OPTIMAL ALLOCATION OF PMU CONSIDERING CONTROLLED ISLANDING OF POWER SYSTEM USING HYBRID OPTIMIZATION ALGORITHM 1 Deebiga Kandasamy, 2 Raqib Hussain A 1 PG scholar, Assistant Professor, 2 Department of EEE K.S.Rangasamy College of Technology Thirunchengode, India 1 deebigak@gmail.com, 2 raqibanwar@gmail.com Abstract This paper proposes an optimal allocation of phasor measurement unit (PMU) placement model under controlled islanding of power system such that the power network remains observable under controlled islanding condition in addition to normal operation condition. The optimization objective of proposed model is to reduce the number of installed PMUs by using hybrid optimization algorithm. The objective is pooled together with a weighting variable such that the optimal solution with less PMU number would be achieved from the model. To decrease the number of required PMUs, the significance of zero-injection bus is considered and incorporated into the model. IEEE standard systems used to test the presented model. Results are presented to exhibit the effectiveness of the proposed technique. Keywords Phasor Measurement Units (PMU), Zero Injection Constraints, Optimal placement, Integer Linear Programming (ILP), Optimization Techniques, Controlled Island I. INTRODUCTION The Power System needs to be operationally secure, i.e. with minimal probability of blackout and equipment damage. An important component of power system security is the system s ability to withstand the effects of contingencies. A contingency is basically an outage of a generator, transformer and or line, and its effects are monitored with specified security limits. A Synchrophasor is a phasor that is time stamped to an extremely precise and accurate time reference. Synchronized phasor (synchrophasors) give a real-time measurement of electrical quantities across the power system[1]. The resultant time tagged phasor can be transmitted to a local or remote receiver at rates up to 60 samples per second. Continuously measures voltages and current phasors and other key parameters and transmits time stamped messages [2]. The overwhelming set of power system data associated with a large number of PMUs would make it advantageous to examine the use of a minimum number of PMUs [3]. In the last decade, a great research was carried out on the optimization techniques applied to the PMU installation problem. The proposed methods are classified evolutionary algorithms and mathematical programming approaches, and meta-heuristics[4], such as canonical genetic algorithm [5], non-dominated sorting genetic algorithm [6], simulated annealing [7], exhaustive search[8], Tabu search [9], recursive Tabu search [10], particle swarm optimization [11], iterated local search [12],immunity genetic algorithm [13] and binary search algorithm [14].Despite some benefits, the major drawback allied with intelligent search-based methods is that they do not assurance that a globally optimal resultis found Generally, the existing OPP models concerns the normal operation scenario i.e, without any disturbance in integrated system, ensuring that the entire power system remains a single observable island [4]. Major faults in the power system may lead parts of the network unstable. Cascading failure of the power system leads to instability in phase angle, voltage and frequency. In that case, trying to maintain system integrity and operating the system entirely interconnected is very difficult and may cause propagation of local weaknesses to other parts of the system[15]. One approach to deal with this circumstances is to split the electric power network into smaller sub-networks, called islands. The basis for forming the islands is to minimize the imbalance between generation and load in each island. As a solution, controlled islanding (CI) is employed by system operators as a preventive measure, in which the interconnected power system is separated into several planned islands prior to catastrophic events [16],[17]. To operate each island with stability after islanding, it is necessary to give OPP scheme which can keep the network observable for the after islanding condition as well as normal condition. In this paper, an ILP model of OPP considering controlled islanding (OPP-CI) is proposed. This model is able to determine the minimal number and optimal location set of PMUs so that to provide the full network observability in normal operation as well as in controlled islanding scenario. the concept of topological observability is adopted and the following simple rules have been applied rule1: When a PMU is placed at a bus, the voltage and current phasor is known for that particular branch. If Voltage and Current phasor of one end of the branch is known, then the other side may be computed easily by using Ohm's Law [15]. This shows if a PMU placed at one bus, then the buses incident to PMU installed bus also become observable. rule 2: When there is no current injection at a bus, the power flow in any one of the incident lines can theoretically be calculated by using Kirchhoff s current law (KCL), when the power flow in the remaining of the connected lines are known. The rest of the paper is organized as follows. Section II gives detailed explanation of optimal placement of PMU and formulation of Zero-Injection Constraints in Section III. Simulation result of PMU placement in Section IV, and the paper concludes with probable future developments in Section V. 10 ISSN 2348-7852 (Print) ISSN 2348-7860 (Online)
II. OPTIMAL PLACEMENT OF PMU To make an interconnected system observable, it is not required to place PMUs in all the buses. Observability may be categorized as Numerical Observability and Topological Observability [14. This approach is used for minimizing optimal PMU locations Optimal PMU placement problem i.e., minimum PMU placement for system observability, can be formulated as a combinatorial optimization problem using an Integer Linear Programming (ILP) method. The PMU placement method has been presented in this paper serves the following objective: It minimizes the total number of PMUs required to be installed in the system considered to make it completely observable considering the Zero Injection Constraints. This objective can be formulated as for an n bus system, if the PMU placement vector having elements defines chance of PMUs at a bus, i.e., and if w i is cost related to placement of PMU at bus, then objective function for the minimum PMU placement problem can be defined as follows. Subject to the following constraints: Where e is a unit vector of length, i.e., e= [1 1 1 1...1] T x= [x 1 x 2...x n ] and A is the Network Connectivity matrix of the system, i.e. Consider the IEEE-30 bus system shown in Fig.1 Let the binary value (0 or 1) is set to variable which is connected to bus i. Variable is assigned to one (1) if a PMU is placed at bus i, zero (0) otherwise. The problem formulation of optimal placement of PMUs for IEEE-30 bus system can defined as Subject to bus observability constraints defined as follows: Bus 1 (2) Bus 2 (3) Bus 3 (4) Bus 4 (5) Bus 5 (6) Bus 6 (7) Bus 13 (14) Bus 14 (15) Bus 15 (16) Bus 16 (17) Bus 17 (18) Bus 18 (19) Bus 19 (20) Bus 20 (21) Bus 21 (22) Bus 22 (23) Bus 23 (24) Bus 24 (25) Bus 25 (26) Bus 26 (27) Bus 27 (28) Bus 28 (29) Bus 29 (30) Bus 30 (31 ) The objective function in (1) represents the minimum number of PMUs required for optimal system observability, General observability constraints [(2) (31)] gives solution of the problem for optimal system observability. III. ZERO-INJECTION CONSTRAINTS Bus does not have any load or generation nor any measurement devices. Such buses are intermediate points in electrical power systems between generators and load is known as zero-injection bus. Current injection at zeroinjection bus into rest of the system is known as an exactly zero. By the effect of Zero Injection Constraint, it is possible to allow some buses to be unobservable; however theses buses will be finally observable with the help of Kirchoff's Law and Nodal Equations [18]. The total number of PMUs can further be reduced when zero injection buses are also integrated in the PMU placement problem by the following conventions: 1) In a set of buses which includes zero Injection bus may have at most one Unobservable bus, then the unobserved bus will be considered as Observable by using Kirchoff's Current Law. 2) For a zero injection bus i, number of unobservable buses in each group can be defined by a zero injection bus and its neighboring buses is at most one. For the sake of illustration, consider the IEEE 30 bus system which has bus- 6, 9, 22, 25, 27 and 28 as a zero injection bus. Hence, the remodeled ILP problem formulation integrating zeroinjection constraints can be defined as Bus 7 (8) Bus 8 (9) Bus 9 (10) Bus 10 (11) Bus 11 (12) Bus 12 (13) 11 ISSN 2348-7852 (Print) ISSN 2348-7860 (Online)
Fig.1 Single line diagram for IEEE-30 bus system (32) Subject to bus observability constraints defined as follows: Bus 1 (33) Bus 2 (34) Bus 3 (35) Bus 4 (36) Bus 5 (37) Bus 6 (38) Bus 7 (39) Bus 8 (40) Bus 9 (41) Bus 10 (42) Bus 11 (43) Bus 12 (44) Bus 13 (45) Bus 14 (46) Bus 15 (47) Bus 16 (48) Bus 17 (49) Bus 18 (50) Bus 19 (51) Bus 20 (52) Bus 21 (53) Bus 22 (54) Bus 23 (55) Bus 24 (56) Bus 25 (57) Bus 26 (58) Bus 27 (59) Bus 28 (60) Bus 29 (61) Bus 30 (62) Zero-injection constraint: Bus 6 (63) Bus 9 (64) Bus 22 (65) Bus 25 (66) Bus 27 (67) Bus 28 (68) Solving the ILP problem (36), (37) (51) we identify buses 3,5,13,17,20,23 for PMU placement. Therefore, it is shown that when considering zero-injection constraints, the number of PMUs to be installed is reduced by three. The optimal system observability is attained with six PMUs, while the optimal system observability is attained with Nine PMUs without considering Zero Injection Constraints. IV. CONTROLLED ISLANDING The most important threats for power system is cascading failure. Cascading failure of the power system result in uncontrolled splitting of network and causes power imbalance results in system blackout. Intentional islanding or controlled islanding is to reduce the real power imbalance within the islands to help in restoration of power system. The main advantages of controlled islanding of power systems can be listed as follows: It can separate weak and healthy part of power system Controlled region of power system is maintained results in better security compared with entire network of power system. Various Techniques has been proposed on this topic for controlled islanding. Based on constraints, the networks are splitted. Controlled island models of the Standard IEEE Systems has been utilized in our research work for Optimal Placement of PMU. Compared to (33-68), the observability constraints of OPP- CI model are modified as follows: CI where A, Network connectivity matrix for post-islanding i j network, which is defined as For example, assuming that the controlled islanding is in effect for the IEEE 30-bus system following a cascading fault, as shown in Fig. 1, the whole system is divided into two subsystems and several lines are opened during the slanding process. According to (33-62), the observability constraints for OPP-CI can be written as follows: Bus 1 (2) Bus 2 (3) Bus 3 (4) Bus 4 (5) Bus 5 (6) Bus 6 (7) Bus 7 (8) Bus 8 (9) Bus 9 (10) Bus 10 (11) Bus 11 (12) Bus 12 (13) Bus 13 (14) 12 ISSN 2348-7852 (Print) ISSN 2348-7860 (Online)
Bus 14 (15) Bus 15 (16) Bus 16 (17) Bus 17 (18) Bus 18 (19) Bus 19 (20) Bus 20 (21) Bus 21 (22) Bus 22 (23) Bus 23 (24) Bus 24 (25) Bus 25 (26) Bus 26 (27) Bus 27 (28) Bus 28 (29) Bus 29 (30) Bus 30 (31 ) V. SIMULATION Considering standard IEEE 30 bus system, the objective function of optimal placement can be formulated as equation (32). Observability constraints can be formulated as equations [(33)-(62)]. Zero-injection constraints are modeled for the considered bus system as in equations [(63)- (68)]. Simulation of PMU placements have been carried out for the IEEE 14 bus system and IEEE 30 bus system using MATLAB Solver. Linear Programming (LP), Mixed Integer Linear Programming (MILP) and Integer Linear Programming (ILP) are used to solve the problem to deduce the most effective solution Fig. 2 Single Line Diagram for IEEE-30 Bus System with PMU Placement using ILP TABLE I. Comparative Study of Mode of Operation IEEE Bus System IEEE 14 bus IEEE 30 bus No. No. of of Observabilit PMU PMU y % s s Mode of Operatio n Normal 3 71 6 80 Observabilit y % mode Controlle 4 83 11 85.71 d Island Table I brings out the simulation result of IEEE-14 and 30 bus system for optimal placement of PMU including zero injections using various mathematical optimization algorithms. System observability is maintained with the minimum number of PMUs by considering modeling of zero-injection constraints. Optimal placement of PMU has been attained with the minimum number of PMUs and nearest observability of buses is shown in Fig.2 for IEEE-30 bus system.. VI. CONCLUSION An effective OPP method should guarantee complete observability of a power network under various operation conditions. continuous failure of the power system results in blackout. To avoid this, power system could be operated in controlled islanding mode. In this paper, an hybrid optimization model considering controlled islanding of power system is proposed. The proposed model guarantees complete observability of power network for normal condition as well as controlled islanding condition. REFERENCES [1] G. Phadke, Synchronized phasor measurements in power systems, IEEE Comput. Appl. Power, vol. 6, no. 2, pp. 10 15, Apr. 1993. [2] A. Enshaee, R. A. Hooshmand, and F. H. Fesharaki, A newmethod for optimal placement of phasor measurement units to maintain full network observability under various contingencies, Elect. Power Syst. Res., vol. 89, no. 1, pp. 1 10, Aug. 2012. [3] Nikolaos M. Manousakis, George N. Korres, Pavlos S. Georgilakis," Taxonomy of PMU Placement Methodologies IEEE Trans. Power Syst., vol. 27, NO. 2, pp. 1070 1076, May 2012. [4] F. J. Marın, F. Garcıa-Lagos, G. Joya, and F. Sandoval, Genetic algorithms for optimal placement of phasor measurement units in electric networks, Electron. Lett., vol. 39, no. 19, pp. 1403 1405,Sep. 2003. [5] Milosevic and M. Begovic, Nondominated sorting genetic algorithm for optimal phasor measurement placement, IEEE Trans. Power Syst., vol. 18, no. 1, pp. 69 75, Feb. 2003. [6] T. L. Baldwin, L. Mili, M. B. Boisen, and R. Adapa, Power system observability with minimal phasor measurement placement, IEEE Trans. Power Syst., vol. 8, no. 2, pp. 707 715, May 1993. [7] S. Azizi, A. S. Dobakhshari, S. A. N. Sarmadi, and A. M. Ranjbar, Optimal PMU placement by an equivalent linear formulation for exhaustive search, IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 174 182,Mar. 2012. [8] J. Peng, Y. Sun, and H. F. Wang, Optimal PMU placement for full network observability using Tabu search algorithm, Elect. Power Syst. Res., vol. 28, no. 4, pp. 223 231, May 2006. [9] Nikolaos C. Koutsoukis, Nikolaos M. Manousakis, Pavlos S. Georgilakis, George N. Korres "Numerical observability method for optimal phasor measurement units placement using recursive Tabu search method" 13 ISSN 2348-7852 (Print) ISSN 2348-7860 (Online)
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