• 조명전기설비학회논문지
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• 제23권2호
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• pp.175-181
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• 2009

본 논문에서는 전력계통의 안정도를 향상시키기 위하여 동기 발전기와 정지형 무효전력 보상기에 대한 파라미터 자기조정 퍼지제어기의 설계 기법을 제시한다. 제안된 퍼지제어기의 파라미터 자기조정 알고리즘은 퍼지제어기의 추론값과 전력계통안정화 장치의 출력값들 사이의 오차를 감소시키는 두 개의 방향 벡터를 사용하는 최급강하법에 기초를 둔다. 전력계통안정화 장치로부터 얻어진 입 출력 데이터쌍을 사용하여, 퍼지추론 규칙의 전건부와 후건부에서의 파라미터들은 제안된 최급강하법에 의해 자동조정되고 학습되어진다. 시뮬레이션 결과, 제안된 퍼지제어기가 종래의 제어기보다 우수한 제어성능을 보임을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 B
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• pp.1111-1113
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• 2003

This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than a conventional PI speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PI controller and is proved robust about parameter variation through Matlab/Simulink simulation.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.

• Journal of Power Electronics
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• 제14권1호
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• pp.105-114
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• 2014

The sliding mode control (SMC) strategy is applied to a permanent magnet synchronous machine vector control system in this study to improve system robustness amid parameter changes and disturbances. In view of the intrinsic chattering of SMC, a current sliding mode control method with a load sliding mode observer is proposed. In this method, a current sliding mode control law based on variable exponent reaching law is deduced to overcome the disadvantage of the regular exponent reaching law being incapable of approaching the origin. A load torque-sliding mode observer with an adaptive switching gain is introduced to observe load disturbance and increase the minimum switching gain with the increase in the range of load disturbance, which intensifies system chattering. The load disturbance observed value is then applied to the output side of the current sliding mode controller as feed-forward compensation. Simulation and experimental results show that the designed method enhances system robustness amid load disturbance and effectively alleviates system chattering.

• Journal of Electrical Engineering and Technology
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• 제6권1호
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• pp.1-7
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• 2011

This paper proposes an enhanced noise robust algorithm for fault location on double-circuit transmission line for the case of single line-to-ground (SLG) fault, which uses distributed parameter line model that also considers the mutual coupling effect. The proposed algorithm requires the voltages and currents from single-terminal data only and does not require adjacent circuit current data. The fault distance can be simply determined by solving a second-order polynomial equation, which is achieved directly through the analysis of the circuit. The algorithm, which employs the faulted phase network and zero-sequence network with source impedance involved, effectively eliminates the effect of load flow and fault resistance on the accuracy of fault location. The proposed algorithm is tested using MATLAB/Simulink under different fault locations and shows high accuracy. The uncertainty of source impedance and the measurement errors are also included in the simulation and shows that the algorithm has high robustness.

• Journal of Power Electronics
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• 제18권4호
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• pp.1075-1085
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• 2018

To deal with the operation performance degradation of permanent magnet synchronous machine (PMSM) drives with uncertainties and unmodeled dynamics, this paper presents a finite-time nonlinear disturbance observer (FTNDO) based discretized integral sliding mode (DISM) composite control scheme. Based on the reaching-law approach, a DISM speed controller featuring a superior dynamic quality and global robustness against disturbances is constructed. This controller can avoid the reaching phase and overlarge control action. In addition, a sliding mode differentiator based FTNDO is devised and extended to the discrete-time domain for disturbance estimation. The attractive features of the FTNDO are that it can provide a finite-time converging estimation and alleviate the chattering effect in conventional sliding mode observers, while retaining robustness to parameter variations. By feeding the estimate forward to the pre-stage DISM controller, both disturbances and chattering can be significantly suppressed. Moreover, considering the estimation error of a FTNDO caused by discrete sampling, a stability analysis of the composite controller is discussed. Experimental results validate the superiority of the presented scheme.

• Journal of Power Electronics
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• 제18권5호
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• pp.1523-1535
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• 2018

High-performance Phase-Locked Loops (PLLs) are critical for grid synchronization in grid-tied power electronic applications. In this paper, a new single-phase All Digital Phase-Locked Loop (ADPLL) is proposed. It features fast transient response and good robustness under distorted grid conditions. It is designed for Field Programmable Gate Array (FPGA) implementation. As a result, a high sampling frequency of 1MHz can be obtained. In addition, a new OSG is adopted to track the power frequency, improve the harmonic rejection and remove the dc offset. Unlike previous methods, it avoids extra feedback loop, which results in an enlarged system bandwidth, enhanced stability and improved dynamic performance. In this case, a new parameter optimization method with consideration of loop delay is employed to achieve a fast dynamic response and guarantee accuracy. The Phase Detector (PD) and Voltage Controlled Oscillator (VCO) are realized by a Coordinate Rotation Digital Computer (CORDIC) algorithm and a Direct Digital Synthesis (DDS) block, respectively. The whole PLL system is finally produced on a FPGA. A theoretical analysis and experiments under various distorted grid conditions, including voltage sag, phase jump, frequency step, harmonics distortion, dc offset and combined disturbances, are also presented to verify the fast dynamic response and good robustness of the ADPLL.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.91-93
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• 2007

This paper presents multi-PI controller of IPMSM drive using fuzzy and neural-network. In general, PI controller in computer numerically controlled machine process fixed gain. To increase the robustness, fred gain PI controller, Multi-PI controller proposes a new method based fuzzy and neural-network. Multi-PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• 한국산학기술학회논문지
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• 제1권2호
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• pp.49-55
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• 2000

본 논문에서는 피드백 사용형 2차 반복 학습제어 방법이 수렴 성능의 향상과 외란에 대한 강인성 향상에 덧붙여 학습제어의 피드백 항을 이용함으로써 초기 조건 오차가 있음에도 불구하고 이를 극복할 뿐만 아니라 기존의 알고리즘보다 더 빠른 수렴 능력이 있음을 확인한다. 또한 불안정한 결과를 낳는 높은 학습 제어 게인의 경우에도 피드백 항을 추가한 본 학습제어 방법에 의해 안정화됨으로써, 빠른 응답 특성과 강인성 향상을 가져올 수 있음을 보인다. 그리고 본 알고리즘을 선형화시킨 로보트 매니 퓰레이터의 선형 시변 시스템 모델에 대해 적용한 시뮬레이션 결과를 통해 초기 조건 오차의 극복 능력이 뛰어남을 확인하고 시스템의 안정화와 강인성 향상에 기여함을 확인한다.

• 대한전기학회논문지:전력기술부문A
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• 제51권9호
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• pp.441-450
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• 2002

In this paper, optimal tuning problem of power system stabilizer using IA-QFT is investigated to improve power system dynamic stability in spite of parameter variation and disturbance uncertainties. The most important feature of QFT is that it is able to deal with the design problem of complicated uncertain plants. However, loop shaping is currently performed in computer aided design environments manually and it is usually a trial and error procedure. It is difficult to design a controller to satisfy all specifications manually. To solve this problem, a study of design automation using IA needs to be taken into account. The robustness of the proposed controller has been investigated on a single machine infinite bus model. The results are shown that the proposed PSS using IA-QFT is more robust than conventional PSS.