By Richard Irwin, Bentley Systems
Data is at the heart of the digital transformation. The data explosion allows companies to create, capture, access, monitor and analyze more information every day. However, problems can arise in terms of what data to use, how to properly use it and how to turn it into useful information that affects decisions across the whole organization. Operational analytics can solve those problems.
Operational analytics is an industry-recognized emerging business process that focuses on improving day-to-day operational performance with the power of sophisticated analytics. It is a process that converges information technology, operational technology and engineering technology by transforming historical and real-time data into actionable just-in-time data for improving operational efficiencies using predictive techniques. Data aggregation and analysis tools are used to provide clarity and context for decision making and business planning, as well as to provide a platform for organizational strategy. The software that enables the process is configurable and provides day-to-day visibility into the performance of existing assets. It also offers predictive analytics to allow utilities to improve their operations. This can be used in conjunction with an existing model to extrapolate relevant information as and when it is required, extending asset performance modeling capabilities for real-time operations.
|FIGURE 1: The Complete Operational Analytics Solution|
There are many forms of analytics that perform well within their own right. Descriptive and diagnostic analytics provide insight into what happened and why it happened, but nothing about what will happen in the future. Predictive analytics takes that a step further. Traditional business intelligence provides users with conventional and dashboard reporting in near to real time.
What is needed is a solution that combines the level of reporting for management, the data mining capability to look closely at what happened and what is currently happening in real time, and the predictive capacity offered to forecast events and opportunities. Operational analytics offers descriptive, diagnostic and predictive analytics for a complete analytical solution (Figure 1).
Operational Analytics and Utilities
Transmission and distribution (T&D) organizations generate a lot of data. This has been accelerated with the arrival of the Industrial Internet of Things (IIoT) and the explosion of big data, where the deployment of millions of smart meters and other grid devices is generating huge amounts of data. Managing, interpreting and turning this data into actionable information is where operational analytics comes to prominence, giving utilities the ability to collect, analyze and act on the information they receive.
Gartner, a U.S.-based information technology research and advisory company, released its “Top Strategic Predictions for 2016 and Beyond: The Future Is a Digital Thing” in October 2015. One of the predictions is that 1 million Internet of Things (IoT) devices will be purchased and installed every single hour by 2021. If this prediction is accurate it means the need to start harnessing the IoT starts now. Not only will data grow in volume and size, but it also will vary in type because of the large variety of data sources. This is why aligning operational technology (OT) with information technology (IT) and also engineering information technology (ET) is important (Figure 2).
The data convergence of operational, IT and engineering data can bring many benefits, such as improved performance, reduced costs and risk and greater flexibility.
|FIGURE 2: . IT-OT-ET Convergence for Operational Analytics|
With assets spread over a wide geographical area, it’s important to have all of the available information in one place to get a clear and concise picture of health, condition and performance right down to the component level. By monitoring a variety of parameters connected to health and condition, decisions can be made earlier through analytics. Analytics can help utilities determine how likely it is that a failure or significant event will occur, so a contingency plan can be activated before it happens.
Bringing Visibility to Operations
Operational analytics help users gain extra visibility into their assets’ performance, effectiveness and efficiency across transmission and distribution (T&D) systems. Within substations, operational analytics can monitor the condition of transformers using sensors to measure a variety of parameters, alerting engineers to any problem that may arise due to oil temperatures, dissolved gas anomalies and more. In the field, transmission tower lifecycles can be extended by calculating and modeling the life span using corrosion, environmental, geospatial and maintenance history data, to name but a few.
In addition, line inspections can be improved by using handheld devices to upload and download inspection data live from the field. Asset health indexing empowers utilities with proof needed to make defensible asset investment decisions, formulating asset life extension strategies where possible to do so safely and reliably.
|FIGURE 3: Typical Power Dashboard Displaying Transfix Gas Levels and Alarm Status|
The risk of failure increases due to age and condition of T&D assets. It is essential to know how assets are performing at all times. For example, monitoring the level of dissolved gasses and the temperature of the cooling oil that circulates within transformers 24/7 identifies potential problems quickly (Figure 3). This allows assets to be taken off line or repaired in a safe window, reducing costly failures and unplanned maintenance expenditure and ensuring the grid’s integrity and availability. Failures within the grid also can lead to costly clean-ups and high-level investigations, and even loss of reputation with affected customers.
Case Study Example
A large electricity transmission company in the U.K. had several hundred substation transformers situated in England and Wales. About 100 of these transformers were identified as being “at risk” from failure due to their age or condition or both. The determination of the failure risk is achieved through monitoring the dissolved gasses in the cooling oil, which circulates within each transformer. This gas is analyzed by using Hydran units of varying age and capability. Only a small percentage of the units had logging capabilities to enable the company to remotely gather readings for analysis. The company was therefore incapable of correctly identifying impending failures and trends to predict future problems.
Using remote devices throughout the substations to collect data from Hydran dissolved gas monitoring systems, data is transmitted by general packet radio service (GPRS) to a Web server for display and analysis with the software.
By taking data from assets in more than 40 substations, and monitoring these levels using multiple techniques, engineers now are warned of any potential failures in plenty of time in the form of SMS or email. These are sent in accordance to alarm levels set for various measurable parameters within each transformer, such as Dralim Oil analysis, SF6 gas levels and DTS (the measurement of temperature along the length of a transmission line through use of optical fibers). Users can view any transformer via a TreeView structure or layout by asset or route. In addition, they can view assets on a geographic basis through the ordnance survey maps incorporated into the system.
Immediate benefits were reduced operating expenses, where the more data they received from their assets meant an increase in targeted risk management and enhanced business decision making; more informed and organized maintenance regimes; and a reduced costs of retrofits to implement condition monitoring.
With the aid of analytics to monitor and analyze condition and performance, transformers diagnosed with potential failure can be proactively taken off line. The utility avoids expensive cleanup costs, can store the knowledge gained for “family” failures, and can obtain potential “grey” spares for other units-with no loss to the grid. Further solutions included using the data for inspection records, as well as line surveys using handheld devices. Bringing in weather data has also been of significant benefit, helping the utility identify the relationship between current transformers and the environment.
Another strategy involved predicting the corrosion rate of the steel tower network to determine the life of a network through the structures degradation.
The company required a strategy that would enable them to identify problem lines and individual towers based on their location and history, and use the data to determine the best intervention programs of painting or bar or tower replacement. In addition, it enabled the utility to generate the best strategies for financial expenditure.
|FIGURE 4: Example of the Corrosion Index Showing the Likelihood that Towers will Corrode because of Emissions from Nearby Power Station|
This was created through a care and risk evaluation model. The model takes into account all aspects of condition that affect the degradation of steel, zinc and organic coatings on all above-ground steelwork. This includes temperature; humidity; time of wetness; pollution in the form of air-borne sulfur dioxide; location in altitude; proximity to sea, lakes, reservoirs, rivers, minor and major roads; and history, including installation date, coating records and maintenance history. The model is then used to calculate the long-term risk of the towers and display them color coded individually on a map within the dashboard (Figure 4).
This marks the first time it was possible to predict the expected condition of transmission towers across a network selection. This allows preventive and replacement strategies to be planned and costed across a long-term strategy.
Richard Irwin is a senior marketing manager for Bentley System’s operational analytics platform, Amulet. He has worked within the analytics industry for over 10 years. In his role as senior marketing manager, Richard works with the sales teams to coordinate marketing opportunities, as well as managing the Gartner account to learn more within the analytics world. Based in the United Kingdom, Richard holds a Master’s degree in sociology from Aberdeen University and a Master’s degree in IT from Heriot Watt, Edinburgh.