• Journal of the Korean Solar Energy Society
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• v.27 no.2
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• pp.29-35
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• 2007

Monitoring system is constructed for evaluating and analyzing performance of installed 50kW grid-connected PV system and have been monitored since October 2005. As climatic and irradiation conditions have been varied through long-term operation, there is necessity for evaluating numerical values of PV(Photovoltaic) system performance to observe the overall effect of environmental conditions on their operation characteristics. This paper presents performance monitoring results and analysis on component perspective(PV array and power conditioning system) and global perspective(yield, losses) of PV system for one year monitoring periods.

• Journal of the Korean Solar Energy Society
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• v.33 no.6
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.

• ETRI Journal
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• v.33 no.6
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• pp.857-863
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• 2011

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

• Smart Structures and Systems
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• v.11 no.5
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• pp.555-567
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• 2013

An Remote Corrosion Monitoring (RCM) system consists of an anode with low potential, the metallic structures against corrosion, an electrode to provide reference potential, and a data-acquisition system to ensure the potential difference for anticorrosion. In more detail, the data-acquisition (DAQ) system monitors the potential difference between the metallic structures and a reference electrode to identify the correct potential level against the corrosion of the infrastructures. Then, the measured data are transmitted to a central office to remotely keep track of the status of the corrosion monitoring (CM) system. To date, the RCM system is designed to achieve low power consumption, so that it can be simply powered by batteries. However, due to memory effect and the limited number of recharge cycles, it can entail the maintenance fee or sometimes cause failure to protect the metallic structures. To address this issue, the low-overhead energy harvesting circuitry for the RCM systems has designed to replenish energy storage elements (ESEs) along with redeeming the leakage of supercapacitors. Our developed energy harvester can scavenge the ambient energy from the corrosion monitoring environments and store it as useful electrical energy for powering local data-acquisition systems. In particular, this paper considers the energy harvesting from potential difference due to galvanic corrosion between a metallic infrastructure and a permanent copper/copper sulfate reference electrode. In addition, supercapacitors are adopted as an ESE to compensate for or overcome the limitations of batteries. Experimental results show that our proposed harvesting schemes significantly reduce the overhead of the charging circuitry, which enable fully charging up to a 350-F supercapacitor under the low corrosion power of 3 mW (i.e., 1 V/3 mA).

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.117-127
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• 2024

Purpose: The purpose is to conduct basic research to save energy and improve the safety of manufacturing plant infrastructure by comprehensively monitoring energy management, temperature, humidity, dust and gas, air quality, and machine operation status in small and medium-sized manufacturing plants. Method: To this end, energy-related data and environmental information were collected in real time through digital power meters and IoT sensors, and research was conducted to disseminate and respond to situations for energy saving through monitoring and analysis based on the collected information. Result: We presented an application plan that takes into account energy management, cost reduction, and safety improvement, which are key indicators of ESG management activities. Conclusion: This study utilized various sensor devices and related devices in a smart factory as a practical case study in a company. Based on the information collected through research, a basic system for energy saving and safety improvement was presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.316-325
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• 2018

In this paper, to integrate distributed IT resources and manage human resource efficiently as purpose of cost reduction, infrastructure of wind turbine monitoring system have been designed and developed on the basis of SaaS cloud. This infrastructure hierarchize data according to related task and services. Softwares to monitor conditions via the infrastructure are also developed. Softwares are made up of DB design, field measurement, data transmission and monitoring programs. The infrastructure is able to monitor conditions from SCADA data and additional sensors. Total time delay from field measurement to monitoring is defined by modeling of step-wise time delay in condition monitoring algorithms. Since vibration data are acquired by measurements of high resolution, the delay is unavoidable and it is essential information for application of O&M program. Monitoring target is gearbox in wind turbine of MW-class and it is operating for 10 years, which means that accurate monitoring is essential for its efficient O&M in the future. The infrastructure is in operation to deal with the gearbox conditions with high resolution of 50 TB data capacity, annually.

• Nuclear Engineering and Technology
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• v.43 no.4
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• pp.343-354
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• 2011

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1555-1562
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• 2017

A loose part monitoring system is used to identify unexpected loose parts in a nuclear reactor vessel or steam generator. It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method. The purpose of this study is to propose a mass estimation method using a Markov decision process and compare its performance with a method using an artificial neural network model proposed in a previous study. First, how to extract feature vectors using discrete cosine transform was explained. Second, Markov chains were designed with codebooks obtained from the feature vector. A 1/8-scaled mockup of the reactor vessel for OPR1000 was employed, and all used signals were obtained by impacting its surface with several solid spherical masses. Next, the performance of mass estimation by the proposed Markov model was compared with that of the artificial neural network model. Finally, it was investigated that the proposed Markov model had matching error below 20% in mass estimation. That was a similar performance to the method using an artificial neural network model and considerably improved in comparison with the conventional methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.8
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• pp.90-97
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• 2013

In recent years, the many electric saving methods are studied because of difficulty of meeting the demand. The electric energy management such as monitoring of branch power consumption, demand control, metering, power quality monitoring, electric safety monitoring can make energy saving. The purpose of this paper is to develop a system which can provide the integrated management of various functions required for energy management by consumers. In this system all functions which were embodied into each devices are integrated into intelligent meter. The developed systems are tested and implemented by installing at consumer electric distribution panel.

• Journal of Magnetics
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• v.18 no.4
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• pp.473-480
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• 2013

A new concept design of electromagnetic energy harvester is proposed for powering a tire pressure monitoring sensor (TPMS). The thin coil strap is attached on the circumferential surface of a rim and a permanent magnet is placed on the brake caliper system. When the wheel rotates, the relative motion between the magnet and the coil generates electrical energy by electromagnetic induction. The generated energy is stored in a storage unit (rechargeable battery, capacitor) and used for TPMS operation and wireless signal transmission. Innovative layered design of the strap is provided for maximizing energy generation. Finite Element Method (FEM) and experiment results on the proposed design are compared to validate the proposed design; further, the method for design improvement is discussed. The proposed design is excellent in terms of durability and sustainability because it utilizes the everlasting rotary motion throughout the vehicle life and does not require material deformation.