K. Z. Latt

Bio: K. Z. Latt is an academic researcher. The author has contributed to research in topics: SCADA. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Digital Twin Modeling and Operating State Assessment of Substation Equipment

Abstract: The comprehensive perception of substation equipment status is the precondition of ensuring safe operation and realizing intelligent patrol inspection of substation. With the development of digital power grid, higher requirements are put forward for the fine management of power equipment. Digital twin technology is one of the ideal ways to realize the real-time perception and evaluation of the running state of equipment by using digital technology such as sensor, Internet of Things, virtual reality and artificial intelligence. Aiming at the problem of precision constraint and error accumulation of physical model, a digital twin modeling method for substation equipment based on multi-parameter perceptual data and a method for equipment operation state evaluation is proposed.

A Solution for an Industrial Automation and SCADA System

Abstract: The need to have increasingly reliable and effective systems led to the rapid development of SCADA systems, the systems that can be found everywhere today. SCADA (Supervisory Control and Data Acquisition) systems are used for supervise and control different industrial processes. One of these solutions is presented in this article. This solution includes the implementation of a platform for operating systems such as Windows XP, 7, S or 10, 11, but also for the porting of all the functionalities of this platform to the Windows Compact Embedded real-time operating system. The testing and evaluation of this solution led to its validation.

K-Means Clustering Algorithm and LSTM based Short-term Load Forecasting for Distribution Transformer

Abstract: It is critical to forecast the electric load for a region. Traditional electric load forecasting frequently predicts the load of multiple transformers in the region after directly summing them, but directly predicting the total load after accumulating the load of each distribution transformer will weaken the individual time-series characteristics and reduce prediction accuracy, so it is necessary to forecast the total load of the region while appropriately retaining the individual time-series characteristics. To address the aforementioned issues, this paper clusters transformer load curves, narrows the load characteristics of the same category of transformers using the weighting concept. It stores the load characteristics of multiple transformers in a neural network. This paper develops a virtual transformer load forecasting model in which the sum of all transformer loads in the region is a number of times the virtual transformer load forecasting result.