Power

Digital disruption in the power industry

15th November 2017
Lanna Deamer
0

Globally, increasing demand for energy and power encourages companies operating in energy and power industry to adopt solutions that can help them enhance production output with minimum errors and reduced down-time. The products are offered specifically for the energy sector to enhance operations in the energy data management area.

By Anand Shanker, Research Analyst at MarketsandMarkets.

Industry 4.0 solutions help power plant owners, operators, and Original Equipment Manufacturers (OEMs) in the power industry make improved business decisions based on performance and operational readiness of their plant equipment.

According to the MarketsandMarkets forecast, the Industry 4.0 market in energy and power was valued at $1.30m in 2016 and is expected to reach $3.22bn by 2022, at a CAGR of 16.33% between 2017 and 2022.

IoT and power industry
Industry 4.0 is being led by IoT and it plays an important role in condition monitoring and predictive and proscriptive maintenance of assets.

Plant operators need to monitor and control the plant more efficiently, and for doing so, the adoption of advanced technologies such as HMI is increasing significantly in the energy and power industry.

IoT provides flexibility to accommodate new energy sources, better management of assets and operations, greater reliability and enhanced security.

Big Data to transform power industry
The energy and power industry has recognised the benefits of big data as it plays a vital role in solving business problems in utility companies.

In this vertical, the big data solutions are gaining traction in various processes such as seismic data analysis, smart grid analytics, and data analysis related to production, testing, logging, and many other operations. Each year, smart grids and smart meters generate hundreds of terabytes of data, which include unstructured and semi-structured data.

Companies in the energy and power industry have analysed this huge amount of data to get real time access to the situation. Being largely customer-centric, the energy companies are also making a shift toward providing more personalised products and services to their customers.

Big data plays an important role in providing trends and patterns by analysing the data, which in turn are useful for product and service upgradation and enhancement.

Real time monitoring in battery management
Real time monitoring can provide frequent information on batteries which can help protect the batteries. Real time monitoring can help replace manual checks by information available at monitoring systems.

Sensor modules collect the voltage and temperature data from the batteries and data is transferred in real time can help supervisors identify issues if any and which will lead to operational efficiency.

Predictive maintenance
Predictive maintenance helps in lowering operating and capital costs by facilitating proactive servicing and repair of assets while allowing more efficient use of maintenance personnel and replacement components.

Predictive maintenance enables companies to accurately diagnose and prevent failures in real time, which is vital in critical infrastructure applications.

Battery failures can prove to be highly expensive in terms of repair costs, in addition to the delay in transporting goods from the resulting downtime. Predictive battery analytics can also help predict battery failures which allows the supervisors to reduce reliability risk and improve uptime.

The need for longer battery life, reduced energy consumption, and lower costs will lead companies to provide intelligent solutions.  

Cognitive power electronics systems
Power electronics systems equipped with intelligence unit can monitor data from sensors and the data can be used to detect faults in the electronic system for real time optimisation of an application.

A power converter with monitoring capabilities would be able to detect impedance changes of a battery, enter into a safe state and send information to external systems for further evaluation.

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