Implementation of the Residue Method in Steam Superheater Fault Detection | Chapter 4 | Contemporary Perspective on Science, Technology and Research Vol. 9

Steam superheaters are susceptible to various defects that can affect the system's operation differently. The maintenance of a constant temperature at the turbine input is critical to optimal running conditions of a steam turbine. This is achieved through regulation of the steam temperature in the superheater, which is partitioned into three sections with equipment installed at each connection point to enable the injection of condensates for steam cooling. Nonetheless, controlling the output temperature can be a challenge due to the transfer time delay between the points where water is sprayed and the points where the steam temperature is measured. To address this challenge, a temperature control system was developed and tested in three environments using an electronic simulator, the block-oriented simulation hardware BORIS, data acquisition board dSpace, and MATLAB R2020a Simulink with a PI controller model. These simulations enabled the study of the superheater's function in both standard and fault conditions, with the possibility of controlling the system structure and detecting faults through appropriately implementing weighting matrices.
 
The proposed method has a notable advantage of being able to detect and locate faults based on real-time information derived from measured process variables. It is easy to implement and provides multiple benefits, such as detecting errors in airflow control modules, highlighting steam temperature measurement problems, identifying accuracy issues in steam flow measurements, and displaying the controller output.
 
Overall, this innovative technique can enhance the safety and efficiency of steam superheaters in thermal power plants. Its straightforward implementation and numerous advantages make it a valuable addition to the existing fault detection and localization methods.
 
In conclusion, the ability to maintain steam temperature and pressure within optimal ranges is crucial for the efficient operation of steam turbines. Superheaters are employed to regulate steam temperature, and the injection of condensates allows for cooling of the steam. While controlling the steam pressure is relatively straightforward through adjusting the fuel flow command, regulating the output temperature can present challenges due to transfer time delays. Therefore, the development of a temperature control system that utilizes simulations to study the function of the superheater in standard and fault conditions can enable the identification of faults and control of the system structure, ensuring optimal turbine performance.

Author(s) Details:

Camelia Adela Maican,
Department of Automation and Electronics, Faculty of Automation, Computers and Electronics, University of Craiova, Romania.

Virginia Maria Radulescu,
Department of Medical Informatics and Biostatistics, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, Romania.

Cristina Floriana Pana,
Department of Mechatronics and Robotics, Faculty of Automation, Computers and Electronics, University of Craiova, Romania.

Please see the link here: https://stm.bookpi.org/CPSTR-V9/article/view/14169

Fault Diagnosis in Power System Using Hybrid Fuzzy-GA Approach | Chapter 3 | Research and Developments in Engineering Research Vol. 8

 In this member, an intelligent technique of Fuzzy – GA (FGA) approach is projected for detecting faults. This system reduces the time significantly in recognizing the exact location of the incorrect section and also detects the incorrect circuit breaker (CB) in the network for further disease.  With the advancement of technologies it should imperative to have a fixed, secure and uninterrupted supply of power to photoelectric systems in addition to to ensure the identification of sins occurring in these arrangements quickly and efficiently for fear that of any casualty. Operators must identify the defective divisions as soon as possible in order to reduce interruption times and increase aid dependability. This method demands drastically less thought to keep the database and makes crucial design regions. Shealthy for the healthy portion and Sisland for the faulty portion by means of post defect condition with CB’s and Relays. Next, it decides membership function (or partnership function) for every potential defect segment.  The best choosing approach is used to select the defect segment that is most controllable. The method is related, tested, and applied with the understanding studies on an IEEE 33kV distribution scheme and a Micro-Grid system. The main advantages of the submitted approach are its elasticity, reduced memory necessities, and reduced calculating time. In addition to the advantages filed above, the fuzzy-GA computerized data in system is significantly quicker and more exact.

Author(s) Details:

Murali Matcha,
Department of Electrical Engineering, Medi-Caps University, Indore, M.P, India.

Siddheswar Kar,
Department of Electrical Engineering, Medi-Caps University, Indore, M.P, India.

Neha Verma,
Department of Electrical Engineering, Medi-Caps University, Indore, M.P, India.

Please see the link here: https://stm.bookpi.org/RADER-V8/article/view/11950

Fault Detection Method Based on k-Nearest Neighbor Normalization and Weight Local Outlier Factor for Multimode Process Circulating Fluidized Bed Boiler | Chapter 10 | Research and Developments in Engineering Research Vol. 1

 Mode changes in contemporary's sophisticated industrial processes influence unanticipated shutdowns, that could reduce the age of critical system and result in high support costs. A technique that can recognize the fault of equipment operating in miscellaneous modes is wanted to prevent this issue. In light of this, we suggest a novel mistake detection method that makes use of the k-nearest neighbor normalization-located weight local eccentricity factor (WLOF). The suggested approach completes activity local normalization using neighbors to take into account potential way changes in the normal dossier, and WLOF is used for fault discovery. In contrast to statistical forms, such as principal component analysis (PCA) and free component analysis (ICA), the local aberration factor (LOF) uses the density of neighbors. However, common LOF is greatly afflicted by the distance between allure neighbors, performance can deteriorate when faulty dossier is adjacent to usual data. To improve the defect discovery performance of LOF, the proposed plan multiplies proportionally by the distance middle from two points each neighbor. A circulating fluidized bed boiler and a multimode mathematical case were used to assess the influence of the suggested method. The results of the experiments show that the suggested order performs better than conventional PCA, essence PCA (KPCA), k-nearest neighbor (kNN), and LOF. In particular, distinguished to conventional techniques, the submitted method raised detection accuracy by 20%. Therefore, the projected technique is appropriate to a real process operating in multiple fads.

Author(s) Details:

Minseok Kim,
Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea.

Seunghwan Jung,
Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea.

Baekcheon Kim,
Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea.

Jinyong Kim,
Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea.

Eunkyeong Kim,
Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea.

Jonggeun Kim,
Artificial Intelligence Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea.

Sungshin Kim,
Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea.

Please see the link here: https://stm.bookpi.org/RADER-V1/article/view/10096

Recent Development of Intelligent Shunt Fault Classifier for Nigeria 33-kV Power Lines | Chapter 9 | Newest Updates in Physical Science Research Vol. 9

 This research describes a novel way to employing artificial neural networks (ANNs) to improve transmission line protection. The suggested technique feeds four different neural network structures instantaneous voltages and currents on a transmission line during normal and fault conditions. The structures are then expertly merged to provide a system that can more effectively detect and diagnose shunt problems. The report goes into great detail about the design process as well as the many simulations that were run. The accuracy and mean square error (MSE) of the created system are examined, and the findings reveal that this approach is capable of identifying and classifying all probable shunt faults on the 33-kV Nigeria power lines in less than 1ms with a high level of precision. When evaluated under various shunt fault types with varying resistances and distances, the system's performance demonstrates that it can be used to improve distance line protection in 33-kV Nigeria power lines.

Author (s) Details

A. A. Awelewa
Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria.

P. O. Mbamaluikem
Department of Electrical/Electronics Engineering, Federal Polytechnic Ilaro, Ilaro, Ogun State, Nigeria.

View Book :- https://stm.bookpi.org/NUPSR-V9/article/view/1940