“Smart” seems to have become the operative word for businesses across the globe these days. Smarter manufacturing, smarter devices, smarter logistics, and inevitably, smarter power; every organization wants to be associated with this new buzzword. If we take a step backward, what does ‘smarter’ mean in the context of the human brain? Simply put, a smarter individual is someone who can assimilate, retain, and recall information quickly and make immediate decisions based on the information available to him/her. Within this cycle, the notion of ‘intelligence’ is the ability to convert available information into insights, which, in turn, help reach a decision. In the context of automated systems, data is analogous to information and the conversion of information into insights, or ‘predictive analytics’ has become the most valuable input for making decisions, be those automated or manual. The possibilities of applying predictive analytics across business verticals are endless, and we are already seeing manifestations in the power sector in the form of smart meters, predictive asset analytics, and reduced T&D losses.
The challenges faced by the power generation sector are not just limited to minimizing consumption and reducing losses. The problem is much more fundamental, with the imminent depletion of resources used for power generation, be it water or fossil fuels, and the risks and high costs associated with alternative sources such as nuclear energy, wind, solar energy etc. Hence, more and more emphasis is being placed on efficient energy management by power producers, distributors, and consumers. The establishment of a smart grid is a key initiative to improve efficiency, maintenance and planning. The smart grid is essentially an automated system that connects all entities in the power sector, allowing them to interact with each other in real-time. Using historical data, future consumption patterns can be predicted. Any abnormal change in consumption can be tracked and the cause of such changes can be detected, thereby ensuring that usage of power-inefficient equipment can be minimized. With concrete data available at hand, consumers can be convinced of the need to replace or repair energy-inefficient devices by providing them details on long-term cost implications using predictive analytics. Enterprises are also using cloud-based analytics to generate actionable insights. For instance, some organizations use business intelligence software and data from sensors to detect occupancy rates in buildings at different time intervals. Automated systems can then use this data to control consumption in real-time and eventually reduce energy costs.
One of the key challenges faced by power generation units is tackling downtime due to equipment failure. The ideal way to optimize power generation efficiency would be to devise a way in which operators can anticipate equipment failures with a fair degree of accuracy and accordingly schedule maintenance. During maintenance, staging equipment can be used to minimize, or even eliminate, downtime. Real-time waveform monitoring and other predictive analytics methods can be used to generate timely insights on equipment performance and help avoid downtime occurring due to overloading, voltage fluctuations, and damages to ancillary equipment.
In countries such as India, transmission and distribution losses are still more than a staggering 20%, which is ironical for a country that is facing a significant power deficit. However, the potential for reducing T&D losses with the implementation of a nation-wide smart grid is enormous. With rapid technological advances and the emergence of IoT, it is becoming easier to track and monitor disruptions in supply. Analytical tools can derive relevant and real-time data from equipment such as sensors, smart meters, and other communications devices and generate actionable insights for T&D companies. This approach, also referred to as asset analytics, helps T&D companies improve productivity based on measures such as asset health, criticality, and maintenance scheduling.
By using machine learning, the asset health can be monitored using algorithms that can track variables such as the condition of the asset, weather, failure frequency etc. These algorithms can be based on analytical techniques such as logistic regression, neural networks etc.
The aforementioned ways are just a tip of the iceberg; there is an enormous potential that big data analytics offers us to ensure optimal utilization of power. Quite evidently, we no longer have many other options remaining with rapidly depleting resources. It is high time we become a lot smarter with not just the way we consume power, but also generate, transmit and distribute it.
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