Leveraging Analytics for Power Transmission and Distribution

Leveraging Analytics

The Power Transmission and Distribution industry is experiencing major changes from its historical business structure of a vertically integrated utility to a combination of several different models. However, the basic function of the industry, to produce and to deliver power, safely and reliably, has not changed. The restructuring of the industry has created uncertainty that has contributed to limited investment. Improvements in technology and improvements in the infrastructure.Moreover, pressures continue to reduce the cost of the electricity. Increasing reliability demands, aging infrastructure, increasing loading of components, and Increasing availability of operational data are the main challenges today.

Managing a reliable grid cost-effectively, is a growing challenge for Power Transmission and Distribution industry, particularly when viewed in the larger context of system complexity, aging assets, and increased customer expectations. Improving assets performance is a pressing issue in ensuring reliability for the utilities. Capturing data and record keeping are at the heart of asset management. This data can come from multiple sources throughout the organization. Existing methods comprise of multiple independent tele-supervisory subsystems, with a decentralized configuration, designed to measure and monitor individual or a group of assets. Existing monitoring, control, and protection systems are driven by established practices that have been proven in the utility market. However, opportunities to evaluate new control or protection systems exist that leverage the availability of more information and new devices.

There is an enormous potential that predictive analytics offers us for Improving Power Generation Efficiency, optimizing Power Consumption, and Reducing T&D Losses.

  • To ensure reliability and to gain additional value, the assets must be managed in new ways using available data as well as expanded data sets. Opportunities exist to capture and better utilize presently available data from relays, DFRs, and other IEDs, to better monitor assets, locate faults, and perform new functions. Additional opportunities exist to improve sensors, communications, capture new information and present information in new ways to enhance performance.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.
  • 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. Many enterprises are using cloud-based analytics to generate actionable insights. For example, 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.
  • Technology improvements and increasing need to better manage assets have facilitated the implementation of substation automation. The use of automation increases the availability of information and the ability to improve control over the system. However, more work is required to better manage the information and develop methods and algorithms to automate systems to improve system performance and reliability.Transmission and distribution losses are still more than a staggering percentage, 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 and IoT, 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.  Nirmalya’s Asset Management System (nPravaahAMS) developed on an Internet of Things (IoT) platform, resides on top of all disparate utility systems and provides a consolidated view of the assets from a central location. This system integrates information from various existing systems such as Supervisory Control and Data Acquisition (SCADA), Substation Automation System (SAS), Distribution Automation System (DAS), Advanced Meter Infrastructure (AMI) etc. as well communicate with intelligent and communicable devices like energy meters, remote terminal unit (RTU), remote monitoring unit (RMU), and other equipment. The collated data is analyzed using intelligent algorithms and helps in real time decision support to the utility managers by giving them a complete overview of asset performance across the utility. This also helps in proactive asset maintenance deferring large capex requirements due to system breakdown.

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.Opportunities exist to study the increasing amounts of available data and identify new phenomena that can lead to the development of new devices and operating practices.The Power Transmission and Distribution industry also needs to build robust system to overcome the challenges and become smarter with not just the way we consume power, also transmit and distribute with the help of disruptive technologies.

About the Author

Biraja Prasad Nath is the Founder and CMD ofNirmalya Labs Private Limited.