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Field data to fleet decisions - embarking on the journey

By Ravi Kiran
22-02-2021 | 5 min read

Digital transformation is changing the playing field for power generation companies.

We can either see change as a challenge or view it as an opportunity to evolve and perform better than ever before.

In order to take  full advantage of the power of modern data,  a data-driven culture must be embraced

I’ve outlined below some practical steps to help make that happen.

1. Determine what asset information exists and the value it can bring

There are two types of data to consider.

First, look for all existing data this could include malfunction modes, historical condition and historical operational information and is the data you’ve been collecting over years of operation...that means it could be anywhere!

You may have information in the form of written reports, spreadsheets, and sensor data. And don’t forget about the knowledge your technicians are storing in their heads.

The more diagnostic data available, the better for your utility. The further back your data goes, the better able you’ll be able to reliably predict future scenarios.

The second type of data to consider, is the data you can begin generating. You might collect additional data by installing more sensors on assets, taking readings more often, surveying technicians and more. Even seemingly small factors such as temperature or noise levels, when used correctly, can provide more insight.

2. Identify assets critical to revenue generation

Using all of the data and information you’ve sourced in step one and identifying which critical power generation plant assets most affect revenue is an important next step.

Examining asset data and plant history, managers should look to answer questions such as:

  • Which assets are associated with failures or are underperforming?
  • Are there any assets on which it is difficult to determine remaining life cycle?
  • Have any components been replaced too soon with the realisation afterwards they could continue to provide optimal output?

Answers to these and other questions can help identify what plant areas require more focused management.

You can also gauge where improved management could quickly result in positive change such as increased revenue.

3. Analyse. Analyse. Analyse.

In real estate they always talk about “Location. Location. Location.”

In a data-driven culture the focus is on analysis – all  kinds of it. Fortunately, digital technology applies physics, math and artificial intelligence (AI) perspectives to understand utility assets from all angles.

Information surrounding asset construction and design details along with the equipment’s data history in your plant are a rich source of clues about current and future operation. But advanced analysis techniques are needed to draw actionable insights from all the data.

Lumada solutions analytics turn all the plant and asset information into intelligence. Advanced data analytics also helps the utility to:

  • Gauge current condition
  • Identify possible sources of a problem
  • Plan maintenance effectively
  • Send work crews out with the right information to reduce waste
  • Predict when a component will fail or productivity will drop
  • Simulate how the equipment will act in future.
  • Obtain a view of likely future market trends
  • Make decisions based on collaborative asset intelligence
  • Stay competitive in today’s market.

As we discussed previously, the entire organization benefits when every team has a holistic view of the utility’s assets. The asset manager will offer up predictions of how the assets will perform in the future. Traders and operations planners can share market demand and price forecasts.

This collaborative asset intelligence can lead to decisions about asset maintenance and capacity bids that make sense and earn profits.

4. Start small

Embracing digital transformation can seem an insurmountable task. That’s why we talk about it as a “digital journey.” Take it one step at a time. Utility plants, after all, manage vast asset fleets spread out across immense facilities. One good thing about Lumada solutions is that it is a modular software with which the organization can start small.

For example, you might start collecting a critical asset’s data, reviewing sensors, and combining data from all the different sources on the one platform. This approach lets you begin to create a data-centric environment without overwhelming team members. Starting small has other advantages too:

  • See benefits sooner to encourage wider adoption of the project
  • Lower capital required at the outset
  • Generate new revenue sooner

At most utilities, implementation of a Lumada solutions will take six months.

This involves conditioning data, applying machine learning to gauge information from technicians and engineers, installing sensors where needed, and teaching staff about the new process. Yet, the modularity of DE makes a step forward on the digital journey more attainable.

Utilities face many challenges today: aging assets, stagnant budgets, new operating risks resulting from distributed energy resources and a workforce in transition.

Taking advantage of collaborative asset intelligence and advances in predictive and prescriptive analytics can help you meet the expectations of high reliability in spite of these challenges. Applying a step-by-step approach, you too can reap the benefits of our Lumada solutions.

Hitachi Energy has helped utility customers around the world embrace digital transformation and become more data-driven. Capturing market potential with the assistance of advanced data analytics is easier than you might expect.

Companies that act now will be better positioned to turn change and challenges into opportunities. Let’s talk about finding your best way forward on the digital journey.

    Ravi Kiran
    Practice Leader - Energy Planning & Trading - Asia Pacific
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    Ravi Kiran has worked with Energy & Utilities focused enterprise applications for over 18 years. His key areas of interest include planning and optimisation solutions for both asset performance management as well as energy portfolio management. His recent work includes predictive and prognostic approach to asset risk management with a view of helping organisations mature from traditional time-based maintenance practices to condition or risk-based asset management. You can connect with him at LinkedIn.

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