March 17, 2020 Last updated March 23rd, 2020 247 Reads share

Managing Performance of Energy Assets with IoT

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For an IoT based energy monitoring solution, managing the performance and condition of assets and equipment is a primary yet rudimental aspect. Maturing energy products in an IoT-driven environment have become predictive and are now pursuing the ability to conduct automated operations

IoT based asset management system enables power generating companies and energy utilities to digitally manage their energy production and distribution operations. However, these systems also face barriers primarily associated with data management. It is difficult for systems to transform the continuous inflow of data into meaningful and actionable information and that too in real-time.

Processing and analyzing this data on a large scale as that of a power generation and T&D facility is no small feat to achieve. However, handlers rely heavily on this interpreted information to conduct repairs on broken pieces of equipment and maintain current operations.

IoT ecosystems infused into these facilities along with the application of sensors allow sharing of the raw collected data and help asset management systems to use this information to track the performance of assets. This data can provide useful insights based on the following two categories:

Health and Maintenance

The amount of data transferred mainly depends on the number of sensors and the component on which they are integrated. Even the most elementary asset management systems can use this data to predict the health of the parts on which they are deployed along with their operational efficiency. At present, these systems compare the actual performance of the assets against the set benchmarks to determine their health. Doing this helps inspection and repair teams to conduct condition-based maintenance while keeping the equipment downtime as low as possible.

However, even though these systems tell about the condition and performance of the assets, certain decisions like when to conduct maintenance still have to be taken manually. With the integration of cognition based technologies like artificial intelligence and machine learning, much more is expected from the current asset performance management systems.

Asset Management as per User’s Expectations

Energy generating companies have some additional goals to be accomplished similar to any other enterprise. These goals may be to ensure adequate profit margins, meet regulatory compliance or provide everyone with sufficient energy. These goals must be accomplished in order to boost overall operational excellence and fulfill customers’ needs.

We now know that predicting potential component failures is a key capability that asset management systems offer. However, the savings of an energy utility can be substantially huge given if the technology can be used for detecting every possible breakdown or malfunction. The money then saved can hence be used by the energy utilities to extend their reach to people located in more remote locations.

Facing Scalability Challenges During Asset Management

Utilities have a prime objective to provide the population of people around its vicinity with adequate and uninterrupted power supply. To do so, these energy providers are required to scale up their processes to stretch their services to the increased population every year.

However, given the complexity of asset management systems, it is not an easy task to scale them up to meet future needs. They are not only required to be scaled in the saturated market but are also required to be extended to new geographies. Hence, the energy suppliers must have access to the distantly located devices or energy meters so that they can supply energy on a pay-to-use basis.

From Scheduled Maintenance to Effective Maintenance

By far, the most advantageous feature of asset maintenance systems is the predictive identification of probable machine failures. Through, advanced analytics data is arranged and analyzed via AI and ML-based models to identify apparent performance degradation of equipment and also its failure.

This paradigm shift from reactive to preventive methodologies can help utilities to optimally use energy management and diagnostic tools. Subsequently, these predictive AI modules can also be used to react simultaneously with a component or process undergoing a constant dip in its effectiveness. By leveraging these modules, an enterprise or a business can eventually program its facility to conduct automated operations and take preventive actions without any manual intervention.

Predictive Maintenance and Superior Safety Systems

As per consultants and experts, the data that fuels an asset maintenance system can also be integrated with other business systems such as GISs, ERPs, and other work management applications.

By using lakes of data from end devices, contextual frameworks can also be developed to boost the operational safety. Power generation companies can also use these frameworks to enhance their operational excellence and install safety systems. Special sensors can be used to monitor parameters like temperature and pressure of a system and predict failures of an asset/equipment that can result in catastrophic situations.

Conclusion

By leveraging contemporary technologies like AI, ML, cloud computing, and IoT, utilities can develop asset performance management systems to manage their equipment and assets effectively. Since the IoT market equally depends on the hardware and software capabilities, it is important to make sensors smart and intelligent. This will help utilities to eventually adopt automation practices and significantly increase the overall ROI.

 

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Apoorva Verma

Apoorva Verma

Apoorva Verma is the Digital Marketer at Biz4Group, based out of Orlando, FL the United States. She has 10+ years of experience in boosting IT-based start-ups to success.

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