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On-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed.

In this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier.

The proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1].

In this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.

This paper aims to present a modified MEC algorithm for demagnetization modeling of the PM motor. One of the major issues that the designers of the permanent magnet (PM) motors are faced with is the demagnetization of magnets because of high temperatures and armature reaction. Demagnetization will weaken the magnetic properties of the magnet and lead to a reduction in the performance of the motor. Therefore, it is essential to provide appropriate methods for modeling this phenomenon. One of these methods that has a compromise between accuracy and time consumption is the magnetic equivalent circuit (MEC). In this paper, the MEC method is used for modeling the demagnetization phenomenon for the newly introduced ring winding axial flux PM (RWAFPM) motor. The proposed algorithm can take the demagnetization into account through a time-stepping model and also correct the value of the knee point flux density.

The modified MEC method is used for demagnetization modeling. The modified algorithm can take into account demagnetization and also renew the knee point at each step to increase the accuracy of the modeling. In addition, the proposed algorithm has a very high and fast execution speed so that the computation time of the MEC algorithm compared to the FEM model is reduced from 3 h to 35 s. In this case, the simulations have been performed on a core i5@ 2.3 GHz/8GB computer. The FEM model is used to verify the validity of the MEC results.

The obtained results show that at the high temperature, RWAFPM motor is severely vulnerable to demagnetization. At the temperature of 140°C, the demagnetization rate of 35% has occurred. So, it is necessary to use the high-temperature magnet in this motor or modify the motor structure in terms of demagnetization tolerant capability.

The RWAFPM motor is introduced for use in ship propulsion and traction systems. For this reason, an accurate estimation of demagnetization tolerant of this motor in different working conditions can show the strengths and weaknesses of this structure.

To design a pulse power water treatment system, it is necessary to design a reactor optimally. One of the most essential types of reactors used in water treatment is the dielectric barrier discharge (DBD) reactor. The purpose of this paper is to model the electric field in the two types of planar and coaxial reactors to have an accurate analytical formula for using in the optimal design according to the required electric field of the treatment.

The method proposed in this paper focuses on the voltage of different areas in the reactor and different boundary conditions to obtain the surface charge density. In this regard, parameters of the dielectric and treated material, as well as the reactor dimension, have been affected in the equations. To confirm the analytical results, the finite element method simulation has been performed, and it shows the accuracy of this method.

The exact analytical equation of the electric field is found within the discharge zone of the planar and coaxial DBD reactors. These equations can predict the values of different parameters of the reactor required to purify the material before each design and it does not even require simulation.

The electric field formula presented in this paper can allow the manufacturers of pulse power water treatment systems to optimize their design easily, cost-effectively and in less time. Also, the formulas provided are completely general and remain effective for all materials.

Pulsating torques cause a number of problems in electrical machines, including mechanical vibrations, acoustic noise and the depreciation of mechanical equipment. In induction motors, the slot skewing method is an effective way to solve these issues; however, it has some drawbacks such as output torque drop, stray loss intensification due to inter-bar currents and iron loss increment. Besides, slot skewing may not be practical in higher-rated induction motors. In this regard, this paper introduces a modified non-skewed rotor (MNSR) structure as a possible alternative to the skewed designs.

The proposed structure includes a two-segmented rotor with an intermediate ring between the rotor parts that are mounted on the shaft with a relative shift angle. Detailed information about the idea and structure of the MNSR as well as its manufacturing aspects will be presented in the second section of the paper. First, the working principle of the proposed design is described via analytical equations to provide an insight into the concept. The shifting angle will then be calculated by analyzing the harmonic contents of the electromagnetic torque. Finally, the validity of the analytical method will be verified by developing three-dimensional finite element models.

It is demonstrated that by using the proposed rotor structure, the torque ripple has been reduced to a satisfactory level without significantly affecting the mean torque, unlike the skewing method. Furthermore, the new method could avoid the disadvantages of the skewing method while enhancing other motor characteristics such as iron loss. Also, the total volume of the MNSR is equal to the initial design, and the mass and material differences are also negligible.

In this paper, a MNSR is introduced as a possible alternative to the skewed patterns. The study mainly focused on electromagnetic torque profile characteristics, i.e. the mean torque enhancement and the ripple reduction. The MNSR structure can be used for general purposes and high-performance applications, especially where excellent torque characteristics are required.

Interturn winding faults, one of the most important causes of power transformers failures, cannot be detected by existing detection methods until they develop into high‐level faults with more severe damage to the transformer. The purpose of this paper is to describe development of a new discrete wavelet transform (DWT) based approach for detection of winding interturn faults.

The following approach was accomplished for development of the proposed fault detection method in this study. The DWT was first applied to decompose the terminal current signals of a transformer, which in turn were obtained from simulations using a finite elements method model of the transformer, into a series of wavelet components. Based on the characteristic features associated with interturn faults extracted from the decomposed waveforms of the terminal currents, a detection scheme was developed. An experimental setup was used to validate the proposed detection method.

The results of this study demonstrate the efficacy of DWT applied on terminal currents of the transformer to identify interturn faults on the windings well before such faults lead to a catastrophic failure. It is believed that, based on the present findings, there definitely exists scope for improving interturn fault diagnosis with wavelet transform.

Performing more detailed studies to find all relevant characteristics of the wavelet transform in this application, identifying the location of the faulted turns along winding, applying the method for indicating early stages of turn insulation deterioration and evaluating other type of wavelets for this application would be some future directions of this research.

With the proposed method, it is becoming possible to detect early signs of the fault occurrence, so that the necessary corrective actions can be taken to prevent long‐lasting outages and reduce down times of the faulty power transformer. The method will be particularly useful as a complement for the classical protection devices of the power transformers.

Some recent studies have been carried out regarding the application of DWT for discrimination between an internal fault and other disturbances such as magnetizing inrush and external faults. This paper extends those studies for the detection of interturn faults using more quantitative and qualitative characteristics features.