Mobile based data collection for models In predictive maintenance of transformers.
Abstract
"Mobile-Based Data Collection for Models in Predictive Maintenance of Transformers" addresses the need for efficient predictive maintenance strategies in the field of electrical engineering. The research explores the development of a mobile application designed for data collection to enhance predictive maintenance models for transformers. Transformers are key equipment in a power grid. The stability of the power system is depending more and more on the safety and reliability of transformers. Health Index (HI) is a practical tool that combines complex condition data into a single value as a comparative indication of the overall condition of a transformer. Most of the existing health index procedures are based on laboratory or on-site test data, with few considering the actual operating time. In this research, a comprehensive assessment method of transformer operating condition is proposed. Based on the HI model, this method first considers the aging process of the transformer insulation as well as the load and operating environment to form the theoretical health index HI1. The aging process is characterized by the operating time of the transformer, the load rate, and the pollution level. The test health index HI2 is then formed based on on-site tests.The asset health indexing software integrates data from a multitude of sources as the basis of predictive analytics Health index tool combines the result of various aspects. Utilities traditionally have not had a primary concern for collecting and storing substation equipment data in a way that would allow for much automatic analysis. The asset health solution allows both online and offline data from many different systems and databases to be combined in an automated way to allow the Utility to take advantage of the many features and functions within the tool. Mobile applications as a data collection tool provides for a rigorous approach that collects data as submitted by the field inspector/technician in real-time and feeds it into model to guide asset-management decisions in relative time.