Wing Technique: A Novel Approach for the Detection of Stator Winding Inter-Turn Short Circuit and Open Circuit Faults in Three Phase Induction Motors

Ballal, Makarand Sudhakar,Ballal, Deepali Makarand,Suryawanshi, Hiralal M.,Mishra, Mahesh Kumar The Korean Institute of Power Electronics 2012 JOURNAL OF POWER ELECTRONICS Vol.12 No.1

This paper presents a novel approach based on the loci of instantaneous symmetrical components called "Wing Shape" which requires the measurement of three input stator currents and voltages to diagnose interturn insulation faults in three phase induction motors operating under different loading conditions. In this methodology, the effect of unbalanced supply conditions, constructional imbalances and measurement errors are also investigated. The sizes of the wings determine the loading on the motor and the travel of the wings while their areas determine the degree of severity of the faults. This approach is also applied to detect open circuit faults or single phasing conditions in induction motors. In order to validate this method, experimental results are presented for a 5 hp squirrel cage induction motor. The proposed technique helps improve the reliability, efficiency, and safety of the motor system and industrial plant. It also allows maintenance to be performed in a more efficient manner, since the course of action can be determined based on the type and severity of the fault.

Wing Technique: A Novel Approach for the Detection of Stator Winding Inter-Turn Short Circuit and Open Circuit Faults in Three Phase Induction Motors

Makarand Sudhakar Ballal,Deepali Makarand Ballal,Hiralal M. Suryawanshi,Mahesh Kumar Mishra 전력전자학회 2012 JOURNAL OF POWER ELECTRONICS Vol.12 No.1

This paper presents a novel approach based on the loci of instantaneous symmetrical components called “"Wing Shape”" which requires the measurement of three input stator currents and voltages to diagnose interturn insulation faults in three phase induction motors operating under different loading conditions. In this methodology, the effect of unbalanced supply conditions, constructional imbalances and measurement errors are also investigated. The sizes of the wings determine the loading on the motor and the travel of the wings while their areas determine the degree of severity of the faults. This approach is also applied to detect open circuit faults or single phasing conditions in induction motors. In order to validate this method, experimental results are presented for a 5 hp squirrel cage induction motor. The proposed technique helps improve the reliability, efficiency, and safety of the motor system and industrial plant. It also allows maintenance to be performed in a more efficient manner, since the course of action can be determined based on the type and severity of the fault.

ANN Based System for the Detection of Winding Insulation Condition and Bearing Wear in Single Phase Induction Motor

Ballal, M.S.,Suryawanshi, H.M.,Mishra, Mahesh K. The Korean Institute of Electrical Engineers 2007 Journal of Electrical Engineering & Technology Vol.2 No.4

This paper deals with the problem of detection of induction motor incipient faults. Artificial Neural Network (ANN) approach is applied to detect two types of incipient faults (1). Interturn insulation and (2) Bearing wear faults in single-phase induction motor. The experimental data for five measurable parameters (motor intake current, rotor speed, winding temperature, bearing temperature and the noise) is generated in the laboratory on specially designed single-phase induction motor. Initially, the performance is tested with two inputs i.e. motor intake current and rotor speed, later the remaining three input parameters (winding temperature, bearing temperature and the noise) were added sequentially. Depending upon input parameters, the four ANN based fault detectors are developed. The training and testing results of these detectors are illustrated. It is found that the fault detection accuracy is improved with the addition of input parameters.

How Does Family Succession Impact Family Firms' Innovation?

Ballal, Juili Milind,Bapat, Varadraj Asian Society for Innovation and Policy 2019 Asian Journal of Innovation and Policy Vol.8 No.2

Family business is the oldest and the most prevalent type of entity in the world. In India, 85% of the enterprises are owned and/or managed by families, contributing to two-third of GDP. Thus the survival of family firms, which also generates 79% of private sector employment, is of paramount importance. Effective succession planning and innovation to gain competitive edge are the two key ways to ensure family firm survival. In this paper, the interplay between family succession and innovation is qualitatively studied using case study approach. Successors and Predecessors are interviewed to gain insights in the areas of succession planning and innovation. It is observed that family succession has a positive relationship with innovation, i.e. the presence of founding family members in the ownership and/or management of the enterprise has a positive influence on innovation tendency of the family firms. The findings contribute to the family business literature on succession planning and innovation, and their inter-relationship.

Detection of Incipient Faults in Induction Motors using FIS, ANN and ANFIS Techniques

Ballal, Makarand S.,Suryawanshi, Hiralal M.,Mishra, Mahesh K. The Korean Institute of Power Electronics 2008 JOURNAL OF POWER ELECTRONICS Vol.8 No.2

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

A Control and Protection Model for the Distributed Generation and Energy Storage Systems in Microgrids

Ballal, Makarand Sudhakar,Bhadane, Kishor V.,Moharil, Ravindra M.,Suryawanshi, Hiralal M. The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.2

The microgrid concept is a promising approach for injecting clean, renewable, and reliable electricity into power systems. It can operate in both the grid-connected and the islanding mode. This paper addresses the two main challenges associated with the operation of a microgrid i.e. control and protection. A control strategy for inverter based distributed generation (DG) and an energy storage system (ESS) are proposed to control both the voltage and frequency during islanding operation. The protection scheme is proposed to protect the lines, DG and ESS. Further, the control scheme and the protection scheme are coordinated to avoid nuisance tripping of the DG, ESS and loads. The feasibility of the proposed method is verified by simulation and experimental results.

[ 177 Lu]Lu-DOTAGA.Glu.(FAPi) 2 Radionuclide Therapy: a New Treatment Option for Patients with Glioblastoma Multiforme

Sanjana Ballal 대한핵의학회 2024 핵의학 분자영상 Vol.58 No.1

In this case report, we present the clinical management of a 52-year-old female patient with a recurrent right temporo-parietalglioblastoma multiforme (GBM). The patient presented with symptoms of headache and loss of balance and recurrence onmagnetic resonance imaging (MRI). To evaluate the fibroblast activation protein inhibitor (FAPi) expression in the recurrentlesion, an exploratory [68 Ga]Ga-DOTA.SA.FAPi PET/CT scan was performed. The imaging results revealed FAPi expressionin the lesion located in the right temporo-parietal region. Based on the findings of FAPi expression, the patient underwent[177Lu]Lu-DOTAGA.Glu.(FAPi)2 treatment. After completing two cycles of [177Lu]Lu-DOTAGA.Glu.(FAPi)2 therapy, afollow-up [68 Ga]Ga-DOTA.SA.FAPi PET/CT scan was conducted. The post-treatment imaging showed a significant reductionin FAPi uptake and regression in the size of the lesion, as well as a decrease in perilesional edema, as observed on theMRI. Furthermore, the patient experienced an improvement in symptoms and performance status. These results suggestthat [68 Ga]Ga-DOTA.SA.FAPi monomer imaging and [177Lu]Lu-DOTAGA.Glu.(FAPi)2 dimer therapeutics hold promisefor patients with recurrent GBM when other standard-line therapeutic options have been exhausted. This case highlights thepotential of using FAPi-based theranostics in the management of recurrent GBM, providing a potential avenue for personalizedtreatment in patients who have limited treatment options available.

Extended Wing Technique Approach for the Detection of Winding Interturn Faults in Three-phase Transformers

Makarand Sudhakar Ballal,Hiralal Murlidhar Suryawanshi,Bhupesh Nemichand Choudhari 전력전자학회 2015 JOURNAL OF POWER ELECTRONICS Vol.15 No.1

This paper presents a novel approach to diagnose interturn insulation faults in three-phase transformers that operate at different loading conditions. This approach is based on the loci of instantaneous symmetrical components and requires the measurement of three input primary winding currents and voltages to diagnose faults in the transformer. The effect of unbalance supply conditions, load variations, constructional imbalance, and measurement errors when this methodology is used is also investigated. Wing size or length determines the loading on the transformer. Wing travel and area determine the degree of severity of fault. Experimental results are presented for a 400/200 V, 7.5 kVA transformer to validate this method.

Detection of Incipient Faults in Induction Motors using FIS, ANN and ANFIS Techniques

Makarand S. Ballal,Hiralal M. Suryawanshi,Mahesh K. Mishra 전력전자학회 2008 JOURNAL OF POWER ELECTRONICS Vol.8 No.2

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

ANN Based System for the Detection of Winding Insulation Condition and Bearing Wear in Single Phase Induction Motor

M. S. Ballal,H. M. Suryawanshi,Mahesh K. Mishra 대한전기학회 2007 Journal of Electrical Engineering & Technology Vol.2 No.4

This paper deals with the problem of detection of induction motor incipient faults. Artificial Neural Network (ANN) approach is applied to detect two types of incipient faults (1). Inter-turn insulation and (2) Bearing wear faults in single-phase induction motor. The experimental data for five measurable parameters (motor intake current, rotor speed, winding temperature, bearing temperature and the noise) is generated in the laboratory on specially designed single-phase induction motor. Initially, the performance is tested with two inputs i.e. motor intake current and rotor speed, later the remaining three input parameters (winding temperature, bearing temperature and the noise) were added sequentially. Depending upon input parameters, the four ANN based fault detectors are developed. The training and testing results of these detectors are illustrated. It is found that the fault detection accuracy is improved with the addition of input parameters.