Induction Machine Stator Fault Tracking using the Growing Curvilinear Component Analysis

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB® software for further analysis using AI techniques. The authors present a Growing Curvilinear Component Analysis (GCCA) neural network that is capable of detecting and follow the evolution of the stator fault using the stator current signature, making online fault detection possible. For this purpose, a topological manifold analysis is carried out to study the fault evolution, which is a fundamental step for calibrating the GCCA neural network. The effectiveness of the proposed method has been verified experimentally

Vehicle to grid system to design a centre node virtual unified power flow controller

Centre node unified power flow controller can fulfil various power flow control objectives, such as the needs of reactive shunt and series compensation, phase shifting and ensure higher degree of control freedom. However, as they are expensive, they are not widely used. The potential of a low-cost solution that utilises the capabilities of plug in electric vehicle (PEV) in vehicle-to-grid mode of operation for the design of a centre node virtual unified power flow controller (CVUPFC) using PEV charging stations is explained. Simulations are performed to establish that the proposed CVUPFC improves the power quality utilising PEV charging stations as DC bus for the converters with higher degree of freedom to control

The h-EXIN CCA for Bearing Fault Diagnosis

This paper presents the hierarchical EXIN CCA, which represents a novel and reliable approach to complex pattern recognition problems. The methodology is based on the EXIN CCA, which is an extension of the Curvilinear Component Analysis, for data reduction, and neural networks for data classification. The effectiveness of this condition monitoring scheme is verified in a demanding bearing fault diagnostic scenario

Feedback Linearization Based Nonlinear Control of SynRM Drives Accounting for Self- and Cross-Saturation

This article proposes a nonlinear controller based on feedback linearization (FL) for synchronous reluctance motor (SynRM) drives which takes into consideration the magnetic saturation. The proposed nonlinear FL control based control technique has been developed starting from the theoretical definition of an original dynamic model of the SynRM taking into consideration both the self- and the cross-saturation effects. Such a control technique permits the dynamics of both the speed and axis flux loops to be maintained constant independently from the load and the saturation of the iron core in both constant flux and variable direct axis flux operating conditions. Finally, sensitivity of the performance of the proposed FL control versus the variation of the main motor parameters has been verified. The proposed technique has been tested experimentally on a suitably developed test setup. The proposed FL control has been further compared with the classic field-oriented control (FOC) in both constant flux and variable flux working conditions

Space-vector State Dynamic Model of the SynRM Considering Self, Cross-Saturation and Iron Losses and Related Identification Technique

This article proposes a space-vector dynamic model of the Synchronous Reluctance Motor (SynRM) including both self-saturation, cross-saturation effects, and iron losses. The model is expressed in state form, where the magnetizing current has been selected as a state variable. The proposed dynamic model is based on an original function describing the relationship between the stator flux and the magnetizing current components, improving a previously developed magnetic model. Additionally, the proposed model includes, besides the magnetic saturation, also iron losses. The proposed model requires 11 coefficients, among which 6 describe the self-saturation on both axes and 5 describe the cross-saturation. This paper presents also, from one side a technique for the estimation of the parameters of the magnetic model, and from the other side a purposely developed methodology for measuring the iron losses resistance as well as its variation with the speed and stator current amplitude. The proposed parameter estimation technique has been tested in both numerical simulation and experimentally on a suitably developed test set-up and the proposed model has been thus validated experimentally

Sensorless Control of Induction Motors by the MSA based MUSIC Technique

This paper proposes a speed sensorless technique for induction motor drives based on the retrieval and tracking of the rotor slot harmonics (RSH). The RSH related to the rotor speed is first extracted from the stator phase current signature by the adoption of two cascaded ADALINEs (ADAptive Linear Element), whose output is the estimated slot harmonic. Then, the frequency of this slot harmonic as well as the speed is estimated by using minor space analysis (MSA) EXIN neural networks, which work on-line to iteratively compute the frequency of the slot harmonics based on MUSIC spectrum estimation theory. Thanks to its sample-based learning and the reduced mean square frequency estimation error, the speed estimation is fast and accurate. The proposed sensorless technique has been experimentally tested on a suitably developed test set-up with a 2-kW induction motor drive. It has been verified that this algorithm can track the rotor speed rapidly and accurately in a very wide speed range, working from rated speed down to 1.3 % of it

Development of sustainable ORC applications in the tertiary sector: a case study in the Mediterranean climate

In recent decades, climate change strong advancement has led many countries, especially the most developed ones, to a greater sense of environmental responsibility. On a global, European and national level, adaptation/mitigation strategies and actions aimed at improving energy-environmental sustainability and resilience in the tertiary sectors have been increasingly intensified. In this sector, therefore, plays a fundamental role the integration/introduction of technologies able to operate an efficient conversion of energy, such as indeed Organic Rankine Cycle (ORC) plant, other than renewable energy sources, in order to reduce both energy consumption and pollutant emissions. Within this scenario, the aim of this work is to investigate the potential application of a cogeneration ORC system powered by solar collector and geothermal sources, by evaluating its energy, environmental and economic advantages and limitations. To this purpose a case study involving the coverage of the energy needs of a hotel located in Catania (Southern Italy) has been simulated and analyzed. The outcomes put in evidence the importance of the operative conditions in optimizing the productivity of an ORC plant, especially when associated with renewable energy sources, although at the moment investment and supply costs are still quite high

Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network

Stator-based faults are one of the most common faults among induction motors (IMs). The conventional approach to IM control and protection employs current sensors installed on the motor. Recently, most studies have focused on fault detection by means of stator current. This paper presents an application of the Growing Curvilinear Component Analysis (GCCA) neural network aided by the Extended Park Vector Approach (EPVA) for the purpose of transforming the three-phase current signals. The GCCA is a growing neural based technique specifically designed to detect and follow changes in the input distribution, e.g. stator faults. In particular, the GCCA has proven its capability of correctly identifying and tracking stator inter-turn fault in IMs. To this purpose, the three-phase stator currents have been acquired from IMs, which start at healthy operating state and, evolve to different fault severities (up to 10%) under different loading conditions. Data has been transformed using the EPVA and pre-processed to extract statistical time domain features. To calibrate the GCCA neural network, a topological manifold analysis has been carried out to study the input features. The efficacy of the proposed method has been verified experimentally using IM with l.lkW rating and has potential for IMs with different manufacturing conditions

Adaptive Feedback Linearization Control of SynRM Drives With On-Line Inductance Estimation

This article proposes an adaptive input-output Feedback Linearization Control ( FLC ) techniques for Synchronous Reluctance Motor ( SynRM ) drives, taking into consideration the iron losses. As a main original content, this work proposes a control law based on a new dynamic model of the SynRM including iron losses as well as the on-line estimation of the static inductances. The on-line estimation of the SynRM static inductances permits to inherently take into consideration the magnetic saturation phenomena occuring on both axes. As a major result, it permits a null stator current steady state tracking error even with a proportional derivative controller. The estimation law is obtained thanks to a Lyapunov-based analysis and thus the stability of the entire control system, including the estimation algorithm, is intrinsically guaranteed. The proposed adaptive FLC technique, has been tested experimentally on a suitably developed test set-up, and compared experimentally with its non-adaptive versions in both tuned and detuned working conditions. Moreover, a sensitivity analysis of the performance of the adaptive FLC to the variations of the stator resistance at low speed has been made. Finally, an analysis of the effects of the iron losses on the control performance and stability at high speed in the field weakening region at medium/high loads has been made

Robust motion control of nonlinear quadrotor model with wind disturbance observer

This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform