Mr. Nihar Raj - Keynote Speaker | DIGITAL UTILITY INDIA | 2022 | AAYERA SUMMITS

Summary

Mr. Nihar Raj (Adani Transmissions) explains how transmission utilities are adopting digitalization to transform “the grid of the future.” By leveraging data analytics, organizations aim to meet business objectives and operational KPIs while improving business continuity and risk de-risking.

1) Digitalization goals and “data fabric”

• Transmission assets (e.g., transformers, circuit breakers, current transformers, converter transformers) are described as having “capacity to speak,” generating operational signals/events.
• A key challenge is not only collecting data, but building an integrated “fabric” across functions such as HR, finance, safety, legal, technology, and bidding/finance data.
• Data visualization is emphasized as a core capability gained through the digital transformation journey.
• Change management is highlighted as part of adoption—enabling teams and driving organizational uptake.

2) Case Study: Climate/weather analytics for continuity and de-risking

Business need: Improve business continuity and reduce operational risk from climate change impacts (e.g., flooding and fog causing trippings).

• Example risk scenario: Flooding at a 765 kV substation, potentially leading to grid imbalance (noting the scale of power transfer around that level).

Approach:

• Combine weather analytics with:
  • failure/trip data, and
  • resource/maintenance planning data.
• Use weather-aware planning to prioritize preventive maintenance and patrolling.
• Enhance workforce/resource planning based on regional climate criticality (e.g., winter fog → line tripping risk).

Outcomes/benefits:

• Prevent major disruptions by acting when adverse weather is forecast.
• Emphasize faster recovery—for example, erecting a 765 kV tower in ~16 days vs ~25 days using data-enabled readiness.
• Strategic point: even with long-term modernization plans (e.g., AIS to GIS), the cost burden remains with customers—so the immediate priority is de-risking existing assets using current asset-management practices.

3) Case Study: Preventing outages to sustain near-99.88% availability (“Power for all”)

Context:

• India’s goal of “24x7 power for all.”
• Increased renewable penetration adds stresses such as:
  • voltage/reactive power issues, and
  • reliability challenges related to aged assets.

Approach:

• Analyze trip history over five years, including:
  • when trips occur (day/night),
  • seasonal patterns (monsoon/winter/summer),
  • storm/month criticality.
• Classify assets by criticality into supercritical / critical / subcritical.
• Use zoning to analyze large corridors (hundreds of km) at finer segment granularity—focusing on “notorious” portions rather than entire lines.

Outcomes:

• Reported overall availability of ~99.88%.
• Reduced operational issues:
  • HVDC line faults (LFFs) reduced by >70%.

Why LFFs matter:

• HVDC faults cause not only interruptions, but also heavy stress on:
  • converter transformers,
  • filters,
  • valves.

Business/asset-life framing:

• Extending equipment life (conceptually around ~15 years) reduces earlier capex needs.
• The “deferred investment” framing links savings to interest/cost of capital over time, aligning with life-cycle replacement thinking.

4) Case Study: Time-motion studies and benchmarking to reduce outage/restoration time (RS component)

Goal: Improve availability, described as depending on (reliability/interruptions) × (events), while also reducing:

• preventive maintenance outage time, and
• breakdown maintenance response time.

Approach:

• Run time-motion studies across regions and sites.
• Benchmark task execution time for specific voltage-level equipment work.
  • Emphasis: avoid “apple-to-apple” mistakes (e.g., 33 kV vs 765 kV circuit breaker replacement).
• Compare execution times per site/team to establish benchmark time.

Operationalization:

• Update SOPs based on benchmarking.
• Use SAP or field mobility apps for maintenance planning and check for outliers in scheduled shutdown activities.

Findings:

• Some sites initially performed efficiently, but later became inefficient due to factors such as the absence of experts (identified as an outlier via data).

Benefits:

• Higher resource productivity.
• Better motivation/reward mechanisms.
• Improved change management via data-driven targets.

5) Case Study: Alarm analytics using SCADA data (no major new capex)

Focus: Use existing SCADA (for substations) to perform alarm analytics and derive actionable engineering insights without major additional investment.

Data scale:

• Example: a 765/400 kV substation generating about ~70 lakh alarm events.

Challenge:

• Engineers may struggle with manual interpretation at this scale.
• Data scientists/analysts can work faster, but tools must be guided and aligned with engineering context.

Implementation method:

• Form a cross-team of:
  • site subject-matter experts (equipment knowledge),
  • automation/IT specialists,
  • programmers.

Key analytics findings:

• Spring charging time variations for circuit breakers:
  • Operator KPI is “spring charged,” but analytics also detects outlier behavior that predicts future failures.
• Missed corrective action insight:
  • Over-focus on keeping the system “live” via operator KPIs could delay deeper issues (example mentioned: historically more DC earth faults).
• Identify value-added vs non-value-added alarms:
  • about ~8 lakh non-value-added alarms.
• Analyze operator behavior:
  • how quickly operators acknowledge critical alarms,
  • differences across regions,
  • risk of alarm fatigue or ineffective acknowledgement.

How results are used:

• Coach operators.
• Run targeted discussions to adjust alarm-handling behavior.

Overall message:

• Deliver outcomes primarily through better use of existing data and analytics workflows, rather than relying on heavy APM software or major capex.

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Main speakers/sources

• Mr. Nihar Raj — Keynote speaker; Head O&M, Adani Transmissions Ltd. (and related technical/committee roles mentioned in the introduction).