Inter-Turn Fault Detection in Rotor of Hydro Generator using Fuzzy Inference System and Field Current Analysis

Problem Definition

The literature survey on fault detection in hydro-generators reveals a significant gap in the existing techniques compared to those used for turbo generators. Specifically, the current approaches for detecting inter-turn short circuit faults in hydro-generators are not as effective in identifying the fault location, leading to a decline in the performance of these traditional models. Additionally, the detection of rotor inter faults in hydro-generators using conventional methods proves to be challenging. These limitations point towards the urgent need for improved fault detection techniques in the field of hydro-generators to ensure optimal performance and reliability. Addressing these issues is crucial for the efficient operation of hydro-generators and for minimizing downtime and maintenance costs associated with faulty equipment.

Objective

The objective of this study is to develop a new fault detection approach for hydro-generators using fuzzy logic. This approach aims to improve fault detection accuracy and efficiency by incorporating temperature data and implementing a fuzzy decision-making system. By utilizing rotor field current and resistance calculations, the proposed method seeks to address the limitations of existing fault detection systems and enhance the performance and reliability of hydro-generators. The ultimate goal is to predict faults in hydro generators, minimize downtime, maintenance costs, and improve the overall efficiency of these traditional models.

Proposed Work

In order to address the issues that were encountered in the standard fault detection systems, a new approach based on Fuzzy logic is developed in this paper. The suggested approach uses the rotor field current to interpolate the output of hydro generator. After this, the effective resistance of the rotor is computed using field voltage at 20 deg Celsius. This value is then mapped to the basic commissioning values of same generator and the total variance in changing resistance will be calculated that is related to the changing number of rotations of rotor poles. The suggested fuzzy model's major goal is to predict faults in hydro generators so that their efficiency is not impeded through any faults that may occur on salient rotor poles.

Furthermore, the presented approach minimizes the requirement for off-line pole drop testing, remove or affirm shorted spins that result from significant vibration and also enabled hydro plants to schedule rotor winding maintenance. The approach incorporates temperature as an additional parameter alongside current variation, and a fuzzy logic-based automatic decision-making system is implemented for fault detection. The proposed work aims to bridge the gap identified in existing fault detection systems for hydro-generators by introducing a novel approach that utilizes fuzzy logic for improved accuracy and efficiency. By integrating temperature data and a fuzzy decision-making system, the model strives to enhance fault detection capabilities and address the limitations of traditional methods. Through the use of rotor field current and resistance calculations, the proposed approach seeks to provide a comprehensive solution for detecting faults in hydro generators, ultimately improving their performance and reliability.

The rationale behind choosing fuzzy logic lies in its ability to handle uncertainty and imprecise information, making it a suitable tool for complex fault detection tasks in critical systems like hydro generators.

Application Area for Industry

This project can be used in a wide range of industrial sectors that rely on hydro generators for their operations. The proposed solutions of using Fuzzy logic for fault detection in hydro generators can benefit industries such as power generation, renewable energy, water management, and manufacturing. These industries often face challenges in efficiently detecting faults in their hydro generators, which can lead to decreased performance and maintenance issues. By implementing the fuzzy logic-based approach, these industries can accurately predict and locate faults in their generators, ensuring optimal performance and reducing downtime for maintenance. Furthermore, the benefits of implementing these solutions include improved efficiency of hydro generators, reduced maintenance costs, and increased reliability of operations.

The fuzzy model's ability to predict faults in advance allows industries to proactively address issues before they escalate, leading to improved overall productivity and performance. Additionally, by minimizing the need for offline pole drop testing and enabling scheduled maintenance of rotor windings, industries can better manage their resources and ensure the longevity of their hydro generator equipment.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in the field of fault detection in hydro-generators. By introducing a new approach based on Fuzzy logic, researchers, MTech students, and PHD scholars can utilize this methodology to address the limitations of traditional fault detection systems. This project can serve as a valuable resource for those looking to pursue innovative research methods, simulations, and data analysis within educational settings. The relevance of this project lies in its application in detecting faults in hydro-generators, which has been a challenge due to the inadequacy of existing techniques used for turbo generators. The use of Fuzzy logic in this context can provide a more effective way to detect and predict faults, particularly inter-turn short circuit faults, in hydro-generators.

By leveraging the rotor field current and computing the effective resistance of the rotor, this approach offers a more accurate and efficient method for fault detection. This project can also be beneficial for researchers and students in the field of electrical engineering, specifically those focusing on power generation and renewable energy. The code and literature generated from this project can be used as a reference for future research endeavors, allowing for further exploration and advancement in fault detection techniques for hydro-generators. In terms of future scope, potential applications of this project could include expanding the use of Fuzzy logic in other areas of fault detection and prediction in power generation systems. Additionally, the development of more sophisticated algorithms and models based on this approach could lead to improved efficiency and reliability in fault detection processes.

Overall, this project has the potential to contribute significantly to academic research, education, and training in the field of electrical engineering.

Algorithms Used

Fuzzy logic is used in the project to develop a new fault detection system for hydro generators. The algorithm uses rotor field current to determine the effective resistance of the rotor, which is then compared to the basic commissioning values of the generator. By analyzing the variance in resistance changes, the model predicts faults in the rotor poles, enabling early detection and maintenance scheduling. This approach reduces the need for offline testing, detects and confirms shorted turns caused by vibration, and improves the overall efficiency of hydro generators.

Keywords

SEO-optimized keywords: Fault Diagnosis, Intern-turn Short Circuit, Rotor Winding, Synchronous Generator, Temperature Variation, Current Variation, Fuzzy Logic, Automatic Decision-making System, Fault Detection, Fault Identification, Electrical Machinery, Condition Monitoring, Rotating Machinery, Fault Tolerance, Fault Analysis, Electrical Engineering, Power Generation, Predictive Maintenance, Hydro-generator Fault Detection, Fault Detection Techniques, Rotor Field Current, Resistance Calculation, Rotor Poles Rotation, Fuzzy Model, Salient Rotor Poles, Off-line Pole Drop Testing, Shorted Spins, Vibration Analysis, Rotor Winding Maintenance.

SEO Tags

fault diagnosis, intern-turn short circuit, rotor winding, synchronous generator, temperature variation, current variation, fuzzy logic, automatic decision-making system, fault detection, fault identification, electrical machinery, condition monitoring, rotating machinery, fault tolerance, fault analysis, electrical engineering, power generation, predictive maintenance