Incipient Fault Diagnosis in Stator Winding of Synchronous Generator: A CMFFLC Technique

ABSTRACT

In the paper, a combined strategy of moth–flame optimization (MFO) algorithm and fuzzy logic controller (FLC) for incipient fault diagnosis of the synchronous generator is proposed. The motivation behind the proposed topology is to analyse the beginning issues exhibited by an asynchronous generator under different situations like healthy and unhealthy conditions. Initially, a synchronous generator is assessed in the ordinary condition and from that point onwards, fault is made in the synchronous generator and the framework practices are checked and signals are measured which can be viewed as mis-shaped waveforms. For the collection of data-set from the input current signal, MFO is presented which extracts the signal and structures the possible data-sets. In light of the fulfilled data-set, the FLC performs and diagnoses the kind of fault that has happened in the stator winding of the synchronous generator. In order to evaluate the effectiveness of the proposed method, the incipient faults are analysed. The proposed technique is implemented in MATLAB/Simulink platform and this is approved utilizing execution measures, for example, accuracy, precision, recall, and specificity. Likewise, the proposed method is analysed with factual measures, for example, the root mean square error, mean absolute percentage error, mean bias error, and consumption time; and the execution is evaluated by utilizing the examination at various strategies like artificial neural network, fuzzy, and adaptive neuro fuzzy inference system techniques.

KEYWORDS:

• FLC
• Incipient fault
• MFO
• Synchronous generator
• Three-phase fault and stator winding current

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Vidyasagar Boorgula

Vidyasagar Boorgula obtained his Bachelor's degree in Electrical and Electronics Engineering from JNTU College of Engineering, Anantapur in 1996. Then, he obtained his Master's degree in Electrical Power Systems in 2001from JNTUCEA. He is pursuing his PhD degree in EEE majoring in Artificial Intelligence Techniques applied to find the incipient faults in synchronous generators, from the Jawaharlal Nehru Technological University, Hyderabad, India. He is also an ISTE life member. Currently, he is an associate professor at the Trinity College of Engineering and Technology, Karimnagar. His specializations include artificial neural networks, genetic algorithm, and wavelet transforms to analyse and classify the internal faults in synchronous generators.

Corresponding author. E-mail: [email protected]

S. S. Tulasi Ram

S. S. Tulasi Ram received the B.Tech, M.Tech, and Ph.D degrees in Electrical and Electronics Engineering from Jawaharlal Nehru Technological University, Hyderabad, India, in 1979, 1981, and 1991, respectively. Currently, he is a professor at the Electrical and Electronics Engineering Department, College of Engineering Kukatpally, Jawaharlal Nehru Technological University, Hyderabad. His fields of interest include HVDC transmission systems, power system dynamics, power quality in power electronics, and smart energy management.

E-mail: [email protected]