By Er. Ifrah Yousuf

On a cold winter evening in Kashmir, thousands of households switch on heaters, cooking appliances and lights as temperatures fall. 

Electricity demand rises, transformers work harder and distribution feeders face mounting pressure. 

Somewhere in the network, a piece of equipment may be approaching failure even before a breakdown becomes visible to consumers.

Traditional systems often discover such trouble after a fault has occurred. Artificial intelligence provides the possibility of detecting it earlier, giving engineers time to intervene before a transformer trips or an entire neighbourhood loses power.

Kashmir needs to take this technology seriously because reliable electricity affects homes, hospitals, businesses and industries alike.

AI can change how power utilities work by helping them spot problems early and fix them before they lead to outages.

The power sector has long invested in generating electricity and transmitting it over long distances. Consumers, however, experience the system through its final stretch, a network of feeders, transformers, switches and cables that delivers electricity to individual homes and businesses. 

Failures along this last stretch can turn a technically sound supply system into an unreliable service.

Engineers have traditionally depended on scheduled inspections, established operating rules and experience gained from years in the field. 

These methods remain important, but modern distribution networks generate far more information than any individual can examine continuously. 

Smart meters, sensors and automated switches record voltage, current, equipment temperatures, consumption and outages. The challenge lies in turning these readings into decisions before a problem escalates.

Artificial intelligence can help bridge that divide. 

Machine learning systems analyse historical records, identify unusual behaviour and estimate what might happen next. A smart grid that only records events tells engineers what has already happened. 

A grid equipped with predictive intelligence can help them decide what needs attention before the next disruption.

Consider a transformer that has served a neighbourhood for years. Its temperature begins rising beyond the levels expected under comparable loads. The change might appear insignificant during a routine inspection, but an AI system analysing equipment performance over time could recognise it as a possible sign of deterioration. 

Engineers could inspect the transformer, investigate the cause and arrange repairs during a planned maintenance window.

The same principle applies to circuit breakers whose operating times gradually increase or cables showing readings that suggest insulation problems. 

Predictive maintenance helps utilities direct crews towards equipment showing signs of trouble instead of treating all assets according to fixed schedules. It can also help them arrange spare parts and maintenance work before a failure disrupts consumers.

The financial implications are strikingly significant. 

Emergency repairs, equipment damage and prolonged outages impose costs on utilities and the people who depend on them. Better predictions can help direct limited maintenance budgets towards assets that need attention most. 

The technology cannot guarantee that faults will disappear, but it can give engineers a better basis for deciding where to intervene.

Demand forecasting presents another opportunity. 

Electricity consumption changes with weather, working hours, holidays and public events. Rooftop solar can reduce demand from the grid during sunny afternoons before consumption rises again at dusk. Electric vehicles can add substantial loads when owners plug them in after returning home. Heatwaves and major cricket matches can also alter consumption in ways that historical averages may fail to capture.

Machine learning models can examine consumption records alongside weather forecasts and other demand trends. They can estimate electricity needs for the coming hour, day or season, with predictions tailored to individual feeders and neighbourhoods. As fresh information becomes available, the models can update their forecasts.

Better estimates help utilities prepare sufficient capacity, reduce the risk of overloaded equipment and avoid unnecessary expenditure on power purchases or infrastructure. 

Consumers ultimately benefit when planning decisions are based on actual demand rather than broad assumptions about how much electricity a region might use.

AI can also assist engineers during an outage. By analysing signals from smart meters, protection devices and field sensors, a system can help identify the likely location of a fault. Maintenance crews can then head towards the affected section rather than spend valuable time searching along a long distribution line.

The technology can also help balance loads between neighbouring feeders when one faces excessive demand and another has spare capacity. 

By coordinating switches, voltage regulators, capacitors and batteries, AI systems can support better network performance and help maintain suitable voltage levels for hospitals, factories and other facilities that depend on reliable electricity.

These capabilities, however, depend on the quality of the systems beneath them. Older equipment may lack the sensors and communication links needed to supply useful data. 

Integrating AI with existing infrastructure can be expensive, particularly when utilities must modernise several parts of a network before predictive systems become effective.

Cybersecurity presents another concern. As more grid operations become digitally connected, unauthorised access can threaten equipment and disrupt electricity supply. 

Utilities must protect their systems while ensuring that engineers can understand, test and verify the recommendations produced by algorithms. A prediction should support professional judgement, with responsibility for critical decisions remaining clear.

Training will be equally important. Engineers and control room operators need the skills to interpret AI-generated alerts, distinguish genuine faults from misleading signals and respond appropriately. 

Investment in software alone will achieve little if the people operating the network lack the training or authority to use it effectively.

AI should extend the abilities of the lineman inspecting a transformer, the control room operator responding to an outage and the engineer planning for a rise in demand. Their field experience and judgement remain essential, particularly when an unfamiliar situation falls outside the data used to train a model.

The real test of a smarter grid will be found in ordinary homes and workplaces. 

A shopkeeper should be able to keep business running, a hospital should receive suitable power and a family should spend a winter evening without an avoidable interruption. 

These outcomes depend on equipment, people and sound decisions working together.

Kashmir’s power distribution system can benefit from predictive intelligence, provided utilities approach it as an investment in public service rather than a purchase of fashionable technology. Better data, modern equipment, trained staff and transparent oversight must accompany its adoption.

An algorithm cannot keep the lights on by itself. Its value lies in giving an engineer enough time, information and freedom to act before a small fault becomes a large disruption. 

That is where the future of power distribution begins, way before a household notices that anything might have gone wrong.

• The author is an electrical engineer and power sector technocrat specialising in power distribution, project management and sustainable energy. She can be reached at [email protected].