• 대한전기학회논문지:전력기술부문A
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• 제52권10호
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• pp.602-609
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• 2003

This paper describes the fault location calculation using neuro-fuzzy systems in combined transmission lines with underground power cables. Neuro-fuzzy systems used in this paper are composed of two parts for fault section and fault location. First, neuro-fuzzy system discriminates the fault section between overhead and underground with normalized detail coefficient obtained by wavelet transform. Normalized detail coefficients of voltage and current in half cycle information are used for the inputs of neuro-fuzzy system. As the result of neuro-fuzzy system for fault section, impedance of selected fault section is calculated and it is used as the inputs of the neuro-fuzzy systems for fault location. Neuro-fuzzy systems for fault location also consist of two parts. One calculates the fault location of overhead, and the other does for underground. Fault section is completely classified and neuro-fuzzy system for fault location calculates the distance from the relaying point. Neuro-fuzzy systems proposed in this paper shows the excellent results of fault section and fault location.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 봄 학술발표회 논문집
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• pp.423-430
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• 2004

A new active neuro-control strategy for seismic response reduction using modal states is proposed. In order to apply the neuro-control strategy to the given structural system it is needed to select state variables used as inputs into the neural network. If the degrees of freedom of the analytical model is large, there are so many possible combinations of the state variables. And selecting state variables is very complicated and troublesome task for the designer. In order to avoid this problem, the proposed control system adopts modal states as inputs. Since the modal states contain the information of the whole structural system's behavior, it is proper to use modal states as inputs of the neuro-controller. The simulation results show that the proposed the proposed active neuro-control strategy is quite effective to reduce seismic responses. In addition, the consuming time for training proposed neuro-controller is quite shorter than that for the conventional neuro- controller. The results of this investigation, therefore, indicate that the proposed active neuro-control strategy using modal states as the inputs could be effectively used for control seismically excited structures.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2188-2190
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• 2004

This paper presents an implementation of power system stabilizer using inverse dynamic neuro controller. Traditionally, mutilayer neural network is used for a universal approximator and applied to a system as a neuro-controller. In this case, at least two neural networks are used and continuous tuning of neuro-controller is required. Moreover, training of neural network is required considering all possible disturbances, which is impractical in real situation. In this paper, Taylor Model Based Inverse Dynamic Neuro Model (TMBIDNM) is introduced to avoid this problem. Inverse Dynamic Neuro Controller (IDNC) consists of TMBIDNM and Error Reduction Neuro Model (ERNM). Once the TMBIDNM is trained, it does not require retuning for cases with other types of disturbances. The controller is tested for one machine and infinite-bus power system for various operating conditions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• 산업공학
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• 제10권3호
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• pp.125-132
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• 1997

We proposed a 2-step neural network approach for estimating machining parameters of electric discharge wire cut. The first step net, which is described as a backward neuro-estimation, is designed for estimating coarse cutting parameters while the second phase net, as a polishing forward neuro-estimation, is utilized for determining fine parameters. Sequential estimation procedure, based on backward and forward net, is performed using the net's approximation capability which is M to 1 and 1 to M mapping property. Experimental results an given to evaluate the accuracy of the proposed 2-step neuro-estimation.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.483-497
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• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• 전력전자학회논문지
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• 제18권4호
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• pp.349-358
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• 2013

In this paper, the sensorless MPPT algorithm is proposed where the performance of varied duty ratio change has been improved using multi-layer neuro-fuzzy that aligns with neuro-fuzzy based optimized membership function. Since the change of duty ratio of sensorless MPPT is varied by using the neuro-fuzzy, the MPPT response speed is faster than the convectional method and is able to reduce the steady-state ripple. The neuro fuzzy controller has the response characteristics which is superior to the existing fuzzy controller, because of the usage of the optimal width of the fuzzy membership function. The effectiveness of the proposed method has been verified by simulations and experimental results.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1602-1607
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• 2003

Vibration causes noise and sometimes makes structure unstable. Especially, due to the efforts of lightening, deformation of flexible structure is increased in its shape. Just a little disturbance can cause vibration and low damping ratio makes residual vibration last long time. This research is concerned with the model reference neuro-controller design for the vibration suppression of smart structures. By using a model reference neurocontroller, which is one of the algorithms of adaptive control, we performed an adaptive control of flexible cantilever plate and opened box structure with piezoelectric materials. The proposed adaptive vibration control algorithm, a model reference neuro-controller, was proved in its effectiveness by applying to an opened box structure. The model reference neuro-controller is implemented with DSP, and the real-time adaptive vibration control experiment results confirm that the model reference neuro-controller is reliable.

• 대한전기학회논문지
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• 제43권4호
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• pp.543-552
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• 1994

This paper presents a decentralized quadratic regulation architecture with feedforward neural networks for the control problem of complex systems. In this method, the decentralized technique was used to treat several simple subsystems instead of a full complex system in order to reduce training time of neural networks, and the neural networks' nonlinear mapping ability is exploited to handle the nonlinear interaction variables between subsystems. The decentralized regulating architecture is composed of local neuro-controllers, local neuro-identifiers and an overall interaction neuro-identifier. With the interaction neuro-identifier that catches interaction characteristics, a local neuro-identifier is trained to simulate a subsystem dynamics. A local neuro-controller is trained to learn how to control the subsystem by using generalized Backprogation Through Time(BTT) algorithm. The proposed neural network based decentralized regulating scheme is applied in the power System Stabilization(PSS) control problem for an imterconnected power system, and compared with that by a conventional centralized LQ regulator for the power system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 추계학술대회 논문집 전력기술부문
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• pp.412-415
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• 2003

It is important to classily fault types, discriminate fault section and calculate the fault location by any detecting technique for combined transmission lines. This paper proposes the technique to classily the fault types and fault section using neuro-fuzzy systems. Neuro-fuzzy systems are composed of three parts to perform different works. First, neuro-fuzzy system for fault type classification is performed with approximation coefficient of currents obtained by wavelet transform. The second neuro-fuzzy system discriminates the fault section between overhead and underground with detail coefficients of voltage and current. The last neuro-fuzzy system calculates the fault location with impedance in this paper, neuro-furry system shows the excellent results for classification of fault types and discrimination of fault section.