本論文提出以基於方向選擇性的蟻群算法來解決配電網重構問題。在蟻群算法的基礎上，提出了基於概率的搜索方法，在費洛蒙中添加了方向資訊，設計了基於降低之前已搜索路徑概率的搜索策略，對鄰域執行選擇性搜索操作，在探索的過程中打破隨機無序的向下一個城市移動的特性而根據費洛蒙進行移動，避免迅速向個體最佳位置移動，並且在該點附近徘徊，出現過早的局部收斂。因此保證粒子向最佳位置移動的同時，使得整個種群根據費洛蒙可迅速良性發展，最終達到收斂。
本文以網損最小、負載均衡最優、開關操作次數少為重構之最佳化目標，並將電壓，過載電流的限制條件化為偏離懲罰函數，減少了限制條件，並考慮了負載波動等不確定因素，對IEEE33配電網算例進行驗證，配電網網路重構之後，在負載波動的影響下，網路仍能保持穩定，並得到了整體變化負載不影響最優解網路結構的結論，表明方向選擇蟻群最佳化算法對配電網的初始解不具有依賴性，算法的穩定性良好。最後對通過改變目標權重和懲罰因子，對多組聯絡開關斷開位置進行分析，提出了重負載偏好的方法，並對69節點的配電網路進行驗證，不僅可求得最佳配置，亦可縮短計算時間。

In this thesis, an ant colony algorithm based on directional selection is proposed to solve the problem of distribution network reconfigurations. On the basis of ant colony optimization algorithms, the search direction is based on the direction information which can reduce the probability of previously searched paths. It will be chosen when the searching on the neighborhood. During the exploration process, it breaks the characteristics random down to any cities, but to move according to the pheromone. Thus, this method can avoid the rapid move to the individual best position and the appearance of premature convergence in the vicinity of the wandering.
The optimization objective for network configurations includes three items: (i) the minimum network loss, (ii) the optimal load balancing, and (iii) the least switching operations. The voltage overload current is deviated to the penalty function, reducing the constraint conditions. The IEEE33 distribution network example is used for verification. The network after this reconfiguration can still remain stable underload fluctuations. It can be concluded that the change of load does not affect the optimal network structure result. This result also demonstrates that the directional selection ant colony optimization algorithm is independent of the initial configuration of the distribution network. Numerical experiments reflect that the algorithm provides a suitable solution in terms of stability.
Finally, by changing the objective weights and penalty factors, groups of switch off position are analyzed A reduction in calculation time can be observed on the 69-node testing network without sacrificing the solution quality.

ABSTRACT III
CHAPTER 1 INTRODUCTION 1
1.1 Motivations 1
1.2 Review of Algorithm Methods 2
1.2.1 Tradition Mathematics Methods 2
1.2.2 Heuristics Methods 2
1.2.3 Intelligent Algorithms 3
1.2.4 Hybrid Algorithms 4
1.3 Review of the Distribution Models 5
1.4 Contributions and Outline of the Thesis 6
CHAPTER 2 FLOW CALCULATIONS OF DISTRIBUTION NETWORK RECONFIGURATIONS 7
2.1 Distribution Network: Model Descriptions 7
2.1.1 Distribution Network Directed 7
2.1.2 Simplified Distribution Network 8
2.1.3 Power Flow Calculations 9
2.2 Optimization Objectives 11
2.2.1 Minimum Power Loss 12
2.2.2 Load Balance 12
2.2.3 Reliability Improvements 13
2.2.4 Minimum Switching Operations 13
2.3 Ant Colony Optimization Algorithm 14
2.3.1 Ant Colony Optimization 14
2.3.2 Applications in Network Problems 16
2.3.3 Improved ACO 19
2.4 Determination of Parameters 22
2.4.1 Performance Analysis of the ACO 22
2.5 Summary 24
CHAPTER 3 DISTRIBUTION NETWORK RECONFIGURATION BY DIRECTIONAL SELECTION ANT COLONY OPTIMIZATION 25
3.1 Distribution network optimization reconstruction model with multi-target 25
3.1.1 Objective Function 25
3.1.2 Constraints 26
3.1.3 Changing load 27
3.2 ACO for Distribution Network Reconfigurations 28
3.2.1 Minimum Spanning Tree 28
3.2.2 ACO for Distribution System Reconfiguration 29
3.3 Summary 32
CHAPTER 4 SIMULATIONS RESULTS 33
4.1 IEEE33 System 33
4.1.1 Reconfiguration Results 34
4.1.2 Effects of Penalty Factors 36
4.1.3 Effects of Weighting Factors 37
4.1.4 Summary 39
4.2 Effects of the Changing Loads 40
4.2.1 Optimal Solution Evaluations 41
4.3 PG&E 69 nodes Simulation 44
4.3.1 Reconfiguration Results 45
4.3 Summary 48
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 49
5.1 Conclusions 49
5.2 Future Works 50
REFERENCE 51
APPENDIX 1 CORRESPONDENCE OF THE NUMBER OF THE BRANCHES AND THE NODES 53

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