Wind Suite empowers renewable energy asset owners with Artificial Intelligence to overcome the challenges they are facing
Renewable energy asset owners are facing challenges such as decreasing revenues, aging assets and increased down times. At the same time, their assets are generating significant amount of data which can help them overcome these challenges. However, they lack the capabilities to exploit these data efficiently.
Tarentum Energy has developed an Artificial Intelligence powered solution that increases revenue by optimizing asset management in a data-centric manner. In this solution called Wind Suite, production forecasting, asset management and predictive maintenance modules work in unison to drive operational decisions with a focus on revenue optimization.
The capable modules of Wind Suite, which brings Artificial Intelligence to renewable energy asset management:
- Revenue Cockpit: Implementation of a dashboard to watch key metrics: “Lost” Revenue, Revenue Based Availability, Energy Based Availability, Capacity Factor
- Forecast+: Use advanced analytics to better forecast short term production, reduce balancing costs and increase profits.
- Power Curve Analyzer: Analyze turbine power curves, maximize turbine output and revenue by recommendation for shifting the power curve up.
- Maintenance Planner: Plan and track all maintenance activities in one place; see real-time progress and avoid potential delays via advance notifications.
- Operations Tracker: Achieve peace-of-mind from miles away by knowing the turbines are operating within normal behavior limits and costly human errors are eliminated.
- Predictive Maintenance: Reduce unplanned maintenance and downtime by harnessing large amounts of data to produce alerts ahead of time to prevent breakdown and loss revenue.
1. Revenue Cockpit
Tarentum provides a live dashboard for viewing the wind power plant critical information and performing reporting and analysis instruments. Main features of the Revenue Cockpit:
- A general overview of all wind power plants on a live map
- A live map with information such as wind speed, production, temperature and a summary of last days production and loss revenue
- A summary table reflecting the summary KPIs of all sites focused on key revenue metrics such as Production, Capacity Factor, Revenue Based Availability, Energy Based Availability, Time Based Availability, Power Efficiency of the turbines and sites.
- The site page will represent a live map of the site representing each turbine
- Each turbine will show live information such as wind speed, production, temperature
Tarentum Wind Energy Forecast uses AI based approach to help wind energy clients with forecasts that minimize uncertainty. Wind energy predictions can negatively impact grid operations and profitability for intraday markets.
Tarentum’s unique approach benefits from combining the advantages of it’s machine learning model with additional input from commercially available forecast models. Each model has it’s own strengths for certain scenarios and Tarentum+ can provide higher accuracy prediction to end- users. Tarentum provide Day Ahead (GÖP) and Intraday (GİP)production forecasts to the Client.
Tarentum’s unique approach benefits from combining the advantages of it’s machine learning model with additional input from commercially available forecast models. Each model has it’s own strengths for certain scenarios and Tarentum+ can provide higher accuracy prediction to end-users.
AI powered Tarentum Wind Energy Forecast advantage starts with the power of historical data available for training machine learning model. There are 3 key stages to Tarentum’s proprietary model. First layer is getting inputs from Plant, Scada and Weather along with physical weather models. Second stage is optimization of Weather and physical model using machine learning. Final stage combines ML based meteorological results with Turbine specific information. The final deliverable after all 3 stages is to provide power forecasts for the farm. For Tarentum+ users the model also takes other forecasts as input for enhanced accuracy.
The Client will have access to a dedicated online portal WindSuite and will have access to Tarentum+ forecast and comparison of historical model accuracy of each forecast model.
3. Power Curve Analyzer
Tarentum Power Curve Analyzer uses an AI and Machine Learning based approach to help wind energy clients analyze their power curves in order to provide a clean power curve model that would enable the tool to detect any unoptimized area in the power curve model of each turbine.
Tarentum’s unique approach benefits from using data science tools and applications of AI to outline any unoptimized points in the power curve in order to identify the cause and improve the power curve and increase turbine outputs. The Power Curve Analyzer will present customers with a baseline power curve acting as a benchmark for turbine performance enabling detection in shifts in the power curve, non-performance analysis (icing, curtailment, hysteresis etc.) and yaw performance analysis.
4. Maintenance Planner
Tarentum Maintenance Planner is a maintenance management tool where high-level plans are entered by the user and then are visualized for tracking which maintenance activities are on track along with commenting and documentation attaching capabilities. After the integration, the product is ready for use. No training data are required. The user simply needs to enter maintenance plans and update the system on a regular basis.
Screens showing on target, delayed, planned maintenance activities and a one stop portal for accessing comments and documents associated with maintenance activities.
5. Operations Tracker
Tarentum Operations Tracker uses an AI based approach to create a normal behavior model for turbine parameters and enabling the user to identify anomalies. The classical approach to maintenance relies on the service teams to identify faults following the defect; the modern approach integrates predictive maintenance that allows avoiding any fault prior to occurrence. Tarentum Operations Tracker works 24/7 reviewing behavior of all critical signals and alerting the user of any anomaly.
6. Predictive Maintenance
Tarentum Predictive Maintenance generates a warning (flag) if it predicts a major component (i.e. generator, gearbox) may have a breakdown in the near future using ML based techniques (primarily anomaly detection).
Predictive maintenance systems in general maximize wind turbines’ uptime by early detection of potential failures of various turbine components with the aim of reducing unplanned maintenance and downtime by harnessing large amounts of data to produce alerts ahead of time to prevent breakdown costly components such as gearboxes, main bearings and generators and loss revenue. Analyzing both high frequency vibration data and SCADA data, the product utilizes a wide range of statistical analyses and machine learning techniques such as normal behavior model development, clustering and anomaly detection methods to predict potential failures.
While the initial focus is on wind energy, the capabilities are easily transferable to other renewable energy sources, hence making it a solution available to a $80 B global market. With the ever increasing importance of renewable energy for our planet, we believe our solution can play an important role in making the transition smoother by using Artificial Intelligence.