Relay Load Management With Predictive Neutral Sensing for BESS
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Solution Overview
Problem
Conventional load management systems for battery energy storage systems require extensive infrastructure modifications, lack user control and flexibility, operate reactively rather than predictively, and fail to integrate with modern smart home ecosystems, leading to inefficient battery utilization and unexpected load disconnections.
Innovation Solution
An intelligent relay-based system employing machine learning algorithms, comparator-based neutral sensing, and mobile application control to dynamically manage 120V and 240V loads without additional wiring, using high-speed comparator circuits and temporal convolutional neural networks for predictive load management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional load management systems use extensive infrastructure modifications (separate electrical panels, additional wiring), then load management capability is improved, but installation cost and complexity increase significantly
Solution Approach 1:
The patent combines multiple load management functions into a single integrated relay module that interfaces with the existing electrical panel through minimal wiring. Instead of installing separate electrical panels and extensive wiring infrastructure, the system merges the relay control unit, sensing circuitry, and communication interfaces into one compact device that leverages existing electrical infrastructure.
Solution Approach 2:
The relay module is designed to perform multiple functions including load prioritization, load shedding, user notification, system monitoring, and communication with external devices. This multi-functional design eliminates the need for separate dedicated systems for each function, reducing overall installation complexity while maintaining comprehensive load management capability.
2Device complexity
If static relay-based switching systems use predetermined rules for load shedding, then system simplicity is maintained, but adaptability to changing usage patterns is lost
Solution Approach 1:
The system transitions from static predetermined load shedding rules to dynamic adaptive load management. The relay module continuously monitors system conditions and automatically adjusts load prioritization based on real-time battery state of charge, power availability, and configured user preferences. This dynamic behavior allows the system to adapt to changing usage patterns without requiring manual reconfiguration.
Solution Approach 2:
The system implements feedback mechanisms where the relay module continuously monitors battery status, power generation, and load conditions, then uses this information to automatically adjust load shedding decisions. The system also provides feedback to users through notifications and interfaces, allowing users to observe system behavior and adjust preferences accordingly, creating a closed-loop adaptive system.
3Reliability
If heavy isolation transformers are used for grid failure detection, then sensing reliability is improved, but response time increases by 10 milliseconds or more
Solution Approach 1:
The patent replaces heavy mechanical isolation transformers with electronic sensing circuitry that directly monitors grid voltage and neutral conditions. This substitution eliminates the mechanical/transformer-based sensing mechanism and uses solid-state electronic components to detect grid failures and neutral conditions, achieving sub-millisecond response times while maintaining sensing reliability.
Solution Approach 2:
The system uses lightweight current transformers and voltage sensing circuits as intermediaries to detect grid conditions without requiring heavy isolation transformers. These intermediary sensing elements provide the necessary electrical isolation and condition detection with minimal response delay, serving as a bridge between the grid and the control system.
4Device complexity
If load management systems operate reactively rather than predictively, then system simplicity is maintained, but battery utilization efficiency decreases
Solution Approach 1:
The system performs preliminary actions by proactively managing loads based on predicted future battery state of charge and power availability. Instead of waiting for battery depletion to trigger load shedding, the relay module anticipates future conditions and pre-adjusts load prioritization to prevent critical situations, thereby improving battery utilization efficiency while maintaining reasonable system complexity.
Data Source
AI summary
A load management system integrates comparator-based neutral sensing, machine learning prediction, and relay control into a single integrated “AC” board requiring no additional wiring. A highspeed comparator circuit detects grid failures in sub millisecond timeframes, providing clean data to a temporal convolutional network that predicts load requirements 24 hours in advance with integration of external data sources such as weather and time of use pricing. The system automatically manages 120V and 240V circuits during grid transitions, learning from user override patterns to continuously improve performance. A mobile application provides real-time monitoring and control. The integration of low-latency sensing with predictive machine learning enables performance improvements exceeding 40% in battery runtime compared to conventional systems, while reducing installation time and cost.