확률론적 의사결정기법을 이용한 태양광 발전 시스템의 고장검출 알고리즘

조현철,이관호,Cho, Hyun-Cheol,Lee, Kwan-Ho 한국융합신호처리학회 2011 융합신호처리학회 논문지 (JISPS) Vol.12 No.3

태양광 발전 시스템의 고장검출은 고장으로 인해 발생되는 기술적 및 경제적 손실을 최대한 줄이기 위한 첨단 기술로 각광을 받고 있다. 본 논문은 푸리에 신경회로망과 확률론적 의사결정법을 이용한 태양광 발전 시스템의 새로운 고장진단 알고리즘을 제안한다. 우선 태양광 시스템의 동적 모델링을 위하여 최급강하 기반 최적화 기법을 통해 신경회로망 모델을 구성하며 GLRT 알고리즘을 이용하여 태양광 시스템의 확률론적 고장검출 기법을 제안한다. 제안한 고장검출 알고리즘의 타당성 검증을 위하여 태양광 고장검출 테스트베드를 제작하여 실시간 실험을 실시하였으며 이 때 태양광으로부터의 신호는 직류 전력선 통신을 이용하였다. Fault detection technique for photovoltaic power systems is significant to dramatically reduce economic damage in industrial fields. This paper presents a novel fault detection approach using Fourier neural networks and stochastic decision making strategy for photovoltaic systems. We achieve neural modeling to represent its nonlinear dynamic behaviors through a gradient descent based learning algorithm. Next, a general likelihood ratio test (GLRT) is derived for constructing a decision malling mechanism in stochastic fault detection. A testbed of photovoltaic power systems is established to conduct real-time experiments in which the DC power line communication (DPLC) technique is employed to transfer data sets measured from the photovoltaic panels to PC systems. We demonstrate our proposed fault detection methodology is reliable and practicable over this real-time experiment.

모델매칭 기법을 이용한 시스템 섭동을 갖는 비선형 크레인시스템 제어

조현철,이진우,이영진,이권순,Cho, Hyun-Cheol,Lee, Jin-Woo,Lee, Young-Jin,Lee, Kwon-Soon 한국항해항만학회 2007 한국항해항만학회지 Vol.31 No.6

크레인 시스템은 항만 터미널 등의 산업현장에서 무거운 물체를 이송하는데 사용되는 장비로서 그 정확성과 신속성을 동시에 만족시키기 위한 연구가 활발히 진행되고 있다. 본 논문은 적응제어기의 일종인 모델매칭 기법을 이용하여 복잡한 3 자유도 비선형 크레인의 제어 시스템에 대한 연구를 제안한다. 피드백 선형화(feedback linearization)를 통해 비선형 크레인 모델을 선형화한 후 PD 제어기를 적용하여 선형 공칭 모텔을 구한다. 이 모델은 시스템 섭동을 갖는 실시간 시스템 모델과 함께, 리아푸노브(Lyapunov) 이론을 적용하여 실시간 섭동에 의해 발생되는 제어오차를 감소하기 위한 보조 제어규칙의 산출에 이용된다. 또한 리아푸노브 안정성이론을 적용하여 구성한 크레인 제어시스템의 안정성 해석을 실시한다. 컴퓨터 시뮬레이션을 통해 제안한 알고리즘의 타당성을 검증하며 기존의 제어방식과 비교 분석하여 그 우수성을 입증한다. Crane systems are very important in industrial fields to carry heavy objects such that many investigations about control of the systems are actively conducted for enhancing its control performance. This paper presents an adaptive control approach using the model matching for a complex 3-DOF nonlinear crane system. First, the system model is linearized through feedback linearization method and then PD control is applied in the approximated model. This linear model is considered as nominal to derive corrective control law for a perturbed crane model using Lyapunov theory. This corrective control is primitively aimed to compensate real-time control deviation due to partially known perturbation. We additionally study stability analysis of the crane control system using Lyapunov perturbation theory. Evaluation of our control approach is numerically carried out through computer simulation and its superiority is demonstrated comparing with the classical control.

온라인 확률분포 추정기법을 이용한 확률모델 기반 유도전동기의 고장진단 시스템

曺賢哲(Hyun Cheol Cho),金廣秀(Kwang Soo Kim),李權純(Kwon Soon Lee) 대한전기학회 2008 전기학회논문지 Vol.57 No.10

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

LB-DECK 콘크리트 바닥판의 구조거동 연구

조현철(Cho Hyun Chul),노병철(Lho Byeong Cheol),조규대(Cho Gyu Dae),곽종원(Kwark Jong Won) 대한토목학회 2006 대한토목학회 학술대회 Vol.2006 No.10

Lyapunov Redesign 기법을 이용한 태양광 발전 시스템의 안정한 적응형 컨버터 제어기법

조현철(Hyun-Cheol Cho),박지호(Ji-Ho Park),김동완(Dong-Wan Kim) 대한전기학회 2012 전기학회논문지 P Vol.61 No.4

Energy conversion systems such as power inverters and converters are basically significant in establishing photovoltaic power systems to enhance power effectiveness. This paper proposes a new converter control method by using the Lyapunov redesign approach. We construct the proposed control mechanism linearly composed of nominal control and auxiliary control laws. The former is generally designed through a well-known power electronic technology and the latter is implemented to compensate real-time control error due to uncertain natures of converter systems in practice. For realizing adaptive control capability in the proposed control mechanism, a control parameter vector is estimated by utilizing a steepest descent based optimization method. We carry out numerical simulation with Matlabⓒ software to demonstrate reliability of the proposed converter control system and conduct a comparative study to prove its superiority by comparing with a generic converter control methodology.

신경회로망 제어기와 동적 베이시안 네트워크를 이용한 시변 및 비정치 확률시스템의 제어

조현철(Hyun Cheol Cho),이진우(Jin Woo Lee),이영진(Young Jin Lee),이권순(Kwon Soon Lee) 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.7

본 논문은 비정치(nonstationary) 통계특성 및 시변 동특성을 갖는 확률 프로세서의 정밀제어를 위한 신경회로망 제어기와 동적 베이시안 네트워크(DBN; Dynamic Bayesian Networks) 기반 모델링 기법을 제안한다. 신경망 제어기는 재귀형 구조의 일종인 Multilayer Perceptron-Infinite Impulse Response(MLP-IIR) 신경회로망으로 하였으며 실시간 시스템 오차를 보상하기 위한 온라인 학습법 또한 제안한다. DBN은 확률 프로세서의 확률모델링을 위하여 설계되며 또한 예측 제어기를 구성하는 보조시스템으로 이용된다. 제안한 제어기법의 타당성을 검증하기 위하여 컴퓨터 시뮬레이션이 실시되었으며 성능의 우수성을 입증하기 위하여 기존의 제어기법과 비교검토하였다. This paper presents a novel control approach for stochastic processes with nonstationary statistics and time-varying dynamics by using neural network control and dynamic Bayesian network modeling. We design a MLP-IIR neural network as a controller and propose online learning algorithm for compensating real-time system error due to such natures. We statistically represent relationship of input and output of a stochastic system by using a DBN model, which is technically used in constructing of a predictive control system. We evaluate our control method through computer simulation and demonstrate its superiority by comparing with the traditional control.

신경회로망 독립성분해석을 이용한 음향센서 기반 대전력기기의 고장진단 알고리즘

曺賢哲(Hyun-Cheol Cho),李晉宇(Jin-Woo Lee),李榮珍(Young-Jin Lee),李權純(Kwon-Soon Lee) 대한전기학회 2008 전기학회논문지 Vol.57 No.5

We present a novel fault diagnosis methodology using acoustic sensor systems and neural independent component analysis for large-scaled power machines. Acoustic sensors are carried out to measure sounds generated from power machines whose signal is used to determine whether fault is occurred or not. Acoustic measurements are independently mixed and deteriorated from original source signals. We propose a demixing algorithm against such mixed signals by means of independent component analysis which is achieved based on information theory and higher-order statistics to derive learning mechanism.

출력편차의 통계학적 신호처리를 통한 태양광 발전 시스템의 고장 위치 진단 기술

조현철(Hyun Cheol Cho) 대한전기학회 2014 전기학회논문지 Vol.63 No.11

Fault detection and diagnosis (FDD) of photovoltaic (PV) power systems is one of significant techniques for reducing economic loss due to abnormality occurred in PV modules. This paper presents a new FDD method against PV power systems by using statistical comparison. This comparative approach includes deviation signals between the outputs of two neighboring PV modules. We first define a binary hypothesis testing under such deviation and make use of a generalized likelihood ratio testing (GLRT) theory to derive its FDD algorithm. Additionally, a recursive computational mechanism for our proposed FDD algorithm is presented for improving a computational effectiveness in practice. We carry out a real-time experiment to test reliability of the proposed FDD algorithm by utilizing a lab based PV test-bed system.

확률 및 통계이론 기반 태양광 발전 시스템의 동적 모델링에 관한 연구

조현철(Hyun Cheol Cho) 대한전기학회 2012 전기학회논문지 Vol.61 No.7

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

A Multiple-Channel ANC With Neural Secondary-Path Model for Railway Train Systems

조현철(Hyun Cheol Cho),김새한(Sae Han Kim),이권순(Kwon Soon Lee),배종일(Jong Il Bae) 대한전기학회 2010 대한전기학회 학술대회 논문집 Vol.2010 No.11

This paper propose a novel active noise control (ANC) approach by employing IIR filter and neural network techniques, which is suitable to effective interior noise reduction for the systems. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to create primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network is connected in series, the output of a IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for optimal selection of ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed to realize online learning mechanism. Stability analysis of the proposed ANC system is achieved in which we demonstrate sufficient conditions of stability from a IIR filter module. Lastly, we carry out numerical study to test our ANC methodology with realistic interior noise measurement obtained from the Korean trains.