• ETRI Journal
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• 제31권6호
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• pp.806-808
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• 2009

A new computationally efficient algorithm-based propagator method for two-dimensional (2-D) direction-of-arrival (DOA) estimation is proposed, which uses two parallel uniform linear arrays. The algorithm takes advantage of the special structure of the array which enables 2-D DOA estimation without pair matching. Simulation results show that the proposed algorithm achieves very accurate estimation at a computational cost 4 dB lower than that of standard methods.

• 조명전기설비학회논문지
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• 제21권9호
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• pp.101-108
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• 2007

State estimators are used to monitor the operating states of power systems in modern EMS. It iteratively calculates the voltage profile of the currently operating power system with voltage, current, and power measurements gathered from the entire system. All the measurements are usually assumed to be obtained simultaneously. It is practically impossible, however, to maintain the synchronism of the measurement data. Recently, phasor measurements synchronized via satellite are used for the operation of these power systems. This paper describes the modified state estimator used to support the processing of synchronized phasor measurements. Synchronized phasor measurements are found to provide synchronism of measurement data and improve the accuracy/redundancy of the measurement data for state estimation. The details of the developed state estimation program and some numerical results of operation are presented.

• 대한전기학회논문지
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• 제33권9호
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• pp.364-371
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• 1984

This paper describes a method for the state estimation in electric power system. The state values are estimated through the weighted least square method considering the bad data. Then, the bad data are identified by using sensitivity coefficients of power system after being detected the bad data through the distribution of T. This method was applied to the model power system, and, the results of test for proposed method are given.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.425-426
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• 2007

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, conventional optimization algorithm, such as weighted least squares (WLS) method, has been widely used. But these algorithms have disadvantages of converging local optimal solution. In these days, a modern heuristic optimization methods such as Particle Swarm Optimization (PSO), are introducing to overcome the problems of classical optimization. In this paper, we suggested parallel particle swarm optimization (PPSO) to search an optimal solution of state estimation in power systems. To show the usefulness of the proposed method over the conventional PSO, proposed method is applied on the IEEE-57 bus system.

• 대한전기학회논문지
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• 제37권7호
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• pp.427-433
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• 1988

본 논문에서는 계통을 분할하여 분할된 지역간의 경계선로에 대학 조류측정을 하여 계통을 축소하는데 근거한 계통분할 알고리즘을 제시한다. 즉 전력계통을 여러개의 종속계통으로 나누면 반그시 경계선로가 파생되는데 이 경계선로 조류를 정확히 추정할 수 있으면 각 종속계통에 대한 상태추정을 개별적으로 행할 수 있으므로 각 종속계통에 대한 경계선로 조류를 추정하는 면밀한 방법을 제시하였다. 까라서, 제시된 알고리즘을 쓰면 종속계통에서 상태추정을 개별적으로 수행할 수 있기 때문에 계산시간과 메모리 용량을 현저히 줄일 수 있다. 제시된 알고리즘을 IEEE 샘플계통에 적용하여 설계통 적용 가능성을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.214-215
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• 2006

In power systems, the state estimation takes an important role in security control. At present, the weighted least squares(WLS) method has been widely used to the state estimation computation. This paper presents an application of Adaptive Evolutionary Algorithm(AEA) to state estimation in power systems. AEA is a optimization method to overcome the problems of classical optimization. AEA is employed to solve state estimation on the 6 bus system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.291-293
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• 2006

In power systems, the state estimation takes an important role in security control. At present, the weighted least squares(WLS) method has been widely used to the state estimation computation. This paper presents an application of Particle Swarm Optimization(PSO) to state estimation in power systems. PSO is a modern heuristic optimization method to overcome the problems of classical optimization. PSO is employed to solve state estimation on the IEEE-30 bus system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.178-180
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• 2001

State estimation in power system is to estimate state variable value which minimizes the error from the real state measured by the gauge and connection state of the circuit breaker. In the past, it was difficult to determine measure function considering the correlation of the measured values. In this paper, an extended state estimation is proposed to process easily various kinds of estimation variable. The proposed algorithm is developed by expanding state variable concept based on many measured values and treating correlation between estimation variable and state variable, it is considered that the state variable satisfy some limitations named "Equality Limitation conditions".

• Journal of Power Electronics
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• 제13권4호
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.