International business machines corporation (20240135280). MONITORING TRANSFORMER CONDITIONS IN A POWER DISTRIBUTION SYSTEM simplified abstract

MONITORING TRANSFORMER CONDITIONS IN A POWER DISTRIBUTION SYSTEM

Organization Name

international business machines corporation

Inventor(s)

Dzung Tien Phan of Pleasantville NY (US)

MONITORING TRANSFORMER CONDITIONS IN A POWER DISTRIBUTION SYSTEM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240135280 titled 'MONITORING TRANSFORMER CONDITIONS IN A POWER DISTRIBUTION SYSTEM

Simplified Explanation

The abstract of the patent application describes a system for monitoring transformers, predicting energy loss, failure rates, and optimal replacement times based on sensor data and failure rate prediction models.

• The system receives sensor data from transformers during operation.
• It generates energy loss data based on the sensor data.
• It trains a failure rate prediction model using failure data to calculate failure probability distribution data.
• It generates replacement data based on the energy loss data, failure probability distribution data, and transformer specifications.

Potential Applications

This technology can be applied in the power distribution industry to improve maintenance schedules and reduce downtime of transformers.

Problems Solved

1. Predicting energy loss and failure rates of transformers can help prevent unexpected failures and minimize disruptions in power supply. 2. Determining optimal replacement times for transformers can optimize maintenance schedules and reduce costs associated with premature replacements.

Benefits

1. Improved reliability and efficiency of transformer operation. 2. Cost savings through optimized maintenance schedules. 3. Enhanced safety by reducing the risk of transformer failures.

Potential Commercial Applications

Predictive maintenance services for power distribution companies to optimize transformer performance and reduce maintenance costs.

Possible Prior Art

One possible prior art could be predictive maintenance systems for industrial equipment, such as rotating machinery or HVAC systems, that use sensor data and failure prediction models to optimize maintenance schedules.

What is the accuracy of the failure rate prediction model in real-world scenarios?

The accuracy of the failure rate prediction model may vary depending on the quality and quantity of sensor data available, as well as the complexity of the failure prediction algorithms used.

How does the system handle different types of transformers with varying specifications?

The system likely takes into account the specific specifications of each transformer, such as size, age, and operating conditions, to generate accurate energy loss data and failure rate predictions tailored to each individual transformer.

Original Abstract Submitted

an embodiment includes receiving, by a transformer monitoring system associated with a transformer, sensor data from one or more sensors during operation of the transformer. the embodiment also includes generating, by the transformer monitoring system, energy loss data representative of a predicted energy loss of the transformer based at least in part on the sensor data. the embodiment also includes training, by the transformer monitoring system, a failure rate prediction model using failure data, resulting in a trained failure rate prediction model that calculates failure probability distribution data indicative of a time at which a failure of the transformer is most likely to occur. the embodiment also includes generating, by the transformer monitoring system, replacement data representative of an optimal time for replacing the transformer based at least in part on the energy loss data, the failure probability distribution data, and specification data for the transformer.