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amulet 3d 250pxThe power of analytics can never be underestimated. Analytics within utilities, if used correctly, can enable them to respond more rapidly and effectively with issues regarding improved grid operations, performance customer engagement, and financial management. By utilizing the industrial internet of things (IIOT) and capturing the large amounts of data that is generated, analytics plays an integral part in staying ahead of the competition. Condition monitoring has traditionally been off-line or operated locally on-site, and the company recognized the power of moving to real time condition monitoring in terms of their short and long term asset management strategies.  Bentley’s Operational Analytics software was applied to interface to all forms of condition monitoring on site providing a single window into the assets on the substations.

A large electricity transmission company owns and maintains the high voltage power transmission network within the UK, supplying 57 million people with electricity across a network of 4,500 miles of power lines and 340 substations.

Condition monitoring has traditionally been off-line or operated locally on-site, and the company recognized the power of moving to real time condition monitoring in terms of their short and long term asset management strategies.  Bentley’s Operational Analytics software was applied to interface to all forms of condition monitoring on site providing a single window into the assets on the substations.  Analytics was applied to analyze the data in real time, generating alarms and notifications on asset health.  This has allowed them to manage the operation of their assets by extending asset life and maintaining operation where previously they would have had to shut down the transformer. The solution has formed a major part of the submission to the Ofgem regulator for the 8 year regulatory review. By using this level of analytics, the large electricity transmission company moved from “what hap­pened?” to “what is happening now and what should I do about it?”

Introduction

Data is at the heart of digital transformation. With the explosion data meaning that we are able to create, capture, access, monitor, and analyze more and more data every day, problems can arise in terms of what data to use, how we can use it properly and how can we turn the data into useful information that affects our decisions across the whole organization. This is where data analytics, and in particular operational analytics, answers those problems.

Priorities in the Electric utilities

There are many problematic areas within the electric utilities arena that are becoming major priorities. An ageing infrastructure is central to the inability to ensure a reliable, cost-effective, secure, and sustainable supply of energy. Temporary solutions can be put in place, such as monitoring asset health, structural integrity, condition monitoring and corrosion management, to stem the flow. Rising costs of generation and transmission, not to mention asset failure, loss of production with unplanned downtime, and operational expenditure. Transparency in environmental regulation and compliance is also a must when it comes down to emissions monitoring and reporting, while capturing,  

institutionalizing and retaining domain knowledge from an ageing workforce is also a must. Operational analytics is a solution that can bring all of these challenges together under one roof, bringing a wide variety of capabilities to bring visibility, increased performance and reduced costs.

What is Operational Analytics

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 analytical opportunities for 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.

Other Forms of analytics

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 (see Figure 1).

Fig 1Figure 1. The complete operational analytics solution

Operational analytics and utilities

T&D organizations generate a lot of data. This has been accelerated with the arrival of the Industrial Internet of Things and the explosion of big data, where the deployment of millions of smart meters and other grid devices is generating a huge amount 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 predicts that by 2021, 1 million IoT devices will be purchased and installed every single hour* – so the need to start harnessing the IoT starts now. Not only will data grow in volume and size, but it will also vary in type due to the large variety of data sources. This is why aligning operational technology (OT) with information technology (IT) (and also engineering information technology, or ET) is so important (refer to Figure 2). Operational analytics benefits such as cost and risk reductions and enhanced performance and flexibility.

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. The data convergence of operational, IT and engineering data can bring many benefits, such as improved performance, reduced costs and risk, and greater flexibility.

How IT and OT Convergence can help Utilities

Operational analytics can help utility companies drive operational efficiency by providing a broader view of their assets and how they are performing. With assets spread over a wide geographical area, it’s important to have all of the available information in one place to give you 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 via analytics that help to 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 the operation

Operational analytics capability has been used to help users gain extra visibility into their assets’ performance, effectiveness, and efficiency across transmission and distribution. Within substations, operational analytics has been used to 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, the lifecycle of transmission towers can be extended by calculating and modeling the life span using corrosion, environmental, geospatial, and maintenance history data, to name but a few. Additionally, 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 the proof to make defensible asset investment decisions, formulating asset life extension strategies where possible to do so safely and reliably.

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 (see Figure 4). This allows assets to be taken off line or operated on in a safe window, reducing costly failures and unplanned maintenance expenditure, ensuring the integrity and availability of the grid. Failures within the grid also incur costly clean ups and high-level investigations, even loss of reputation with its customers if affected.

Case Study example

A large electricity transmission company in the UK had several hundred substation transformers situated in England and Wales of which approximately 100 were identified as being “at risk” from failure due to their age and/or condition. The determination of the failure risk is achieved through the monitoring of 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, where 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.

Figure 3. Typical power dashboard displaying transformer condition monitoring parameters and asset attribute details which can be used to calculate the overall Asset Health Index (AHI) score for transformer assets. Figure 3. Typical power dashboard displaying transformer condition monitoring parameters and asset attribute details which can be used to calculate the overall Asset Health Index (AHI) score for transformer assets.

Using remote devices throughout their substations to collect data from Hydran dissolved gas monitoring systems, data was transmitted by GPRS to a Web server for display and analysis with the software. By taking data from assets within over 40 substations, and monitoring these levels using multiple techniques, engineers 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 that have be 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 have the ability to view any transformer via a treeview structure or layout by asset or route. They can also view assets on a geographic basis through the OS maps incorporated into the system.

Figure 4. Typical power dashboard displaying transfix gas levels and alarm status.Figure 4. Typical power dashboard displaying transfix gas levels and alarm status.

Immediate benefits resulted in a reduction in OPEX, where the more data they received from their assets meant an increase in targeted risk management and enhanced business decision making; maintenance regimes were more informed and organized, and a reduction in the cost of retrofitting of condition monitoring could also be implemented. With the aid of analytics to monitor and analyze the condition ad performance, transformers that have been diagnosed with potential failure can be proactively taken off line, have no expensive cleanup costs, store the knowledge gained for ‘family’ failures, and 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 to help spot the relationship between current transformers and the environment.

Figure 5. Example of the corrosion index showing the likelihood that towers will corrode due to emissions from nearby power station.Figure 5. Example of the corrosion index showing the likelihood that towers will corrode due to emissions from nearby power station.

Another strategy used was predicting the corrosion rate of the steel tower network to determine the life of a network through the degradation of the structures. 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, bar or tower replacement, as well as generate the best strategies for financial expenditure. This was created through a care and risk evaluation model. The model took into account all aspects of condition that affects 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 (refer to Figure 5). This marked the first time it was possible to predict the expected condition of transmission towers across a selection of the network. This allowed preventive and replacement strategies to be planned and costed across a long-term strategy.

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RichardIrwin 175Richard Irwin - Senior Marketing Manager for AssetWise Amulet, Bentley Systems.  Richard is a for Bentley System’s Operational Analytics platform, Amulet, with over 10 years’ experience in working within the analytics industry.  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 Masters in IT from Heriot Watt, Edinburgh.

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