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Optimal siting and sizing of Distribution Flexible AC Transmission Systems (D-FACTS) devices in power distribution networks maximizes loadability, compensates reactive power, minimizes power loss and enhances voltage profile. The search for optimal size and locations of these devices in radial distribution networks is challenging and requires robust scheduling. This dissertation presents the application of improved bacterial foraging algorithm (IBFA) to the optimal siting and sizing of Distribution Static Compensator (D-STATCOM) in radial distribution networks for power loss minimization and voltage profile enhancement. Radial distribution network power flow model and algorithm was developed based on the Bus Injected to Branch Current matrix (BIBC) technique. The IBFA was modelled with three adaptive run-length units (linear, quadratic and exponential) and the cell-to-cell signalling mechanism was eliminated. A multi-objective function comprising of total active power loss and network bus voltage deviation was formulated for use in the IBFA. The effectiveness and applicability of the approach was demonstrated on standard IEEE 33-bus radial distribution network and the 50-bus Canteen Feeder in Zaria distribution network for steady-state constant load model. The results obtained are compared with those of the conventional BFA; and with Analytical and Bat Algorithm (BA) approaches reported in literature. For the standard IEEE 33-bus test network, the optimal location and size of D-STATCOM were determined respectively as bus 30 and 2577 kVar by the BFA method, while the IBFA approach obtained the optimal site and size of the D-STATCOM in the network to be bus 26 and 3351 kVar respectively. The BFA approach produced a 5.87 % reduction in overall network power losses and an 82.88 % improvement in voltage profile when compared with the base-case scenario. Similarly, the IBFA approach resulted in 5.83 % drop in total power losses of the network and 87.96 % improvement of the voltage profile. An average computational time of 7.2 seconds and 4.9 seconds were obtained for the BFA and IBFA approaches respectively. The results obtained using the IBFA approach showed a 28.5 % and 1.1 % reduction in active power loss and size of D-STATCOM respectively when compared with those of analytical approach. Also, when compared with the BA results, the IBFA approach showed a 50 % improvement in the overall network voltage profile. For the 50-bus Canteen Feeder, bus 41 and 227.8 kVar were found as the optimal site and size of D-STATCOM using BFA method, while for IBFA approach, the optimal site and size of the D-STATCOM in the network were determined as bus 22 and 138 kVar respectively. The overall network power loss was reduced by 22.8 % and the voltage profile improved by 6.10 % using the BFA approach while for the IBFA approach, a 26.53 % reduction in total power loss and a 6.21 % improvement in voltage profile were achieved as compared with the base-case results.




1.1 Background Information1
1.2 Problem Statement4
1.3 Aim and Objectives5
1.4 Research Methodology5
1.5 Research Limitations6
1.6 Organisation of the Dissertation 6
2.1 Introduction8
2.2 Review of Fundamental Concepts8
2.2.1 Flexible AC Transmission System (FACTS) Controllers8 Classification of FACTS Controllers9 Categories of FACTS Controllers9
2.2.2 Distribution Flexible AC Transmission Systems (D-FACTS)9
2.2.3 Overview of Distribution Static Compensator (D-STATCOM)10 Basic Operating Principle of D-STATCOM11 Modelling of the D-STATCOM14
2.2.4 Optimal Siting and Sizing Techniques15 Analytical Techniques15
viii Conventional Optimization Techniques15 Meta-heuristic Optimization Techniques16
2.2.5 Power Flow Analysis for Radial Distribution Networks16 Computation of Load Current17 BIBC Matrix Formation18 Forward Sweep19 Power Losses19
2.2.6 The Standard IEEE and Zaria Distribution Networks20 Standard IEEE 33-bus Distribution Network20 Zaria Distribution Network21
2.2.7 Concept of Bacterial Foraging Algorithm (BFA)22 Chemotaxis22 Swarming23 Reproduction24 Elimination and Dispersion24
2.2.8 Improved Bacterial Foraging Algorithm (IBFA)27 Linearly Adaptive Bacterial Foraging Algorithm28 Quadratic Adaptive Bacterial Foraging Algorithm29 Exponentially Adaptive Bacterial Foraging Algorithm29
2.3 Review of Similar Works30
2.3.1 Summary of Literature38
3.1 Introduction39
3.2 Materials39
3.2.1 Personal Computer39
3.2.2 MATLAB 2013a Software39
3.2.3 Distribution Network Parameters40
3.3.1Acquisition of Relevant Data40
3.3.2 Base-case Power Flow Analysis40
3.3.3 Multi-Objective Algorithm Formulation42 Problem Formulation42 Constraints43
Equality Constraints43
Inequality Constraints43
3.3.4Replication of the Improved Bacterial Foraging Algorithm44 Parameter Selection46
3.3.5 Implementation47
3.3.6 Comparison of Results49
4.1 Introduction50
4.2 The Standard IEEE 33-Bus Radial Distribution Network50
4.2.1 Case I: Network without D-STATCOM (Base Case)50
4.2.2 Case II: Network with D-STATCOM51 Network Voltage Profile with D-STATCOM using BFA and IBFA54 Performance Comparison for Voltage Profile Improvement55 Network Power Loss with D-STATCOM using BFA and IBFA56 Performance Comparison for Power Loss Reduction57
4.3 The 50-bus Canteen Feeder60
4.3.1 Case I: Network without D-STATCOM (Base Case)60
4.3.2 Case II: Network with D-STATCOM62 Network Voltage Profile with D-STATCOM using BFA and IBFA65 Network Power Loss with D-STATCOM using BFA and IBFA66
5.1 Summary69
5.2 Conclusion69
5.3 Significant Contribution71
5.4 Recommendations for Further Work71




1.1 Background Information
Technically, networks are categorized as either compact or large depending on their level of complexity. Large technical networks are known to be spatially extended with large number of structural components. Telecommunication networks, traffic networks, railroad networks and electric power networks are all classified as large technical systems (Tchokonte, 2009). Electric power networks are considered among the greatest and most sophisticated contemporary technological break-through comprising of generation, transmission and distribution networks (Murty, 2008). Electricity is generated at generating stations and transported via transmission networks to the final stage of power transfer (distribution networks) where power is directly delivered to consumers by electricity utilities (Murty, 2008). Figure 1.1 shows the major components that constitutes an electric power system.
Figure 1.1: Major Components of an Electric Power System (Kersting, 2012) Distribution networks are mostly radial in nature with very rare exceptions. A typical distribution network consists of distribution substation, feeders, switches, fuses, transformers, voltage regulators, meters and circuit breakers as shown in Figure 1.2.
Power Feeders
Interconnected Transmission System
Bulk Power Substation
Subtransmission Network
Distribution Substation
Transmission Network
Distribution Network
Subtransmission Line
Disconnect Switch
Voltage Regulation
Circuit Breaker
Primary Feeders
Secondary Feeders
Figure 1.2: Schematic Diagram of a Simple Distribution Network (Kersting, 2012) The deregulation of power networks to augment the imbalances between generation and consumption results in loss of the networks passive nature. This scenario subsequently results in technical issues that may affect the power quality of the entire network (Machowski et al., 2011). Poor voltage quality in distribution networks which can be due to variations in consumers’ demand forces electrical equipment to draw more than their rated current, resulting in excessive heating capable of inflicting severe damages (Watson andMiller, 2015). Practically, voltage quality remains the greatest threat to system stability considering the fact that electrical devices interact via the terminal voltage. Traditionally,load reduction, load shedding, series voltage regulators and shunt capacitors are among several techniques employed in arresting the issues of voltage quality in power networks.
The advent of power electronics based controllers, or Flexible AC Transmission System (FACTS) controllers such as Static VAR Compensator (SVC), Thyristor Controlled Reactor (TCR), Static Compensator (STATCOM) etc has led to improved ways of enhancing voltage profile and reducing losses in power networks (Singh et al., 2012). Considering the fact that most electric loads draw lagging current, there is an increase in the demand for reactive power in distribution networks. Distribution Flexible AC Transmission Systems (DFACTS) devices such as Dynamic Voltage Restorer (DVR), Distribution Static Compensator (D-STATCOM), Unified Power Quality Conditioner (UPQC) and Static Synchronous Series Compensator (SSSC) have been developed for operation in distribution networks. The excellent features of D-STATCOM that includes less harmonic distortion, low power losses, zero resonance, compact size, low cost and high regulatory capability makes it superior over other DFACTS devices (Gupta andKumar, 2015). Analytical and numerical techniques have been employed in the search for optimal position and size of FACTS devices in power networks. But their computational complexity and inability to fully incorporate the nonlinearity of the power networks prompted the search for better and more robust techniques (Ming et al., 2014).
The evolution of meta-heuristics algorithms has led to a new, faster and robust approach of solving complex power system optimization problems (Binitha andSathya, 2012). Recently, a novel meta-heuristic optimization algorithm, known as Bacterial Foraging Algorithm (BFA) was developed by Passino in 2002. Inspired by the foraging behaviour of Escherichia Coli (E. Coli) bacteria found in human intestine, the algorithm is developed to mimic how bacteria forage over a landscape of nutrients to handle parallel non-gradient optimization problems (Xing andGao, 2014). The algorithm is highly recognised for its broad coverage of search area (global and local search
simultaneously) and multi-focused nature. However, the BFA, like all other meta-heuristic algorithms is characterized by high computational time, complexity and the possibility of getting trapped in local minima as shortcomings (Passino, 2012). This research work applied an improved bacterial foraging algorithm (IBFA) to optimally site and size of D-STATCOM in radial distribution networks via a multi-objective function for power loss minimization and voltage profile enhancement.
1.2 Problem Statement
Due to the non-linear nature of most consumers’ loads, there is an increased demand for reactive power in the distribution network of power systems. As such, up to 13% of total power generated is wasted in the distribution level depending on the network stability (Yuvaraj et al., 2015). Highly sophisticated and efficient equipment (D-FACTS devices) have been developed specifically to mitigate these losses incurred at the distribution stage. Analytical (Eigen value analysis, Modal analysis, Index method, etc), conventional optimization (Liner Programming, Non-Linear Programming, Mixed Integer Non-Linear Programming, etc) and meta-heuristic optimization (Immune Algorithm, Firefly Algorithm, Bat Algorithm, etc) techniques have been deployed in the search for optimal placement and size of D-FACTS devices in distribution networks to resolve such problems. However, these techniques are characterised by multiple assumptions, inherent inaccuracy, complexity and high computational time as drawbacks (Ming et al., 2014). With the recent evolution of meta-heuristics algorithms, some of these shortcomings (multiple assumptions and inherent inaccuracy) have been addressed. Although the bacterial foraging algorithm (BFA) has shown promising results when applied in solving complex power system optimization problems, it is insufficient due to its complexity, high computational time and possibility of being
trapped in local minima. This research seeks to apply an improved BFA for optimal siting and sizing of D-STATCOM in radial distribution networks.
1.3 Aim and Objectives
The aim of this research is to apply an improved bacterial foraging algorithm (IBFA) to the optimal siting and sizing of D-STATCOM in radial distribution networks for power loss minimization and voltage profile enhancement. In order to achieve this aim, the following objectives are set forth:
1. To apply the Improved Bacterial Foraging Algorithm (IBFA) to the optimal siting and sizing of D-STATCOM on standard IEEE 33-bus radial distribution network in MATLAB 2013a environment;
2. To validate the IBFA approach by comparison with conventional BFA and with the works of Hussain and Subbaramiah, (2013) and Yuvaraj et al., (2015); and
3. To implement and compare the BFA and IBFA approaches for 50-bus Canteen Feeder in Zaria distribution network.
1.4 Research Methodology
The objectives outlined in this research were actualized in line with the following procedures:
1. Acquire the relevant network data (line data, bus data, network base voltage, etc.);
2. Perform network base-case power flow analysis based on the Bus-Injected-to-Branch-Current matrix (BIBC);
3. Formulate the IBFA adaptive run-length units with appropriate tuneable and scaling factors, and also eliminating the cell-to-cell signalling mechanism in MATLAB 2013a environment;
4. Formulate a multi-objective function for the standard IEEE 33-bus radial distribution network comprising of the total network active power loss and bus voltage deviation to be used in the IBFA;
5. Represent the obtained results (voltage profile improvement, power loss reduction, etc.) in graphical, bar-chart and tabular form; and
6. Repeat the fourth and fifth procedures on the 50-bus Canteen Feeder in Zaria distribution network.
1.5 Research Limitations
The limitations of this research includes:
1. The use of D-STATCOM only for compensation;
2. Unavailability of data for other Feeders in the Zaria distribution network; and
3. Unavailability of resources for on-line application of the research resulting in virtual simulation platform only
1.6 Organisation of the Dissertation
Chapter One presents the general introduction of the dissertation while Chapter Two presents the fundamental concepts that includes all relevant assumptions, theories and modelling equations needed for the actualization of this research and a comprehensive review of similar works carried out. Chapter Three presents an elaborate procedure for the actualization of this research which comprises of all the materials and the methodology adopted. The results obtained from the application of the theoretical and modelling concepts presented in the two previous chapters and their discussions are presented in Chapter Four. Chapter Five covers the conclusion,significant contribution
of the research and recommendation for further research. Finally, relevant references and Appendices are presented at the end of this research.


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