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Title Page

Contents

List of Abbreviations 17

Abstract 19

Chapter 1. Introduction 21

1.1. Problem Statement 21

1.1.1. Optimal Placement and Sizing of Multiple Distributed Generations 21

1.1.2. Islanding Detection Method for Multiple DGs 22

1.1.3. Inverter Controller 22

1.1.4. Power Management for Multiple DGs 23

1.1.5. Load Modeling 23

1.2. Objective of Research 24

1.3. Dissertation Outlines 25

1.4. Research Publications 27

Chapter 2. Optimal Placement and Sizing of Multiple Distributed Generations 28

2.1. Introduction 28

2.2. Optimal Placement of Multiple DGs 30

2.2.1. Reduction of Power Loss by Connecting DG 30

2.2.2. Selection of Optimal Location in Transmission Network 36

2.2.3. Selection of Optimal Location in Distribution Network 39

2.3. Optimal Sizing by Using Kalman Filter Algorithm 42

2.3.1. Estimation by Using Kalman Filter Algorithm 42

2.3.2. Finding Actual Values 47

2.3.3. Evaluation of Estimation Performance 48

2.3.4. Selection of Optimal Sizes in Real Distribution System 50

2.4. Optimal Sizing by Using LMM and RMP 52

2.4.1. Description of Power Loss by Using Reduced Multivariate Polynomial 52

2.4.2. Description of Sizes of DGs by Using Lagrange Multiplier Method 53

2.4.3. Sampling Procedure with Consideration of Segment Dividing 56

2.4.4. Estimation of Sizes of DGs by Using RMP 58

Chapter 3. Islanding Detection Method for Multiple DGs 60

3.1. Introduction 60

3.2. Islanding Detection Method without Non-Detection Zone 62

3.2.1. Non-Detection Zone(NDZ) by OUV/OUF Method 62

3.2.2. Description of Distribution Network 63

3.2.3. Grid-Connected Single DG System 65

3.2.4. Grid-Connected Multiple DG Systems 69

3.2.5. Implementation of Islanding Detection Method 69

3.3. Case Studies in Single DG System 71

3.3.1. Similar Load Consumption and DG Output 71

3.3.2. Different Load Consumption and DG Output 72

3.3.3. Consideration of Signal to Noise Ratio 73

3.4. Case Studies in Multiple DG Systems 74

3.4.1. Similar Load Consumptions and DG Outputs 75

3.4.2. Different Load Consumptions and DG Outputs 75

3.4.3. Connection of another DG System with Different Switching Frequency 77

3.4.4. Connection of another DG System with Same Switching Frequency 77

3.4.5. Connection of another Load 78

3.4.6. Connection of Nonlinear Sensitive Load 79

3.4.7. Short-Circuit Fault Clearance 81

Chapter 4. Inverter Controller 82

4.1. Introduction 82

4.2. Islanding Detection Methods 84

4.2.1. Distributed Generation System 84

4.2.2. Inverter's Switching Frequency Based IDM 85

4.2.3. P-V Characteristics of Load Based IDM 85

4.3. Design of Novel Inverter Controller 88

4.3.1. Conventional Inverter Controller 88

4.3.2. Design of Novel Inverter Controller 92

4.4. Case Studies for Inverter Control 95

4.4.1. Different θref(이미지참조) 95

4.4.2. Start of Inverter 96

4.4.3. Changed Pref and Zero Qref(이미지참조) 97

4.4.4. Changed Pref and Non-Zero Qref(이미지참조) 98

4.5. Case Studies for Islanding Detection 99

4.5.1. Different Load Consumption and DG Output 99

4.5.2. Same Load Consumption and DG Output 101

4.5.3. Weak Grid 101

4.5.4. Application of SFIDM 102

Chapter 5. Power Management for Multiple DGs Based on Renewable Energies 104

5.1. Introduction 104

5.2. The Proposed Power Management Method 106

5.2.1. DG System with BESS 106

5.2.2. Amount of Available Power Management in Feeder 107

5.2.3. Power Management in Islanded Feeder 109

5.2.4. Grid-Connection of Islanded Feeder 113

5.3. Inverter Control of DG Systems 115

5.3.1. Control of Utility-Owned DG for Grid-Connected Operation 115

5.3.2. Control of Utility-Owned DG for Islanding Operation 116

5.3.3. Control of Utility-Owned DG to Synchronize Feeder with Grid 118

5.4. Case Studies 119

5.4.1. Case-1: When Total Output Power from DGs is Larger Than Total Load Consumptions 119

5.4.2. Case-2: When Total Output Power from DGs is Smaller Than Total Load Consumptions 121

5.4.3. Case-3: Grid-Connection 124

Chapter 6. Load Modeling 126

6.1. Introduction 126

6.2. Load Data Acquisition for Static Model 128

6.2.1. Data Acquisition from K-EMS 128

6.2.2. Data Classification and Valid Data Screening 129

6.3. K-EMS Real Data Based Load Modeling 131

6.3.1. Selection of Load Model Structure 131

6.3.2. Kalman Filter Based ZIP Model Parameter Estimation Algorithm 132

6.3.3. Algorithm Verification Using PSS/E Data 135

6.3.4. Results of Load Modeling Based on Real K-EMS Data 137

6.4. DFR Real Data Based Load Modeling 141

6.4.1. Selection of Load Model Structure 141

6.4.2. Levenberg-Marquardt Algorithm 143

6.4.3. Results of Dynamic Load Modeling Based on Real DFR Data 144

Chapter 7. Conclusions 146

References 150

List of Tables

Table 2.1. Comparison of RMSE Values 49

Table 2.2. Comparison of RMSE Values 50

Table 2.3. Comparisons of Power Losses Corresponding to the Combinations of Sizes of DGs 51

Table 2.4. RMSE Values of the First-Order RMP and the Forth-order Kalman-Filter Algorithm 59

Table 3.1. Sizes and Parameters of All Devices in Fig. 3.2 64

Table 3.2. Output Voltage Harmonics of DG-1 System 74

Table 3.3. States of Circuit Breakers to Carry Out Seven Case Studies 75

Table 3.4. Change of Output Voltage Harmonics in Percent of Rated Voltage After the Induction Motor is Connected 81

Table 5.1. When Power Generation from All DGs and BESSs is Greater than Power Consumed in All Loads 109

Table 5.2. When Power Generation from All DGs and BESSs is Less than Power Consumed in All loads 109

List of Figures

Fig. 2.1. IEEE benchmarked 31-bus system. 31

Fig. 2.2. Power flow from the κ-th generator to the several loads. 32

Fig. 2.3. Power flow from the several generators to the l-th load. 32

Fig. 2.4. Simplified circuit with only power generations and consumptions. 33

Fig. 2.5. Simplified unit circuit between two buses. 33

Fig. 2.6. Power losses corresponding to the variations in size of DG. 36

Fig. 2.7. The power loss sensitivities of related buses. 37

Fig. 2.8. The power-margins, PM of related buses.(이미지참조) 38

Fig. 2.9. The OLIs of related buses. 38

Fig. 2.10. A part of Do-gok distribution network in Seoul, Korea. 40

Fig. 2.11. The OLIs of related buses in three Areas. 41

Fig. 2.12. Comparison of power losses according to different locations of multiple DGs. 41

Fig. 2.13. Procedure to obtain data samples of the multiple DGs and power loss required before applying the Kalman filter algorithm. 44

Fig. 2.14. Steps to estimate the optimal size of multiple DGs in two phases by applying the Kalman filter algorithm. 45

Fig. 2.15. Convergence of states for estimation of multiple DGs' size. 47

Fig. 2.16. Convergence of states for estimation of total power loss. 48

Fig. 2.17. Estimation performance of the Kalman filter algorithm for each DG. 49

Fig. 2.18. Estimation performance of total power loss. 50

Fig. 2.19. Estimation results for optimal sizes of three DGs. 51

Fig. 2.20. Procedure to obtain data samples of the multiple DGs and power losses with consideration of segment dividing. 57

Fig. 2.21. Estimation performances of the first-order RMP and the fourth-order Kalman-filter algorithms. 58

Fig. 3.1. Non-detection zone (NDZ) by the OUV/OUF based method. 63

Fig. 3.2. Distribution power network interconnected with multiple DG systems. 64

Fig. 3.3. Simplified distribution network with single DG system. 65

Fig. 3.4. Modeling of distribution network with the estimated impedances. 66

Fig. 3.5. Variation of impedance, Zsw corresponding to mf.(이미지참조) 68

Fig. 3.6. Implementation of the proposed islanding detection algorithm based on the orthogonal characteristic of high frequency components. 70

Fig. 3.7. Responses of a-phase voltage at the terminal of DG-1 after the islanding operation at 0.5 s: (a) Magnitude (b) Phase. 71

Fig. 3.8. Voltage response at high switching frequency (15 kHz) of PWM inverter in the islanding and pre-islanding operations. 72

Fig. 3.9. Responses of α-phase voltage at the terminal of DG-1 after the islanding operation at 0.5 s: (a) Magnitude (b) Phase. 73

Fig. 3.10. Voltage response at high switching frequency (15 kHz) of PWM inverter in the islanding and pre-islanding operations. 73

Fig. 3.11. Responses in the case study-3.4.1. 76

Fig. 3.12. Responses in the case study-3.4.2. 76

Fig. 3.13. Responses in the case study-3.4.3. 77

Fig. 3.14. Responses in the case study-3.4.4. 78

Fig. 3.15. Responses in the case study-3.4.5. 79

Fig. 3.16. Responses in the case study-3.4.6. 80

Fig. 3.17. Responses in the case study-3.4.7. 81

Fig. 4.1. Practical Do-gok distribution power network in a part of Seoul, Korea. 84

Fig. 4.2. Islanding detection performances of the SFIDM and the OUV/OUF IDM. 86

Fig. 4.3. The P-V characteristics corresponding to the curves of Pinv and PLoad.(이미지참조) 86

Fig. 4.4. Phasor diagram to describe the Park's transformation. 89

Fig. 4.5. Block diagram of phase-locked loop (PLL). 89

Fig. 4.6. Phasor diagram to describe the Park's transformation when PLL is not ideal. 90

Fig. 4.7. Block diagram of conventional inverter controller. 91

Fig. 4.8. Block diagram of proposed inverter controller by using magnitude and phase angle of voltage. 93

Fig. 4.9. Phasor diagram to describe the operation of phase-shifter in the proposed controller. 93

Fig. 4.10. Phasor diagram to describe the relationship among the Vfilter, the Vs, and the V.(이미지참조) 94

Fig. 4.11. Performances of proposed controller corresponding to the synchronized and zero θref.(이미지참조) 96

Fig. 4.12. Comparison of conventional and proposed controllers in starting response performance. 97

Fig. 4.13. Comparison of two controllers' performances when the Pref is changed and the Qref is zero.(이미지참조) 98

Fig. 4.14. Comparison of two controllers' performances when the Pref is changed and the Qref is 0.31 pu.(이미지참조) 99

Fig. 4.15. Comparison of islanding detection performances with the OUV IDM when the amount of load consumption and the DG output power are different. 100

Fig. 4.16. Comparison of islanding detection performances with the PVIDM when the amount of load consumption and the DG output power are same. 101

Fig. 4.17. Performances of the conventional and proposed controllers when the grid impedance is increased. 102

Fig. 4.18. Comparison of islanding detection performances with the SFIDM. 103

Fig. 5.1. Distributed generation and battery energy storage systems. 106

Fig. 5.2. Electrical circuit model of battery in BESS. 107

Fig. 5.3. Distributed generations connected to a part of practical distribution network at Do-gok area in Seoul, Korea. 108

Fig. 5.4. Power management algorithm for the only DGu,1.(이미지참조) 112

Fig. 5.5. Protection algorithm for DGu,1 and DGu,2(이미지참조) 112

Fig. 5.6. Grid-connection algorithm for DGu,1 and DGu,2(이미지참조) 114

Fig. 5.7. Block diagram of inverter controller using magnitude and phase of voltage. 115

Fig. 5.8. Phasor diagram to describe the output power of inverter. 116

Fig. 5.9. Block diagram of voltage magnitude-controller (M-Ci) and phase-controller (P-Ci) for utility-owned DG during the islanding operation.(이미지참조) 117

Fig. 5.10. Block diagram of voltage magnitude-controller (M-Cs) and phase-controller (P-Cs) to synchronize the feeder and grid voltages. 118

Fig. 5.11. Powers generated from DGs before and after islanding (Case-1). 120

Fig. 5.12. Other results (Case-1): (a) reactive powers of DGs, (b) voltages of DGs, (c) states of circuit breakers, and (d) frequency of utility-owned DGs. 121

Fig. 5.13. Powers generated from DGs before and after islanding (Case-2). 123

Fig. 5.14. Other results (Case-2): (a) reactive powers of DGs, (b) voltages of DGs, (c) states of circuit breakers, and (d) frequency of utility-owned DGs. 123

Fig. 5.15. Results for Case-3: (a) powers from the utility-owned DGs, (b) states of circuit breakers, (c) synchronization of α-phase voltage at Brk2, (d) synchronization of α-phase voltage at Brk6, (e) α-phase current from DGu,1, and (f) α-phase current from DGu,2.(이미지참조) 125

Fig. 6.1. K-EMS data acquisition process 129

Fig. 6.2. K-EMS raw data classification 130

Fig. 6.3. Data screening process 130

Fig. 6.4. Estimation algorithm to determine parameters of ZIP model 134

Fig. 6.5. Estimation results using noise-free PSS/E data 135

Fig. 6.6. Estimation results using noise-included PSS/E data 136

Fig. 6.7. Characteristics of measured load and load model at substation-A (without tap change)-10:02:32 a.m., February 8, 2011 (pz＝0.3851, pI＝0.3362, pp＝0.2787, qz＝3.2397, qI＝-0.1611, qQ＝-2.0786).(이미지참조) 137

Fig. 6.8. Validation of load model which is represented in Fig. 6.7. with anther data achieved from substation-A - 10:35:20 a.m., February 9, 2011 138

Fig. 6.9. Characteristics of measured load and load model at substation-A (with tap change) - 10:27:00 a.m., February 8, 2011 (pz＝0.6245, pI＝-0.3000, pp＝0.6755, qz＝5.4470, qI＝-5.4659, qQ＝1.0189).(이미지참조) 139

Fig. 6.10. Characteristics of measured load and load model at substation-B - 06:07:00 a.m., February 9, 2011 140

Fig. 6.11. Characteristics of measured load and load model at substation-C - 05:57:44 a.m., February 8, 2011 141

Fig. 6.12. ZIP + IM model 142

Fig. 6.13. Dynamic load modeling by using DFR data at substation-N 144

Fig. 6.14. Static load modeling by using DFR data at substation-N 145

초록보기

This dissertation presents studies on distributed generation (DG) and its connected power system. The DG changes entire powerflows and the construction of additional transmission lines are determined by the powerflows. At the view point of the power system, therefore, selection of locations of multiple DGs is one of the most important issues. In addition, because the powerflows are affected by the sizes of DGs, selection of sizes of multiple DGs is important as much as the selection of locations. However, selection of locations or sizes of multiple DGs are not simple problem. To solve the powerflows, the entire power systems should be analyzed by numerical method and it requires much time for calculation. Moreover, the calculation time increases exponentially corresponding to the increase of number of DGs. The calculation time to select optimal locations is dramatically reduced by using the sensitivity of power loss and the derivative of the sensitivity. Also, the calculation time to select optimal sizes is reduced by using the Kalman filter algorithm.

The DGs usually supplies electrical power independently from the utility. Therefore, islanding detection should be seriously considered to protect the power system and DG itself. The passive islanding detection method (IDM) does not impact the power system, but it has large non-detection zone (NDZ). In contrast, the active islanding detection method has relatively smaller NDZ or no NDZ. However, the active IDM harms the stability of the power system. To solve the problems of the passive and active IDMs, switching frequency component of inverter is used. The switching frequency based IDM (SFIDM) does not harm the stability of the power system while it detects the islanding operation without the NDZ.

According to the increase of power consumption, peak load demand becomes a serious problem. Because the DG is much smaller than the conventional power plant, it responds quickly to the peak load demand. The response of the DG mainly depends on inverter because most of the DGs are implemented with inverters. In general, the inverter impacts on the stability of power system if it is implemented by focusing on fast response and an active IDM is used. The conventional inverter control algorithm is based on the Park's transformation, and it controls the output power indirectly through currents. The response of inverter is improved without any impact on the stability by developing a method to control the output power directly.

The islanding detection algorithm conventionally stops DG when an islanding is detected. However, the DG can continuously supply power if the DG is controlled directly or indirectly by the utility. The continuous operation of multiple DGs improves the power system reliability by preventing widespread power outage if the DG occupies large ratio of entire power supply. In contrast to the utility owned DG, independent power producer (IPP) owned DG is not controlled by the utility. However, the utility can manage the IPP owned DG with indirect way by using the islanding detection method in each DG. The range of power outage is reduced by the developed management algorithm.

The studies on DG are progressed by assuming that all loads consuming constant power. To extend the studies to real power system, accurate power system model is required. In general, parameters of generator and transmission equipment are given by the manufacturer or achieved by testing procedure. In contrast, it is difficult to get the parameters of loads because it is determined by the consumer. Consequently, the load model is inaccurate comparing to the models of generator and transmission equipment. To reflect the load characteristics accurately, the static load modeling and dynamic load modeling is conducted by using the Kalman filter and Levenberg-Marquardt algorithms, respectively.

참고문헌 (75건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Reliability modeling of distributed generation in conventional distribution systems planning and analysis 네이버 미소장
2 Improving the voltage profiles of Distribution Networks using multiple Distribution Generation Sources 네이버 미소장
3 “Optimization of embedded generation sizing and siting by using a double trade-off method,” IEE Proceedings of Generation, Transmission and Distribution, Vol. 152, No. 4, pp. 503-513, July 2005. 미소장
4 Optimal Distribution Voltage Control and Coordination With Distributed Generation 네이버 미소장
5 Overview of Anti-Islanding Algorithms for PV Systems. Part I: Passive Methods 네이버 미소장
6 Overview of Anti-Islanding Algorithms for PV Systems. Part II: ActiveMethods 네이버 미소장
7 An islanding detection method for distributed generations using voltage unbalance and total harmonic distortion of current 네이버 미소장
8 New Islanding Detection Method for Inverter-Based Distributed Generation Considering Its Switching Frequency 네이버 미소장
9 A Scalable Power-Line-Signaling-Based Scheme for Islanding Detection of Distributed Generators 네이버 미소장
10 Sandia Frequency-Shift Parameter Selection to Eliminate Nondetection Zones 네이버 미소장
11 A Simple Technique for Islanding Detection With Negligible Nondetection Zone 네이버 미소장
12 Control for Grid-Connected and Intentional Islanding Operations of Distributed Power Generation 네이버 미소장
13 Selection of Optimal Location and Size of Multiple Distributed Generations by Using Kalman Filter Algorithm 네이버 미소장
14 “Selection of Optimal Location and Size of Distributed Generation Considering Power Loss,” The Transactions of KIEE, Vol. 57, No. 4, pp. 551-559, April 2008. 미소장
15 “New Islanding Detection Method for Inverter-Based Distributed Generation Considering Its Switching Frequency,” in proc. of IEEE IAS Annual Meeting 2009, pp. 1-8, Huston, Texas, USA, October 2009. 미소장
16 “An Investigation of Islanding Detection Method by Using High-Frequency Component of Inverter-Based Distributed Generation”, in proc. of KIEE Annual Conference 2008, pp. 1-2, July 2008. 미소장
17 “A Method to Select Optimal Size of Multiple Distributed Generation System by Using the Reduced Multivariate Polynomial Model”, in proc. of KIEE Annual Conference 2009, pp. 56-57, July 2009. 미소장
18 “An Impact Assessment for Active Islanding Detection Method Corresponding to Impedances of Distribution Line”, in proc. of KIEE Annual Conference 2010, pp. 441-442, July 2010. 미소장
19 p,p′-Dichlorodiphenoxydichloroethylene induced apoptosis of Sertoli cells through oxidative stress-mediated p38 MAPK and mitochondrial pathway 네이버 미소장
20 “A Study on Load Modeling of Industrial Area Based on DFR measurements”, in proc. of KIEE Annual Conference 2011, pp. 182-183, July 2011. 미소장
21 IEEE Power and Energy Magazine 네이버 미소장
22 Adequacy assessment of distributed generation systems using Monte Carlo Simulation 네이버 미소장
23 Effect of Load Models in Distributed Generation Planning 네이버 미소장
24 An integrated distributed generation optimization model for distribution system planning 네이버 미소장
25 Analytical approaches for optimal placement of distributed generation sources in power systems 네이버 미소장
26 Optimal distributed generation location using mixed integer non-linear programming in hybrid electricity markets 네이버 미소장
27 “Introduction to Optimal Estimation,” London: Springer Verlag, pp. 149-183, 1999. 미소장
28 Generalized Generation Distribution Factors for Power System Security Evaluations 네이버 미소장
29 Network reconfiguration in distribution systems for loss reduction and load balancing 네이버 미소장
30 Optimum Size and Location of Shunt Capacitors for Reduction of Losses on Distribution Feeders 네이버 미소장
31 Optimal sizing of capacitors placed on a radial distribution system 네이버 미소장
32 DG allocation using an analytical method to minimize losses and to improve voltage security 네이버 미소장
33 A Kalman Filtering Approach to Rapidly Detecting Modal Changes in Power Systems 네이버 미소장
34 “Power System Analysis,” 2nd ed., Singapore: McGraw-Hill, pp. 234-227, 2004. 미소장
35 A Reduced Multivariate-Polynomial Model for Estimation of Electric Load Composition 네이버 미소장
36 Benchmarking a reduced multivariate polynomial pattern classifier. 네이버 미소장
37 Islanding detection of inverter-based distributed generation 네이버 미소장
38 Evaluation of anti-islanding schemes based on nondetection zone concept 네이버 미소장
39 Distributed generation islanding-implications on power system dynamic performance 네이버 미소장
40 Comparative analysis between ROCOF and vector surge relays for distributed generation applications 네이버 미소장
41 Active anti-islanding method for PV system using reactive power control 네이버 미소장
42 Application of distribution line carrier-based protection to prevent DG islanding: an investigating procedure 네이버 미소장
43 p,p′-DDE fails to reduce the competitive reproductive fitness in Nigerian male guppies 네이버 미소장
44 On the local identifiability of load model parameters in measurement-based approach 소장
45 Discrete sliding-mode control of a PWM inverter for sinusoidal output waveform synthesis with optimal sliding curve 네이버 미소장
46 Induction machine parameter identification using PWM inverter at standstill 네이버 미소장
47 Characteristics and modeling of harmonic sources-power electronic devices 네이버 미소장
48 The path of the smart grid 네이버 미소장
49 “Development of Distributed Generation in China,” in proc. of IEEE PES. General Meeting, pp. 1-7, July 2009. 미소장
50 Investigation of Positive Feedback Anti-Islanding Control for Multiple Inverter-Based Distributed Generators 네이버 미소장
51 A $Q$– $f$ Droop Curve for Facilitating Islanding Detection of Inverter-Based Distributed Generation 네이버 미소장
52 Impact of Load Frequency Dependence on the NDZ and Performance of the SFS Islanding Detection Method 네이버 미소장
53 IEEE Recommended Practice for Utility Interface of Photovoltaic (PV) Systems, IEEE Std.929-2000, 2000, pp. 1-26. 미소장
54 IEEE Standard for Interconnecting Distributed Resources With Electric Power Systems, IEEE Std. 1547-2003, 2003, pp. 1-16. 미소장
55 Power Management and Power Flow Control With Back-to-Back Converters in a Utility Connected Microgrid 네이버 미소장
56 Power-Sharing Method of Multiple Distributed Generators Considering Control Modes and Configurations of a Microgrid 네이버 미소장
57 Performance Evaluation of Active Islanding-Detection Algorithms in Distributed-Generation Photovoltaic Systems: Two Inverters Case 네이버 미소장
58 An Islanding Detection Method for a Grid-Connected System Based on the Goertzel Algorithm 네이버 미소장
59 A Correlation-Based Islanding-Detection Method Using Current-Magnitude Disturbance for PV System 네이버 미소장
60 Battery modeling for energy aware system design 네이버 미소장
61 Accurate electrical battery model capable of predicting runtime and I-V performance 네이버 미소장
62 Dynamic Modeling and Control of a Grid-Connected Hybrid Generation System With Versatile Power Transfer 네이버 미소장
63 The application of load models of electric appliances to distribution system analysis 네이버 미소장
64 Load modeling for power flow and transient stability computer studies 네이버 미소장
65 Load modeling of electric locomotive using parameter identification 소장
66 Development of composite load models of power systems using on-line measurement data 소장
67 Reducing Identified Parameters of Measurement-Based Composite Load Model 네이버 미소장
68 Nonlinear dynamic model evaluation from disturbance measurements 네이버 미소장
69 An interim dynamic induction motor model for stability studies in the WSCC 네이버 미소장
70 Composite load modeling via measurement approach 네이버 미소장
71 Measurement-based dynamic load models: derivation, comparison, and validation 네이버 미소장
72 On the parameter estimation and modeling of aggregate power system loads 네이버 미소장
73 Multiple Solutions and Plateau Phenomenon in Measurement-Based Load Model Development: Issues and Suggestions 네이버 미소장
74 A Novel Parameter Identification Approach via Hybrid Learning for Aggregate Load Modeling 네이버 미소장
75 “Numerical Optimization,” 2nd ed., New York: Springer, pp. 258-259, 2006. 미소장

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