• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.6
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• pp.11-17
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• 1983

In this paper, the application of the explicit M.R.A.C. algorithms to the D.C. motor speed control using the microprocessor is studied. The adaptation algorithms are derived from the gradient method and the exponentially weighted least square [E.W.L.S.] method. In order to minimize the computational instability of the E.W.L.S. method, the adaptation algorithm of UDUt factorization method is developed, and because of the characteristics of the D.C. motor (dead-aone phenomenon) , the SM. gra-dient type algorithm is also improved from the gradient type algorithm. Computer simulations and experiments show that these algorithms adapt well to the rapid change of the reference input and the load.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.735-737
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• 2017

An Off grid or remote solar electric systems are an energy supply to our home or to our companies without the utility of Grid at all. Off grid solar systems are very important for those who live in remote locations especially for developing countries where getting the electric grid is extremely expensive, inconvenient or for those who doesn't need to pay a monthly bill with the electric bill in general. The main critical components of any solar power system or renewable energy harvesting systems are the energy storage systems and its charge controller system. Energy storage systems are the essential integral part of a solar energy harvesting system and in general for all renewable energy harvesting systems. To provide an optimal solution of both high power density and high energy density at the same time we have to use hybrid energy storage systems (HESS), that combine two or more energy storage technologies with complementary characteristics. In this present work, design and simulation we use two storage systems supercapacitor for high power density and lithium based battery for high energy density. Here the system incorporates fast-response supercapacitors to provide power to manage solar smoothing and uses a battery for load shifting. On this paper discuss that the total energy throughout of the battery is much reduced and the typical thermal stresses caused by high discharge rate responses are mitigated by integrating supercapacitors with the battery storage system. In addition of the above discussion the off grid solar electric energy harvesting presented in this research paper includes battery and supercapacitor management system, MPPT (maximum power point tracking) system and back/boost convertors. On this present work the entire model of off grid electric energy harvesting system and all other functional blocks of that system is implemented in MATLAB Simulink.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.