The Great Divide: Avoiding conflict between owners and OEMs

In wind energy, long-term service agreements (LTSAs) between turbine manufacturers and asset owners were designed to bring clarity and stability to operations. On paper, they do exactly that. Responsibilities are clearly defined, risks are allocated, and performance is measured against agreed benchmarks. In practice, however, there are misaligned incentives that produce tension and even conflict. Beyond contract wording, there is a big, untapped opportunity to smooth this process by how data is collected, compiled, and presented. 

Tension and conflict between owners and OEMs do not arise because one party is underperforming or acting in bad faith. In fact, both sides are typically doing exactly what they are supposed to do. The friction arises because the contract defines success in a way that does not always align with the operational reality of the wind farm.

OEMs are incentivized to meet contractual obligations, most commonly availability targets, while managing service costs efficiently across large fleets. Asset owners, on the other hand, are focused on maximizing energy production and revenue from each turbine. These priorities overlap, but they are not identical.

When things are going well, this misalignment remains largely invisible. When they are not, it becomes very apparent.

Consider periods with elevated equipment failures. For OEMs, this translates into rising costs: more technician hours, more component replacements, tighter margins, and potential exposure to penalties. For asset owners, the same situation means lost production, revenue shortfalls, and operational disruption. The result is a strained relationship, where each interaction risks becoming adversarial.

The OEM may feel they are being pushed beyond contractual obligations. The asset owner may feel their concerns are not being addressed with sufficient urgency. Discussions shift from collaboration to negotiation, and from problem-solving to position-taking.

This dynamic is not unique to any one market. It is a structural feature of how wind assets are operated globally. The question, then, is not how to eliminate the tension entirely, but how to reduce it in a way that benefits both parties. Increasingly, the answer lies in technologies that understand not just the data generated by turbines, but also the realities of how wind farms are operated and serviced.

This can be achieved through three dimensions of how data is handled:

1. Results compilation designed for asset owner and OEM collaboration
2. Utilization of machine learning for predictive maintenance
3. AI + data models applied to performance optimization

Results compiled for asset owner and OEM collaboration

The wind industry has no shortage of data. Modern turbines generate vast streams of information through SCADA systems and other monitoring platforms. This could, in theory, provide full transparency, but in practice it most often creates overload. Access to data does not automatically translate into insight. Plotting graphs is useful, but it is often time-consuming and open to interpretation.

To be effective, analytics must go beyond detection. They must explain, especially when asset owners and OEMs are negotiating action. 

In order to advocate with the OEM, asset managers need to understand what the issue is, why it matters, what is likely causing it, and what should be done about it. Just as importantly, the OEM needs to understand and feel confident in the diagnosis.

Here, the quality and source of evidence become critical. Insights are far more effective when they are grounded in data that both parties trust. 

How the information is presented is key where clear explanations, supported by concise visuals, enable faster alignment. When both sides can see the issue, understand its cause, and quantify its impact, the conversation becomes less adversarial and more collaborative.

Predictive maintenance ML

When applied effectively, predictive maintenance AI enables early detection of issues that would otherwise lead to failures, identifying abnormal patterns in temperature, vibration, or operational behavior well before they escalate. The immediate benefit is clear: fewer unexpected breakdowns — a pain point that both sides experience at the same time.

Critically, this does not mean overwhelming service teams with constant alerts. Effective systems distinguish between early-stage issues that can be addressed during routine maintenance and critical risks that require immediate action. Most signals can be surfaced well in advance, allowing them to be integrated into existing workflows without creating unnecessary urgency.

When this balance is achieved, predictive maintenance is no longer seen as intrusive. It becomes a tool that helps both parties operate more efficiently.

AI + data models applied to performance optimization

While availability has traditionally been the primary contractual metric, it does not fully capture how well a turbine is performing. Subtle deviations (whether due to control settings, yaw alignment, or sensor inaccuracies) can reduce output without triggering alarms. For asset owners, these issues represent lost revenue. 

For OEMs, they represent an opportunity. No manufacturer wants its turbines to be perceived as underperforming. Demonstrating strong production performance and high-capacity factors is central to maintaining reputation. OEMs improve outcomes and strengthen their standing with clients when performance issues are clearly identified and accompanied by straightforward corrective actions.

Clearly identified issues increase production and also build trust.

Bridging the divide

Across these measures, technology serves not just as an analytical layer, but as a bridge. It provides a common, objective view of performance; one that reflects both contractual realities and operational outcomes enabling more constructive interactions. OEMs and asset owners can work toward the same goal: maximizing the value of the asset.

As wind portfolios continue to scale and expectations rise, this capability will become increasingly important. It is incumbent upon owners and OEMs to up their game in compiling, analyzing, and presenting data as well as allocating the resources to train all parties on the process.

Bora Tokyay is the CEO of Kavaken.com, a company focused on improving the performance and profitability of renewable energy assets through advanced analytics and generative AI. Kavaken works with asset owners, operators and insurers to transform operational data into clear, data-backed insights that support faster decisions, stronger collaboration, and measurable performance improvements.

Kavaken | www.kavaken.com