• 대한전기학회논문지:전력기술부문A
• /
• 제48권2호
• /
• pp.127-135
• /
• 1999

Multivarialbe Boiler-Turbine H_\infty Control System Genetic Algorithm Weighting Functions $W_1$(s), $W_2$(s), and design parameter $\gamma$ that are given by Glover-Doyle algorithm, to optimally follow the output of reference model. The first method to do this is that the gains of weighting functions $W_1$(s), $W_2$(s), and design parameter are optimized simultaneously by genetic algorithm with the tournament method that can search more diversely, in the search domain which guarantees the robust stability of system. And the second method is that not only by genetic algorithm with the roulette-wheel method that can search more fast, in that search domain. The boiler-turbine H_\infty control system designed by theabove second method has not only the robust stability to a modeling error but also the the better command tracking preformance than those of the H_\infty control system designed by trial-and-error method and the above first method. Also, this boiler-turbine H_\infty control system has the better performance than that of the LQG/LTR contro lsystem. The effectiveness of this boiler-turbineH_\infty control system is verified by computer simulation.

• 한국태양에너지학회 논문집
• /
• 제38권2호
• /
• pp.1-13
• /
• 2018

This paper deals with the NREL (National Renewable Energy Laboratory) 5-MW reference wind turbine. The controller which include MPPT (Maximum power point tracking) control algorithm and tower load reduction control algorithm was designed by MATLAB Simulink. This paper propose a tower damper algorithm to improve the existing tower damper algorithm. To improve the existing tower damper algorithm, proposed tower damper algorithm were applied the thrust sensitivity scheduling and PI control method. The thrust sensitivity scheduling was calculated by thrust force formula which include thrust coefficient table. Power and Tower root moment DEL (Damage Equivalent Load) was set as a performance index to verify the load reduction algorithm. The simulation were performed 600 seconds under the wind conditions of the NTM (Normal Turbulence Model), TI (Turbulence Intensity)16% and 12~25m/s average wind speed. The effect of the proposed tower damper algorithm is confirmed through PSD (Power Spectral Density). The proposed tower damper algorithm reduces the fore-aft moment DEL of the tower up to 6% than the existing tower damper algorithm.

• Smart Structures and Systems
• /
• 제20권4호
• /
• pp.483-498
• /
• 2017

Real-Time Hybrid Simulation (RTHS) is a powerful and cost-effective dynamic experimental technique. To implement a stable and accurate RTHS, time delay present in the experiment loop needs to be compensated. This delay is mostly introduced by servo-hydraulic actuator dynamics and can be reduced by applying appropriate compensators. Existing compensators have demonstrated effective performance in achieving good tracking performance. Most of them have been focused on their application in cases where the structure under investigation is subjected to inputs with relatively low frequency bandwidth such as earthquake excitations. To advance RTHS as an attractive technique for other engineering applications with broader excitation frequency, a discrete-time feedforward compensator is developed herein via various optimization techniques to enhance the performance of RTHS. The proposed compensator is unique as a discrete-time, model-based feedforward compensator. The feedforward control is chosen because it can substantially improve the reference tracking performance and speed when the plant dynamics is well-understood and modeled. The discrete-time formulation enables the use of inherently stable digital filters for compensator development, and avoids the error induced by continuous-time to discrete-time conversion during the compensator implementation in digital computer. This paper discusses the technical challenges in designing a discrete-time compensator, and proposes several optimal solutions to resolve these challenges. The effectiveness of compensators obtained via these optimal solutions is demonstrated through both numerical and experimental studies. Then, the proposed compensators have been successfully applied to RTHS tests. By comparing these results to results obtained using several existing feedforward compensators, the proposed compensator demonstrates superior performance in both time delay and Root-Mean-Square (RMS) error.

• Journal of Power Electronics
• /
• 제16권3호
• /
• pp.1110-1121
• /
• 2016

The mathematical model of a three phase PWM converter under the stationary αβ reference frame is deduced and constructed based on a Proportional-Resonant (PR) regulator, which can replace trigonometric function calculation, Park transformation, real-time detection of a Phase Locked Loop and feed-forward decoupling with the proposed accurate calculation of the inductance voltage vector. To avoid the parallel resonance of the LCL topology, the active damping method of the proportional capacitor-current feedback is employed. As to current vector error elimination, an optimized PR controller of the inner current loop is proposed with the zero-pole matching (ZPM) and cancellation method to configure the regulator. The impacts on system's characteristics and stability margin caused by the PR controller and control parameter variations in the inner-current loop are analyzed, and the correlations among active damping feedback coefficient, sampling and transport delay, and system robustness have been established. An equivalent model of the inner current loop is studied via the pole-zero locus along with the pole placement method and frequency response characteristics. Then, the parameter values of the control system are chosen according to their decisive roles and performance indicators. Finally, simulation and experimental results obtained while adopting the proposed method illustrated its feasibility and effectiveness, and the inner current loop achieved zero static error tracking with a good dynamic response and steady-state performance.

• 한국산업경영시스템학회:학술대회논문집
• /
• 한국산업경영시스템학회 2002년도 춘계학술대회
• /
• pp.387-396
• /
• 2002

Batch processes are a significant class of processes in the process industry and play an important role in the production of high quality speciality materials. Examples include the production of semiconductors, chemicals, pharmaceuticals, and biochemicals. With on-line sensors connected to most batch processes, massive amounts of data are being collected routinely during the batch on easily measured process variables such as temperatures, pressures, and flowrates. In this paper, multivariate SPC charts for on-line monitoring of the progress of new batches are developed which utilize the information in the on-line measurements in real-time. We propose the formation of statistical model which describes the normal operation of a batch at each time interval during the batch operation. An on-line monitoring scheme based on the proposed method can handle both cross-correlation among process variables at any one time and auto-correlation over time. And the control limits for the monitoring charts are established from sound statistical framework unlike previous researches which use the external reference distribution. The proposed charts perform real-time, on-line monitoring to ensure that the batch is progressing in a manner that will lead to a high-quality product or to detect and indicate faults that can be corrected prior to completion of the batch. This approach is capable of tracking the progress of new batch runs, identifying the time periods in which the fault occurred and detecting underlying cause.

• 한국정밀공학회지
• /
• 제19권8호
• /
• pp.147-154
• /
• 2002

In this paper, a path navigation algorithm through use of a sensor platform is proposed. The sensor platform is composed of two electric motors which make panning and tilting motions. An algorithm for computing a real path and an obstacle length is developed by using a scanning method that controls rotation of the sensors on the platform. An Autonomous Robot Vehicle(ARV) can perceive the given path by adapting this algorithm. A sensor scanning method is applied to the sensor platform for using small numbers of sensor. The path navigation algorithm is composed of two parts. One is to perceive a path pattern, the other is used to avoid an obstacle. An optimal controller is designed for tracking the reference path which is generated by perceiving the path pattern. The ARV is operated using the optimal controller and the path navigation algorithm. Based on the results of actual experiments, this algorithm for an ARV proved sufficient for path navigation by small number of sensors and for a low cost controller by using the sensor platform with a scanning method.

• 한국산학기술학회논문지
• /
• 제19권3호
• /
• pp.584-593
• /
• 2018

유도전동기를 고효율로 제어하기 위한 다양한 연구가 진행되어 왔다. 그 중에서 단위전류당 최대토크 제어기는 최소한의 고정자 전류로 원하는 토크를 제공하기 때문에 유도전동기 드라이브에서 고효율의 동작을 제공한다. 이는 유도전동기를 수학적으로 정밀하게 표현하는 대안모델을 기반으로 제어기가 설계되었기 때문이다. 그러나, 온도 변화에 따른 회전자 저항의 변이는 대안모델의 파라미터와 실제의 유도전동기의 파라미터의 불일치가 발생하여 단위전류당 최대토크 성능을 심각하게 저해하고 단위전류당 최대토크 제어 조건을 만족하지 못하게 하게 있다. 이러한 유도전동기의 운전시에 발생하는 열적 상승으로 인한 파라미터 값의 변화를 고려하는 단위전류당 최대토크적응 제어기가 제안되었다. 본 논문에서는 단위전류당 최대토크적응 제어기가 다수의 운전영역에서도 최소의 고정자 전류로 원하는 토크를 성취하는지를 검토하였다. 실험을 통한 연구에서 회전자의 온도가 증가하더라도 다수의 운전영역에서 25Nm의 토크 명령에서 5%의 차이가 존재하더라도 토크 명령을 정확하게 추구하고 또한, 원하는 토크를 최소한의 고정자 전류로 얻어짐을 확인함으로써 단위전류당 최대토크적응 제어기의 우수성을 검증하였다.

• 지능정보연구
• /
• 제26권2호
• /
• pp.131-145
• /
• 2020

산업혁신의 흐름에 발맞추어 다양한 분야에서 활용되고 있는 IoT 기술은 빅데이터의 접목을 통한 새로운 비즈니스 모델의 창출 및 사용자 친화적 서비스 제공의 핵심적인 요소로 부각되고 있다. 사물인터넷이 적용된 디바이스에서 누적된 데이터는 사용자 환경 및 패턴 분석을 통해 맞춤형 지능 시스템을 제공해줄 수 있어 편의 기반 스마트 시스템 구축에 다방면으로 활용되고 있다. 최근에는 이를 공공영역 혁신에 확대 적용하여 CCTV를 활용한 교통 범죄 문제 해결 등 스마트시티, 스마트 교통 등에 활용하고 있다. 그러나 이미지 데이터를 활용하는 기존 연구에서는 개인에 대한 사생활 침해 문제 및 비(非)일반적 상황에서 객체 감지 성능이 저하되는 한계가 있다. 본 연구에 활용된 IoT 디바이스 기반의 센서 데이터는 개인에 대한 식별이 불필요해 사생활 이슈로부터 자유로운 데이터로, 불특정 다수를 위한 지능형 공공서비스 구축에 효과적으로 활용될 수 있다. 대다수의 국민들이 일상적으로 활용하는 도시철도에서의 지능형 보행자 트래킹 시스템에 IoT 기반의 적외선 센서 디바이스를 활용하고자 하였으며 센서로부터 측정된 온도 데이터를 실시간 송출하고, CNN-LSTM(Convolutional Neural Network-Long Short Term Memory) 알고리즘을 활용하여 구간 내 보행 인원의 수를 예측하고자 하였다. 실험 결과 MLP(Multi-Layer Perceptron) 및 LSTM(Long Short-Term Memory), RNN-LSTM(Recurrent Neural Network-Long Short Term Memory)에 비해 제안한 CNN-LSTM 하이브리드 모형이 가장 우수한 예측성능을 보임을 확인하였다. 본 논문에서 제안한 디바이스 및 모델을 활용하여 그간 개인정보와 관련된 법적 문제로 인해 서비스 제공이 미흡했던 대중교통 내 실시간 모니터링 및 혼잡도 기반의 위기상황 대응 서비스 등 종합적 메트로 서비스를 제공할 수 있을 것으로 기대된다.