The Japanese government has set the target reduction in CO₂ emissions by 46% in 2030 compared to their 2013 level, and achieving this represents big challenges to be overcome. Because of the land and weather characteristics, Japan has to rely heavily on newly installed PVs to achieve the 2030 goal. Supply and demand balancing and also voltage control at distribution grids have to be dealt with for successful integration of the PVs. Voltage control schemes using reactive power control of the PV systems have been widely discussed and a number have been proposed. This paper focuses on fairness of the displacement power factor at each PV connected to the same distribution feeder and it compares the performances of several control methods. The results indicate that the maximum PV installation share is between 62.5 and 100% with no power factor deviation. And autonomous power factor control, which this paper proposes, has the possibility to realize 100% PV installation share with a power factor deviation smaller than 0.05.

This paper presents a torque estimation method for variable speed induction motors for factory tests that does not use a torque or rotational speed meter. A new algorithm, which takes into account the torque reduction component due to stray load loss that varies with the main flux level and rotational speed, is proposed to accurately estimate the torque, which is based on the cross product of the stator flux linkage and current vectors. Pre-determining two parameters, the parallel equivalent iron loss resistance and percentage stray load loss, is the key to accurate torque estimation using this algorithm. The authors also show a method to identify the two parameters only from a rated voltage no-load test and nameplate values. This allows determining the percentage stray load loss for individual test machines without performing actual load testing with torque and rotational speed measurements. The proposed method is carried out on V/f-controlled test machines fed by three types of voltage waveforms: sine, square, and pulse width modulation drives. The performance of the proposed torque estimation is verified by comparing the estimated and measured torques.

This study focuses on the development of an electric impact driver integrated with inductively coupled wireless power transfer. As it is important for impact drivers to be light weight, a control method of wireless power transfer that does not require a DC converter is proposed for the inductively coupled wireless power transfer between the impact driver and battery pack. Specifically, the dead time of the inverter deployed on the primary side of the wireless power transfer is controlled based on the primary current via the algorithm for the proposed control method. In order to verify the effectiveness of the proposed control method, operating simulations of wireless power transfer are conducted using a circuit simulator. The results confirm that the proposed control method can effectively control the wireless power transfer in an electric impact driver. In particular, the proposed method prevents output over-voltage and primary over-current. Based on the numerical findings, a prototype electric impact driver integrated with wireless power transfer is assembled. The proposed control method is applied to the wireless power transfer controller. Furthermore, screw-tightening and idling tests are conducted using the prototype. The results show that the screw-tightening and idling operations can be successfully performed by the prototype without failure. This study contributes to the development of electric impact drivers integrated with wireless power transfer technology as well as to the realization of new waterproof electric impact drivers that can be operated at construction sites even during rainy weather.

This study presents the design of a load-side angle controller for an electromagnetic motor with motor-/load-side encoders, a reduction gear, and low-stiffness coupling. A method that estimates a shaft torsional torque and feeds back the estimated value to the controller is proposed to suppress shaft torsional vibration during high-speed operation. However, feedback of the shaft torsional torque deteriorates the disturbance suppression characteristics in the low-frequency range and may lead to an angle tracking error. Therefore, this study aims to improve the disturbance suppression characteristics by suppressing the amount of feedback of the low-frequency component of the torsional torque. The effectiveness of the proposed load-side angle controller is demonstrated by experiment.

This paper proposes a novel V/f control method for switched reluctance motor (SRM). The proposed method does not require rotor position information. In addition, the wide operating range including magnetic saturation region of the SRM and high torque/ampere ratio are achieved without complicated pre-measurement or finite element analysis (FEA). This paper proposed the following three methods and confirmed their effectiveness: (A) a virtual rotor magnetic flux is generated by controlling the zero-phase current with a PI controller, which realizes the V/f control for the SRM, (B) the damping control gain is designed to be stable at all operating points since the rotor magnetic flux and inductance value change dynamically according to the operating conditions, and (C) a high torque/ampere is achieved by applying both i₀ = i_q control and i_d =0 control. In the experiment, the proposed method achieved the stable operation in all N-T regions for the tested SRM. In addition, the current RMS value is reduced by 39% at the torque of 0.05p. u. compared with no MTPA control, which is the almost performance as the drive with the position sensor.

This study aimed to introduce a simple optimization method that can enhance the feedback control system of mechatronic systems. This method can cope with unexpected plant perturbations without needing any dynamic models or frequency response knowledge of the system. The method uses support vector machine (SVM) techniques to fine-tune feedback controller parameters according to the experimental classification results. The method separates “success” and “failure” classes using hyperplanes and optimizes the parameters to achieve the desired performance. It enables control engineers to attain the desired robust performance without relying on any dynamic models, even for unstable and multi-output systems. To demonstrate the effectiveness of this method, we apply it to an inverted pendulum robot and show that it can achieve the desired performance against individual variations. This method can be applied to various mechatronic systems that face uncertainties and disturbances in their operating environments and has the potential to be widely used in the field of mechatronics.

The paper proposes a method for reducing the zero-sequence current in a dual inverter with a common DC-bus. Motor drive systems are required to have higher efficiency, higher power, and smaller size in the electrification of industrial machinery. Hence, 6-step operation on one side of a dual inverter with a common DC-bus is effective. However, increasing the zero-sequence current is problem. Therefore, the proposed method effectively reduces the zero-sequence current in a 6-step operation on one side. The effectiveness of the proposed method is demonstrated by experimental results using an open-end winding PMSM and dual inverter with a common DC-bus. The proposed method reduces the peak-to-peak values of the zero-sequence current by 91.9% and improves the THD of the phase current by 68.7pt. compared to the conventional method. The reduction effect of the proposed method is also shown to be effective at higher motor speed. Therefore, the proposed control method achieves effective reduction of zero-sequence current in high-efficiency operation with the 6-step operation on one side.

This paper proposes a modulation strategy for a two-motor drive system that shares a battery and drives two main motors at variable speeds. Two-motor drive systems are required to reduce the DC link capacitor current to reduce system size and extend system life. The proposed method reduces the current of the DC link capacitor by outputting an appropriate combination of voltage vectors at the two inverters to offset the current of the DC link ripple. The effectiveness of the proposed method is demonstrated using experimental results of two inverters with the same and different output conditions. Compared with the interleave method, the proposed method reduced the root-mean-square (RMS) value of the DC link capacitor current by up to 50.4% when the output conditions of the two inverters were the same and 50.3% when the output conditions were different. Therefore, the proposed method can effectively reduce the current flowing in the DC link capacitor of a two-motor drive system.

This study proposes a nondestructive method to evaluate switching power supply circuits using subsurface magnetic field imaging with pulse-width modulation. DC-DC converters are widely used to transform the voltage of lithium-ion batteries and other devices in many applications such as electric vehicles, telecommunications, medicine, uninterruptible power supplies, and various consumer products. However, relatively few methods are available to assess their reliability. Although a nondestructive evaluation method using subsurface magnetic field imaging with electromagnetic field reconstruction was proposed to address this problem in a previous work, the upper limit of the response frequency of this method was dependent on the magnetic sensor. In this study, a pulse-width modulation method was incorporated into the circuit to measure the magnetic field distribution of a DC-DC converter operating above the response frequency of magnetic sensors. We report an evaluation of the operation of a DC-DC converter in terms of the measured magnetic field distribution.

This study proposes a simplified deadbeat (DB) predictive torque control (PTC) strategy for driving an open-end winding permanent magnet synchronous motor (OEW-PMSM) to reduce the torque and current ripple. The torque and current ripple are large because the conventional finite set PTC (FS-PTC) uses only one voltage vector in the sampling period. To avoid these problems, a simplified PTC strategy based on discrete space vector modulation (DSVM) is proposed to split the voltage vector into a virtual voltage vector and select the optimal voltage vector. Furthermore, the DB method is used to estimate the reference voltage angle and to thereby select an area that includes the candidate voltage vectors to reduce the computational burden. Among these candidate voltage vectors in the selected area, the optimal voltage vector can be selected by calculating the cost function. Therefore, one or more voltage vectors are used in the sampling period, reducing the torque and current ripple. The effectiveness of the proposed strategy is verified through simulation and experimental results.

Multiport converters with a large number of ports have been explored for integrated generation units or batteries. An attractive candidate multiport converter has been developed based on the modular multilevel converter (MMC) with cell loading. Accordingly, this study proposes a control strategy for a multiport converter based on the MMC operated under high power imbalances among cells. Owing to this power imbalance, the multiport converter requires an additional circulating current (intra-arm balancing current) to provide balanced three-phase grid currents and balanced capacitor voltages in all cells. In a previous study, the minimum required intra-arm balancing current was calculated offline and applied according to the loading condition. This method may require a high-capacity memory for the controller. In contract, this study proposes an active control method for minimizing the intra-arm balancing current online. Experimental results reveal that the multiport converter achieves balanced three-phase currents with a total harmonic distortion of 3.13% and balanced capacitor voltages with an error of 0.1% under maximum power imbalance among cells.

This paper introduces a practical approach for transformer design optimization using a novel hybrid Dowell artificial neural network (HDANN) model. This model is a highly efficient and accurate method to estimate leakage inductance, which is an important parameter that affects the performance of transformers and power converters. The model combines the conventional hybrid Dowell's model, which uses analytical equations, and an artificial neural network, which uses machine learning techniques. It is integrated into an optimization program to optimize the design of a transformer in terms of its size and loss. We investigated the HDANN design scope by testing various transformer conditions. The results provided an in depth understanding of the capabilities and limitations of the HDANN model for transformer design. By understanding the HDANN design scope, the optimization program was implemented in a multidomain circuit simulation, which includes electric and magnetic circuits. This allows a high-speed co-simulation using the optimized transformer design that considers the geometry and material characteristics for the desired circuit specification. It showed that the HDANN offers significant advantages over existing design optimization methods, including improved ease of application, accuracy, and efficiency. The effectiveness of the optimization using the HDANN with the defined geometric parameters was demonstrated by the circuit analysis results of a phase-shift full-bridge converter. Summarizing, the proposed method can potentially revolutionize how transformers are designed and implemented for various applications, leading to increased design reliability as well as reduced power loss and size.

This study proposes a novel data-driven method based on Bayesian approach. A d/q axis flux map and the non-linear torque characteristics of wound-field motors were estimated using a single layer neural network to reduce the number of finite element analysis and load tests. Moreover, to improve estimation accuracy even with a small number of learning data, training data-set to be input into machine learning were selected based on a Bayesian approach. The proposed method improves the estimation accuracy compared to that of the conventional data-driven method. The proposed method is validated by applying it to the numerical and experimental problem. Moreover, estimated results are compared with those of the conventional methods.

This study proposes a decision strategy for the power discharged from batteries in electric vehicles (EVs) with a bidirectional battery charger (BBC) in single-phase three-wire distribution feeders (SPTWDFs), where reverse power flow to the utility grid is perfectly prevented. The active power consumed by the domestic consumer connected to the BBC for EVs is calculated using the detected load currents. The newly proposed control strategy determines the reference value for the bidirectional DC-DC converter in the BBC with the active power derived from the detected load currents. The basic principle of the proposed control strategy of the discharged power from the battery in EVs is discussed in detail and confirmed by digital computer simulation using a power electronics simulator (PSIM). The simulation results show that the amplitudes of the source-side currents flowing to the utility grid, are almost zero with the proposed control strategy. Therefore, it is concluded that the reverse power flow to the utility grid is perfectly prevented by the proposed control strategy.

In recent years, environment perception systems have been developed to achieve autonomous operation of existing railways. To make the system reliable, fusing multiple sensor data is effective, and the sensors have to be calibrated for the sensor fusion. In this study, we propose a method to calibrate a LiDAR and a monocular visible light camera using rail detection results. By utilizing existing rails as targets, our proposed method does not require any new target installations and can carry out calibration before the train starts running. Considering some of the rail shape features, rails can be robustly detected using LiDAR data. With the camera, 2D rails are detected by referring to an existing method and converted to 3D rails. Extrinsic parameters of a sensor are calculated by using the point correspondence of the detected rails in different coordinate systems. Experiments showed that our method reduced the object location error due to inappropriate extrinsic parameters, and the estimated parameters were more accurate than another known method in use.