Leveraging the IIOT to improve O&M at Indian coal fired plants

4 March 2020

The Industrial Internet of Things can help address some of the serious challenges currently being faced by India’s fleet of operating coal plants.

Traditionally, power plant operation & maintenance (O&M) is governed by OEM instructions coupled with the expertise and experience of O&M staff and the processes adopted/evolved by individual plants or by a cluster of plants managed by a single owner/ authority. In most cases the emphasis is on ensuring maximum plant availability, performing time based scheduled maintenance and breakdown maintenance. Not much attention or effort is devoted to controlled operation of the plant based on health of the assets or on condition based/predictive maintenance that can help in improving the plant’s operational efficiency and also extend plant life. However, with the ever increasing squeeze on capex and growing concern for the environment prospects for the addition of new coal fired utility plants are diminishing. Hence, the emphasis is on improving the performance of existing plants and prolonging asset life so as to extract the best possible returns from the plants over the longest time.

How can the IIOT (Industrial Internet of Things) be leveraged to strengthen O&M practices at Indian power plants, leading to improved power plant performance, better asset management and plant life extension?

Challenges and opportunities in India

The mainstay of Indian power generation is a fleet of coal fired plants that together contribute about 75% of the country’s power generation. Though there has been a steady decline in the coal contribution over recent years thanks to the growth of renewables, coal fired plants will continue to shoulder the bulk of power generation for a good few years yet. On the other hand, investment in new coal fired plants has reached a plateau and is unlikely to see a revival in the near future. At the same time, growth in the renewable sector, largely solar capacity, means increasing fluctuations in the power demanded of coal fired units and the requirement that these units are flexible enough to adjust power output in response to the availability of renewable power and overall load demand.

The combination of these factors has placed an enormous burden on the operating coal fired plants, which must remain in operation with minimal downtime, while at the same time being capable of operating flexibly and also able to achieve extended plant life. In addition, owing to the pressures deriving from sluggish power demand and low tariffs, generation costs must also be kept low to enable power producers to survive in a demanding market. This means plants have to revamp their O&M strategies and explore all possible options for improving plant performance and maximising utilisation of existing assets, while at the same time keeping the assets healthy.

In addition, the dwindling number of experienced and knowledgeable O&M staff is leading to lack of expertise at the plant level. This has already started to impact plant performance and is likely to become an increasingly acute problem in the years to come.

Summarising, the major challenges being faced by existing coal fired plants are in achieving:

  • flexibility in operation to adjust to the fluctuating load demand;
  • improved plant performance under varying load conditions;
  • improved plant availability;
  • increased asset life to prolong plant operation; and
  • adequate availability of expertise and knowledge for day to day operation and particularly for trouble shooting.

While the challenges are daunting, they also present opportunities for plant managers to explore and take full advantage of technologies to manage plant assets in a more structured manner. For many years, IT in power plants has been confined to the control & instrumentation, centred on DCS, SCADA, PLCs and connected instrumentation – not any more. The emergence of Industry 4.0, or IIOT as it is known in the power plant business, has opened up enormous possibilities for handling all these O&M challenges in a much better way.

Industrial Internet of Things – what does it mean?

While it is certain that IIOT is going to bring huge changes to the power industry, the first hurdle power engineers face is to understand the meaning of buzz words like IIOT, edge device, real time analytics, prognosis, etc, and how to figure out what is required for their plant and why.

The following paragraphs aim to improve understanding, in order to help plant engineers to take informed decisions.

IIOT is a combination of IT (information technology) & OT (operational technology) and essentially consists of three stages:

  • 1. asset digitisation;
  • 2. data collection and storage; and
  • 3. management/control of the systems of interconnected assets through data analytics and formation of algorithms (as shown in Figure 1).

The potential benefits to Indian coal plants can be summarised as follows:

Evolution – Learning from the past to improve plant performance through data collection and data analytics

Improved control – Continuously tracking operating status; keeping plant operation within the given pre-defined boundary conditions without crossing limits. Helps improve plant reliability and extend plant life.

Health, safety and environment (HSE) – Ensuring safe operation, with tracking and alerts in the case of any hazards, minimising pollution in all forms, gaseous (NOX, SOX, CO2 etc), liquid effluent and solid (ash) through optimised operation.

Adaptive operation – Improved flexibility in adapting to varying load demands and changes in fuel quality.

Predictive maintenance – Introduction of disruptive changes in maintenance philosophy. Instead of reactive (ie,
time based, planned, and break down) maintenance, allows plants to adopt condition based maintenance based on equipment health monitoring and failure prognosis.

Remote monitoring and expert analysis – Enables remote monitoring of plants at enterprise level and also facilitates analysis of critical O&M issues through analytics using a combination of algorithms & machine learning. Over and above this, it can also help to make available advice from OEM experts and others located anywhere in the world, if so desired.

Today all these features are possible to build in but does every plant need everything? It is important to understand the relevance of each of these features in terms of significance in the context of a specific power plant and prioritise them depending on economics.

Are Indian coal plants ready for IIOT?

1st stage: plant digitisation. All most all the power plants in India built in last 10-15 years have most of the sensors required for plant digitisation already installed and fortunately a lot of them are smart sensors that are IIOT compliant. Also, all the power plants are equipped with PLCs, DCS and SCADA. Hence, the first stage of IIOT, ie, plant digitisation, largely exists, except for some areas that may call for sensor replacement or require additional sensors.

However, the requirements for implementing the second and third stages of IIOT are not readily available in Indian coal fired power plants and need to be provided if IIOT compliance is to be achieved.

2nd stage: data collection and storage. Data collection per se is being done through DCS, SCADA and PLCs. However, only some of these data are used for auto/manual control while most of the data collected remains unused. Only in the event of system/equipment failure is data, around the time of failure, looked into and analysed manually by experts to ascertain the cause of failures. With IIOT, the data extraction at plant level will more or less remain as it is today but the data collection, storage and analysis will go through a sea change. All the data collected will be effectively used to extract meaningful information for further analysis on a continuous basis. This means large volumes of real time data will be collected and stored
in a centralised location (not at plant level) and would be made accessible for data analytics. 3rd stage: data analytics, formation of algorithms, actions. Data analytics essentially means sorting and examining the large volume of data with the help of specialised software in order to extract meaningful information to help in prediction of equipment/system behaviour. Such analysis and the conclusions drawn from it enable the plant operators to run the plant in the optimum manner and also help in improving understanding of the heath of the individual equipment.

Such understanding of equipment behaviour enables operators to adopt a condition based approach to maintenance of equipment.

As such data analytics can be used to improve plant overall performance by improving:

  • reliability – by equipment modelling using artificial intelligence and statistics;
  • performance – by equipment level thermodynamic modelling; and
  • plant optimisation – at plant, equipment, and process level, as appropriate.

Specific Indian coal plant O&M challenges and the IIOT

Thus far I have described broadly the stages of the IIOT that are common for all applications and the role that it can play in the power industry as a whole. The paragraphs below now describe how IIOT can address the specific concerns/challenges being faced by Indian power plants, with a few typical examples by way of illustration.

Flexibility in plant operation and improving plant performance

With the rapid growth of renewables, mainly solar, a large chunk of power generation is coming such sources, which get priority over other forms of power generation simply because they burn no fuel and are environmentally friendly. However, the flip side is that the power generation from renewables is fluctuating and uncertain to a great extent. This fluctuating and uncertain nature of power supply from renewables puts the onus on fossil fuel based plants (mainly coal fired in case of India) to flexibly ramp up or down, or even start up or shut down selectively so that power flows to the grid remain steady and meet the demand all the time. This problem of continuously adjusting power generation from coal fired plants is becoming more acute as the renewable contribution to power generation is becoming more significant.

By and large, almost all coal fired plants in India are designed for baseload operation. This means they are designed to operate at a relatively high steady load. However, as the present mode of operation increasingly deviates from the desired operating zone, it is essential to take another look at the plant design and its ability to adopt a new operating mode & regime and at the same time keep plant performance at the optimum level so as to maintain operating expenditures, including fuel consumption, at the lowest levels given the constraints of operation.

Traditional O&M processes, as practiced by the plants up to now, can no longer meet such challenges. Essentially, plant operation needs to be more agile, interactive and smart. In the case of a large fleet of plants clustered in one place or scattered across India operated by a single entity, systems are needed that enable the fleet to be operated efficiently both at the individual level and also at enterprise level.

Up until now, the instrumentation & control systems used to operate Indian coal fired power plants, ensure safety and monitor performance mostly throughmanual intervention. The missing link is interaction between plant systems and AI enabled optimised operating procedures, through equipment/system level thermodynamic modelling.

This can be achieved by:

  • critical review of original plant design;
  • modifications needed to make the plant better suited for fluctuating and low load operation;
  • mapping of existing plant instrumentation systems including suitability of installed sensors;
  • mapping of the sensor landscape and finalising requirements for replacement of existing sensors and installation of additional sensors;
  • integration of thermodynamic models of equipment/ systems;
  • interconnection of systems to enable automated systems to operate in sync with each other at any given load condition; and
  • revision of the plant operating philosophy to take account of built-in AI.

Typical example: The condenser and its associated systems/equipment have a significant role to play in plant performance. A below par condenser can severely affect plant performance; however, in reality, instances of degraded condenser performance are not so easily noticeable to the plant operators. An integrated thermodynamic model encompassing the condenser, with built-in intelligence and analytics, can certainly help in identifying and eliminating the causes of poor performance on a real time basis. The model can be further enriched with added intelligence and by integrating it with plant load management to achieve more agility in the management of fast changing load demand. The operating approach for many plant systems and items of equipment can be similarly improved with IIOT, resulting in an overall improvement in plant performance.

Improving availability & life of assets

To improve plant reliability and asset life, it is imperative that the health of dynamic equipment such as turbines, pumps, compressors and static components such as boiler tubes and high pressure high temperature piping be improved. This is possible with equipment friendly operation and health-condition based in-time maintenance rather than reactive or time based maintenance. The asset management maturity model is shown in Figure 2.

By embedding/installing the right sensors (most of the relatively new coal plants already have almost all the sensors required), it is possible to have continuous feedback on equipment functioning and its health. This stream of information can be organised and analysed, and inferences made, via stages 2 and 3 of the IIOT. Such analysis and inferences would then inform the plant maintenance team as to the equipment condition and would also provide advice on corrective measures in a user friendly interactive mode in case the asset is found to be in stressed condition. This would help in getting the asset to run in a less stressed condition, thereby extending the life of the equipment.

The condition monitoring and analysis system can be utilised further to predict equipment breakdown through prognosis, enabling the maintenance crew to plan equipment maintenance before it actually breaks down, thereby avoiding costly repairs and interruptions in plant operation.

Such condition based in-time action for maintenance can vastly improve plant reliability and availability and also extend the life of individual assets.

Typical example: ID (induced draft), FD (forced draft), and PA (primary air) fans are critical for boiler operation and any failure of any of these fans severely affects plant performance since most power plants do not have any standby fans to fall back on. The condition monitoring and analysis system if installed would continuously map the major parameters of equipment function, such as vibration, discharge vs pressure, etc, and based on the analytics will assess equipment health, identify issues if any and also predict the time to fail if it continues to operate.

Such analysis and prediction would help operators to take in-time corrective actions and plan equipment maintenance, if required, to avoid breakdown. The maintenance approach can be further upgraded from predictive to prescriptive by leveraging the knowledge and capabilities of predictive and preventive maintenance to fully optimise system performance.

Creating digital twins for better performance

In addition, an IIOT enabled plant can relatively easily take a step further by creating digital twins. Creating a digital twin of a plant is essentially a step towards plant digitalisation. It means developing a simulation model with all the systems, equipment, instrumentation, etc, exactly as they exist in the plant, with all the characteristics and information related
to each and every plant component, and the model is made dynamic to mirror the real time performance of the actual plant.

It is relatively easy to create a digital twin of a new plant as all plant data; drawings are readily available in digital form. In the case of older plants, data, drawings are either often not available or only available in physical form. Hence, the task is to first create a plant model within a 3D platform and then enrich the 3D model by embedding plant/system/component level data. The model can then be made dynamic with the help of AI.

The digital twin model not only helps in understanding what a parameter change would look like, it can also be used to analyse data dynamically and empower O&M personnel to better understand plant operation in order to build projections and forecasts. At enterprise level it enables experts and management to view real time plant operation, with alerts and future projections to help and strengthen decision making mechanisms.

Opportunities created by IIOT

In summary, coal fired power plants in India are facing serious challenges owing to the emergence of renewables and changes in market dynamics. Growth in the renewable sector forces the coal fired plants designed, and until recently being operated, as baseload units to be flexible to adjust power generation to match grid demand in sync with renewable generation, which is fluctuating and uncertain. This coupled with low tariffs and a squeeze on capex makes it imperative for power plants to optimise their performance, improve plant reliability/ availability and also prolong asset life.

The emergence of IIOT creates an opportunity to effectively address these challenges being faced by Indian coal fired power plants. However, to extract maximum mileage from deployment of IIOT it is necessary to carry out a critical review of plant specific issues and customise the adoption of IIOT applications for individual units.


Arun Ramamurthy and Pramod Jain, The Internet of Things in the power sector, opportunities in Asia and the Pacific, ADB Sustainable Development Working Paper Series, Aug 2017.

Bob Denison, The fourth industrial revolution in power generation, Siemens AG, 2016.

Author: Anjan Bhattacharya, Tata Consulting Engineers

Figure 2. Asset management maturity model (Source: ARC Advisory Group)
Figure 1: IIOT functional model

Linkedin Linkedin   
Privacy Policy
We have updated our privacy policy. In the latest update it explains what cookies are and how we use them on our site. To learn more about cookies and their benefits, please view our privacy policy. Please be aware that parts of this site will not function correctly if you disable cookies. By continuing to use this site, you consent to our use of cookies in accordance with our privacy policy unless you have disabled them.