Wind energy operations and maintenance (O&M) presents a unique set of challenges that are driving stakeholders to seek advanced solutions such as artificial intelligence (AI). There are multiple applications of AI in wind O&M. One, AI helps to avoid costly unplanned downtimes, through predictive maintenance. Two, it is being used to for better forecasting of wind power generation in a bid to avoid deviation penalties. Three, with AI, risky work involving manual labour is automated, especially at hard-to-access sites. Four, initial investments in AI are being undertaken to reduce overall wind O&M costs. According to Renewable Watch’s interactions with industry stakeholders, O&M accounts for almost 20-25 per cent of a wind project’s levellised cost of energy, making even modest cost reductions highly impactful. In this context, AI is emerging as a game-changing solution. It is reshaping how the wind sector anticipates failures, optimises performance and reduces operational costs.
Against this backdrop, this article delves into AI’s role in wind O&M, how AI is integarted in India’s wind sector, followed by the challenges and future outlook…
AI’s role in wind O&M
While supervisory control and data acquisition (SCADA) systems can provide basic performance monitoring, their lack of interpretative sophistication has been identified in wind power set-ups, limiting their usefulness in identifying issues before they escalate into failures. To address these challenges, industry stakeholders advocate for a shift towards big data-driven diagnostics and predictive analytics. AI is emerging as a critical tool in this effort, enabling predictive maintenance, fault detection and autonomous inspection, while also supporting grid integration and workforce optimisation.
Digital twin models, powered by AI and machine learning (ML), can simulate turbine components and their operating conditions in real time, enabling operators to visualise and forecast degradation pathways. This approach does not depend on the presence of modern sensor infrastructure. Instead, non-intrusive sensing technologies, including drone-based imagery, radar-based acoustic signature analysis and multi-sensor fusion, can extract vital operational data from legacy turbines. Tata Consultancy Services (TCS) has also underscored how legacy wind farms, many of which are nearing their end-of-life design, can adopt digital twins, radar-based sensing and AI analytics to extend asset life, improve diagnostics and reduce operational costs without retrofitting physical sensors.
AI’s capabilities in wind energy O&M extend beyond predictive maintenance and digital twins. AI assists in site identification and wind resource evaluation by analysing meteorological data to optimise turbine placement, which directly affects project viability. For grid connectivity and forecasting, AI enhances the predictability of wind output, allowing utilities to better match supply with demand, thereby improving grid stability. Condition monitoring systems can now track critical components such as gearboxes and bearings in real time, while AI-powered risk management tools analyse historical failure data to flag vulnerabilities and suggest mitigation strategies. In large-scale projects, AI supports workforce planning by ensuring efficient deployment and safety compliance. Moreover, AI facilitates smart component interfacing by reconciling technical specifications across diverse original equipment manufacturer (OEMs) components, enabling integrated turbine systems at minimal cost. TCS suggests that this can prove to be particularly valuable in markets like India, where mixed fleets are common.
However, industry estimates regarding AI’s cost-saving potential vary. At the AI in Renewables conference organised by Renewable Watch in April 2025, estimates ranged widely – while some projected savings as high as 50 per cent with full-scale AI integration, others took a more conservative view, estimating 5-6 per cent, acknowledging practical implementation limitations.
AI adoption in India’s wind sector
Several companies in India illustrate how AI adoption is reshaping wind O&M operations. Suzlon Energy has introduced AI-driven predictive maintenance systems to boost the performance and life span of its turbines, enabling it with an 83 per cent probability of predicting turbine gearbox failures 45 days in advance. The company has also established AI-driven optimisation and digitisation platforms supported by SCADA integration and dual monitoring centres in Pune and Melbourne. Vestas India has an industry analytics platform, Scipher, which applies ML to enable easy access to portfolio-wide asset visualisation, predictive maintenance and wind/solar power forecasting. ReNew employs AI and ML tools to predict generation from wind and solar assets, leading to more stable grid operations and fewer penalties from schedule deviations.
Adani Green Energy Limited (AGEL) uses CCTV anomaly detection systems to monitor wind turbines 24/7 and plans to integrate SAP-based notifications to support both preventive and breakdown maintenance workflows in the future. Furthermore, AGEL is integrating AI into its hybrid wind-solar projects to enable real-time monitoring and optimisation of power output and grid integration. AMPIn Energy Transition has implemented AI-based control and scheduling mechanisms, automated forecasting and scheduling systems to enhance operational efficiency. The company has also deployed drone-based inspections and geotagging of employees, supporting real-time asset monitoring and performance tracking. Additionally, automated
reporting dashboards help identify top-performing assets, while pilot projects are under way to optimise power purchase agreement provisions.
Beyond individual case studies, a broader trend is emerging. Companies across the board are increasingly combining drones with AI analytics for blade inspections, enhancing safety, accelerating inspection times and yielding more accurate diagnostics compared to traditional methods.
Challenges and future outlook
Despite advancements, several systemic challenges remain. Data interoperability remains a hurdle, as disparate systems from OEMs, operators and third-party service providers often do not easily integrate with predictive maintenance technology. Cybersecurity is another concern, as the real-time nature of AI-based monitoring necessitates robust safeguards against data breaches, especially given the increasing sophistication of cyberthreats. Additionally, there is a noticeable skill gap – O&M professionals in India are not trained in AI tools and techniques, indicating the need for extensive capacity-building. As AI tools become more complex, there is also a need for standardisation in performance metrics and regulatory oversight to ensure consistency and accountability across the sector.
To address the systemic challenges facing AI adoption in the wind energy O&M space, the most effective overarching solution lies in the creation of a national AI-enabled digital infrastructure framework specific to the renewable energy sector. This framework would standardise data protocols, cybersecurity norms, performance metrics and capacity-building pathways across the industry.
To address the skill gap, the framework must include a national skilling roadmap with mandatory AI training modules for O&M technicians, supported by partnerships with technical institutions, OEMs and digital service providers. Furthermore, the establishment of a central performance benchmarking registry under the Ministry of New and Renewable Energy would enforce metric standardisation and accountability. Lastly, regulatory clarity can be ensured by integrating this framework within India’s digital public infrastructure vision, enabling the sandbox testing of AI tools and defining liability, data privacy and auditability norms.
India’s wind energy sector will soon undergo a digital renaissance, with AI at the forefront of O&M innovation. From real-time condition monitoring and predictive maintenance to autonomous inspections and digital twins, AI is driving a shift toward data-centric, proactive asset management. Many companies, including Suzlon, ReNew, AGEL and Vestas India, are leading this transition by embedding AI into their O&M ecosystems. Their initiatives reflect a broader industry trend – the transition towards smart, self-diagnosing wind assets capable of adapting to operational and environmental changes in real time.
As AI adoption accelerates, fuelled by falling sensor costs and rising computational capacity, India’s wind farms are set to become not only more efficient but also more resilient and sustainable. The future of wind O&M lies in intelligent systems that reduce operational costs, increase power yield and extend asset life – all critical to India’s path towards a cleaner energy future.
Nguồn: https://renewablewatch.in/2025/05/02/optimising-performance-role-of-ai-in-wind-om/