On-line monitoring and diagnostics of permanent magnet synchronous motors (PMSM) is becoming important with the increasing demand in PMSM applications. Most of the research effort focuses on motor current signature analysis (MCSA) as it can provide remote, on-line monitoring at low cost. However, all types of defects that produce asymmetries in the PMSM rotor or load produce identical rotor rotational frequency components. This is a serious limitation when applying MCSA since it can produce false indications and degrade the sensitivity of fault detection. The requirement of complex time-frequency analysis techniques is another limitation of MCSA for applications with speed variations. In this paper, the feasibility of using analog Hall sensor signals to complement MCSA for detection and classification of rotor and load related defects is investigated. It is shown that Hall sensors installed in machines for initial rotor position estimation can be used with minimal hardware modifications, to detect and classify signatures produced by the rotor and load for cases where MCSA fails, even during transient conditions. Experimental testing performed on an IPMSM under eccentricity, local demagnetization, and load unbalance conditions show that the reliability and sensitivity of fault detection in PMSM systems can be improved at low added cost.

In this study, we propose a wind power generation system model for operating modular multilevel converter (MMC) in a hardware-in-the-loop simulation (HILS) application. The application of the MMC is a system that connects wind power to a grid through high-voltage direct current (HVDC) in the form of back-to-back connected MMCs, whereas a HILS is a system used to test or develop hardware or a software algorithm with real time. A real-time operation model of the MMC is required to conduct a HILS experiment. Although some studies have introduced the HILS model of MMCs for grid connection using PSCAD/EMTDC, it is difficult to find a study in the literature on the model using Matlab/Simulink, which is widely used for power electronic simulation. Hence, in this paper, we propose a real-time implementation model employing a detailed equivalent model (DEM) using MATLAB/Simulink. The equivalent model of both wind power generation system and MMC are presented in this paper. In addition, we describe how to implement components such as a variable resistor that is not provided in the Simulink’s library. The feasibility of the proposed model is demonstrated with real-time operation of a wind power generation system.

Variable speed systems have been widely applied to home appliances. One of the applications is vector control scheme of a washing machine using low‐resolution hall sensors. Vector controller with binary‐type hall sensors is cost effective. However, due to electrical and mechanical non‐uniformity related to hall sensors, the position obtained by hall sensors does not change linearly, and resultant bumps in the position information degrade control performance. To solve this problem, a new position observer that generates linearly changing position signal is proposed. The proposed scheme removes necessity of knowing mechanical parameters and additional calculation process by utilising a Kalman filter for speed detection. The feasibility and effectiveness of the proposed algorithm are verified through experimental results carried out with a washing machine.

This article proposes a new parameter estimation method for surface-mounted permanent magnet (SPM) motors by employing the model reference adaptive system (MRAS) technique. The proposed algorithm is applied to an SPM motor used in washing machines. To determine washing and dewatering patterns, a washer tries to detect the size of load in the beginning of the operation. Hence it requires an accurate estimation of system parameters. The proposed estimation method identifies stator inductance and flux-linkage by using d- or q-axis current error, and its value is used for estimation independently with respect to each parameter. Therefore, the proposed scheme can get rid of the instability in inductance and flux estimation caused by the cross-coupling between d- and q- quantities. In this article, the stator resistance is detected by using the recursive least square (RLS) method to reduce measurement noises and to eliminate dead-time effect. Simulation results show that the proposed scheme tracks the parameters well even when there is transient states in rotor speed and the load conditions, and experimental results verify the validity and efficacy of the proposed algorithm.

This paper proposes a new modified level, increased nearest level modulation (NLM) method to improve the THD quality and enhance the quality of output voltage of modular multilevel converters (MMCs). Offshore wind farm links need connections from onshore national grids, and this paper is consistent with higher level voltage generation techniques by using MMC. By changing the on-number sub-modules (SM) at the middle position after using the round function, the generated gate logic by using the proposed method maintains a fixed magnitude value of phase voltage with a maximal number of (2 N + 1) levels, where N is the number of sub-modules per arm. The proposed method maintains the advantages of the conventional NLM such as simple implementation and low switching loss and avoids the disadvantages of the existing NLM method such as capacitor voltage ripple, peak value of arm inductor voltage, complexity in topology, etc. Both the advantages and disadvantages of the proposed and the conventional schemes are also studied, and an illustrated comparison shows the THD result differences for four methods. The system is simulated using MATLAB-Simulink to validate the feasibility of the proposed scheme and carried out by a hardware-in-the-loop simulation (HILS) system. By reviewing the simulation and experimental results, it was found that the proposed scheme improves the output voltage and THD quality.

Accurate estimation of submodule capacitance in modular multilevel converters (MMCs) is essential for optimal performance and reliability, particularly in motor drive applications such as permanent magnet synchronous motor (PMSM) drives. This paper presents a novel approach utilizing recurrent neural networks with long short-term memory (RNN–LSTM) to precisely estimate capacitance in MMC-based PMSM drives. By leveraging simulation data from MATLAB, the LSTM neural network is trained to predict capacitance based on voltage, current, and their temporal variations. The proposed LSTM architecture effectively captures the dynamic behavior of MMCs in PMSM drives, providing high-precision capacitance estimates. The results demonstrate significant improvements in estimation accuracy, validated through mean squared error (MSE) metrics and comparative analysis of actual versus estimated capacitance. The method’s robustness is further confirmed under varying operating conditions, highlighting its practical utility for real-time applications in power electronic systems.