network partitioning using harmony search and equivalencing for distributed computing

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    Optimal partitioningDistributed computingHarmony Search AlgorithmNetwork equivalencing

    2012 Elsevier Inc. All rights reserved.

    1. Introduction

    A power system is a complex network with a large numbercomponents and interconnections. The ever growing size andcomplexity of the system and the advances in computingtechnology have given an insight into parallel processing anddistributed computing for power system computations. Networkdecomposition is an essential for parallel processing and adistributed computing approach to any power system problem.The large interconnected network may be optimally divided intoclusters. In order to reduce the overall execution time thereshould be a balance between the size of the cluster and theinterconnection between the clusters. An objective function isformed such that it reflects these factors and is solved using theevolutionary algorithm.

    Over the past decades a number of algorithms have been pro-posed in literature for optimal network tearing [1]. The techniquesincludematrix decomposition, successive approximation, dynamicprogramming and heuristic clustering approaches. These methodstend to form clusters with fewer interconnections but fail to bal-ance with the size of the clusters. Some of the evolutionary op-timization techniques such as simulated annealing [14], geneticalgorithm [7], ant colony optimization [20] and tabu search [2]have also been applied to optimal partitioning of the network.These methods try to minimize an objective function that reflects

    Corresponding author.E-mail address: (G. Angeline Ezhilarasi).

    a balance between the two, so the objective function is formedsuch that it reflects the features of parallel and distributed process-ing. Network partitioning based on voltage variation at the loadbuses relative to other buses is proposed for voltage margin cal-culations in [32]. A decomposition algorithm is used for partition-ing the network for distributed reactive power optimization in apower system in [33]. Meta heuristic algorithms have been usedfor clustering web documents. However these methods are com-putation intensive and involve procedures based on natural selec-tion crossover andmutation. It also requires a large population sizeand occupies more memory.

    In this paper one of the recently evolving heuristic algorithmscalled the Harmony Search Algorithm (HSA) is used to solve thepartitioning problem of large scale power networks. The harmonysearch algorithm has been applied tomany optimization problemsin engineering and design. To mention a few it is used for solvingstructural optimization problems in [15]. An improved harmonysearch is applied to optimal economic power dispatch [4,17],dynamic economic dispatch with wind energy is solved in [22]and a hybrid swarm intelligence based harmony search is usedto solve economic dispatch in [21]. The HS algorithm has beenused for transmission network planning [30]. In [26], a multiobjective HS algorithm is used to solve the optimal power flowproblem and in [27] environmental economic dispatch is solvedusing the same. In software engineering the HS algorithm hasbeen used for the task assignment problem [34] and a novelglobal harmony search algorithm is described in [35]. Self adaptiveharmony search is proposed in [31] for expert system applications.A novel derivative of harmony search for discrete optimizationJ. Parallel Distrib. Comp

    Contents lists available a

    J. Parallel Dis

    journal homepage: www

    Network partitioning using harmony seadistributed computingG. Angeline Ezhilarasi , K.S. SwarupDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai - 60

    a r t i c l e i n f o

    Article history:Received 19 January 2011Received in revised form21 December 2011Accepted 20 April 2012Available online 8 May 2012

    Keywords:Network decomposition

    a b s t r a c t

    Power system has a highlyresources for centralized coninto clusters. The network pnumber of nodes in a clusteone of the recently developeIn this work, the HS algorithequivalencing is done to reprThe algorithm is found to bemethod gives accurate result0743-7315/$ see front matter 2012 Elsevier Inc. All rights reserved.doi:10.1016/j.jpdc.2012.04.006ut. 72 (2012) 936943

    t SciVerse ScienceDirect

    rib. Comput.

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    0036, India

    interconnected network that requires intense computational effort andtrol. Distributed computing needs the systems to be partitioned optimallyartitioning is an optimization problem whose objective is to minimize theand the tie lines between the clusters. Harmony Search(HS) Algorithm isd meta heuristic algorithms that can be applied to optimization problems.m is applied to the network partitioning problem and power flow basedesent the external system. Simulation is done on IEEE Standard Test Systems.ery effective in partitioning the system hierarchically and the equivalencings in comparison to the centralized control.

  • aG. Angeline Ezhilarasi, K.S. Swarup / J. Par

    problems has been proposed in [11]. In [9] a hybrid method hasbeen proposed combining the harmony search method with thesequential quadratic programming method and a global harmonysearch algorithm is proposed for unconstrained optimizationproblems as well. The exploratory power of the HS algorithm infinding the optimumsolution is given in [5] and [12] discuses aboutthe parameter setting free harmony search algorithm. Hence theliterature shows the applicability of the HS algorithm to a widerange of optimization problems.

    This paper is organized as follows. Section 2 deals with theproblem formulation of network partitioning. Section 3 outlinesthe Harmony Search Algorithm and Section 4 explains theimplementation of the HS algorithm to the network partitioningproblem. Section 5 presents the simulation results to assess theeffectiveness of the proposed method on its application to thenetwork partitioning problem.

    2. Network partitioning problem

    A power system is a highly interconnected network, geograph-ically distributed over a wide area. In order to enhance the com-putational performance with the available resources distributedcomputing is adopted, for which the network should be optimallypartitioned into clusters. The Partitioning problem has the mainobjectives namely (1) To minimize the number of nodes in a clus-ter; (2) To minimize the number of lines in a cluster; and (3) Tominimize the number of tie lines between the clusters. The firsttwo objectives represent the computational load on each clusterand the third represents the communication between the clusters.Hence network partitioning can be viewed as a combinatorial opti-mizationproblem. It can bemathematically represented as follows.Min C(N, L,M) = N + L+ M (1)where,N maximum number of nodes in a clusterL maximum number of lines in a clusterM maximum number of tie lines between the clusters, , weighting factors for each term.

    This optimization problem is subject to the constraint thatthe nodes in each cluster must form a connected graph. Thisconstraint checks the observability of the network at the instantof decomposition of the network.

    3. Harmony search algorithm

    The Harmony Search (HS) algorithm is a meta heuristicalgorithm developed recently based on the improvisation ofharmony in music composition. This method is based on theimprovisations done by the musician to obtain a better harmony.Musical improvisation is analogous to the optimization processseeking an optimal solution. Each musician corresponds to eachdecision variable; a musical instruments pitch range correspondsto decision variables value range; musical harmony at a certaintime corresponds to the solution vector at a certain iteration;and audiences aesthetics corresponds to objective function. Justlike musical harmony is improved time after time, the solutionvector is improved iteration by iteration. The harmony in themusicrepresents the solution vector of any optimization problem andthe improvisations made by the musician represents the local andglobal searches towards the optimum solution. There is no needfor initial values of decision variables in the HS algorithm. Unlikeother heuristic optimization algorithms that use a gradient search,HS uses a stochastic random search. The search is based on theharmonymemory considering rate and the pitch adjusting rate andso the derivative information of the previous iteration becomes

    unnecessary.The optimization procedure of the HS algorithm is as follows:llel Distrib. Comput. 72 (2012) 936943 937

    Fig. 1. Harmony search methodology for network partitioning.

    1. Initialize the optimization problem and the algorithm parame-ters.

    2. Initialize the Harmony Memory (HM).3. Improvise a new harmony from the Harmony Memory.4. Update the Harmony Memory.5. Repeat Steps 3 and 4 until the termination criterion is reached.

    4. Implementation of HS to network partitioning

    The HS algorithm is proposed to optimally partition the powernetworks into individual clusters, such that the clusters areconnected networks. The implementation of the HS algorithm tothe partitioning problem is explained by means of a flowchart inFig. 1.

    In this clustering problem, the variables in the harmonymemory are the nodes or the buses in the network. The initialsystem data such as nodes and lines and their connectivity is takenfrom the original interconnected network. A set of random vectorsare generated varying between 1 to n without any redundancyto create the initial harmony memory, n being the number ofnodes in the network. The random memory is partitioned intothe desired number of clusters. The connectivity of the derivedclusters is determined using the graph traversing methods. Thenumber of tie lines linkin


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