Virtualitics improves energy production of wind turbines
- Anomaly-detection algorithms revealed the root cause of high temperature operational malfunctions which required excessive servicing, costing more than $500,000 per year in downtime and energy losses.
- Interactive data exploration in 3D empowered wind farm engineers, analysts and operators to visualize and identify the key driver for underperformance: the OEM designed cooling system as the culprit, which was underpowered relative to the wind turbine designer’s recommendations.
- AI routines highlighted drivers of energy production and identified ideal operational conditions with respect to auxiliary power.
- The wind farm’s owner operator commented: “The visual insights derived from VIP would have enabled our team to work with the manufacturer to upgrade the cooling system sooner and at relatively low cost, saving millions of dollars in lost production, warranty costs, insurance, spare parts and equipment expenses.”
A leading multinational company providing energy and automation solutions for efficiency and sustainability looked for improvements in both identifying and understanding equipment failures. The company’s businesses are composed of multiple physical and digital assets that include wind and solar power, energy storage, backup critical power, cooling, and industrial automation controls. These assets support some of the world’s most mission critical facilities. The large number of components in complex industrial systems can fail at any time, due to a variety of causes ranging from grid disruptions to mechanical and electrical faults, and random weather events. As shown in this whitepaper, monitoring, detecting and predicting the root causes of asset failure and underperformance is essential for Industry 4.0 operations. The customer applied Virtualitics Immersive Platform to easily map more than 150 IoT data parameters and more than 50 million data points across two sites to diagnose, identify and visualize the key drivers of equipment failure and energy output.
Using Virtualitics Immersive Platform (VIP) to characterize equipment failure
The company wanted to analyze two industrial wind turbine sites located in different facilities more than 60 miles (100 Kilometers) apart. The turbine’s performance was captured through 150 individual IoT sensors controlled by a PLC system. Operators and OEM Engineers monitored these facilities through a SCADA system with built in time series historian views and automated alerts and alarms. These alerts notified maintenance personnel of events such as operation errors, software malfunctions, grid outages, equipment breakdowns, or abnormal temperatures. The company was aware of some of the big challenges they faced with their existing analytical software tools:
- Limited capability and time to explore data. Operators and analysts often needed to pull in data science and IT resources in order to perform impactful analysis.
- These time consuming analyses required the use of expensive external consultants and software coding experts who performed the analysis on difficult to comprehend tools and blackbox models.
- Solid knowledge and understanding of the systems often led to fragmented decision making whose impact varied in how it was tracked or even measured. Operators and engineers could not visualize the complete set of interactions that most affected the key performance metrics.
- False alarms created a narrative where you couldn’t always trust the time-based alerts or alarms driven by standard predictive models.
- The company wasted significant resources not being able to properly assess and proactively react to unexpected deviations in performance of their mission critical assets. Unscheduled maintenance and costly downtime events resulted in more than $5 million of unplanned expenses over 10 years due to a lack of actionable insights connecting human intelligence and vast amounts of IoT data.
- The company’s multinational team and the OEM’s factory engineers based in Europe and Asia could not easily communicate or share insights based on a unified view of asset-specific advanced analytics models.
VIP’s powerful technology meets you where you operate best
VIP’s powerful visualizations enabled the company to look at data in more ways than one. Whereas traditional SCADA, Process Data Historians, and Asset Performance Management tools focus on informing with 2D line charts, data tables, and pre-set alarms and alerts, VIP delivers insights in a visual, collaborative and intuitive manner. VIP understands that the story is often not as accessible or easy to communicate to your entire audience or extended set of collaborators.
Equipment operators at the organization have an intimate knowledge of their systems and equipment, and must respond rapidly to demanding and unexpected changes in the process and physical environment. Manufacturers have subject matter expertise in specific subdomains such as the electrical, mechanical, and thermal subcomponents of the assets but lack a transversal understanding across geographical sites and a limited view of hybrid facilities where there may be equipment from many different manufacturers and vendors in operation. Service technicians and field workers understand how to perform efficient and targeted repairs if the defective conditions are identified in a specific and clear way by the manufacturer or engineering team. Achieving this type of effective team coordination involves analytics beyond analyzing trends.
To solve this challenge, the first step was to render the real-world environment of the wind turbines as a digital twin representation. This approach enabled everyone in the team to link the data exploration with data and enable all disciplines to instantly begin to understand their data.
VIP’s Smart Mapping instantly helps you understand why equipment is performing in a certain way
Using one of VIP’s patented AI routines, Smart Mapping, the company was able to immediately characterize what was driving energy production in these two windmills. Smart Mapping is able to rank which features in the dataset were most influential in driving performance of our target variable, Energy Production, and almost instantly suggest the most optimal visualization in order to interpret and explain the insights. With a few button clicks, and in a no-code environment, multiple stakeholders were able to quickly validate or learn something new from their data.
In the image above, the suggested plot uses 5 dimensions to communicate the insights. Our target variable, Energy Production, is on color. We can quickly assess states of high energy production and lower production. Unlike 2D visualizations, we are immediately able to identify areas for exploration (indicated by the annotations in the image).
From the visualization we are able to see and fully experience that the biggest contributors to performance are wind speed and auxiliary power. We also notice that there are two different performance ranges within auxiliary power, conditions in which the wind turbine operates with low auxiliary power draw (regardless of wind speed) and conditions in which the turbine operates with high auxiliary power draw, and simultaneously high bearing temperatures within the nacelle’s gearbox. With regards to bearing temperatures, we learn that it can be a leading indicator of reduced performance when the temperature gets too high and the overall system begins to tap out the maximum auxiliary power range as the cooling system ramps up. While veteran operators at the company understood that bearing temperature was an important metric to monitor, they did not understand that there was a subtle relationship between bearing temperature and auxiliary power that was deterministic of performance. This level of characterization became a necessary tool for the company to create a common language across the mechanical engineering, maintenance, and electrical power teams.
VIP’s Anomaly Detection quickly finds the problem
Using another no-code embedded AI-routine, the company was able to identify two large anomalies; potential malfunctions due to high bearing temperatures and high Line Side Converter (LSC) temperatures. These two subcomponents were initially analyzed in a siloed fashion by the operator, with the problem of bearing temperatures tackled by the maintenance department through more frequent oil replacements, and the problem of LSC temperatures heavily analyzed by the power engineering team to identify disturbances in the grid
In this image, the haloed points represent points that were detected as anomalies. As wind speed increases, energy production remained strangely stagnant for these points. When this happens, it follows that the turbine malfunctioned and required service just a few days later (indicated by the PLC state in the color Red).
By applying the temperature variable on color, we are able to identify a likely culprit of the error state in analyzing the high bearing temperatures (shown in color Red).
Using a similar process to the bearing temperature anomaly, we were able to explore the high Line Side Converter (LSC) anomaly, represented by the large Blue point in the first visualization. In the image below, we’re able to see a point (haloed and color Purple) that indicates high converter temperatures that are causing a PLC error.
Using color to differentiate the range in LSC temperature, we were able to see a sudden spike in converter temperature (indicated by color Red) and a likely reason to cause the error, an underpowered cooling system as designed by the manufacturer which resulted in high bearing temperatures and high Line Side Converter temperatures at critical points in the power curve. Turbine failures occurring at these critical points, in high wind speeds, resulted in severe mechanical and electrical damage to the wind turbines due to sudden hard stops.