• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.671-674
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• 1997

The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FIT, DWT(Discrete Wavelet Transform), and Fisher's criterion are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 10-class power quality disturbances are also provided.

• 한국전자통신학회논문지
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• 제18권1호
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• pp.115-126
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• 2023

무작위 및 주기적인 변동하는 재생에너지 발전 전력 품질 교란으로 인해 발전 변환 송전 및 배전에서 더 자주 발생하게 된다. 전력 품질 교란은 장비 손상 또는 정전으로 이어질 수 있다. 따라서 서로 다른 전력 품질 외란을 실시간으로 자동감지하고 분류하는 것이 필요하다. 전통적인 PQD 식별 방법은 특징 추출 특징 선택 및 분류의 세 단계로 구성된다. 그러나 수동으로 생성한 특징은 선택 단계에서 정확성을 보장하기 힘들어서 분류 정확도를 향상하는 데에는 한계가 있다. 본 논문에서는 16가지 종류의 전력 품질 신호를 인식하기 위해 CNN(Convolution Neural Networ)과 LSTM(Long Short Term Memory)을 기반으로 시간 영역과 주파수 영역의 특징을 결합한 심층 신경망 구조를 제안하였다. 주파수 영역 데이터는 주파수 영역 특징을 효율적으로 추출할 수 있는 FFT(Fast Fourier Transform)로 얻었다. 합성 데이터와 실제 6kV 전력 시스템 데이터의 성능은 본 연구에서 제안한 방법이 다른 딥러닝 방법보다 일반화되었음을 보여주었다.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권9호
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• 전기학회논문지
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• 제65권5호
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• pp.711-717
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• 2016

Recently, fuel cell generation system with high energy efficiency and low CO2 emission is energetically interconnected with distribution power system. Especially, MCFC(molten carbonate fuel cell) operating at high temperature conditions is commercialized and installed as a form of large scale power generation system. However, it is reported that power system disturbances such as harmonic distortion, surge phenomenon, unbalance current, EMI(Electromagnetic Interference), EMC (Electromagnetic Compatibility) and so on, have caused several problems including malfunction of protection device and damage of control devices in the large scale FCGS(Fuel Cell Generation System). Under these circumstances, this paper proposes countermeasure algorithms to prevent power system disturbances based on the modelling of PSCAD/EMTDC and P-SIM software. From the simulation results, it is confirmed that proposed algorithms are useful method for the stable operation of a large scale FCGS.

• IEIE Transactions on Smart Processing and Computing
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• 제4권5호
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• pp.330-339
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• 2015

Power quality (PQ) problems are becoming a big issue, since delicate complex electronic devices are widely used. We present a new denoising technique using discrete wavelet transform (DWT), where a modified correlation thresholding is used in order to reliably detect the PQ disturbances. We consider various PQ disturbances on the basis of IEEE-1159 standard over noisy environments, including voltage swell, voltage sag, transient, harmonics, interrupt, and their combinations. These event signals are decomposed using DWT for the detection of disturbances. We then evaluate the PQ disturbance detection ratio of the proposed denoising scheme over Gaussian noise channels. Simulation results also show that the proposed scheme has an improved signal-to-noise ratio (SNR) over existing scheme.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.472-477
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• 2013

An internet-based, real-time GPS synchronized wide-area power system frequency monitoring network(FNET) has been monitoring wide-area power system frequency in continuous time in the United States. This paper analyzes the FNET measurement to the verified disturbances in the US eastern interconnected power system and simulates it using the dynamic system model. By comparing the frequency measurements with its simulation results to the same disturbances in detail, this paper finds that the sequence of monitoring points to detect the frequency fluctuation caused by the disturbances is matched well in the measured data and the simulation results. The similarity comparison index is also proposed to quantify the similarity of the compared cases. The dynamic model based simulation result is expected to compensate for the lack of FNET measurement in its applications.

• 전기전자학회논문지
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• 제25권4호
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• pp.710-715
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• 2021

전력선 신호는 다양한 원인으로 인하여 이상 현상을 보일 수 있다. 대표적인 이상 현상으로는 일시적인 진폭의 증가 혹은 감소(swell/sag), 클립핑에 따른 진폭의 일시적인 평탄화(flat top), 고조파 왜곡(harmonic distortion) 등이다. 고품질의 전력 신호를 위하여는 이러한 이상 현상의 검출 및 대응이 필요하다. 본 연구에서는 변곡점 검출법을 활용하여 전력선 신호의 이상 현상을 검출한다. 변곡점은 국부적인 최대값/최소값 그리고 기울기가 변하는 지점으로 정의된다. 전력선 신호는 정현파이기 때문에 최대값과 최소값 부근에서 변곡점이 존재하며 이상 현상이 발생하는 곳에서 추가적인 변곡점이 발생한다. 본 연구에서는 대상 신호에서 검출된 변곡점과 정상 신호의 변곡점을 비교하여 이상 현상을 판단한다. 아울러 비용함수를 정의하여 이상 현상이 발생하는 시점을 추정한다. 컴퓨터 모의실험으로 다양한 이상 현상에 대한 제안된 방법의 유용성을 검증한다. 일시적인 진폭의 증가/감소의 경우, 변곡점의 위치는 정상 신호와 동일하며, 변곡점에서의 진폭에서만 차이가 발생한다. 고조파 왜곡이나 평탄화된 진폭의 경우 추가적인 변곡점이 발생하여 비용함수가 큰 값을 보인다. 이러한 이상 현상 간의 차이를 이용하여 이상 현상을 분류할 수 있다.

• 제어로봇시스템학회논문지
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• 제4권3호
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• pp.321-328
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• 1998

When we control the level of the steam generator in the nuclear power plants, a swell and shrink arises from many disturbances such as feed water rate, feed water temperature, main steam flow rate, and coolant temperature. If we use the conventional type of PI controller in this system, we will not have stability during controlling at lower power, the removal function of disturbances, and a load follow-up control effectively. In this paper, we study the application of a 2-Degree of Freedom(2-DOF) PID controller to the level control of the steam. generator of nuclear power plants through the simulation and the experimental steam generator. We use the parameters $\alpha$, $\beta$, $\gamma$ of the 2-DOF PID controller for the removal of disturbances and the parameters Kp,Ti,Td of the conventional type of PID controller for controlling setpoint. The back-propagation learning algorithm of neural network is used for tuning the 2-DOF PID controller. We can find satisfactory results of the removal of the disturbances and the tracking function in the change of setpoint through the simulation and experimental steam generator.

• Journal of Information Processing Systems
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• 제15권1호
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• pp.116-126
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• 2019

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.

• Journal of Power Electronics
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• 제16권4호
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• pp.1375-1386
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• 2016

To decouple the secondary saliencies in sensorless permanent magnet synchronous machine (PMSM) drives, a repetitive control (RC) in the angle domain is proposed. In this paper, the inductance model of a concentrated windings surface-mounted PMSM (cwSPMSM) with strong secondary saliencies is developed. Due to the secondary saliencies, the estimated position contains harmonic disturbances that are periodic relative to the angular position. Through a transformation from the time domain to the angle domain, these varying frequency disturbances can be treated as constant periodic disturbances. The proposed angle-domain RC is plugged into an existing phase-locked loop (PLL) and utilizes the error of the PLL to generate signals to suppress these periodic disturbances. A stability analysis and parameter design guidelines of the RC are addressed in detail. Finally, the proposed method is carried out on a cwSPMSM drive test-bench. The effectiveness and accuracy are verified by experimental results.