Gallen (47.43°N, 9.31°E). 3 drones (drone_01–03) covering panel zones A–D, spaced at realistic 60–90 second intervals.
Anomaly rate intentionally set at ~55% (11/20 rows).
Anomaly Types & Severity
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hot_spot — Localized cell failure · severity 6.8–8.9 · temp delta +17–26°C
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bypass_diode — String-level failure · most critical · severity 9.1–9.7 · temp delta +28–31°C
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soiling — Dirt/dust accumulation · lowest priority · severity 2.4–3.1 · temp delta +5–6°C
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delamination — Physical panel degradation · severity 5.3 · temp delta +12°C
Since Microsoft Fabric is an end-to-end analytics platform, we never have to leave Fabric to write code. For example, if we want to enrich our solar panel inspection data, we can do so using a Notebook (a Data Scientist's favorite) with PySpark — or plain Python for smaller datasets (under 100 million rows).
Picture a heart attack. A dirty solar cell or panel is like a clogged artery — and if enough arteries stop pumping, the whole system seizes up and fails. One common way this plays out comes down to frame design and how panels are installed. Most solar panel frames trap a small amount of water along the bottom edge.
When that water carries any dirt or debris, it leaves behind a soiling deposit as it evaporates, which then causes the affected cells to overheat. The panel shown below is likely suffering from exactly this: a buildup of dust along its lower edge creating partial shading — a clear example of which is visible in the image.
With Microsoft Fabric, we can create smart alerts for any observed variable — such as dirt/dust accumulation or localized cell failure — enabling technical teams to receive automated notifications via email or Microsoft Teams. For example: "Solar panel A14: anomaly detected — localized cell failure (confidence: 94%)." This eliminates the need for constant dashboard monitoring, unless real-time observation is specifically required.
Microsoft Fabric captures drone flight paths in real-time and stores them for post-mission analytics. For the pilot, live geo-tracking improves situational awareness across large or complex installations. For drone operators, recorded flight routes provide verifiable coverage evidence — supporting ESG audits and ensuring no panel zone is missed between inspection cycles.
