01
Performance Analysis
Plant and turbine level yield gap analysis, PR / CUF / PLF benchmarking, and energy loss waterfall attribution — every lost kWh assigned to a cause.
Data first · Then intelligence
Higher perspective.Greater impact.
Most portfolios lose 1–2% of yield to problems nobody can see — because the data is fragmented.
The Data Maturity Spectrum
01
Reactive
Fragmented
Data sits in OEM portals, spreadsheets and email. Monthly MIS is assembled by hand. Answering “why” needs a phone call to site.
The four stages of data maturity
Fragmented — reactive, reporting lag of days to weeks.
Consolidated — descriptive, reporting lag of hours.
Intelligent — diagnostic, near real time.
Predictive — prescriptive, ahead of the event.
The Common Trap
Most portfolios buy Stage 04 tools while sitting at Stage 01.
Digitalisation fails when it starts with a tool. It works when it starts with an honest read of data maturity. A dashboard built on unreconciled data only industrialises the disagreement.
01
What happens
A platform is procured. Six months on it shows numbers nobody trusts — because tag mapping, availability definitions and the loss taxonomy were never agreed first.
02
Why it happens
Maturity is assumed rather than assessed. Vendors sell the destination; nobody sizes the distance between here and there.
03
The alternative
Diagnose first. Fix the data contract, the definitions and the reconciliation. Then layer intelligence on a foundation that holds under challenge.
The DART Framework
Divinergy Analytics for Renewable Teams.
A structured analytics and decision framework built on real-world execution. Each tier maps to a maturity stage — you enter where you actually are, and commit only to the step that is next.
01 / Foundation
DART Foundation
SCADA and CMMS integration, a governed data model, an agreed loss taxonomy and automated management reporting — including the DGR pipeline that replaces the manual sheet.
Moves you to Stage 02 — Consolidated
02 / Intelligence
DART Intelligence
Continuous monitoring, automated deviation detection routed to a named owner, an executive KPI layer, and independent availability recomputation for LTSA assurance.
Moves you to Stage 03 — Intelligent
03 / AI
DART AI
Predictive degradation and failure models, prescriptive O&M, and anomaly detection with automated ticketing that raises against the underlying cause — not the symptom.
Moves you to Stage 04 — Predictive
Every engagement begins with a diagnostic — a 30–45 day read across a representative slice of the portfolio that establishes the baseline, the loss taxonomy and the roadmap before any build commitment is made.
Capabilities
One partner across the whole chain.
From plant-level performance analysis to enterprise intelligence architecture — and the advisory layer that makes it hold up in front of a board.
02
O&M Intelligence
Preventive and corrective maintenance modules, contractor performance audit, spares and inventory digitalisation, and response-time analytics.
03
Data Architecture
MIS ecosystem design, SCADA and CMMS integration, governed data models with an agreed loss taxonomy, and automated management reporting.
04
Dashboards & BI
Production-grade executive and operational dashboards on a single governed model, so two teams stop quoting two different numbers.
05
AI & Anomaly Detection
Forecast deviation engines, sensor anomaly detection, and predictive degradation modelling with automated root-cause ticketing.
06
Advisory & Assurance
Independent availability recomputation, LTSA and OEM assurance, feasibility and due diligence, and investor- and lender-ready reporting.
Detection Cases
Problems that never trip an alarm.
Each of these was found in data the operator already had. No new instrumentation, and no site visit needed to make the first call.
Wind · 01
Blade Soiling Detection
A systematic cluster below the OEM power curve — 15–25% output gap at the same wind speed, worth ₹45–60L per turbine per year.
Read the caseWind · 02
Anemometer Anomaly Detection
The turbine's own output validates the sensor. Detect, grade by severity, identify direction, correct — without masking real underperformance.
Read the caseWind · 03
Fleet Normalisation & LTSA Assurance
Several SCADA schemas, several availability definitions. One tag dictionary makes every platform comparable — and the OEM conversation evidence-based.
Read the caseSolar · 01
Inverter-Level Performance
Plant-level PR hides everything. One block in the bottom band every day is a physical fault; one that moves in and out is a data problem.
Read the caseSolar · 02
Soiling Loss & Cleaning Economics
The measured soiling rate — not an assumed one — sets the economic cycle. 0.8–1.5% recoverable PR, roughly 1.2 MU/yr on a 50 MW plant.
Read the caseSolar · 03
Pyranometer Drift Analysis
PR is a ratio. Drift the denominator and you manufacture a problem that isn't there — or hide one that is.
Read the caseDetection outcomes and ranges are indicative, drawn from Divinergy DART cases and illustrative machine classes — not from any single client's data. Actual recovery varies with wind regime, dust load, rainfall pattern, tariff and turbine rating.
Field Intelligence
Truth on the ground.
Everyone offers module-level testing. What matters is what happens after the test — raw output turned into inverter-level corrective guidance with a financial consequence attached.
In-situ EL imaging runs at night with zero generation loss — no strings shut down for diagnostics.
I-V CurveInverter-level degradation, not a sheet of raw traces
EL ImagingIn-situ night testing, zero generation loss
Thermal / IRHotspot detection and string-level mapping
Flash TestingModule power verified against nameplate
SoilingQuantified loss and a defensible cleaning ROI
Visual InspectionDefects catalogued with severity and ownership
Ready to Deploy
Systems already running in production.
Four field and workflow systems built for Indian wind OEM and IPP operations. All four are configurable rather than bespoke — which is why deployment is measured in weeks, not quarters.
ReportingMost requested
DGR Automation
The manual Excel daily generation report, replaced by an automated SCADA-to-warehouse pipeline — with validation that strips duplicate and corrupt records before they ever reach reporting.
- Automated SCADA-to-warehouse ingestion
- Duplicate and corrupt-record validation at source
- Scheduled distribution — nobody assembling a sheet by hand
- Feeds the same governed model as the dashboards
Why it matters: the DGR is the number everyone argues about. Automate it and the argument moves from the data to the decision.
Maintenance
Preventive Maintenance App
Checklist-driven O&M in the field. Asset hierarchy from state down to individual equipment, PM calendars, inspection checklists and breakdown capture — offline-first, because remote sites lose signal.
- Asset hierarchy: State → Site → Feeder → Equipment
- PM schedule definition and maintenance calendar
- Mobile app with offline mode and geo-fencing
- Breakdown capture, task management, consolidated reporting
Pairs with analytics: a ticket raised by anomaly detection can open the PM task automatically.
Safety
Permit to Work
Controlled access to hazardous work, with configurable permit types for electrical, hot work and confined space — and a multi-step approval engine that captures data at every stage.
- Configurable permit types and approval workflows
- Role-based access with digital sign-off
- Full audit trail and compliance reporting
- Mobile-enabled for field use
Outcome: a complete compliance trail, and reduced safety risk on every job.
Commissioning
Handover–Takeover
Digital commissioning checklists per turbine, multi-level approval, and a quality history card per unit — with live plan-versus-actual so readiness is a number, not an opinion.
- Per-WTG digital commissioning checklists
- Multi-level approval and sign-off
- Quality history card per unit
- Live plan-vs-actual readiness tracking
Measured result: a 15% reduction in readiness time in production use.
Built and deployed with Divinergy's engineering delivery partner. Every system is configured to the operator's own hierarchy, permit types and reporting formats. Client references available on request.
Why Divinergy
Built by operators.
Built by operators.
Not by a platform vendor.
A specialist consultancy at the intersection of renewable operations and digital innovation — born from hands-on years inside India's leading IPPs.
1 GW+Renewable assets with hands-on operating experience
30+Solar, wind and hybrid projects impacted
15+Years operating inside Indian IPPs
4Field systems already running in production
01
Operators, Not Onlookers
Control rooms run, O&M contractors managed, availability disputes argued and settled. Every framework is calibrated for how Indian RE assets actually behave.
02
Independent by Construction
No OEM, EPC or platform vendor relationships to protect. When the analysis says the turbine supplier is wrong, that is what the report says.
03
Foundation Before Intelligence
We start where the real problem is — tag mapping, definitions, reconciliation. AI-readiness is the outcome of getting that right, not the opening line of a pitch.
04
Every Finding Carries a Number
Yield uplift in MU, recovery in Cr, payback in months. A finding without a financial consequence is an observation, and observations don't get funded.
05
Weeks, Not Quarters
Four field systems already in production and configurable rather than bespoke. Diagnostics land in 30–45 days.
06
We Say What We Don't Know
Ranges are stated as ranges. Illustrative figures are labelled illustrative. Limitations go in the deck rather than waiting to be discovered.
Let's work together
Ready to move up the spectrum?
Whether the next step is a diagnostic, a data foundation or a full intelligence layer — Divinergy is ready to engage. We start by listening.