Can you tell us about your background and how you became the solo analyst for Southern Power’s $450 million renewable energy fleet?
I sometimes joke that my career is a “fusion reactor” of two forces: a childhood spent tinkering with discarded radio parts in Mumbai, learning how invisible waves carry information, and an adult obsession with turning raw data into living, breathing intelligence. After earning my MS in Data Science at Indiana University, I racked up twelve peer-reviewed papers, six patents, and a textbook because academia let me ask “why?” but the energy sector needed someone to ask “why not now?” When Southern Power called, they had 50 renewable sites throwing off terabytes of SCADA and PI data, yet only gut-feel diagnostics. I showed them a prototype: a Python pipeline on Databricks that predicted ice accretion on wind blades 48 hours in advance and explained itself with SHAP, no black boxes, just actionable color codes on a Power BI dashboard. That demo cut projected downtime by 15 percent, so leadership asked, “How many people built this?” I said, “Just me and a legion of algorithms.” They handed me the keys to a $450 million fleet and the mandate to make each turbine, panel, and battery string think for itself. Being the solo analyst isn’t a headcount accident; it’s a design choice. I automate the grunt work, let models handle the midnight shift, and reserve human bandwidth for curiosity, the most renewable resource.
What inspired you to combine your expertise in algorithms with the renewable energy sector?
Growing up in coastal Mumbai, “renewable” wasn’t a buzzword but a daily necessity. Monsoon outages meant homework by candlelight, and I remember thinking, “If electrons had a heartbeat, someone would spot the arrhythmia before the lights died.” Years later, while debugging a neural-network model at Indiana University, I fed it a week of wind-turbine SCADA logs just for fun and plotted the hidden-layer activations. The shapes looked eerily like electrocardiograms. That was my lightning bolt: renewable assets are living organisms in disguise, and algorithms are the stethoscopes they’ve been waiting for.
I’d spent years optimizing ad bids and credit-risk scores, important work, but it didn’t move the planetary needle. Energy did. Marrying machine learning with renewables felt like giving data science its most heroic use case: turning terabytes of “digital noise” into fewer blackouts, safer blades, and a greener grid. The climate challenge is physics-heavy and capital-intensive, yet oddly data-rich and insight-starved—the perfect playground for an algorithmic tinkerer. In short, I didn’t pivot to renewable energy; the sector’s unanswered math pivoted to me, and I’ve been translating its silent signals into megawatt-level decisions ever since.
You’ve developed end-to-end machine-learning pipelines for wind turbines and battery storage. Can you walk us through a specific challenge you faced and how you overcame it?
Ice isn’t just frozen water on a blade; it’s a shape-shifter buried inside noisy SCADA streams. My biggest hurdle was the missing “ground-truth” label: operators only record ice after a shutdown, leaving a desert of zeros with a few lonely 1’s. Add micro-climate drift, what works in Kansas collapses in Colorado, and the data were nearly untrainable.
How I cracked it:
1. Reverse-labeling with physics – I built a “virtual blade” that fuses SCADA pitch, power, yaw, and NOAA icing indices. When the virtual blade dropped below 0 °C and actual power slipped outside its Cp-curve for three 10-second ticks, I stamped a synthetic ice label. Boom: 300 real positives became 18,000 training events.
2. Cluster-aware meta-learning – K-Prototypes grouped turbines by altitude, rotor diameter, and prevailing wind roses; each cluster got its own lightweight XGBoost dialects for turbines.
3. SHAP gatekeeper – Every alert must show ambient temperature in its top-3 SHAP drivers; if not, it’s quarantined. That rule alone cut down false positives by 42%.
4. Edge de-noise loop – A micro ONNX model runs on each PLC: if vibration spectra suggest an ice chunk has already broken off, the PLC silences the alarm before it spams the control room.
Result: 48-hour lead time, 15% higher accuracy, and zero premature curtailments last winter. Most importantly, technicians trust it—every red flag arrives with a SHAP “X-ray” that explains why the algorithm sees ice, turning a black-box warning into a data-backed maintenance decision.
Your work includes catching battery-storage oscillations in real-time. How has this capability improved the efficiency of Southern Power’s operations?
Think of a 250 MW battery farm as a symphony of inverters; when one drifts off tempo, the entire performance can collapse within seconds. Before my system went live, we spotted oscillations only after the plant’s EMS automatically derated the output or, worse, after an unscheduled trip, which cost megawatt-hours and shortened equipment life. I built a two-layer defense. First comes a high-frequency FFT that runs on one-hertz PI tags and flags harmonic spikes above 2.5 hertz. Second is the proprietary Operational Stability Index, OSI, which blends those spikes with DC bus ripple and temperature deltas, then streams a single score to a live Dash dashboard. This pipeline cut detection latency from eight minutes to nine hundred milliseconds, allowing operators to curtail the rogue string rather than the entire site. In the last fiscal quarter, that agility prevented three full site trips, preserved about 2.1 GWh or roughly 470 thousand dollars in arbitrage revenue, and reduced inverter mean time to repair by 28 percent. OSI trends now feed predictive maintenance schedules, so we replace IGBTs forty percent closer to their true end of life instead of on conservative calendar intervals, stretching capital budgets by another 320 thousand dollars a year. In short, real-time oscillation detection has turned our batteries from reactive components into proactive grid balancers, driving both uptime and EBITDA upward.
You’ve created a proprietary Operational Stability Index. Could you explain how this index works and provide an example of how it’s been applied to solve a real-world problem?
The Operational Stability Index (OSI) is a single zero-to-one-hundred score that tells our operators, at a glance, how peacefully a battery string or inverter stack is behaving. Under the hood, it is a weighted blend of four real-time signals that historically precede faults:
1. Harmonic factor—the energy in the two-to-three-hertz band extracted by a rolling FFT
2. State-of-charge ripple—minute-to-minute excursions that hint at control loop hunting
3. DC bus voltage noise—captured as the standard deviation of the bus over the last thirty seconds
4. Thermal delta—the gap between the IGBT case temperature and the ambient rack temperature
Every second, these features are normalized with an adaptive Z-score, multiplied by risk weights learned from three years of fault history, then pushed through an exponential decay so yesterday’s turbulence does not haunt today’s score. The math yields an OSI of one hundred when the asset is whisper-quiet; anything below seventy triggers a yellow advisory, and anything below fifty is red.
Real-world win: Last October, our Texas BESS showed an OSI slide from eighty-eight to sixty-two in under five minutes during an otherwise calm afternoon. The dashboard highlighted a spike in harmonic factor while the other components stayed green, pointing to an incipient controller oscillation rather than a thermal issue. Operators isolated one inverter group instead of throttling the entire site, dodging a full trip and preserving about seven hundred megawatt-hours of discharge capacity for the evening peak. Post-mortem analysis confirmed a failing phase current sensor that would have remained invisible inside aggregate plant KPIs. The OSI alert turned a potential two-hour outage into a twenty-minute swap and saved roughly one hundred fifty thousand dollars in avoided imbalance penalties and lost arbitrage revenue.
Your data-engineering architecture using Azure Databricks, PySpark, and Airflow has freed up significant manual work hours. Can you share a before-and-after scenario that illustrates the impact of this optimization?
Before optimization, every Monday looked like a mini relay race. A technician exported sixty gigabytes of PI data and SCADA logs to local CSV files, zipped them, uploaded them to SharePoint, and emailed a download link. I spent roughly six hours unzipping, merging, and cleaning those files in Excel and Pandas, then loaded them into a SQL staging table for analysis. The cycle repeated mid-week for an “as-needed” snapshot, so the fleet always ran on data that lagged operations by at least forty-eight hours. Any ad-hoc query—like correlating temperature spikes with inverter trips—meant another round of manual extracts and VLOOKUP gymnastics, burning an extra three to four hours each time.
After optimization, the Airflow DAG now triggers every fifteen minutes. It spins up an Azure Databricks job cluster that ingests raw PI historian feeds through the MQTT gateway, lands them as Delta Lake tables, and cleans them with PySpark in place. Feature engineering notebooks populate an online feature store while a lightweight REST API exposes fresh metrics to Dash and Power BI. The entire flow runs unattended in eleven minutes end-to-end, delivers data that is never more than four minutes old, and auto-scales to handle storm-driven surges without human intervention.
Impact:
– Manual wrangling time dropped from twenty-five hours per week to under ninety minutes for occasional audit checks
– Dataset freshness improved from forty-eight hours to fifteen minutes, allowing same-shift interventions instead of next-day fixes
– Ad-hoc analyses that once took half a workday now execute as Spark SQL or DAX queries in under two minutes, enabling on-the-spot decision making during control room calls
– Overall, we logged a nine percent increase in asset availability last quarter because engineers could act on near real-time insights rather than historical post-mortems
In short, the Databricks PySpark Airflow stack turned data plumbing from a bottleneck into a background service, letting human talent refocus from clerical extraction to high-value problem solving.
You’ve translated complex analytics into executive-friendly dashboards. What’s your approach to making technical information accessible to non-technical stakeholders?
I treat an executive dashboard like a news broadcast: the anchor must deliver the headline in ten seconds, then invite curious viewers into the deeper segment. First, I identify the one metric that reflects strategic outcome—profit at risk, avoided curtailment dollars, or carbon megatons saved—and I let that number own the upper-left corner in a font large enough to read from ten feet away. Second, I apply a stoplight grammar that needs no legend: green means sleep well, yellow means investigate, red means pick up the phone now. Third, I embed a story arc behind every tile. Click the revenue figure, and you descend into a waterfall that shows which sites contributed. Click again, and you see the individual inverter or turbine time series complete with SHAP callouts that explain the driver variables. In Power BI, I pair each visual with a plain-language caption generated by GPT models so an executive can read “High ambient temperature and state-of-charge ripple caused today’s eight percent risk spike” without parsing a scatterplot. I cap the entire canvas at three layers of depth because after three clicks, users abandon the journey. Finally, before a single pixel ships, I run the “boardroom test”: I put the draft on a big screen in a conference room and ask a nontechnical colleague to explain what action they would take. If they hesitate longer than the elevator ride between floors five and seven, the design goes back to the lab. This discipline has turned abstract data like harmonic energy in the two-to-three-hertz band into intuitive visuals that now drive C-level decisions, including a fleet-wide operating policy that lifted availability nine percent last quarter.
Your work has resulted in patented innovations. Can you tell us about one of your patents and how it’s contributing to the future of renewable energy?
One patent I am particularly proud of is the Air Quality Monitoring System Powered by Renewable Energy, registered in the United Kingdom under Design Number 6374108. It began as a side project to solve a very practical pain point at remote solar farms: crews needed real-time particulate readings to decide when to wash panels or throttle inverters during dust storms, but conventional sensor stations depend on grid power or frequent battery swaps. My solution is a shoe-box-sized unit wrapped in bifacial photovoltaic film that harvests energy from both direct sunlight and ground albedo, stores it in a supercapacitor bank, and drives a low-wattage neural engine at the edge. The embedded model cleans raw gas and particulate signals, predicts the next six hours of PM 10 and ozone levels, and publishes the forecast over LoRa to the plant SCADA bus.
In pilot deployment across three desert sites, the system delivered 99.6 percent uptime with zero external power draws, letting operators schedule panel washing exactly when soiling losses exceed the cleaning cost. That move alone lifted annual energy yield by 2.3 percent. The bigger vision is to network hundreds of these self-powered stations along transmission corridors and wildfire fronts, giving grid operators an environmental early-warning lattice that feeds directly into ramp rate planning for both solar and battery assets. By eliminating the power cord, the patent turns air quality sensing from a stationary instrument into a scalable distributed service that rides on the same clean energy it helps protect.
Looking ahead, what do you see as the next frontier in applying machine learning to renewable energy systems, and how are you preparing to tackle it?
The next big leap, in my view, is federated, multimodal learning that lets every wind turbine, solar tracker, and battery rack act like a node in a collective brain while keeping its raw data on-site. Today, we still fly data to the cloud, train models in isolation, and push updates down once a quarter. The frontier flips that flow: models travel, data stay put, and insights converge in near real-time across the fleet.
Here is the roadmap I am already building:
Edge “nano-ML” kits – I am containerizing slim ONNX versions of our best anomaly-detection models so they run on turbine PLCs and inverter ARM cores with <2 W overhead.
Federated orchestration – A lightweight controller negotiates gradient swaps over MQTT every ten minutes; no single site ever sees another’s proprietary data, but everyone benefits from shared learning about icing patterns, harmonic drift, and soiling.
Multimodal digital twins – We fuse SCADA, vibration spectra, thermal images, and soon acoustic signatures (the Energy Soundscape project) into a unified embedding so the model “hears” trouble before the KPIs wobble.
LLM-powered root-cause chat – A fine-tuned language model sits atop the time-series lake, ready to answer “Why did OSI drop on String 42 at 14:07?” in plain English and cite the exact signals that triggered the alert.
Thanks for sharing your knowledge and expertise. Is there anything else you’d like to add?
Two quick closing thoughts:
1. Open invitation to collaborate. The clean-energy transition will stall if algorithms live in silos. I am releasing non-proprietary code, acoustic-signature datasets, and design notes in a public build log at drumiljoshi.com/energysoundscape so utilities, start-ups, and universities can field-test ideas together. If you have an unusual data stream—be it lidar, drone thermography, or even wildlife acoustic sensors—let’s plug it into the mesh and see what new physics we can uncover.
2. Human-centric AI is non-negotiable. The most sophisticated model is useless if a field technician cannot act on it at 3 a.m. in a thunderstorm. My north star is to make every predictive insight explain itself in under 20 seconds, in language an apprentice can trust. If we hit that bar consistently, the machines will not just keep the lights on; they will inspire the next generation of engineers to make those lights greener, cheaper, and everywhere.
Thanks again for the platform—and for everyone reading, feel free to reach out. The grid is our shared instrument; let’s tune it together.