• Journal of Electrical Engineering and Technology
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• v.10 no.1
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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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• v.9 no.2
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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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.4
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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 ${\pm}4$[%].

• The KIPS Transactions:PartC
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• v.17C no.4
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• pp.371-378
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• 2010

In this paper, we propose a new energy-efficient routing algorithm for sensor networks that selects a least energy consuming path among the paths formed by node with highest remaining energy and provides long network lifetime and uniform energy consumption by nodes. The pair distribution of the energy consumption over all the possible routes to the base station is one of the design objectives. Also, an alternate route search mechanism is proposed to cope with the situation in which no routing information is available due to lack of remaining energy of the neighboring nodes. Simulation results show that our algorithm extends the network lifetime and enhances the network reliability by maintaining relatively uniform remaining energy distribution among sensor nodes.

• International journal of advanced smart convergence
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• v.6 no.1
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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.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.5
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• pp.199-202
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• 2013

In the cluster mechanism of the wireless sensor network, the network lifetime is affected by how cluster heads are selected. One of the representative clustering mechanisms, the low-energy adaptive clustering hierarchy (LEACH), selects cluster heads periodically, resulting in high energy consumption in cluster reconstruction. On the other hand, the adaptive clustering algorithm via waiting timer (ACAWT) proposes a non-periodic re-clustering mechanism that reconstructs clusters if the remaining energy level of a cluster head reaches a given threshold. In this paper, we propose a re-clustering mechanism that uses multiple remaining node energy levels and does re-clustering when the remaining energy level of a cluster head reaches one level lower. Also, in determining cluster heads, both of the number of neighbor nodes and the remaining energy level are considered so that cluster heads can be more evenly placed. From the simulations based on the Qualnet simulator, we validate that our proposed mechanism outperforms ACAWT in terms of the network lifetime.

• Smart Media Journal
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• v.4 no.1
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11A
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• pp.995-1003
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• 2005

Since the sensor network nodes have a small size and limited battery power, there have been many studies for reducing their energy consumption. Each sensor node can show different energy usage according to the frequency of event sensing and data transmission, and thus they have different lifetime. So, some nodes may run out of energy that causes disconnection of paths and reduction of network lifetime. In this paper, we propose a new energy-efficient routing algorithm for sensor networks that selects a least energy-consuming path among the paths formed by node with highest remaining energy and provides long network lifetime and somewhat uniform energy consumption by nodes. Simulation results show that our algorithm extends the network lifetime and enhances the network reliability by maintaining relatively uniform remaining energy distribution among sensor nodes.

• Nuclear Engineering and Technology
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• v.55 no.1
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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.

• Journal of Wind Energy
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• v.14 no.3
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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.