고조파를 고려한 신경회로망 기반의 정태부하모델링과 계통해석기법 = Static Load Modeling and Power System Analysis Based on Artificial Neural Network and Harmonics

다국어 초록 (Multilingual Abstract)

Effective planning and operation of large electric power networks require more accurate modeling and simulation of the system. A typical load bus is connected to the various loads with different characteristics where each load exhibits different patterns of energy consumption depending upon the voltage and/or frequency of the systems. It is difficult to obtain a good load model for the stability analysis and the load flow calculations in power systems.
Nonlinear loads with harmonics exist in an actual power system where harmonic currents make voltage distortion. The sum of reactive power measured at individual load is different from the measured reactive power at a bus in a power system with linear and non-linear loads.

* A thesis for the degree of Doctor in February 2013.
Typical power system is composed of generation, transmission, distribution and utilization (load) parts. Although other parts of the system have been well researched enough to develop several models, the load models have received less attention.
In this study, ANN(artificial neural network) load modeling technique with consideration of harmonics is introduced for more accurate power system analysis, and an application method of constructed ANN load model is proposed for the power flow calculation. Results of this research can be summarized as follows:
The first, static characteristic experiments are performed for typical single load and characteristic experiments with harmonic source are also performed. Acquired data are used to construct single load model by ANN in consideration of harmonics.
The second, each individual reactive power in a group load affects the aggregated load which is different from composition rate. A new method that determines the degree of impacts by adjusting the coefficient of weight factors of each load was proposed using the least squares error method. The effectiveness of proposed algorithm is demonstrated by simulating three aggregation cases.
The third, application method of ANN load model was proposed for the power flow calculation. Through the case study, the results of the power flow calculation using proposed ANN load model were compared with traditional power flow calculation. When the applied load model was changed, result of power flow calculation was also changed. It means that the amount of generation will be changed and the load model influences on the power system planning and operating.
Results of this research show more accurate load modeling method and resonable application techniques for the power flow calculation. Since precise data for power system analysis can be acquired, the proposed method will be used for power system planning and maintenance.
For the future, numerous experiments are required for various load in better circumstance to understand more accurate characteristics of loads. Moreover, great interests and efforts in various power system applications such as complete harmonic load flow will be needed.

목차 (Table of Contents)

• Ⅰ. 서 론 1
• 1. 연구의 배경 1
• 2. 연구 동향 4
• (1) 국외 연구 동향 4
• (2) 국내 연구 동향 6
• 3. 연구의 범위 및 내용 7
• Ⅱ. 부하모델링 9
• 1. 개요 9
• 2. 부하모델링 접근법 10
• 3. 부하모델링 기법 11
• (1) 수학적 기법을 이용한 정태부하모델링 11
• (2) 수학적 기법을 이용한 동태부하모델링 14
• (3) Non-parametric기법을 이용한 부하모델링 21
• Ⅲ. 제안된 정태부하모델링 및 조류계산 적용기법 32
• 1. 제안된 기법의 개요 32
• 2. 고조파를 고려한 정태부하모델링 기법 37
• 3. 개별부하의 합성에 의한 부하모선의 모델링 41
• (1) 기존의 부하구성비를 이용한 부하합성법의 한계 41
• (2) 영향력 계수 추정 44
• 4. 전력조류계산의 적용 기법 47
• Ⅳ. 실험 및 고찰 51
• 1. 개요 51
• 2. 개별부하 정태특성실험 결과 51
• 3. 정태부하모델링 결과 67
• 4. 부하합성 결과 77
• 5. 조류계산 적용결과 99
• Ⅴ. 결론 108
• 참고문헌 110
• 부록 A. 정태부하 특성실험 결과 117
• 부록 B. 신경회로망에 의한 개별부하모델링 결과 127
• 부록 C. 부하 합성 결과 137