This paper presents an overview of industry trends and practices in the deployment of technologies needed to support the implementation of smart asset analytics in electricity distribution systems. The paper discusses the strategic opportunities and benefits in pursuing specific applications of smart asset analytics e.g., failure prediction, asset optimization and power distribution efficiency improvements, and the role of modern innovative practices in achieving them...

Smart Asset Analytics Power Distribution

Power Distribution is one of the most critical elements of the power supply value chain. It is a regulated activity and a customer inter-facing operation. A Power Distribution Company (DISCOM) has to ensure electricity supply availability, reliability and quality at all times, in compliance with the established regulatory framework and Standard Operational Practice (SOP). An efficient network asset management programme through asset planning, failure analysis and distribution efficiency improvements is essential to manage cost of operations, by reducing network breakdowns and costly asset replacements. The paper aims to evaluate and substantiate the value proposition in adopting advanced asset analytics for failure prediction, asset optimization and efficiency improvements, using modern tools such as big data, Artificial Intelligence (AI) and Machine Learning (ML).

Asset management in Power Distribution

Asset management is an important activity in power distribution system planning, operation and maintenance. It has a direct impact on a DISCOM’s revenue.  The maintenance of network infrastructure comprising assets across the distribution chain from the substation to the consumer premises has to reflect current best practices to meet the stress of fluctuating electricity demands. To this end, DISCOMs need to introduce asset data analytics as part of their strategy for predicting and analyzing faults, reducing downtime and rapid system restoration.

Asset data analytics uses risk-based analysis to manage performance, predict failures and control costs. It benchmarks asset performance and predicts how it is going to perform in the future. Apart from an analysis of the financial aspects of network asset performance, such as cost of downtime, there are technical, regulatory and socio-environmental aspects such as power quality, reliability and carbon footprint.

Innovations in Power Distribution automation and smart grid have resulted in the growth of intelligent devices communicating with the electrical network assets in real time and collecting vast amount of network data in the process. This data after analysis provide vital clues on the health of the electrical assets and operating conditions. A robust asset management helps plan power distribution network for profitable and sustainable operations. Efficient asset management maximizes asset value over its entire life cycle, maximizes ROI and optimizes network operations.

Advanced Asset Analytics in Power Distribution

Advanced asset analytics makes use of modern technologies for remote monitoring of network assets and enables advance warning systems for proactive network management. Recent innovations in Internet of Things (IoT), Cloud computing and AI/ ML now offer cost-effective platform for intelligent asset analytics.

Maintenance of physical systems and associated strategies form an important part of network asset management. This includes corrective maintenance, preventive maintenance, condition-based (predictive) maintenance and proactive maintenance. Advanced asset analytics helps in planning maintenance strategy for various power system components, such as power transformers, distribution transformers, switchgears, overhead lines, underground cables and protection devices.

Accurate, timely and reliable asset information results in better decisions, leading to balancing cost, performance and risks associated with power distribution operations. Advanced asset analytics promises network efficiency improvement, asset optimization, loss minimization and carbon footprint reduction.

Advanced Assets Analytics

Technologies for Advanced Asset Analytics

Advanced asset analytics uses automated data-driven technologies to collect, migrate, process, analyze and present data to aid decision-making and improve asset performance. It involves cyber-physical implementation of asset management using sensors, telemetry, computerized data processing and business intelligence to measure and analyze asset performance data across the life cycle. Ageing infrastructure is a common problem affecting the Power DISCOMs.

Sensors Database

IT and OT Integration in Asset Analytics 

The operation of substations, feeders, transformers, switchgears and other electrical systems responsible for the supply of electricity, as per regulatory norms and standards, comes under the domain of operational technology. Operational Technology refers to industry-specific business operations responsible for monitoring events, processes and devices, making adjustments and applying controls to run the DISCOM business operations. Information Technology provides the backend computing hardware, system software and software applications, which support the power distribution business operations. Advanced asset analytics requires integration of both Information Technology (IT) and operational technology (OT) systems.

Use Cases in Advanced Asset Analytics

Use case: Substation Feeder Monitoring System

Substation feeders are the arteries of the distribution network. Failure of substation assets directly impacts the revenues of the DISCOM, due to cost of system restoration and billing disruption.


Substation asset analytics uses information collected from Fault passage current sensors installed on LT feeders, in real time, and monitors any sign of overloading, short circuit or earth fault. The current and load signals are graphically displayed on a remote Digital Fault Recorder (DFR) and the information is utilized to ascertain possible fault conditions and location of impending fault. Early detection of an emerging fault provides operators with a better understanding of the vulnerable sections of the network and help them take preventive measures before major fault could occur.

Fault passage sensors communicate the status of the network to the Remote Terminal Unit (RTU) using RF communication, and the RTUs relay the network condition data using GSM/ GPRS communication to the Feeder Monitoring Cell, where the data is processed and analyzed by advanced asset analytics program.

By effective condition monitoring of LT distribution systems, the number and duration of the outages can be reduced drastically, thus saving the cost of maintenance and system restoration. This enables DISCOM improve network reliability, operate the network within acceptable limits and avoid regulatory penalties.

Rf Communication

Substation feeder data analytics can yield the following tangible benefits:

  • Quicker detection of a fault condition
  • Determination of the location of fault
  • Isolating of the faulty section of the LV network
  • Re-energizing heathy sections, outside the faulty isolated section

Use Case: Transformer Monitoring System

Distribution Transformer (DT) is the heart of the LV distribution networks. The health of DT needs to be monitored at all times to ensure continuous and reliable electricity supply. Failure of DT due to overloading, overheating, voltage sags and swells, harmonics and transients adversely impacts the distribution assets, asset life expectancy and network performance.

On line monitoring and analytics of DTs make use of sensors to capture the critical parameters of the transformer, analyze data and apply proactive corrections, which prevent overloading, reduce the risk of transformer failure and enhance the life of the transformer. The data is monitored remotely on a smart dashboard application, using GSM/ GPRS, Ethernet, RF or ZigBee communication. The failure of transformer in service could be due to temperature rise, low oil levels, over loading, poor quality of connections or improper installation. These abnormalities can be detected early and major faults can be averted by taking timely action, based on intelligence provided by advanced asset data analytics.  As a result, DT breakdowns is drastically reduced, thus enhancing the life of the network asset.

Use Case: Power Quality Monitoring of the Network

The quality of electricity supply is an issue of growing concern among the DISCOMs. The power quality of an electricity distribution network is affected by power line disturbance such as waveform faults, overloading, capacitor switching transients, impulse transients and total harmonic distortions (THD). The proliferation of energy efficient equipment, renewable energy sources and power electronics results in electrical distortions beyond regulatory limits of harmonics, as specified by IEEE 519: 2014 standard, and voltage quality index, as specified by EN 50160 standard. This can be potentially damaging to the network assets – circuits, relays, protection switches and other equipment, by causing overheating, cable faults and tripping.

Temp Sensor

Ideally, the best electrical supply is pure, sinusoidal waveform of a constant magnitude and frequency. However, many loads are not purely resistive or linear. The presence of magnetizing current, effect of rectification and inherent impedance of certain loads may result in creation of harmonic distortions and electrical imbalance, which may degrade the power quality leading to technical losses and asset deterioration.

Various measurement instruments are used for monitoring power quality in LT distribution systems, e.g., smart meters, protection relays and fault recorders. Nowadays, IoT and Cloud-based technology are used to monitor and analyze power quality of the distribution network and its impact on asset condition. The asset condition data is collected at regular intervals and analyzed to take timely corrective measures and safeguard network assets.

IoT and Cloud-based asset condition monitoring and analytics ensure efficient and trouble-free operation of the distribution network. IoT sensors and telemetry system allow DISCOMs to record vital information regarding power quality of supply, in compliance with the power regulations. They provide real-time information for asset condition monitoring and asset analytics. The sensor records harmonics, inter-harmonic frequencies, voltage, current, active/ reactive power, power factor and frequency usually at the Point of Common Coupling (PCC) of the electricity distribution network. The asset condition data is transmitted over wireless or wired communications to Network Control and Data Analytics Centre.

The solution provides network asset visualization including THD and voltage quality parameter, allowing early fault detection in the LT distribution network. The electrical (V, I, P, Q, PF, THD) and physical parameters (Transformer casing and winding temperature) of the network assets can be measured at user-selectable intervals. The asset parameters are aggregated in a Master Control Unit (MCU) connected to a communication gateway to transmit data to a remote intelligent dashboard. The dashboard is powered by advanced asset analytics application, which performs graphical analysis, and sends out alarms and notifications, allowing DISCOM operators to take proactive control actions. This method eliminates the use of expensive diagnostic instrumentation, such as Power Quality Analyzers and Digital Fault Recorders.

Power Assets

Perfect Sine WaveConclusion

Advanced asset analytics is being increasingly adopted in electrical utilities across the globe for remote monitoring and real-time visualization of vital network assets of DISCOMs. The business case is justified in terms of cost reduction, asset life increase and revenue gains that it can bring, as explained in the use cases above. It can be a useful tool for the DISCOMs to manage their assets more productively with the following benefits:

  • Improving cost efficiency in network operations
  • Increasing life expectancy of network assets
  • Compliance with regulatory standards
  • Improving reliability and quality of power supply
  • Minimizing technical losses and improving efficiency
  • Reducing carbon footprint in asset maintenance

Advanced asset analytics can add significant value to any smart grid implementation, and therefore deserves to be given its due merit in enabling cost-efficiency, process improvement, reliability and quality in power distribution.

Jayant Sinha





Author – Jayant Sinha
Principal Consultant (Energy & Power)
Spectrum Infra Ventures Pvt. Ltd.
Asia Power Quality Initiative (APQI)
NSN Partner

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