Redening
grid intelligence
Analysing networks, optimising switching, and
improving gird reliability with Hytrove AI.
About Us
We are an AI company focused on improving India’s power distribution systems. We
work with DISCOMs to optimise grid operations, enhance eciency, and reduce
downtime.
By using real-time data and advanced analytics, we help utilities build more reliable
and resilient power networks.
Our goal is to make power distribution smarter, more ecient, and better suited for
the future.
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Grid Challenges
Telangana is one of India’s fastest-growing states, with expanding industry,
agriculture, and world-class urban centres. Yet, its power utilities still face the same
challenges that plague DISCOMs across India: fragmented visibility, reactive
decision making, and non existent real time intelligence.
(1) Reactive decision making
Load balancing, switching, maintenance, and loss reduction choices are made on estimates and
intuition rather than live evidence resulting in avoidable outages and suboptimal operations.
(2) Compounding daily costs
Blind operation of the grid drives preventable technical losses, delayed restoration, transformer
damage, and inaccurate energy accounting, all of which compound cost every single day.
(3) Lack of data-driven insights
There is no system that continuously tells what is happening on the network or what is about to
break. Existing tools are static, manually updated, and disconnected from real grid behavior.
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Our Solution Suite
HyTrove applies AI and predictive analytics to give DISCOMs real-time visibility,
failure foresight, and action guidance.
Module 4: Reliability Assessment
Module 2: Switching Optimisation
Module 3: Enhanced Substation Modeling
Module 1: Network Analysis
Module 5: Energy prole Manager
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Modules in depth:
A foundational load-ow and fault-analysis engine that converts the grid into a
single-line digital model for engineering simulation.
Value delivered:
How it works:
Gives utilities a dynamic, editable connectivity model of their network instead of
static drawings or consultant reports
Enables planners to identify under-voltage, overloads, and loss hotspots early,
before they escalate into outages
Allows decisions and studies to be run in minutes instead of weeks, reducing
dependency on external consultants
Becomes the reusable foundation for switching optimisation, reliability
improvement, loss reduction, and upgrade planning
Lets utilities test changes virtually rst, adding new lines or transformers on the
SLD and seeing their eect instantly leads to better investment decisions and
fewer on-ground mistakes.
Builds complete HV/MV/LV topology as a single-line diagram
Runs load-ow to compute voltage drops, technical losses, and current ows
AI-driven validation catches connectivity or voltage-class errors during model
editing.
Module 1: Network Analysis
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This module analyses the network model and load conditions to recommend
optimal switching actions that reduce losses, relieve overloads, improve voltage
proles, and enhance reliability. It produces ranked switching plans with clear
instructions that can be directly implemented in the eld.
Value delivered:
Reduces technical losses and improves voltage quality without new
infrastructure
Relieves overloaded feeders and transformers through optimal reconguration
rather than expensive upgrades
Helps engineers test switching plans digitally before eld execution, avoiding
operational risk
Produces clear, quantied switching orders that shorten decision cycles and
improve reliability
Can be rerun whenever load patterns change, enabling continuous optimisation
instead of one-time studies.
Module 2: Switching Optimization
How it works:
Uses the single-line diagram and load proles to simulate alternate feeder
topologies
Power-ow engine evaluates each option for losses, voltages, and thermal limits
AI helps rank feasible switching plans based on historical performance and
operational patterns.
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This module models the internal topology of substations using a detailed Single Line
Diagram, allowing engineers to analyse busbar congurations, breaker behavior, and
transformer loading against their operational limits
It simulates operational states and switching scenarios to reveal bottlenecks, unsafe
manoeuvres, and true export capacity, enabling planners to validate maintenance
actions, test load transfers, and assess transformer health before implementing
changes in the eld.
Value delivered:
Reveals hidden substation bottlenecks and safety risks that traditional feeder
studies miss
Allows operators to digitally validate switching and load transfers before
executing them on live equipment, thus reducing error and outage risk
Helps extend transformer and switchgear life through condition-based
assessment rather than time-based maintenance
Provides accurate visibility into a substation’s true export capability, leading to
better planning and more reliable network operation.
Module 3: Enhanced Substation Modeling
How it works:
Builds a high-delity single-line diagram inside the substation
Simulates load-sharing, tap-changer operations, and internal voltage drops
AI predicts internal bottlenecks and failure risks based on historical patterns.
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This module calculates and forecasts network reliability by analysing outages, failure
probabilities, protection zones, and restoration pathways across the entire network
topology. It simulates fault scenarios, identies weak segments that drive high
customer downtime, and quanties how network upgrades would improve SAIFI,
SAIDI & overall service continuity.
Value delivered:
Identies weak links and single points of failure through N-1 contingency
simulation before they trigger major outages
Enables data-driven justication for automation, reclosers, and network
upgrades by showing their projected impact on SAIDI/SAIFI
Moves utilities from post-mortem reliability reporting to predictive planning and
risk mitigation
Ensures alignment with regulatory performance targets, helping utilities avoid
penalties and maximise performance-based incentives
Validates restoration pathways and FLISR logic, ensuring that backup feeds have
sucient capacity to pick up load during emergency switching.
Module 4: Reliability Assessment
How it works:
Computes SAIFI, SAIDI, CAIDI for every feeder and region
Runs N-1 contingency simulations to detect single points of failure
AI forecasts reliability degradation by learning outage patterns.
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Module 5: Energy prole Manager
This module ingests historical meter and billing data alongside live feeder
measurements to prole energy behaviour, estimate technical & non-technical
losses, and ag anomalies. It continuously ranks feeders by stress, variance, and
loss risk, enabling engineers to spot suspicious consumption patterns, hidden
losses and degradation trends without running heavy simulations across the entire
network.
How it works:
Automatically ingests 15-min/hourly feeder energy data from SCADA/MDAS
Cleans, aligns, and updates every feeder’s load pattern and loss-risk curve
Uses network impedances to estimate technical loss envelopes
AI assigns anomaly scores (night base-load spikes, sudden jumps, imbalance).
Value delivered:
Gives utilities a daily oversight of loss hotspots, abnormal feeders, and unusual
energy consumption patterns without manual study eort
Helps prioritize eld inspections, metering upgrades, and smart meter rollout
based on actual loss behavior rather than guesswork
Saves engineering time by automatically screening all feeders and surfacing only
the ones needing attention
Provides actionable insights that reduce commercial losses and improve
planning decisions over time.
Why Hytrove?
Designed for the world's most dynamic grid
Built for India
Contact Us:
AI Core.
Built for legacy systems.
Email: business@hytrovetech.com
Hyderabad
Zero Capital expenditure to start.
Solving human-layer problems with cutting-edge tech.
And mainly...
All modules are powered by our advanced, proprietary machine learning algorithms
developed specically for the complexities of evolving power distribution networks.
These models continuously learn from operational data, adapt to regional consumption
behaviours and deliver real time insight without relying on constant reconguration.
No need for any major upgrades, our hybrid model works with your current infrastructure.
Minimal hardware, fast deployment, and no upfront investment required.
Electricity theft and vegetation related outages aren’t technical gaps but operational
headaches. Hytrove uses advanced AI models to automate what’s traditionally human-
dependent.
C
all us at:
+91
9063214778
