• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.675-680
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around the world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance. CMS(Condition Monitoring System) can be used to aid plant operator in achieving these goals. Its aim is to provide operators with information regarding th e health of their machine, which in turn, can help them improve operation efficiency. In this work, wind turbine fault diagnostic algorithm which can diagnose the mass unbalance and aerodynamic asymmetry of the blades is proposed. Proposed diagnostic algorithm utilizes both FFT(Fast Feurier Transform) of the signal from accelerometers installed inside of nacelle and simple diagnostic logic. Furthermore, to verify the applicability of the proposed system, 3W small sized wind turbine system is tested and physical experiments are carried out.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.123-133
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• 1998

In this paper, an algorithm of fault detection and diagnosis during operation of induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input currents is used in monitoring the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrum patterns caused by faults are detected. For the diagnosis of the fault detected, a fuzzy fault tree is designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, is solved. The solution of the fuzzy relation equation shows the possibility of occurence of each fault. The results obtained are summarized as follows : (1) Using clustering algorithm by unsupervised learning, an on-line fault detection method unaffected by the characteristics of loads and rates is implemented, and the degree of dependency for experts during fault detection is reduced. (2) With the fuzzy fault tree, the fault diagnosis process become systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.