Power Protection System Using Historical Data Analysis
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Solution Overview
Problem
Modern electronic equipment is vulnerable to power disturbances, leading to premature equipment failure, revenue loss, and high maintenance costs due to ineffective protection systems that are primarily reactive and fail to anticipate or address local topology issues.
Innovation Solution
A data processing system that collects historical power consumption data to generate sensitivity profiles and load-specific protection specifications for each electrical load, enabling proactive protection, malfunction detection, and identification of problematic load combinations, thereby optimizing protection schemes and reducing downtime and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive protection systems are used to isolate electronic devices from power disturbances, then equipment protection is improved, but the system cannot anticipate or address local topology issues and fails to prevent premature equipment failure
Solution Approach 1:
The system performs preliminary actions by continuously monitoring power characteristics and detecting anomalies before they cause equipment failure. The proactive power protection system analyzes historical data and real-time measurements to identify potential issues, allowing protective measures to be taken before actual failures occur, thus preventing premature equipment failure rather than merely reacting after disturbances happen.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring power characteristics at multiple locations and using this information to adjust protection strategies. The system analyzes feedback from power quality measurements, historical data, and anomaly detection to dynamically modify protection settings, enabling it to anticipate local topology issues and adapt protective measures accordingly.
2Device complexity
If standardized protection schemes are applied to all electrical loads, then system simplicity is maintained, but load-specific sensitivity requirements are not addressed leading to unnecessary trips and downtime
Solution Approach 1:
The system applies local quality by determining sensitivity profiles for individual electrical loads based on their specific characteristics and requirements. Each load receives customized protection specifications tailored to its sensitivity to power anomalies, rather than applying a uniform protection scheme. This allows the system to maintain high equipment uptime by avoiding unnecessary trips while keeping the overall system structure manageable through automated profile generation.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting protection thresholds and settings based on load-specific sensitivity profiles. The protection scheme modifies operational parameters such as trip thresholds and monitoring levels according to each electrical load's characteristics, enabling optimized protection that prevents unnecessary downtime while maintaining system-wide manageability.
3Ease of repair
If technicians are dispatched for routine diagnostics and power cycling, then equipment issues are addressed, but high costs are incurred including technician time, truck dispatch, fuel, and maintenance overhead
Solution Approach 1:
The system implements self-service by automatically detecting power anomalies, diagnosing potential issues, and executing protective actions without requiring technician intervention. The proactive power protection system monitors equipment status, identifies problems early, and responds automatically, eliminating the need for routine technician dispatches for diagnostics and power cycling, thereby significantly reducing maintenance costs while maintaining ease of repair through automated systems.
Solution Approach 2:
The system replaces mechanical intervention with automated electronic monitoring and control. Instead of requiring physical technician presence for routine diagnostics and power cycling, the system uses electronic sensors, data processing, and automated control mechanisms to detect and respond to equipment issues, substituting human mechanical actions with automated electronic systems that reduce costs while maintaining repair effectiveness.
4Reliability
If protection systems respond after fault detection, then protective measures are taken, but the system is not designed to anticipate the need for protective action before fault conditions occur
Solution Approach 1:
The system performs preliminary actions by continuously monitoring power characteristics and detecting anomalies before they develop into actual fault conditions. The proactive power protection system identifies potential issues through pattern recognition and historical data analysis, allowing protective measures to be initiated before faults occur, thus eliminating response time delays and ensuring equipment reliability through advance intervention.
Solution Approach 2:
The system skips the traditional detect-then-respond sequence by rushing through to preventive action. When anomalies are detected, the system immediately executes protective measures without waiting for full fault development, effectively skipping the delayed response phase. This approach maintains reliability by preventing faults while minimizing time loss by acting swiftly upon anomaly detection rather than waiting for confirmed failures.
Data Source
AI summary
Methods, systems, and apparatus for collect historical power consumption data and power consumption statistics for one or more locations and devices at the location to generate historical power consumption and health data (“historical data”). The historical data are used to develop and provide multiple different protection and monitoring functions. The system may be deployed within a single customer location, e.g., within a building or a plant, and local analytics are developed at the location. Alternatively, the system may be distributed among several locations for a particular customer or multiple different customers and include cloud-based analytics in addition to, or instead of, local analytics.