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Optimal placement of phasor measurement units to attain power system observability utilizing an upgraded binary harmony search algorithm

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Abstract

Phasor measurement units (PMUs), which provide time-synchronized measurements of current and voltage phasors, are considered as an advanced tool for monitoring, protection and management of modern power systems. In this paper, a novel method for optimal placement of PMUs for complete observability of power network is presented. However the installation cost of the PMUs in different places differ with each other, which is related to some factors like as the number of branches connectedto the placed bus, a big quantity of reported methods for optimal PMU placement problem considered an equal cost for PMU installation in different places. An upgraded binary harmony search algorithm is utilized in this paper as an optimization method to attain the minimum number of PMUs and their relevant locations considering the installation costs of the PMUs. The proposed method is applied to IEEE 14-bus, IEEE 30-bus, IEEE 39-bus and IEEE 118-bus standard test systems to obtain the optimal PMU placement. The simulation results confirm that the proposed method is efficient in optimal PMUs placement with minimum cost of configuration.

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Abbreviations

PMU:

Phasor measurement unit

OPP:

Optimal PMU placement

UBHS:

Upgraded binary harmony search

SCADA:

Supervisory control and data acquisition

RTU:

Remote terminal unit

WAMPAC:

Wide area monitoring, protection, and control

SMT:

Synchronized measurement technology

GPS:

Global positioning system

SLP:

Sequential linear programming

SQP:

Sequential quadratic programming

IMPs:

Interior point methods

ACO:

Ant colony optimization

ANN:

Artificial neural networks

BFA:

Bacterial foraging algorithms

COA:

Chaos optimization algorithms

EAs:

Evolutionary algorithms

DE:

Differential evolution

ILP:

Integer linear programming

NSDE:

Non-dominated sorting differential evolution

SA:

Simulated annealing

RTS:

Recursive Tabu search

TS:

Tabu search

IGA:

Immunity genetic algorithm

GA:

Genetic algorithm

OMPP:

Optimal multi-stage PMU placement

BPSO:

Binary particle swarm optimization

ILP:

Integer linear programming

HS:

Harmony search

HAS:

Harmony search algorithm

SCUC:

Security constrained unit commitment

HM:

Harmony memory

HMS:

Harmony memory size

HMCR:

Harmony memory considering rate

PAR:

Pitch adjustment rate

NH:

New harmony

NHS:

New harmony size

MaxIter:

Maximum number of improvisations

\(A\) :

Connectivity matrix

\(A(i,j)\) :

Binary connectivity parameter between buses \(\text{ i }\) and \(\text{ j }\)

N:

The number of buses in the system

\(X\) :

A matrix showing installation status of the PMUs in the power system

\(X(i)\) :

The elements of PMU installation matrix

\(X_i\) :

The set of possible range of values for each decision variable

\(x\) :

The set of each decision variable \(\text{ x }_i\)

\(\text{ LX }_i\) :

Lower bound for each decision variable

\(UX_i\) :

Upper bound for each decision variable

\(X_i^t\) :

Element in the \(t \)th row and \(j\)th column of the HM

\(bw\) :

Distance bandwidth

\(rand\) :

Randomly selected number

\(y_{ij}\) :

Element in the \(i\)th row and \(j\)th column of the NH

\(x_{pj}\) :

Element in the \(p\)th row and \(j\)th column of the HM

\(W\) :

Weight array

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    M. Nazari-Heris & B. Mohammadi-Ivatloo

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  1. M. Nazari-Heris
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  2. B. Mohammadi-Ivatloo
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Correspondence to B. Mohammadi-Ivatloo.

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Nazari-Heris, M., Mohammadi-Ivatloo, B. Optimal placement of phasor measurement units to attain power system observability utilizing an upgraded binary harmony search algorithm. Energy Syst 6, 201–220 (2015). https://doi.org/10.1007/s12667-014-0135-3

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  • DOI: https://doi.org/10.1007/s12667-014-0135-3

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