Production is going on.
Predictive insight across a fleet with 715 MW of installed capacity
BCT deployed RETINA360 APM to unify turbine and substation data, introduce proactive alerts and predictive analytics, and tailor reporting to the operator’s needs.
Client
Challenge
The existing centralised platform brought data from different turbine systems onto one screen but offered no proactive alerting and limited predictive analytics. Faults were identified after they had already affected generation, leaving teams to investigate downtime and underperformance reactively.
Reporting could not be adapted adequately to the operator’s requirements. Differences between the two manufacturers’ data formats also made consistent fleet analysis difficult. The platform lacked capabilities such as yaw and pitch misalignment detection and insight into the likelihood of equipment failure.
The customer needed an asset performance platform that could work with existing turbine and substation infrastructure, provide earlier warnings and produce reports suited to its operations.
Reporting could not be adapted adequately to the operator’s requirements. Differences between the two manufacturers’ data formats also made consistent fleet analysis difficult. The platform lacked capabilities such as yaw and pitch misalignment detection and insight into the likelihood of equipment failure.
The customer needed an asset performance platform that could work with existing turbine and substation infrastructure, provide earlier warnings and produce reports suited to its operations.
Solution
BCT deployed RETINA360 APM as a common asset performance and analytics layer across turbine, substation and enterprise systems. It connected the existing turbine SCADA systems and substation monitoring through IEC 104, retaining the underlying infrastructure while replacing the previous monitoring platform.
The platform normalised data from both turbine manufacturers into a common asset model and applied performance and health-scoring logic. Predictive analytics added yaw and pitch misalignment detection, while failure-likelihood alerts helped teams identify conditions requiring investigation.
BCT configured management information and daily generation reports to the operator’s requirements. An AI chatbot using local large language models supported investigation of underperformance and potential breakdowns, alongside continuous monitoring and proactive alerts.
Delivery began with a two-month proof of concept at one wind farm using live SCADA data. Once validated, the approach was extended to the remaining three farms, with targeted customisation for differences between manufacturers. Reusing the pilot approach accelerated the wider rollout.
The customer’s team now operates RETINA360 day to day, while BCT continues to manage platform configuration. This gives the operator direct responsibility for daily monitoring with continued support for configuration changes.
The platform normalised data from both turbine manufacturers into a common asset model and applied performance and health-scoring logic. Predictive analytics added yaw and pitch misalignment detection, while failure-likelihood alerts helped teams identify conditions requiring investigation.
BCT configured management information and daily generation reports to the operator’s requirements. An AI chatbot using local large language models supported investigation of underperformance and potential breakdowns, alongside continuous monitoring and proactive alerts.
Delivery began with a two-month proof of concept at one wind farm using live SCADA data. Once validated, the approach was extended to the remaining three farms, with targeted customisation for differences between manufacturers. Reusing the pilot approach accelerated the wider rollout.
The customer’s team now operates RETINA360 day to day, while BCT continues to manage platform configuration. This gives the operator direct responsibility for daily monitoring with continued support for configuration changes.
Impact
RETINA360 established a unified performance view across four wind farms and two turbine manufacturers. Approximately 700 MW of wind turbine assets are monitored within the portfolio’s 715.3 MW total installed capacity. The platform replaced the previous monitoring tool and consolidated visibility previously spread across separate manufacturer consoles.
Proactive alerts, predictive analytics and customised reporting enabled the operator to investigate developing issues and analyse generation losses more systematically. The pilot also provided a repeatable approach for onboarding the remaining sites.
The source materials report annual energy production improvement of 0.5–1.0%, fault and downtime analysis in under five minutes, and 99.5% asset coverage and application uptime. Their measurement status requires confirmation: the submission presents them as outcomes, while the supporting presentation labels them potential business outcomes.
Proactive alerts, predictive analytics and customised reporting enabled the operator to investigate developing issues and analyse generation losses more systematically. The pilot also provided a repeatable approach for onboarding the remaining sites.
The source materials report annual energy production improvement of 0.5–1.0%, fault and downtime analysis in under five minutes, and 99.5% asset coverage and application uptime. Their measurement status requires confirmation: the submission presents them as outcomes, while the supporting presentation labels them potential business outcomes.
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