https://doi.org/10.5370/KIEE.2023.72.5.571

이재걸(Jae-Gul Lee) ; 이용승(Yong-Seung Lee) ; 정솔영(Sol-Young Jung)

In this paper, we propose a new method to analyze how much surplus renewable energy will occur in what pattern. The difference from existing analysis methods is that seasonal surplus renewable energy patterns can be analyzed. A case study was conducted based on the carbon neutrality scenario in 2050, and it was found that 513.9[TWh] of surplus power was generated. This is because the proportion of solar power generation facilities in Korea's carbon-neutral scenario is very high at 85%, so the trend of generation of surplus power varies depending on the change in the amount of solar power generation. From the perspective of planning a renewable energy-oriented electrical grid, it is very important to rationally predict the amount of surplus power generated in the mid to long term and establish technical alternatives to minimize surplus power. In particular, in the case of mid- to long-term energy storage and power conversion (P2X) technology that needs to consider seasonal characteristics, such seasonal surplus power generation characteristics can be very important information.

https://doi.org/10.5370/KIEE.2023.72.5.580

이영달(Young-Dal Lee)

The wireless power transfer (WPT) systems have to supply a stable output voltage to the battery. However, due to its physically separated structure and unstable communication control between transmitter-side (Tx) and receiver-side (Rx), there should be a battery control converter in front of the battery, causing a large number of components and low power density. To relieve these drawbacks, a new receiver-side post regulator (RSPR) with a simple control method and high power density is proposed in this paper. The proposed RSPR structure can help the WPT system to supply stable output to the battery. Besides, the proposed one can remove complex phase detection and unstable communication control method between Tx and Rx. Moreover, through the integrated single-switch into the rectifier diodes, high power density can be obtained. The effectiveness of the proposed structure is analyzed and verified experimentally by a prototype under 380VDC input and 250W output.

https://doi.org/10.5370/KIEE.2023.72.5.586

박영서(Yeong-Seo Park) ; 김상훈(Sang-Hoon Kim)

In order to minimize copper loss of Permanent Magnet Synchronous Motor(PMSM), Maximum Torque Per Ampere(MTPA) control is essential. The MTPA control method utilizing mathematical models requires accurate information of parameters, but it is difficult to perform an accurate MTPA because the parameters may vary with temperature and load. Therefore, this paper proposes the new MTPA control method using ADALINE(Adaptive Linear Neuron), one of the Artificial Neural Network(ANN). In the proposed method, the locus of the stator current relative to the ?? -axis current is estimated using ADALINE, and the MTPA operating point is determined through the estimated locus. This method is resistant to fluctuations in motor parameters and is easy to implement.

https://doi.org/10.5370/KIEE.2023.72.5.594

정주영(Ju Young Jung) ; 강태훈(Taehoon Kang) ; 장유나(Youna Jang) ; 안달(Dal Ahn)

In this paper, a 10-stage waveguide filter is designed using the coupling matrix method after converting the Chebyshev prototype low-pass filter into an inverter-coupled form through the equivalent circuit conversion method. While the optimization process in the theoretically designed structure is carried out through time and human know-how in the past, this paper shows how to solve it in a sophisticated and efficient way using Synmatrix’s AI tuning program. Through this, we propose a efficient design method for a waveguide filter having a complex coupling structure in the high frequency band. For verification of the design method, tow types of filters are designed and one of the is fabricated. The designed filter has a center frequency of 3.625GHz, a bandwidth of 150MHz, and a reflection coefficient of -20dB in the passband. Another designed filter has a center frequency of 3.84GHz, a bandwidth of 300MHz, and a reflection coefficient of -20dB in the passband.

https://doi.org/10.5370/KIEE.2023.72.5.601

김성훈(Seong-Hun Kim) ; 윤웅희(Ung-Hui Yun) ; 김진규(Jin-Gyu Kim)

The problem of microplastic has recently emerged as the worldwide use of plastics has increased. Although many studies are being conducted to address the microplastic problem, the impact is still insignificant and takes a long time. Therefore, in this study, microplastic degradation studies were conducted according to the amount of particles injected using dielectric barrier discharge that can efficiently decompose microplastic particles. As a result, after 120 minutes of treatment at 0.05 g input, the removal efficiency reached 5.44%, and the carbonyl index (CI) was 1.7308. In addition, the smaller the amount of injected particles, the higher the removal efficiency and CI. However, when considering the absolute removal amount and energy yield, it was necessary to design an appropriate input amount according to the characteristics of the dielectric barrier discharge (DBD) device. This study's findings should be useful for future research on microplastic degradation using DBD techniques.

https://doi.org/10.5370/KIEE.2023.72.5.607

(Abbas Jafar) ; 이명호(Myungho Lee)

Machine learning (ML) has proven to be highly effective in solving complex problems in various domains, thanks to its ability to identify specific data tasks, perform feature engineering, and learn quickly. However, designing and training ML models is a complicated task and requires optimization. The effectiveness of ML models is highly dependent on the selection of hyperparameters that determines their performance. Hyperparameter optimization (HPO) is a systematic search process to find the optimal combinations of hyperparameters to achieve robust performance. Traditional HPO methods such as grid and random search take a lot of computing time when used in large-scale applications. Recently, various automated search strategies, such as Bayesian optimization (BO) and evolutionary algorithms, have been developed to significantly reduce the computing time. In this paper, we use state-of-the-art HPO frameworks, namely BO, Optuna, HyperOpt, and Keras Tuner, for optimizing the ML and deep learning models for the classification tasks and evaluate their comparative performance using two different sets of experiments. The first one uses different ML classifiers to solve the optimal parameter selection problem with HPO. The second one attempts to optimize the convolutional neural network (CNN) architecture using HPO frameworks to improve its performance in the image classification task. We use four publicly available real-world datasets including one image dataset. The experimental results show that HyperOpt -TPE outperforms the other HPO frameworks for the ML classifiers and achieves up to 94.12% of accuracy with 30 minutes for performing the optimization. Similarly, for the CNN model, HyperOpt-TPE outperforms the other HPO frameworks by improving 34% of the classification accuracy, while taking 2 hours and 24 minutes of computing time.

https://doi.org/10.5370/KIEE.2023.72.5.620

문양찬(Yang Chan Moon) ; 임민규(Mingyu Lim)

The personalized video service collects and analyzes user information to provide differentiated services for each user. Additionally, it is impossible to avoid delays when sending a video from a server to a client. However, if prefetching is performed at the edge node, transmission delay can be reduced, and personalized service can be provided by utilizing user information at each edge node. In this paper, we propose a personalized video service based on edge nodes. The proposed method operates so that the edge node performs personalized prefetching in which the number of prefetching is adjusted by calculating the weights of five selection criteria based on the user's viewing record. We found that the proposed method has the highest prefetching accuracy through implementation and experiments using the simulation of user behavior of selecting and viewing videos.

https://doi.org/10.5370/KIEE.2023.72.5.628

최명수(Myeong-Soo Choi) ; 이정훈(Jung-Hoon Lee)

In this paper, a complete proof of Utkin's theorem is presented for the ITSMC(integral terminal sliding mode control) of second order uncertain linear plants when ??≠. The addition of integral action to the TSMCs so called the ITSMC enhances performance in both transient and steady states. Therefore, proof of Utkin’s theorem is essential for ITSMC systems. There are five approaches to designing ITSMCs; control input transformation, sliding surface full transformation, and three sliding surface part transformations, which is for first time pointed out in this paper. The invariance property of the Utkin’s theorem applies only to the first two transformations. Specifically, the sliding mode equation, i.e., the sliding surface, remains invariant for the first two transformations. However, the invariance property of Utkin’s theorem can not apply to the three additional sliding surface part transformations, although they serve as design and stabilization approaches. The sliding surface full transformation first appears in the TSMCs, except in [32], while the three sliding surface part transformations appear for the first time in the TSMCs. This paper includes research on the first three transformations, and further studies will cover the last two sliding surface part transformations. This paper presents three transformation methods that achieve the same performances as those obtained through output prediction, predetermination, and predesign. Through an illustrative example and simulation study, the usefulness of the main results is verified.

https://doi.org/10.5370/KIEE.2023.72.5.643

이동주(Dong-Ju Lee) ; 김종겸(Jong-Gyeum Kim)

Induction motors are most widely used for driving rotating loads in the industrial applications. This induction motor may be operated at the rated output load in some cases, but it is often operated at lower load than the rated load. Induction motor has a low power factor due to its inductive load. A device such as a capacitor is used to compensate for a low power factor, but it is difficult to maintain a constant power factor in response to load fluctuations. Therefore, reactive power compensators such as Statcom are also used to keep constant the power factor that varies depending on load fluctuations. In this study, when the load of the induction motor changes, the changes in active power, reactive power, and apparent power were confirmed for cases where no capacitor was installed, only capacitors installed, and only Statcom installed. In addition, based on this, the efficiency and power factor fluctuations at the power source side were also analyzed.

https://doi.org/10.5370/KIEE.2023.72.5.649

김홍성(Hong-Sung Kim) ; 김유하(Yoo-Ha Kim) ; 최해용(Hae-Ryong Choi) ; 이승요(Seung-Yo Lee)

The power supply part of a PV system is called a PV array which is composed of many PV strings connected in parallel. A PV-string which is a set of PV-Modules is made by serial connection of many PV modules. PV modules presents irregularity of ouput power which is generated due to various factors such as average performance degradation of about 0.923% to 1.54% according to various researches and born power variations over production stages. Power degradation and born power variation are due to various factors such as material interactions(connection state of connectors between PV cells, corrosion, browning of encapsulation materials ...etc) and environment factors such as shading and soiling which refers to the accumulation of snow, dirt, leaves and bird droppings on PV modules. Such various minor factors can make side effects in safety side and economic one. Therefore several methodology for diagnosis of PV-array have been developed, which are classified into three types ? image-based diagnosis approach, model-based approach and data-driven approach. In this paper, a new data-based approach(called data model) with good failure diagnosis reliability and economy is proposed, and verified by simulation using Python, published data and reasonable data generation. Based on the bathtub failure rate function, the fault diagnosis model requires a well-functioning predictor to generate a reference signal which evaluates the output characteristics of PV array under instantaneously varying environmental condition such as solar irradiance, temperature, etc. To implement a new data-based approach, an Artificial Neural Network (ANN) based predictor is applied as a reference generator for PV array’s fault diagnosis. ? value and the RMSE(Root Mean Square Error) are used to evaluate how far away individual learned predictions are from the actual measured values. Based on the reliability obtained from the learning result for a specific PV string, it is confirmed through pair comparison analysis(called t-test) that the learning result(called clustering possibility) between PV strings under different installation conditions is also reliable.

https://doi.org/10.5370/KIEE.2023.72.5.662

오효석(Hyo-Seok Oh) ; 김재영(Jae-Young Kim) ; 최용은(Yong-Eun Choi) ; 문병민(Byung-Min Moon) ; 김재문(Jae-Moon Kim)

Batteries for railway vehicles serve as the fundamental power source to maintain system operations, control power, and emergency power. In this paper, we investigate the operating environment and load usage conditions of lithium batteries applied in a 10-car formation of urban railway vehicles in Korea. We measure the initial capacity and SoC-OCV through charge-discharge experiments and select the partial discharge range based on the operating characteristics of urban railway vehicles. During operation, the load characteristics of railway vehicle batteries supply the load through the charging device, except for a certain section, and the battery discharge time is very short compared to the operating time. The charge and discharge experiments, considering the railway vehicle operating environment, show that the initial voltage of the discharge rapidly decreases, followed by linear discharge, and the charge gradually increases depending on the CV charging method of the railway vehicle. In the future, if we utilize the data acquired through the system established in this paper and the data from urban railway vehicles in operation, it is expected to estimate the State of Health(SoH) of lithium batteries installed in urban railway vehicles and be useful for maintenance.

https://doi.org/10.5370/KIEE.2023.72.5.669

권부석(Buseok Kwon) ; 이종우(Jongwoo Lee)

The FTG-S 917 track circuit device installs an S-bond composed of cables instead of an impedance bond, which is a heavy object, at the boundary point of the existing track circuit, thereby simplifying the facility, improving the comfort of the train, and improving maintenance efficiency and stability. can improve However, as a disadvantage, precise frequency tuning is required during installation, and it is not easy to identify the location and cause of failure when a failure occurs.[1] Because of these problems, it is very important to theoretically analyze and confirm the frequency resonance characteristics of the S-bond boundary section and mutual induction and cancellation with the rail when current flows. In this paper, the electrical characteristic components of the electrical insulation part between the S-bond and the rail of Seoul Subway Line 2 were defined, and the flow of current was modeled through mathematical analysis. The modeled circuit was simulated with Matlab and the results obtained were verified by comparing them with field measurement results.

https://doi.org/10.5370/KIEE.2023.72.5.678

박건원(Gunwon Park) ; 최규형(Kyuhyoung Choi)

This study is aimed at optimizing through preventive maintenance (PM) techniques, such as analyzing failure modes using the FMECA technique for effective maintenance of the points system and suggesting ways to structurally solve problems. is a study about Analysis of failure data occurred in the points system for 10 years in Seoul Metro Lines 1 to 8 was performed. Based on this, 10 high risk items with the highest criticality of the criticality matrix for the failure mode of the points system were selected, and as a result of applying the RCM processor and preventive maintenance (PM) technique, the number of points failures decreased from 6.4 cases/year to 3.4 cases/year. It was analyzed that it decreased by 46.9% per year, and the cumulative failure recovery time decreased significantly by 65.8% from 417 minutes/year to 143 minutes/year.

https://doi.org/10.5370/KIEE.2023.72.5.688

이태훈(Tae-Hoon Lee) ; 주흥진(Heung-Jin Ju) ; 차영광(Young-Kwang Cha) ; 나정효(Jung-Hyo Nah)

The main circuit breaker(MCB) used in railway vehicles has mainly applied a vacuum interrupter(VI) due to its long life and easy maintenance requirements. When an accident such as a short circuit or ground fault occurs in a railway vehicle, the VI may interrupt the fault current. The electrodes of the VI are closely related to the breaking performance, so it is very important to design the shape of them. In particular, when a high breaking capacity is required, such as railway vehicles, most manufacturers control the arc by applying a axial magnetic field electrode in VIs. For a axial magnetic field electrode, by diffusing the arc throughout the electrode surface by a magnetic field, melting of the contact can be minimized and the electrical endurance life can be improved. The breaking performance of VIs could be verified through a high power short-circuit test, but it takes a lot of cost and time, so most manufacturers evaluate the breaking performance through magnetic field analysis, but the validity of analysis result is not verified. In this study, an electrode of a vacuum interrupter applied to a high-speed vehicle was designed, and a magnetic field analysis was performed to verify its effectiveness, and a sample was manufactured for an actual electrode part to measure magnetic flux density. As a result of comparing the analysis and the measured values, similar results were confirmed in most parts except for the center and edge of the electrode, and the validity of the design was verified.

https://doi.org/10.5370/KIEE.2023.72.5.694

방수철(Su-Chul Bang) ; 한소연(So-Yeon Han) ; 윤용호(Yong-Ho Yoon)

With the development of distributed power generation using solar and renewable energy, smart grid-type DC distribution is gradually spreading. However, DC distribution has the disadvantage of being difficult to dissipate naturally because it does not form a zero point when an arc occurs compared to the AC distribution system. A fire occurs due to an arc, causing failure, equipment malfunction, etc., resulting in loss of life and property. In addition, traditional overcurrent breakers and short circuit breakers cannot prevent the occurrence of series arcs in advance. Therefore, the main issue of smart grid type DC distribution is to analyze arc signals to detect arc accidents. This paper aims to diagnose arc signals in real time through existing hole-type sensors and Rogowski coil sensors, report accidents and fires caused by arc generation as well as smart grid-type DC distribution performance, and improve facility safety. The DC arc detection method using the proposed Rogowski coil current sensor and the algorithm for signal analysis were applied to the Smart Grid distributed power system, and the following conclusions were obtained. 1)Diagnosis is possible through real-time measurement of analog/digital signals of power analysis and failure signals, 2)Improvement of power analysis and fault diagnosis technology of power systems such as renewable energy and energy storage devices, 3)Improvement of equipment safety and protection against accidents and fires caused by arcs as well as the performance of renewable energy facilities applied to Smart Grid lines