Application of Multiple Parks Vector Approach for Detection of Multiple Faults in Induction Motors

Vilhekar, Tushar G.,Ballal, Makarand S.,Suryawanshi, Hiralal M. The Korean Institute of Power Electronics 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.4

The Park's vector of stator current is a popular technique for the detection of induction motor faults. While the detection of the faulty condition using the Park's vector technique is easy, the classification of different types of faults is intricate. This problem is overcome by the Multiple Park's Vector (MPV) approach proposed in this paper. In this technique, the characteristic fault frequency component (CFFC) of stator winding faults, rotor winding faults, unbalanced voltage and bearing faults are extracted from three phase stator currents. Due to constructional asymmetry, under the healthy condition these characteristic fault frequency components are unbalanced. In order to balanced them, a correction factor is added to the characteristic fault frequency components of three phase stator currents. Therefore, the Park's vector pattern under the healthy condition is circular in shape. This pattern is considered as a reference pattern under the healthy condition. According to the fault condition, the amplitude and phase of characteristic faults frequency components changes. Thus, the pattern of the Park's vector changes. By monitoring the variation in multiple Park's vector patterns, the type of fault and its severity level is identified. In the proposed technique, the diagnosis of faults is immune to the effects of unbalanced voltage and multiple faults. This technique is verified on a 7.5 hp three phase wound rotor induction motor (WRIM). The experimental analysis is verified by simulation results.

Application of Multiple Parks Vector Approach for Detection of Multiple Faults in Induction Motors

Tushar G. Vilhekar,Makarand S. Ballal,Hiralal M. Suryawanshi 전력전자학회 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.4

The Park’s vector of stator current is a popular technique for the detection of induction motor faults. While the detection of the faulty condition using the Park’s vector technique is easy, the classification of different types of faults is intricate. This problem is overcome by the Multiple Park’s Vector (MPV) approach proposed in this paper. In this technique, the characteristic fault frequency component (CFFC) of stator winding faults, rotor winding faults, unbalanced voltage and bearing faults are extracted from three phase stator currents. Due to constructional asymmetry, under the healthy condition these characteristic fault frequency components are unbalanced. In order to balanced them, a correction factor is added to the characteristic fault frequency components of three phase stator currents. Therefore, the Park’s vector pattern under the healthy condition is circular in shape. This pattern is considered as a reference pattern under the healthy condition. According to the fault condition, the amplitude and phase of characteristic faults frequency components changes. Thus, the pattern of the Park’s vector changes. By monitoring the variation in multiple Park’s vector patterns, the type of fault and its severity level is identified. In the proposed technique, the diagnosis of faults is immune to the effects of unbalanced voltage and multiple faults. This technique is verified on a 7.5 hp three phase wound rotor induction motor (WRIM). The experimental analysis is verified by simulation results.

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.

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.

Fuzzy Inference Based Electricity Theft Prevention System to Restrict Direct Tapping Over Distribution Line

Supriya Jaiswal,Makarand S. Ballal 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.3

Electricity theft is a major concern for power distribution utilities. The increase in non-technical losses give rise to imbalance between electricity supply and demand resulting into overloading of existing distribution network, reduction in reliability and stability of supply and additional tarif posed on genuine consumers. Although, the smart metering systems has resolved meter related power theft problems, however, direct tapping on distribution line remains perpetual issue which should be stringently annihilated. Thus, this paper presents real-time electricity theft detection using energy consumption data of all legal consumers and outgoing distribution transformer energy meter data. In order to prevent the hook-line activity, a fuzzy inference based scheme is implemented in LabVIEW to operate electricity theft prevention system (ETPS). The ETPS develops unsuitable voltage across illegal consumer and hinders normal operation of their appliances. The consumer care unit (CCU) interlocked with ETPS maintains normal supply voltage at legal consumers end. The suitability, fexibility in operation and efectiveness of the proposed ETPS and CCU based theft prevention scheme is experimentally and practically demonstrated as case study under various voltage regulation and energy loss scenarios.

Integrated control scheme for dynamic power management with improved voltage regulation in DC microgrid

Deshmukh, Rohit R.,Ballal, Makarand S. The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.6

This article presents an integrated control scheme to improve power sharing for power management and voltage regulation in DC microgrids. The proposed scheme considers the available power and the stochastic nature of sources to achieve adequate power sharing among them. Therefore, it achieves effective utilization of each source. In addition, the effective use of energy storage systems (ESSs) is also achieved by reducing their charging/discharging cycles. The proposed control scheme improves voltage regulation under various operating conditions. It enhances the stability of microgrids and improves their dynamic response. The proposed control scheme is adaptive to changes in the source or load. It operates without historical/previous data, which reduces the computational burden. The proposed control scheme is experimentally validated under diverse operating conditions.