Evaluation of the Accuracy and Consistency of Variable Reluctance Sensors for Turbine Speed Monitoring in Steam Turbine Generator 1.0 at Tambak Lorok CCPP

Journal Title: Journal of Intelligent Systems and Control - Year 2024, Vol 3, Issue 4

Abstract

Accurate monitoring of turbine speed is essential for ensuring operational stability and efficiency in power generation systems, particularly within the context of low-carbon and renewable energy integration. This study evaluates the performance of three Variable Reluctance Sensors (VRSs)—VRS1, VRS2, and VRS3—used for real-time speed monitoring of the Steam Turbine Generator (STG) 1.0 at the Tambak Lorok Combined Cycle Power Plant (CCPP). The evaluation was conducted using statistical methods, including Root Mean Square Error (RMSE), standard deviation, and two-factor Analysis of Variance (ANOVA) without replication, to assess the accuracy and consistency of the sensors under varying operational conditions. The operational conditions were simulated through a motor controlled by a Variable Speed Drive (VSD), which allows for precise control over speed variations. The results indicate that the VRSs exhibit high accuracy and reliability, with RMSE values ranging from 0.08% to 0.28%. Among the three sensors, VRS3 demonstrated the highest performance, achieving minimal variability, with a standard deviation of 0.000 at a frequency of 50.00 Hz. ANOVA revealed no significant differences in performance between the three sensors (P-value = 1.000), suggesting uniformity in their measurement capabilities. These findings substantiate the suitability of VRSs for turbine speed monitoring in power plants, ensuring operational stability and supporting the integration of renewable energy technologies. The results reinforce the potential of VRSs as a reliable tool for improving the efficiency of sustainable energy systems.

Authors and Affiliations

Afif Abdul Hadi, Ahmad Azmi Fikri

Keywords

Related Articles

Robust Neural Network-Based Trajectory Tracking Control for Mobile Vehicles

The ability of neural network-based control systems for trajectory tracking in wheeled mobile vehicles was evaluated in this study. A significant challenge often encountered is the deviation from the desired trajectory,...

A Few Maclaurin Symmetric Mean Aggregation Operators for Spherical Fuzzy Numbers Based on Schweizer-Sklar Operations and Their Use in Artificial Intelligence

One significant benefit of the Maclaurin symmetric mean (MSM) is that it is a generalization of many extend operators and can consider the interrelationships among the multi-input arguments, such as multi-attributes or m...

Modeling and Control Strategy of Wind-Solar Hydrogen Storage Coupled Power Generation System

Hydrogen production by wind and solar hybrid power generation is an important means to solve the strong randomness and high volatility of wind and solar power generation. In this paper, the permanent magnet direct-drive...

Neural Network-Based Control and Active Vibration Mitigation in a Fully-Flexible Arm Space Robot under Elastic Base Influence: A Luenberger Observer Approach

This study explores dynamic simulation and integrated control in a space robotic arm system characterized by a fully-flexible arm and an elastic base. The elastic base is modeled as a lightweight spring, and the modal sh...

Enhancement of Vehicle Ride Quality Through Semi-Active Suspension: A Full-Scale Quarter-Car Test Rig Evaluation

In the pursuit of optimizing automotive suspension systems, a semi-active suspension system (SASS) utilizing continuous skyhook control has been developed to enhance vehicle ride comfort and handling. This system is spec...

Download PDF file
  • EP ID EP759850
  • DOI https://doi.org/10.56578/jisc030404
  • Views 27
  • Downloads 1

How To Cite

Afif Abdul Hadi, Ahmad Azmi Fikri (2024). Evaluation of the Accuracy and Consistency of Variable Reluctance Sensors for Turbine Speed Monitoring in Steam Turbine Generator 1.0 at Tambak Lorok CCPP. Journal of Intelligent Systems and Control, 3(4), -. https://europub.co.uk/articles/-A-759850