An upgraded binary bat algorithm approach for optimal allocation of PMUs in power system with complete observability

Ravindra, M.; Rao, R. Srinivasa

doi:10.21833/ijaas.2017.010.006

Cited by 6 publications

(7 citation statements)

References 22 publications

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“…In order to determine the optimal number of measuring devices, it will be necessary to minimize the objective function. In [20], phasor measuring units are used, which are devices that measure bus voltage and current phasors. In [20], the connection matrix is built ion the principle that a measuring device on the observed bus only covers its electrical neighborhood.…”

Section: Proposed Objective Functionmentioning

confidence: 99%

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Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm

et al. 2021

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Voltage dips represent a significant power quality problem. The main cause of voltage dips and short-term interruptions is an electrical short circuit that occurs in transmission or distribution networks. Faults in the power system are stochastic by nature and the main cause of voltage dips. As faults in the transmission system can affect more customers than faults in the distribution system, to reduce the number of dips, it is not enough to invest in a small part of the transmission or distribution system. Only targeted investment in the whole (or a large part of the) power system will reduce voltage dips. Therefore, monitoring parts of the power system is very important. The ideal solution would be to cover the entire system so that a power quality (PQ) monitor is installed on each bus, but this method is not economically justified. This paper presents an advanced method for determining the optimal location and the optimal number of voltage dip measuring devices. The proposed algorithm uses a monitor reach area matrix created by short-circuit simulations, and the coefficient of the exposed area. Single-phase and three-phase short circuits are simulated in DIgSILENT software on the IEEE 39 bus test system, using international standard IEC 60909. After determining the monitor reach area matrix of all potential monitor positions, the binary bat algorithm with a coefficient of the exposed area of the system bus is used to minimize the proposed objective function, i.e., to determine the optimal location and number of measuring devices. Performance of the binary bat algorithm is compared to the mixed-integer linear programming algorithm solved by using the GNU Linear Programming Kit (GLPK).

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Section: Proposed Objective Functionmentioning

confidence: 99%

“…Ravindra and Rao [20] proposed an upgraded binary bat algorithm for the optimal location of phasor measuring units. By using the bat algorithm, Yang and Le [21] successfully solved the problem of power quality by optimizing the design of a passive power filter, with the aim of suppressing critical harmonics and improving power factors.…”

Section: Introductionmentioning

confidence: 99%

Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm

et al. 2021

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“…Substitution of Eq. (19) in (17) gives constraint Eq. (20) by changing constraints ≥ 2.With FLVSI-constrained MBILP technique, the allocations 2, 4, 5, 6, 9, 10, 12 and 14 are obtained.…”

Section: Opp Considering Flvsi Redundancy Branch Weight and Zi Consmentioning

confidence: 99%

“…OPP considering line-outage constraints is shown in Eqs. (18)(19)(20). Performance of observability is checked considering CSBOI shown in Eqs.…”

Section: Optimal Pmu Deployment Utilizing Mbilp Formulationmentioning

confidence: 99%

“…The author in [18] proposed multi-objective channel-based methods for allocation of PMU base units and PMU measuring channels. The author in [19] considered branch weight constraints for OPP to minimize the installation cost of PMU in networks. In [20], voltage dissimilarity was measured to find weak buses and proposed PMU configurations to control weak voltage network to obtain real-time data.…”

Section: Introductionmentioning

confidence: 99%

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A multi-constrained binary ILP method for optimal allocation of PMUs in network

Lakshminarayana

Venkatesan

2020

SN Appl. Sci.

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This paper proposes multi-constrained binary integer linear programming (MBILP) method for optimal installation of phasor measurement units in IEEE networks considering Fast Load Voltage Stability Index (FLVSI), branch weight and redundancy constraints to minimize the cost of installation and to accomplish complete network observability. The weak buses of IEEE network are sorted based on their severity through the proposed strategy. A new FLVSI is programmed along with branch weight and redundancy constraints in MBILP to place PMUs in network. As installation cost of PMU for different buses varies with the number of branches or number of channels connected, PMUs should be allotted at bus with minimum cost. Redundancy of bus is computed to measure redundancy of bus network. Priority of PMU placement is considered at weak bus with proposed FLVSI in such a way that the cost is minimized and redundancy is adequate considering both branch weight and redundancy constraints. Ranking is proposed for PMU placement to find weak load bus in network along with the consideration of branch weight and redundancy. Zero injection (ZI) constraint modeling is recommended to minimize allocations further in system without losing observability. Contingency constraints for single-line or PMU loss are considered for allocation of PMUs. The proposed method is compared with ZI and without ZI modeling under general and line-outage or PMU loss cases to show efficacy of method. To estimate observability performance of complete network, a Complete Network Bus Observability Index is suggested. IEEE 14-, 24-, 30-and 57-bus networks are programmed with MATLAB software and compared with standard approaches to validate their efficacy.

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A Systematic Review on Bat Algorithm: Theoretical Foundation, Variants, and Applications

Agarwal

Kumar

2021

Arch Computat Methods Eng

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An upgraded binary bat algorithm approach for optimal allocation of PMUs in power system with complete observability

Cited by 6 publications

References 22 publications

Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm

Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm

A multi-constrained binary ILP method for optimal allocation of PMUs in network

A Systematic Review on Bat Algorithm: Theoretical Foundation, Variants, and Applications

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