Voltage Distribution Modeling Along Stator Windings in Permanent Magnet Synchronous Machines Using Coil-Based Grey Box Models

Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines

Stator winding short-circuit faults are among the most common faults that could occur in permanent magnet synchronous machines (PMSM). Therefore, on-line diagnosis of incipient stator winding short-circuit faults plays an important role contributing to the safe operation of PMSMs. However, the efficacy of known diagnostic methods varies with the types of stator winding configurations in PMSMs. This paper compares the effectiveness of diagnosis of stator winding short-circuit faults in series and parallel winding connections of PMSMs, with the same interior permanent-magnet rotor configuration. Two existing diagnostic methods have been applied to detect the severity of stator short-circuit faults happening in both series and parallel winding connection in PMSMs. Simulation analysis has been carried out in ANSYS Maxwell to compare the severity of magnetic saturation caused by equivalent short-circuit fault in series and parallel winding connected PMSMs. Experimental results lead to the conclusion that PMSMs with parallel winding connections can mask the influence of such stator short-circuit faults under certain circumstances, and thus it is more challenging to detect such incipient faults in parallel-winding-connected PMSM in comparison with series-winding-connected PMSMs.

Through a systematic optimization study using high-fidelity finite-element (FE) models, torque capability of three common synchronous machine technologies, namely a permanent magnet (PM) synchronous machine (PMSM) with V-shaped rare-earth (RE) rotor magnets, a PMSM with deep V-shaped ferrite rotor magnets, and a synchronous reluctance machine (SynRM) with four layers of conforming flux barriers are compared for an application in a 48-V mild hybrid electric car. The optimization of the deep V-shaped ferrite PMSM automatically resulted in a spoke-type PM layout for maximum torque production. Yet, an optimized SynRM with conforming flux barriers was achieved that could produce a higher torque than ferrite PMSM. One machine technology is selected and is subsequently subjected to a large-scale FE model-based drive-cycle design optimization. Various pole-slot combinations are methodically compared in terms of drive-cycle losses, active material cost, torque ripple, and PM demagnetization level. More than 20,000 designs were analyzed over ten energy-centric torque-speed points to identify an optimal design solution. The results of multi-physics analysis incorporating electromagnetics, computational fluid dynamics (CFD), and structural analyses are provided, including a study on two different water jacket concepts. A final design is prototyped and initial experimental results are provided.

Gray box model and artificial neural network (ANN) model of central air-conditioning cold source system have been established with simulation data to predict system operation energy consumption and compare the prediction accuracy difference of models under variable training data. A hybrid model is proposed to combine gray box model and ANN model based on K-means cluster algorithm. The results show that ANN model has higher prediction accuracy than gray box model when the membership of model input variables to trained input data is greater than 0.4. Otherwise, the gray box model has higher energy consumption prediction accuracy. Compared with gray box model and ANN model, prediction accuracy of hybrid model increases 27.7% and 33.85% on average under different training data.

Buildings, consuming over 70% of the electricity in the U.S., play significant roles in smart grid infrastructure. The automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy consumption and demand, as well as improve the resilience to power disruptions. In order to achieve such automatic operation, high fidelity and computationally efficiency building energy forecasting models under different weather and operation conditions are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control operation. However, typical black box models often require long training period and are bounded to weather and operation conditions during the training period. On the other hand, creating a grey box model often requires long calculation time due to parameter optimization process and expert knowledge during the model structure determining and simplification process. An earlier study by the authors proposed a system identification approach to develop computationally efficient and accurate building energy forecasting models. This paper attempts to extend this early study and to quantitatively evaluate how the most important characteristics of a building energy system: its nonlinearity and response time, affect the system identification process and model accuracy. Two commercial building: a small-size and a medium-size commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and validation. The system identification method proposed in the early study is applied to these two buildings that have varying nonlinearity and response time. Adaption of the proposed system identification method based on systems’ nonlinearity and response time is proposed in this study. The energy forecasting results demonstrate that the adaption is capable of significantly improve the performance of the system identification model.

A strategy for interturn short-circuits and static eccentricity diagnosis in permanent magnet synchronous ma-chines (PMSM) is proposed in this paper. In several practical implementations, the PMSM faults produce similar behavior patterns on electrical signals. In these cases, robust diagnostic strategies are required in order to identify the fault, without being influenced by external disturbances. To achieve this goal, a 2D model of PMSM was designed and evaluated by using of a finite element method (FEM). The fault diagnosis is realized by analyzing the behavior pattern of a residual voltages (RV) in the αβ plane. A PMSM prototype with modified stator windings and bearings with eccentric bushings was used to evaluate the proposed diagnosis strategy. Experimental results obtained with this PMSM demonstrate the correct fault diagnosis of both, interturn short-circuits and static eccentricity.

The paper proposes a fast dynamic mathematical model to evaluate the performances of saturated permanent magnet synchronous machines (PMSMs) under stator winding's inter‐turn fault. The parameters of the model can be determined using only manufacturer's data of the healthy machine. Two surface mounted PMSMs have been considered to investigate the validity of the proposed approach; with distributed and concentrated winding. It has been shown that the proposed model predicts the fault current with a reasonable accuracy compared with the non‐linear finite elements analyses and to the experimental results. This model can be incorporated in a global simulation environment of power electronic of electrical device since the computation time is very short.

A strategy for interturn short-circuit fault detection in Permanent Magnet Synchronous Machines (PMSM) is presented. Fault detection is performed by a Residual Voltage (RV) obtained from the difference between the midpoint voltages measured on the semi-windings of each stator phase. To evaluate the performance of the proposed strategy a PMSM model with interturn short-circuit fault and partitioned stator phase windings (semi-windings) is presented. Unlike other strategies that analyze the stator fault on the complete phase windings, the RV obtained from the PMSM dynamic model considering semi-windings allows to detect the incipient stator fault on any of the phase windings. The RV monitoring indicates the occurrence of a fault in stator windings, even for unbalanced supply voltages or asymmetric load conditions. Simulation results to evaluate the performance of the proposed strategy based on the RV signal are presented.

Impact of solar gain estimation on heat loss coefficient determination using in-situ data: Comparing co-heating test with B-splines integrated grey-box modelling

Automated data-driven modeling of building energy systems via machine learning algorithms

The concept of fuzzy instrumental variable and an approach for fuzzy identification of nonlinear discrete time systems were proposed. Convergence conditions for identification in a noisy environment in a fuzzy context were studied. Simulation results for off-line and on-line schemes evidence the good quality of this fuzzy instrumental variable approach for identification and function approximation with observation errors in input and output data.

A hybrid power transmission system (HPTS) is a promising way to save energy in a hydraulic excavator and the electric machine is one of the key components of the system. In this paper, a design process for permanent-magnet synchronous machines (PMSMs) in a hybrid hydraulic excavator (HHE) is presented based on the analysis of the working conditions and requirements of an HHE. A parameterized design approach, which combines the analytical model and the 2D finite element method (FEM), is applied to the electric machine to improve the design efficiency and accuracy. The analytical model is employed to optimize the electric machine efficiency and obtain the stator dimension and flux density distribution. The rotor is designed with the FEM to satisfy the flux requirements obtained in stator design. The rotor configuration of the PMSM employs an interior magnet structure, thus resulting in some inverse saliency, which allows for much higher values in magnetic flux density. To reduce the rotor leakage, a disconnected type silicon steel block structure is adopted. To improve the air gap flux density distribution, the trapezoid permanent magnet (PM) and centrifugal rotor structure are applied to PMSM. Demagnetization and armature reactions are also taken into consideration and calculated by the FEM. A prototype of the newly designed electric machine has been fabricated and tested on the experimental platform. The analytical design results are validated by measurements.

Grey-Box Modeling for Identification and Control: An Emerging Discipline or an Established Technology?

Modeling of automotive Permanent Magnet Synchronous Machines (PMSM)electric drives for Noise, Vibration and Harshness (NVH)assessment involves modeling different components such as electrical machine, power electronics and controller. The usage of transistors for machine control affects the NVH performance, especially within the switching frequency range. Traditionally the method to include power electronics models is to use co-simulation to couple a circuit simulator with an electromagnetic Finite Element (FE)model. However, this approach is computationally expensive. To overcome this limitation, the electromagnetic behavior of the machine can be approximated by using reduced order models. This work compares the co-simulation approach with the system-level model in terms of high frequency NHV behavior. The comparison provides results in terms of time and accuracy.

This article introduces a hybrid consequent-pole brushless wound rotor synchronous machine (CP-BL-WRSM). The CP-BL-WRSM installs permanent magnets (PMs) on the alternative rotor poles. Compared with the conventional BL-WRSM and permanent magnet-assisted brushless WRSM (PM-BL-WRSM), the proposed machine provides advantages of high average output torque, high maximum torque, and high starting torque. In addition, the proposed CP-BL-WRSM’s torque ripple is also lower than the conventional brushless WRSM. The volume of PM in the CP-BL-WRSM machine is lower than the conventional permanent magnet synchronous machines (PMSMs) and PM-BL-WRSM. In this brushless topology, the machine uses three-phase stator windings powered by a single inverter, which provides a unique armature current to the stator windings that comprises of fundamental and third-harmonic components. The fundamental current component is used to generate the main stator field, and the third-harmonic current component is used to induce a harmonic current in the harmonic winding. A diode rectifier installed on the rotor is used to rectify the harmonic current. After the rectification, a direct current (DC) is injected into the field winding of the rotor to form a rotor magnetic field and achieve brushless operation. In order to verify the operation of the proposed CP-BL-WRSM topology and analyze its performance, 2-D finite element analysis (FEA) was used in JMAG-Designer. Finally, the performance of the proposed CP-BL-WRSM topology was compared with the conventional BL-WRSM topology and PM-BL-WRSM topology to verify its higher starting torque, higher average torque, and higher maximum torque and higher minimum torque. The proposed topology can replace the existing highly expensive PM synchronous machines (PMSMs) due to its less PM volume and conventional BL-WRSMs due to its better performance.