The renewable industry can expect a sudden failure, malfunction, or an error–technological barriers can make the situations worse with no preventive measures against the undesirable business impacts. Predictive maintenance in the renewable industry offers the ecosystem to prepare and avoid failure or downtime periods in the production process. With IIoT sensors, Machine Learning, and AI-powered predictive maintenance tools, renewable industry like solar plants or wind turbines gathers huge volumes of Big Data to perform detailed analyses for the optimization of the process and strengthen their ability to prepare against possible risks. This blog post aims at deriving the significance and the benefits of predictive maintenance systems in enhancing renewable industry operations.
Driving Actionable insights with Predictive maintenance
Remote sensors use an interface on either a computer or a smartphone to offer you real-time alerts, once they predict specific issues before a problem can arise. Professionals can be proactive- you can schedule a maintenance outage in priority, purchase the parts, or document the conditions to build the equipment history data. AI tracks and searches for the data patterns to predict and initiate optimization of performance. For instance, a wind turbine operator identifies a situation and can decide to wait till the low-wind period occurs for repair. The early detection helps operators to reduce the downtime and plan prior rather than operating in the crisis mode.
Predictive maintenance also positively impacts the significant reduction of costs involved with the machine or equipment failures and the time you spend to maintain the assets. Additionally, this kind of monitoring of machinery health is crucial to expand the lifespan of the equipment. As per Statista, it is expected that the global predictive maintenance will hit 23.5 billion USD by 2024, to reach an annual growth rate of almost 40% from 2018 to 2024.
Role of IoT-based Predictive maintenance in renewable energy systems.
Predictive maintenance enables the streamlined and efficient management of energy systems to further minimize interruptions. For instance, the energy systems, such as wind turbines or solar power systems are the most productive when they can operate perfectly, without leaving chances for unexpected repairs for parts like bearings, rotating elements, lubrication systems, condensation pumps, etc. which are crucial to continuing operations. In such cases, the predictive maintenance tools act sensitively, to let you pick up the anomalies, maintain the efficiency, and withstand the harsh conditions, to build great conditions for the components.
The IoT sensors can be a replacement for humans on damage assessments to make the maintenance measures more effective. For example, in the case of wind turbines, if lightning strikes any of the turbines, the sensors can retrieve the data about the electricity that passes through every blade. This can also help gauge the differences in performance and help real-time data flow to the control system to send alerts. This can help the team to analyze the site, and understand the blades that require a diagnosis. The team can then stop the wind turbines and reduce the downtime.
AI-driven predictive maintenance in Renewable Energy Sector
AI has become a colleague and virtual assistant in both business and domestic functions. Since the wind and solar systems have inherent variability, the existing electric grids may face serious challenges in accommodating renewable energy. The utility sector needs smart systems that can scale up the incorporation of renewables into the grid and can give equal priority to renewable energy in the energy supply.
Let’s understand the role of AI technology in enhancing the trustworthiness of renewable energy:
1. Intelligent and Centralized Control Centers
The grids can be connected with sensors and devices to collect massive data. When you couple data with AI, they produce new insights for the operators to trigger the control operations. It contributes to the flexibility of energy suppliers to balance demand and supply.
Intelligent storage units can adjust depending upon the supply flow. Nevertheless, the sensors and machines can enrich the integration and efficiency of renewable energy.
2. Asset performance management
The AI-based predictive maintenance software can analyze the sensor data and combine them with real-time monitoring. They assist in condition-specific maintenance, and use Artificial Intelligence to make fault detection and repairs before the asset breaks down.
3. Weather-based tracking control system
The software can build tracking algorithms to track information as per weather conditions. The self-learning technology can integrate forecasting models with solar generation and weather generation operation data. The prediction system tracks the solar generation in advance, and performs solar forecasting efficiently.
Industrial use cases on Predictive Maintenance
Scaling up maintenance process of wind turbines
Predictive maintenance is particularly significant in the offshore sector since there is no direct access to the turbines. The malfunctions can lead to turbine downtime, which creates undesirable economic damage. It needs a lot of time and energy to fix a problem with the old research method. While with predictive maintenance, the technology can use relevant data about the condition of turbines, send it to the central system, and use analytics and Machine Learning to find out what they mean. For instance, if a huge form of wind were to quickly distort a wind blade, then you can control the energy to prevent overheating the gear ratio of the motor. This also helps you analyze the probability of failure and the expiry of specific components.
Tracking anomalies with AI-powered predictive maintenance
With an AI-based predictive maintenance system, you understand in detail how the turbines and plants perform under custom periods while comparing the power curves and analyzing the condition of components. The leading sources of energy like solar and wind energy, depend on weather largely for power generation. The sudden weather fluctuations can affect the energy flow, thereby making the renewable energy supply chain prone to vulnerabilities. AI-powered software analyzes the data from the existing sensors to determine the optimal points for improving the performance of wind turbines. Since it needs no installation of additional sensors, there are no costs involved for the operator.
Conclusion
Many technologies fuse predictive maintenance to extend the life expectancy of equipment and machines in the renewable sector. Predictive maintenance in the renewable industry can optimize operations, improve machine efficiency, and implement real-time monitoring, asset condition monitoring, and maintenance. Plant managers and operations professionals use predictive maintenance technologies to ensure zero unplanned downtime and wish to scale up the operational efficiency.
UptimeAI offers Artificial Intelligence rooted solutions to mitigate the equipment failures of the plant.
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