Recent publications
Renewable energy sources are getting integrated into the electrical power network since many years. The characteristics of these sources are completely different from traditional synchronous generators. This is posing challenges for power engineers from system protection and control perspective. Cases of maloperations of protective relays have been a focus of research across industries and universities. India has been growing in deploying distributed energy resources. Most of the maloperations reported relate to transmission line protection. Studies on maloperation of directional element are not duly addressed from Indian grid perspective. Few cases from India have been chosen for research. Fault data from Type‐4 and Type‐3 wind plant and solar park has been analysed and compared with literature reports. The objective of this paper is to document the behaviour of directional elements for a distributed energy resource connected system in India. In addition, aim is to understand the gaps from existing research to the behaviours of the current directional protections. This would help in identifying mechanisms to ascertain directionality for similar systems in the future. The conclusion from the paper is that the detection of fault direction must be completed before the time inverter control kicks in to operation in any algorithm.
In recent years, various control modes of grid-forming inverter (GFM) have been studied for transient stability and short-term frequency stability in power systems with large amount of renewable energy sources (RES). In this paper, we investigate the trade-off between the two stabilities caused by GFM to get a better knowledge of RES's effective control mode for each interconnection area. Numerical simulations newly reveal that applying GFM to receiving area deteriorates both stabilities in some fault scenarios due to its reactive power output when the terminal voltage drops slightly. Based on the results, a reactive power control strategy of GFM to address the trade-off issues is proposed and RES's effective control mode for each interconnection area is summarized.
This paper proposes a grid-islanding planning method for real-time optimization of grid configuration. Recently, the number of natural disasters and the needs for resilience enhancement technologies have been increasing. As one of these solutions, grid-islanding technology is attracting attention. For grid-islanding, it is desirable to expand the power supply area to reduce the outage damage as much as possible. However, if we expand the area too large, it is difficult to maintain its stability and there is a risk of re-outage. Therefore, we need to propose an appropriate grid-islanding planning method that minimizes the outage damage with maintaining the grid stability. In addition, if the optimal plan is searched by a brute force approach with its huge planning time, fast start of the first restoration and replanning according to dynamic situation changes can't be done. To realize the method, we combined mathematical planning and stability analysis and added a stability estimation function to efficiently search for the optimal plan avoiding the brute force approach. The method was tested with an outage scenario based on the typhoon case and its planning time was about 10 minutes.
Permanent magnet synchronous motors (PMSMs) generate torque ripple through the pulse width modulation (PWM) method of voltage source inverters. In this report, we propose a method to reduce torque ripple during overmodulation in asynchronous PWM. In asynchronous PWM, when the number of pulses decreases during high-speed rotation, three-phase voltage imbalance occurs and torque ripple occurs. By controlling the PWM pulse edge using trapezoidal wave modulation, seamless control from asynchronous PWM down to a single pulse can be achieved. The effectiveness of the proposed method was confirmed by its simulation and experimental results.
In the field of electric aircraft, passing the RTCA DO-160G Section 24 “Icing test” is one of the specific requirements to realize electrification for aircraft power train with high reliability. "No Icing and Bedewing" inside the inverters were considered as a safe side criterion for the standard in this study. To achieve this criterion, we proposed "Sealed inverter filled with low humidity atmosphere", which is realized by the “two functional sealings with flexible and moisture proof material”. “Icing test” in DO-160G was performed with the prototype of high voltage inverter applied the proposed sealing structure. As a result, no icing and bedewing were proved for prototype inverter applied the proposed inverter package, which contributes to the environmental reliability of insulation properties.
The Power and Energy Society Annual Conference was held at Osaka Metropolitan University on September 4-6, 2024. Total number of 383 papers were presented in 47 sessions. In addition to oral presentations and poster sessions, new events such as organized sessions and a joint symposium between Divisions B and C were introduced, contributing to the success of the conference. This report summarizes the conference.
In recent years, earthquakes have frequently occurred in Japan, and the occurrence of events such as the Nankai Trough earthquake and near-field earthquakes is predicted. Traditional seismic isolation systems are effective in reducing damage to buildings, but they are insufficient to prevent infrastructure shutdowns, such as urban gas and elevators, which typically occur at seismic intensities of 4 or lower. To reduce the impact of strong earthquakes to a seismic intensity of 4 or less on a seismic isolation device, we are currently considering a passive 3D seismic isolation system. This system uses fluid levitation for horizontal isolation and a parallel link mechanism with hydraulic cylinders for vertical isolation. In this report, based on prior knowledge of horizontal isolation, we developed a mock-up to determine the required performance of vertical isolation, aiming to reduce vertical acceleration by one-third. Excitations were conducted using random waves and observed seismic waves. The results showed that the vertical isolation behaved as a friction-type mechanism, without resonance, and successfully reduced acceleration by one-fourth. It was confirmed that by combining this with the fluid levitation-type horizontal isolation system, it is possible to reduce the impact of strong earthquakes to a seismic intensity of 4 or less.
The experience of recent massive earthquakes and the damage they have caused have prompted conceptual studies for realizing resilient cities in order to achieve a higher level of safety. While levitation systems have been developed for horizontal seismic isolation that can provide high performance, improvements are needed for vertical seismic isolation. Although many performance verification tests have been conducted in the development of vertical seismic isolation systems, few studies have quantitatively demonstrated the required performance. This paper analyzes the seismic response of levitation base isolation systems based on the characteristics of observed vertical seismic motions, clarifies the required performance of vertical base isolation, quantitatively shows the effects of weight eccentricity and tower ratio on the response, and proposes a threshold value where the response increase is negligible. It also clarifies the tendency for the rocking response to become smaller with horizontal long periodization.
We revealed the habitat status of the fishes in the upper reaches of the Nogawa River in Tokyo using environmental DNA surveys and sampling surveys. These fishes consisted mainly of those living in the river's middle reaches. In environmental DNA analysis, Opsariichthys platypus, Pseudorasbora parva, and Gnathopogon elongatus showed high read counts. It was also found that some confirmed fish species were endangered and non-native species. The results of the two surveys suggested that the environmental DNA survey could complement the sampling survey. Through these surveys and results, we organized and compared the characteristics of both surveys. The environmental DNA survey and sampling survey were found to have advantages and disadvantages in the "sample collection" and "analysis" stages. The biological monitoring survey considered that using both surveys could provide a more accurate picture of inhabiting fishes and invasive alien species invasions. For resource management, ecosystem management, and conservation, more efficient and accurate results will be obtained by combining multiple survey and analysis methods and considering detailed condition settings, depending on the objectives.
Technical innovations for the downsizing of motor systems are necessary to realize energy savings and roomy and comfortable car interior space in electric vehicles. In this paper, a novel motor drive system with multiple high‐speed motors and a magnetic multiple spur gear (MMSG) is proposed for electric vehicles. Furthermore, the factors leading to a high‐power density and high torque density in an MMSG system are clarified.
The paper presents a new fault location method with traveling waves (surge) on distribution line to restore power supply as soon as possible. The proposed method builds a diagram that describes transmission and reflection of traveling waves over the lines and generates pseudo waves based on the diagram. The least error condition including fault point is estimated by numerical analysis comparing measured waves and pseudo waves. The method is robust and accurate without line parameters such as the speed of traveling waves. The method can be applicable for multiple faults and branch line faults without any additional modification.
Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images. Databases of CT images from multiple manufacturers/scanners, disease status, and patient positioning were used. The segmentation accuracy, and accuracy in estimating the structures volume and density, i.e., mean HU, were evaluated. An approach for segmentation failure detection based on predictive uncertainty was also investigated. The model has improved all segmentation accuracy and structure volume/density evaluation metrics compared to a shallower baseline model with a smaller training database (N = 20). The predictive uncertainty yielded large areas under the receiver operating characteristic (AUROC) curves (AUROCs ≥ .95) in detecting inaccurate and failed segmentations. Furthermore, the study has shown an impact of the disease severity status on the model’s predictive uncertainties when applied to a large-scale database. The high segmentation and muscle volume/density estimation accuracy and the high accuracy in failure detection based on the predictive uncertainty exhibited the model’s reliability for analyzing individual MSK structures in large-scale CT databases.
This paper explores, the adoption of a series slave drive for discharge switches to reduce the costs of discharge circuits in high-voltage power supplies for ERF dampers. With this driving method, the charging period of the voltage divider capacitor after the turn-off of the discharge switch is a dead time, which reduces the responsiveness of the system. Thus, a control method to reduce the dead time by turning-off the discharge switch at a higher output voltage than the target was proposed. This method is based on the estimated charge energy of the voltage divider capacitor and the estimated energy consumption of the load with high temperature dependence of the parameters. The circuit was simulated to verify the effectiveness of the proposed control for reducing dead time in the range of discharge voltage amounts from 1kV to 4kV. As a result, the switching cycle of the discharge circuit was reduced by up to 23% at an ERF temperature of 20°C and up to 38% at an ERF temperature of 50°C by reducing the dead time with the proposed control. This confirmed the effectiveness of the proposed control.
We developed a torque stabilization method for the regenerative operation of speed sensorless vector controlled induction motor (IM) drives. Speed sensorless vector control suffers from a torque reduction phenomenon in regenerative operation. Therefore, we analyzed the cause and developed two stabilization methods: one to stabilize the decrease in IM flux and the other to adjust the velocity command in the low-speed range. Time domain simulations and experiments demonstrate the effectiveness of the proposed method.
The introduction of renewable energy whose power outputs fluctuate depending on the weather causes uncertainties in power generation operations. To prevent effects by the uncertainties such as surplus or shortage of fuel tank and maximize the operational profitability, power generation companies require economic fuel risk management. As this risk management, a new power generation planning that maximizes the expected profitability under the uncertainties modeled by numerous scenarios is developed. To reduce the computation time to optimize the plan, the proposed method only considers the effective scenarios affecting the results of the optimization and applies progressive hedging which is a fast optimization method. To evaluate the performance of the proposed method, a weekly plan, which assumes 56 uncertainty scenarios and 27 generators, is constructed. As a result, we confirmed that the computation time can be reduced by 92% compared to a method which only applies the optimization solver, while mitigating the error of the objective function at 0.42%. In three - month plan assumed 14 uncertainty scenarios, the proposed method can optimize less than an hour with parallel processing in a computer.
The main purpose of HEE is to consider the future direction of electrical engineering by studying the past. The history of electrical engineering forms the basis of the technology we need to develop. It is the starting point from which we should approach the future. This article presents the committee’s recent activities.
This paper studies an internet of things (IoT) network where a fusion center relies on multi-view and correlated information generated by multiple sources to monitor various regions. Each region possesses hard age of correlated information (AoCI) constraints for information update, and accordingly we propose a scheduling policy to satisfy such needs and minimize the required wireless resources. We first approximate the problem to a dual bin-packing problem. Secondly, efficient scheduling policies are identified when the age constraints possess special mathematical properties, where the number of channels at most required is analyzed. Optimality conditions of the proposed policies are presented. For general constraints, a two-step grouping algorithm for multi-view (TGAM) is proposed to establish scheduling policies. Under TGAM, the constraints are mapped into a combination of the special constraints. To quickly identify an optimized mapping from a vast solution space, TGAM heuristically groups the regions according to their constraints and then searches for the optimal mapping for each group. Numerical results demonstrate that, compared to a derived lower bound, the proposed TGAM requires only 1.07% more channels. Additionally, the number of regions that can be served by TGAM is significantly larger than the state-of-the art algorithm, given the number of channels.
As the application of generative adversarial networks (GANs) expands, it becomes increasingly critical to develop a unified approach that improves performance across various generative tasks. One effective strategy that applies to any machine learning task is identifying harmful instances, whose removal improves the performance. While previous studies have successfully estimated these harmful training instances in supervised settings, their approaches are not easily applicable to GANs. The challenge lies in two requirements of the previous approaches that do not apply to GANs. First, previous approaches require that the absence of a training instance directly affects the parameters. However, in the training for GANs, the instances do not directly affect the generator’s parameters since they are only fed into the discriminator. Second, previous approaches assume that the change in loss directly quantifies the harmfulness of the instance to a model’s performance, while common types of GAN losses do not always reflect the generative performance. To overcome the first challenge, we propose influence estimation methods that use the Jacobian of the generator’s gradient with respect to the discriminator’s parameters (and vice versa). Such a Jacobian represents the indirect effect between two models: how removing an instance from the discriminator’s training changes the generator’s parameters. Second, we propose an instance evaluation scheme that measures the harmfulness of each training instance based on how a GAN evaluation metric e.g., inception score (IS) is expected to change by the instance’s removal. Furthermore, we demonstrate that removing the identified harmful instances significantly improves the generative performance on various GAN evaluation metrics. The code is available at https://github.com/hitachi-rd-cv/data-cleansing-for-gans.
Digital twins are considered to be one of the most promising technologies for both green transformation and digital transformation. To facilitate the early adoption of digital twin technology, it is crucial to reduce the development time of models that can simulate with low computational cost and high accuracy. However, verifying the accuracy of these models can be time-consuming due to the significant variations in boundary conditions caused by changes in environmental conditions, design requirements, and specifications of products. In this study, we proposed an evaluation method for evaluating the accuracy of reduced order models built through parametric model order reduction. We utilized the conceptual idea of the Monte Carlo method with Latin Hypercube Sampling to randomly and comprehensively generate boundary conditions. Based on these evaluation methods, we have developed an algorithm to determine the order of the projection matrix, which is obtained through singular value decomposition of the original projection matrix generated by parametric model order reduction techniques. We applied this algorithm to a 3D FEM model with linear parameter varying convective heat transfer. The results show that the simulation time of the reduced order model, with an accuracy of 0.1 ℃ or less, can be reduced to 1/200 compared to the original FEM model (full order model), and to 1/12 compared to the original reduced order model generated by conventional parametric model order reduction techniques. This significant reduction in simulation time would enable real-time simulations in the digital twins environment.
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