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Computer Engineering Project Topics

Development of an Optimal Reconfiguration Algorithm for Radial Distribution Electrical Power Networks (a Case Study of Zaria Distribution Network)

Development of an Optimal Reconfiguration Algorithm for Radial Distribution Electrical Power Networks (a Case Study of Zaria Distribution Network)

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Development of an Optimal Reconfiguration Algorithm for Radial Distribution Electrical Power Networks (a Case Study of Zaria Distribution Network)

Chapter One

ย Aimย and Objectives

The aim of this research is to develop an optimal reconfiguration algorithm for radial distributionย networks,ย usingย non-dominatedย sortingย geneticย algorithmย (NSGA-II)ย withย aย viewย toย improvingย theย performances of a radial distribution system through minimizing its active power loss and reduceย totalย voltageย deviation.

Theย objectivesย ofย thisย researchย areย itemizedย asย follows:

  1. Development of an optimal reconfiguration algorithm using fast and elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal location of tie and sectionalizing
  2. Determination of the location and size distributed generation for the distribution
  3. Investigation of the effect of distributed generation placement on the power quality for the distribution
  4. Comparison of the results obtained from the normal and reconfigured network in order to determine the extent of reductions in both active power loss and total voltage deviation
  5. Comparison of the efficiency of the proposed model with existing model as a means of validations.

CHAPTERย TWO

ย LITERATUREย REVIEW

ย ย Introduction

This chapter is divided into two parts. The first part discussed the fundamental concepts relevantย toย theย studyย andย theย second part providesย aย reviewย of relatedย works.

Conventionalย Approach

This section presents an overview of some of the conventional approaches and fundamentalย conceptย critical toย theย concept ofย networkย reconfiguration.

Determinationย ofย Radialย Configuration

The number of possible radial structure for a distribution network can be obtained for a givenย configuration. The number of branches needed to maintain a radial network with redundantย connectionsย is fixed and can beย obtained usingย (Chicco,ย 2013):

Aย completeย distributionย networkย (graph),ย withย 14ย nodes,ย theย numberย ofย possibleย radialย configurationsย areย 1.07ร—ย 1016.ย Aย practicalย distributionย systemย hasย aย numberย ofย branchesย thatย isย farย less than the complete graph, as such the number of possible configuration can be obtained usingย Kirchhoffย matrixย treeย theorem.

Kirchhoffย Matrixย Treeย Theoremย 

Kirchhoffย matrixย theoremย canย beย appliedย toย determineย theย numberย ofย radialย configurationย extractedย fromย aย weaklyย structureย ofย anย electricalย distributionย systemย (Chicco,ย 2013).ย Theย numberย ofย possibleย radial configuration can be obtained by using the determinant of the Laplacian matrix. Theย Laplacianย matrixย is aย squareย matrixย andย can beย obtainedย usingย (Chicco, 2013):

รฌย noย ofย branchesย connectedย toย node.ย forย iย =ย j

Iij

=ย รฏ-ย 1 ifย (iย ยนย j)ย andย nodeย iย adjacentย toย nodeย j

รฏ otherwise

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Theย numberย ofย possibleย radial configurationย can beย obtained using:

fย =ย det(ย Iย ijย )

For real network, the exploration can be quite complex and time consuming or even impossibleย reasonableย computationalย times.ย Asย such,ย theย needย toย discussย otherย possibleย approachesย toย distributionย network reconfiguration.

Heuristicย Approachย toย Distributionย Networkย Reconfiguration

ย Thisย sectionย presentsย anย overviewย ofย someย ofย theย heuristicย approachesย usedย inย networkย reconfiguration.

Branch-and-Boundย Technique

Branch-and-Bound technique is a general algorithm for finding optimal solutions of various optimization problems, especially in discrete optimization. It consists of a systematic enumeration of all candidate solutions, while discarding infeasible solution based on the upper and lower bound of the optimized quantities. The concept of branch-and-bound technique is to search into the graph of the distribution network, with a view to obtaining the minimal loss configuration, while satisfying radial constraint by using:

PTย =ย Min.รฅย Pjย xย j

jย =1

Where:

รฌ1

xย jย =ย รญ

รฎ

Statusย ofย theย branch

Pjย : Branch power loss

The basic idea of the heuristic branch exchange is to compute the change of power losses by operating a pair of switches with the aim of achieving reduction in system active power loss (Zhu, 2009). The main advantage of the Branch-and-bound is its simplicity and ease of comprehension,ย whileย its disadvantages are:

  1. Final system configuration depends on the initial
  2. It is time consuming for selecting and operating each pair of switches 2nย isย theย possibleย switchingย combinations.
  3. The solution is a local optimal, rather than a global optimal

Optimalย Flowย Pattern

If the impedance of all branches in the loop network is replaced by their branch resistances, theย load flow distribution that satisfies the Kirchhoff current law and Kirchhoff voltage law is calledย an optimal flow pattern. When the load flow distribution in a loop is an optimal flow, theย corresponding network power losses will be minimal. The steps of the heuristic algorithm basedย onย optimal flow pattern areย itemized as followsย (Zhu, 2009):

  1. Compute load flow of the initial network.
  2. Close all normal open switches to produce loop
  3. Compute the equivalent currents at all
  4. Replace all branch impedances by their corresponding branch resistance and run a power
  5. Open a switch of the branch that has minimal current value in theย ย Recompute the load flow for the remaining network.

Ruled-Basedย Comprehensiveย Approach

Thisย methodย combinesย bothย theย branchย andย exchangeย methodsย andย aย setย ofย rules.ย Theย rulesย usedย toย selectย theย optimalย reconfigurationย ofย theย distributionย networkย areย formedย basedย onย systemย operation experiences (Zhu, 2009).ย The rule based method can be expressed by the use of theย followingย (Zhu, 2009):

 

CHAPTERย THREE

ย MATERIALSย ANDย METHODS

ย Introduction

This chapter present a details mathematical model equation for computing parameters of theย distribution network, load model, model of the distributed generation, problem formulation and aย detailย of theย developed non-dominatedย sortingย geneticย algorithm (NSGAย II).

Modelย Equationย ofย theย Twoย Busย Distributionย Network

Considered a n busย distribution network, reduced to a two bus distribution network as contained in Figure 3.1.

Modellingย ofย Distributedย Generation

The inclusion of a distributed generation to the radial system results in a network that may not beย strictly radial (weakly meshed network) and their feeders may carry power whose directionย changesย asย aย functionย ofย theย loadingย andย distributedย generationย levels.ย Inย distributionย systemย powerย flow analysis, distributed generation are model as either constant active and reactive power (PQ)ย orย constant active powerย and voltageย (PV)ย model.

Constantย Activeย andย Reactiveย Powerย Model

The constant active and reactive power models are identified with a constant power load modelย except that the current is injected into the bus. (Eminoglu and Hocaoglu, 2008). In this model theย distributed generation is modelled as a negative load which alters the direction of flow of currentย in the radial system (acting as generator). In this model the total load at the bus where theย distributed generation is situated is increased, but its challenge is its ability to handle or controlย their reactive power output in order to maintain a specific terminal voltage. The constant activeย andย reactiveย power modelsย areย expressed using:

CHAPTERย FOUR

RESULTSย ANDย DISCUSSIONS

ย Introduction

This section discusses the results obtained from the standard IEEE network before and afterย reconfiguration. The results obtained after application of reconfiguration model on the Zariaย distributionย network are also analyzedย and discussed.

Comparativeย Studyย Beforeย andย Afterย theย Applicationย ofย Reconfiguration

The developed reconfiguration model was validated using a standard 33 Bus distribution network with line and load data as contained the works of Baran and Wu (1989) and Srinivas etย al.,ย (2009).ย Theย 33ย IEEEย busย testย feederย comprisesย ofย 33ย nodes,ย withย 5ย tieย switches,ย 32ย sectionalizingย switchesย asย shown in Figureย 4.1.

CHAPTERย FIVE

CONCLUSIONย ANDย RECOMMENDATIONS

ย Introduction

Thisย sectionย presentย theย limitation,ย conclusionย andย recommendationย forย furtherย work.

Conclusion

The research has developed an approach to distribution network reconfiguration using enhance non-dominated sorting genetic algorithm (NSGA-II) multi-objective based, considering active power loss and total voltage deviation as the main objective function. The developed algorithm was tested using sample of data extracted from Zaria distribution network. The result revealed several feasible switching state for the various distribution system as compared to the normal network. Optimal locations of tie switches (open branches) were found to be at branches 12, 14 and locations 14, 16, 18 and 25, 20, 10 and 23, 16, 43 for Gaskiya, Railway, Sabo and Canteen distribution network respectively. An improvement in active power loss reduction of 37.14%, 18.22%, 39.21% and 23.42% as compared to the active power loss of the normal network (55.32kW, 17.22kW, 120.08kW and 508.3kW) for Gaskiya, Railway, Sabo and Canteen distribution network respectively. While a reduction in total voltage deviation of 9.43%, 9.02%, 37.81% and 10.72% as compared to the total voltage deviation of the normal network (0.2672V, 0.2340V, 0.9949V and 4.7482V) for Gaskiya, Railway, Sabo and Canteen distribution network respectively. Based on the result obtained, it can be concluded that the total active power and total voltage deviation has been estimated for different switching state, using non-dominated sorting genetic algorithm (NSGA II). Also a noticeable reduction in active power loss and improvement voltageย profileย wereย recordedย forย allย theย sampleย ofย distributionย network,ย withย theย introductionย ofย distributedย generation.

Limitations

Duringย theย courseย ofย this work, certainย limitationย wereย observedย whichย areย itemizedย as follows:

  1. The scope of this work was limited to a balanced network, hence the effect of unbalanced nature of the distribution network were not captured.
  2. The enhance dominated sorting genetic algorithm explore the search space and performs best at a high generation, as such increase the computation burden consequently affecting the convergence
  3. The dynamic nature of load for a typical distribution network was not considered.

Recommendationย forย Furtherย Work

Futureย worksย shouldย considerย theย followingย areas:

  1. This algorithm can be developed and extended to an unbalanced distribution network, so as to capture the exact nature of a distribution system.
  2. The use of hybrid algorithm can be adopted to enhance the convergence time, while simultaneously exploring the search space.

Reference

  • Abdelaziz. A. Y, Mekhamer, S. F, Mohammed. F. M and Badr M. A. L, (2012)โ€ A Modifiedย Particleย Swarmย Techniqueย forย Distributionย Systemย Reconfigurationโ€,ย Onlineย Journalย onย Electronicย andย Electrical Engineering.ย 1(2)/121-128.
  • Baran. M. E and Wu. F, (1989),โ€ Network Reconfiguration in Distribution Systems for Lossย Reductionย and Loadย Balancingโ€,ย IEEEย Transactionย onย Powerย Delivery.ย 4(2)/1402-1403.
  • Bhujel. D, Adhikary. B and Mishra. A. K (2012),โ€A Load Flow Algorithm for a Radial Distributionย Systemย with Distributed Generationโ€,ย IEEEย ICSETย ,ย 7(12)/375.
  • Bhutad.ย Aย .G,ย Kulkarni.ย S.Vย andย Khaparde.ย S.ย Aย (2003),ย โ€œย Three-phaseย Loadย Flowย Methodย forย aย Radialย Distributionย Networksโ€, IEEEย transaction onย powerย system. 781
  • Carcamo-Gallardo.ย A,ย Garcia-Santander.ย Lย andย Pezoa.ย Jย .ย E,ย (2009),โ€Greedyย Reconfigurationย Algorithm for Medium-Voltage Distribution Networksโ€, IEEE Transactions on Power Delivery,ย 24(1)/328. DOIย 10.1109/TPWRD 2008.923997
  • Charlangsut. A, Rugthaicharuencheep. N, Auchariyamel. S, (2012),โ€ Heuristic Optimizationย Techniquesย for Network Reconfiguration inย Distribution Systemโ€,ย 64(1)/1-4.
  • Cheraghi. M and Ramezanpour. P, (2012), โ€œEfficient Fast method for Determining Minimumย Lossย Configurationย inย Radialย Distributionย Networkย Basedย onย Sensitivityย Analysisโ€,ย IEEEย internationalย powerย engineeringย and optimizationย conference Malaysia,ย 46-50.
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