Customer Success Stories

See how organizations across industries are transforming their energy management with Konductor and achieving measurable results.

Real Results from Real Customers

Our customers have achieved significant improvements in energy efficiency, cost savings, and operational reliability using Konductor's platform.

Featured Utility Scale HydroPerformance Monitoring 4 min read

Hydro Asset Owner - Early Underperformance Detection

3 years of software fees paid back in 1 weekend. Konductor detected sustained underperformance at a Scottish Highlands hydro scheme, recovering ~80 MWh of generation.

Key Results

80 MWh
Extra Generation Recovered
£24,000
Financial Impact
30 min
Detection Speed
Immediate
ROI Payback

Challenge: Without an expected performance model, it's impossible to know in the moment how the scheme should be performing. When underperformance goes undetected, thousands of pounds of lost generation can accrue before anyone notices. Monthly manual data downloads and reports might surface issues—but by then, significant generation has already been lost.

Solution: We ingested HMI/PLC telemetry every 10 minutes—river level, power output, and other system parameters—via a secure data pipeline. Live data was continuously compared to Konductor's expected performance model, tuned to the site's characteristics. When the power output of the turbine dropped against the expected performance, the operations team was alerted immediately with clear, actionable context.

Utility Scale HydroCondition Monitoring 4 min read

Hydro Condition Monitoring: Drive-End Bearing Overgrease Detection

Overgrease detected at startup; temperature normalised without downtime. Konductor's condition monitoring distinguished between bearing failure symptoms and benign overgreasing, avoiding unnecessary shutdowns.

Key Results

Immediate
Detection Speed
Zero
Unplanned Downtime
Avoided
Bearing Damage
Documented
Preventive Action

Challenge: After routine maintenance, the Pelton turbine's drive-end bearing showed a temperature spike immediately after startup. Without continuous condition monitoring and a clear baseline, teams can mistake overgreasing symptoms for bearing failure, risking unnecessary shutdowns, emergency callouts, or premature component replacement. Monthly reporting is too slow to separate benign overgrease events from emerging faults.

Solution: The Konductor platform ingested live bearing temperature and vibration from the HMI/PLC. We compared signals against the site's baseline and post-maintenance expected profile. Konductor flagged an abnormal temperature rise without corresponding vibration growth, a classic signature of overgreasing/grease churning rather than mechanical damage.

Featured Utility Scale WindHydroPortfolio Management 6 min read

Mixed Portfolio, One View - Wind & Hydro Consolidation

One login for your whole fleet. An IPP with 14MW wind and 7 hydropower sites (~4MW) achieved faster decisions, higher yield, and less chaos through Konductor's unified platform.

Key Results

1-5%
Energy Uplift
30-50%
Faster Fault Response
18 MW
Assets Consolidated

Challenge: Multi-technology portfolios spread across vendors, protocols, and portals create operational blind spots. Fragmented visibility meant wind and hydro data lived in separate systems with no shared live view. Faults and curtailments were discovered hours later, and every site and contractor followed a different process. Portfolio-level KPIs took days to compile.

Solution: Konductor delivered a single, centralised live view for the entire wind + hydro portfolio and a standardised management process that works the same way across regions and contractors. Live portfolio visualisation, standardised operations with shared processes, unified data pipeline, and smart alerts with expected-vs-actual performance models.

CommercialUtility Scale MicrogridSCADA IntegrationSolarBattery Storage 5 min read

Remote Microgrid SCADA Integration

One secure system. Full visibility. Real-time control across a remote hybrid microgrid comprising solar PV, battery storage, wind turbines, and diesel generators—with no existing documentation.

Key Results

4 months
Implementation Time
100%
Asset Visibility
Zero
Security Compromises
Complete
Documentation Created

Challenge: The client required real-time operational visibility of a remote hybrid microgrid but faced several fundamental challenges: no existing documentation (no network diagrams, IP address lists, or Modbus register maps), heterogeneous technologies from different vendors, remote development constraints, strict security requirements prohibiting direct internet exposure, and no unified view for operators or connected customers.

Solution: Konductor delivered a secure, on-premises SCADA and data integration solution. With no prior documentation available, the project began with structured reverse-engineering: network scanning, manual interrogation of PLCs and controllers to discover Modbus registers, and validation of scaling and units across all asset types. The platform was deployed locally within the site network with controlled outbound data flow.

Utility Scale WindHydroSolarData Quality 4 min read

Sensor Integrity & Data Confidence

Trust the data that drives generation, dispatch, and reporting. Konductor detects sensor failure, drift, and implausible values before they impact operations—distinguishing between true asset issues and instrumentation problems.

Key Results

Real-time
Sensor Fault Detection
High
Data Confidence
Proactive
O&M Response
Accurate
Reporting Metrics

Challenge: Renewable generation performance depends heavily on sensor accuracy, yet sensor health is often unmanaged. Wind speed and river/reservoir level sensors directly influence generation expectations and dispatch decisions. Temperature and vibration sensors are critical for early fault detection, but failures often go unnoticed. Sensors may degrade or drift slowly, producing believable but incorrect data that skews generation set points and KPIs.

Solution: Konductor continuously monitors sensor behaviour alongside asset performance to detect sensor failure, drift, or implausible values. The platform applies contextual checks including detection of flat-lined or frozen signals, cross-comparison of related sensors (e.g. wind speed vs power output), identification of gradual sensor drift, and validation against historical and environmental patterns.

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