• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.146-154
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• 2015

This paper proposes a control method for maintaining the energy level for a supercapacitor energy storage system coupled with a wind generator to stabilize wind power output. Although wind power is green and clean energy source, disadvantage of the renewable energy output power is fluctuation. In order to mitigate the fluctuating output power, supercapacitor energy storage system (SCESS) and wind power simulator is developed. A remaining energy supercapacitor (RESC) control is introduced and analyzed to smooth for short-term fluctuating power and maintain the supercapacitor voltage within the designed operating range in the steady as well as transient state. When the average and fluctuating component of power increases instantaneously, the RESC compensates fluctuating power and the variation of fluctuating power is reduced 100% to 30% at 5kW power. Furthermore, supercapacitor voltage is maintained within the operating voltage range and near 50% of total energy. Feasibility of SCESS with RESC control is verified through simulation and experiment.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.185-194
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• 2020

Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimize the energy consumption of the Wireless Sensor Networks (WSNs). In this paper, a homogeneous and randomly deployed sensor nodes is considered. These sensors are energy constrained elements. The nominal selection of the Cluster Head (CH) which falls under the clustering part of the network protocol is studied and compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CHs in this proposed process is the function of total remaining energy of each node as well as total average energy of the whole arrangement. The algorithm considers initial energy, optimum value of cluster heads to elect the next group of cluster heads for the network as well as residual energy. Total remaining energy of each node is compared to total average energy of the system and if the result is positive, these nodes are eligible to become CH in the very next round. Analysis and numerical simulations quantify the efficiency and Average Energy Ratio (AER) of the proposed system.

• 조명전기설비학회논문지
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• 제21권4호
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• pp.28-35
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• 2007

In this paper, a study was made of the technology used to measure the remaining amount of energy in secondary cells, for which demand is ever increasing. First, the standard data were stored for measurement of the remaining energy and a compare/analysis algorithm was developed. Next, hardware was designed and a prototype that can display the SOC(State Of Charge) through an LCD displayinstrument was created. The small size of the prototype allows it to be portable and its performance is within ${\pm}4$[%].

• 정보처리학회논문지C
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• 제17C권4호
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• pp.371-378
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• 2010

본 논문에서는 센서 네트워크에서 데이터 전송을 위한 경로선택 시 남아있는 에너지가 많은 노드 중에 최소 에너지를 소모하는 경로를 선택함으로써 망의 수명을 연장하고 에너지 효율을 높일 수 있는 새로운 라우팅 알고리즘을 제안한다. 제안 알고리즘의 기본 목표는 다수의 경로간에 에너지 소비를 고르게 분포시키며 에너지를 효율적으로 활용하는 것이며, 에너지 상태정보로는 각 노드의 가용에너지 양과 홉 수를 사용된다. 또한 이웃 노드의 에너지 저하로 인해 관리 노드로의 가용 정보경로가 상실된 경우 간단한 우회 경로 탐색 방안을 제안하였고 이는 에너지 효율성 개선 방안으로 활용될 수 있다. 시뮬레이션을 통해 기존에 제안된 센서 네트워크 라우팅 알고리즘보다 망의 수명이 개선되고 각 노드의 잔류에너지 분포가 더 균일하여 망의 신뢰성이 높아짐을 보였다.

• International journal of advanced smart convergence
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• 제6권1호
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• pp.57-67
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• 2017

In this paper, propose an algorithm to improve network lifetime by equally consuming energy of LEACH - Mobile sensor nodes. LEACH is one of energy efficient protocols. However, we did not consider the mobility of nodes. Therefore, the transmission reception success rate of the moving data is reduced. LEACH-Mobile is a protocol that has improved the drawbacks of these LEACH. However, since LEACH-Mobile has a larger number of data packets and consumes more energy than LEACH, it has a disadvantage that the lifetime of the network is short. In order to improvement these disadvantage, Based on the average of the remaining energy of the node, cluster heads are elected with a number of nodes whose energies are larger than the average of the remaining energy from the member nodes. After that, by trying to increase the lifetime of the network by equalizing the remaining energy. In to confirm whether improve the lifetime of the network, In this paper, the number of nodes and the position of all nodes are varied for each specific round, the rest energy is equalized, and the algorithm which uniformly selected the cluster head is compared with LEACH.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제2권5호
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• pp.199-202
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• 2013

무선 센서 네트워크에서 클러스터링 방식의 네트워크 수명은 클러스터 헤드 결정 방법에 의해서 영향을 받는다. 대표적인 클러스터링 방식인 LEACH(Low-Energy Adaptive Clustering Hierarchy)의 경우 주기적으로 클러스터 헤드를 선택함으로써 클러스터 재구축으로 인한 에너지 소모가 큰 단점이 있다. 한편 ACAWT(Adaptive Clustering Algorithm via Waiting Timer)는 클러스터 헤드의 잔여 에너지 수준이 하나의 특정 임계치에 도달하면 클러스터를 재구축하는 비주기적인 클러스터 재구축 방식을 사용한다. 본 논문에서는 클러스터를 주기적으로 재구축하지 않고 노드의 잔여 에너지 수준(level)을 여러 단계로 설정한 후 클러스터 헤드의 잔여 에너지 수준이 한 단계 낮아지면 클러스터를 재구축하는 방법을 제안한다. 또한 클러스터 헤드 선정 시 이웃 노드 수와 잔여 에너지 수준을 고려함으로써 클러스터 헤드의 분포를 균일하게 유지시켜 네트워크 수명을 연장시킨다. 본 제안 방식의 성능을 평가하기 위해서 Qualnet 기반의 시뮬레이션을 수행하였으며, ACAWT와 성능을 비교한 결과 제안 방식이 네트워크 수명 측면에서 우월함을 확인하였다.

• 스마트미디어저널
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• 제4권1호
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• pp.25-32
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• 2015

Powering wireless sensors with energy harvested from the environment is coming of age due to the increasing power densities of both storage and harvesting devices and the electronics performing energy efficient energy conversion. In order to maximize the functionality of the wireless sensor network, minimize missing packets, minimize latency and prevent the waste of energy, problems like congestion and inefficient energy usage must be addressed. Many sleep-awake protocols and efficient message priority techniques have been developed to properly manage the energy of the nodes and to minimize congestion. For a WSN that is operating in a strictly energy constrained environment, an energy-efficient transmission strategy is necessary. In this paper, we present a novel transmission priority decision scheme for a heterogeneous body sensor network composed of normal nodes and an energy harvesting node that acts as a cluster head. The energy harvesting node's decision whether or not to clear a normal node for sending is based on a set of metrics which includes the energy harvesting node's remaining energy, the total harvested energy, the type of message in a normal node's queue and finally, the implementation context of the wireless sensor network.

• 한국통신학회논문지
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• 제30권11A호
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• pp.995-1003
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• 2005

센서 네트워크를 구성하는 각 센서 노드는 대체로 크기가 작고 배터리의 용량이 한정되어 있기 때문에 에너지소모를 줄이는 방안이 연구되고 있다. 센서 노드는 자신의 위치에 따라서 이벤트 감지와 데이터 전송 펀수 등에 의해 다른 노드와 서로 다른 에너지 소비율을 보일 수 있으며 노드간의 수명도 다르게 된다. 따라서 특정 노드의 에너지 고갈로 경로 단절에 의해 망의 수명을 단축시킬 수 있다. 본 논문에서는 센서 네트워크에서 데이터 전송을 위한 경로 선택에 있어, 남아 있는 에너지가 많은 노드 중에 최소 에너지를 소모하는 경로를 선택함으로써 망의 수명을 연장하고 전체적인 에너지 소비율을 비슷하게 유지하는 라우팅 알고리즘을 제안하였다. 제안된 알고리즘의 목적은 센서 네트워크의 수명을 오래 유지하고 각 센서노드의 에너지를 골고루 사용함에 있다. 시뮬레이션을 통해 기존에 제안된 센서 네트워크 라우팅 알고리즘보다 망의 수명이 개선되고 각 노드의 잔류에너지 분포가 더 균일하여 망의 신뢰성이 높아짐을 보였다.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• 풍력에너지저널
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• 제14권3호
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• pp.34-42
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• 2023

The reduction of operation and maintenance (O&M) costs is a critical factor in determining the competitiveness of wind energy. Predictive maintenance based on the estimation of remaining useful life (RUL) is a key technology to reduce logistic costs and increase the availability of wind turbines. Although a mechanical component usually has sudden changes during operation, most RUL estimation methods use the trend of a state index over the whole operation period. Therefore, overestimation of RUL causes confusion in O&M plans and reduces the effect of predictive maintenance. In this paper, two RUL estimation methods (load based and data driven) are proposed for the bearings of a wind turbine with the results of trend classification, which differentiates constant and increasing states of the state index. The proposed estimation method is applied to a bearing degradation test, which shows a conservative estimation of RUL.