Balance of plant (BoP) constitutes a critical part of a power plant and accounts for 40-45 per cent of the total project cost. The key BoP subsystems for thermal power plants typically include ash handling plants, coal handling plants, circulating water systems, fire water and service water systems, water treatment plants and demineralisation plants, and compressed air systems. However, reasons such as non-proximity to the main plant, redundancy and limited suppliers of the equipment, preference for low costs and the absence of intelligent systems have led to subdued demand for BoP systems and made it a low priority area.
Digitalisation efforts in BoP
Fleet-wide monitoring or remote monitoring has been popular, particularly in the US, since 1995. It involves data acquisition, on-site data intelligence, data processing, diagnostics, expert engineering and operations support to obtain valuable information from the data. It helps monitor multiple units/equipment in real time from a central location by utilising historical data to describe how equipment operates and develops a series of operational profiles. Based on the historical data, it sends an alert in case of any deviation from the historical pattern. The primary target of fleet monitoring is to improve plant reliability and performance. In recent decades, data monitoring and diagnostics have reached a certain level of sophistication, thanks to artificial intelligence. One of the key drivers of remote monitoring is the absence of standby systems in most power plants, which poses a huge downtime risk for achieving the desired capability of the unit. Moreover, there is a need for an early warning system due to complexities in the process, technology and large-sized equipment involved, which has led to the uptake of the fleet monitoring approach.
An emerging trend is the use of predictive analytics for fleet monitoring. Predictive analysis uses the principle of clustering of parameters into groups and establishes normal patterns among them, based on the actual performance data of each machine in its unique operating context. The software establishes a unique operating profile with dynamic bands for each piece of equipment. It uses an advanced cluster-based reasoning to identify process variations that would be virtually impossible to detect otherwise. It also makes use of the advanced pattern recognition technique.
Experience so far
Private power major Tata Power has adopted various digitalisation measures in the BoP system of its Mundra thermal power plant in Gujarat. An interconnection of the BoP system’s programmable logic controller (PLC) to the station distributed control systems (DCS) through an optical fibre link has been established. This has enabled complete monitoring from the DCS. Further, all BoP systems’ PLCs have been time-synchronised with the station GPS via such a communication link. This has facilitated a centralised monitoring of the health and status of the PLC. At the same plant, Tata Power has established switchgear connectivity to the DCS through an optical fibre communication link. This has led to seamless and faster communication, while also facilitating the exchange of an unlimited number of signals.
Tata Power Noida has come up with an Advanced Centre for Diagnostics and Reliability Enhancement (ADoRE), a 24×7 centre that has extended reach and observability on the company’s thermal units at Mundra, Trombay and Maithon for the purpose of fleet-wide monitoring of its units, including the BoP areas.
ADoRE has enabled advanced warnings of incipient failures and impending equipment problems to avoid forced outages and catastrophic failure. The continuous monitoring of health and performance has helped in identifying any subtle changes in equipment behaviour, thereby minimising the operational risks of unacceptable schedule interruptions.
Conclusion
A comprehensive BoP system can lead to improved lifetime performance and ensure complete plant security and safety. Thus, it is imperative to ensure reliable monitoring of BoP systems. Going forward, recognising the multidimensional benefits of a healthy monitoring system, power companies must adopt the practice to enable better functioning and cost effectiveness of its plants and stations. With the help of predictive analytics software, plant engineers will be equipped to take proactive, risk-informed and timely decisions.