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Minimizing the Cost of Wide Area Monitoring Systems by Optimal Allocation of PMUs and Their Communication Infrastructure

  • Research Article-Electrical Engineering
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Abstract

Power system utilities need to replace the conventional measuring and monitoring devices to the phasor measurement units (PMUs)-based wide area monitoring system (WAMS) to be able to move from conventional to smart grids. The challenge of this action is the cost of WAMS. Many researchers have tried to deal with this challenge by optimal allocation of PMUs to find the minimum number that fulfils the full observability of the system. The WAMS is not only PMUs but also it comprises communication infrastructures (CI) such as communication links, switches and phasor data concentrators. This paper proposes an optimization algorithm to solve the problem based on two strategies. In the first strategy, the objective function is to find the minimum number of PMUs, i.e. conventional objective function. While in the second strategy, the objective function is the minimum cost of the WAMS, i.e. cost of PMUs and their CI. The objective function in the two strategies is solved under normal operating condition and under contingencies such as failure of PMU and outage of network lines. The proposed artificial electric field algorithm (AEFA) is applied using two strategies on IEEE standard networks: 14-bus, 30-bus, 57-bus and 118-bus. The results show that the number of PMUs using two strategies is not the same and this means that the second strategy should be chosen to solve this problem. The results are also compared with other optimization algorithms, and they depict that the AEFA has the efficiency and superiority to find minimum cost of WAMS and lesser distance path of establishing communication links with higher observability.

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Abbreviations

AEFA:

Artificial electric field algorithm

BILP:

Binary integer linear programming

CI:

Communication infrastructures

DE:

Differential evolution

GPS:

Global positioning system

ILP:

Integer linear programming

IPSO:

Improved particle swarm optimization

MCDM:

Multi-criteria decision-making

MINLP:

Mixed integer nonlinear programming

MNBO:

Maximum number of times the bus is observed

PDC:

Phasor data concentrator

PMUs:

Phasor measurement units

PSO:

Particle swarm optimization

OPP:

Optimal PMUs placement

SABO:

Sum of all buses observability

VNS:

Variable neighbourhood search

WAMS:

Wide area monitoring system

WLS:

Weighted least square

ZIB:

Zero-injection bus

A :

The binary bus connectivity matrix

AX :

An observability condition

\( a_{i}^{n} \left( t \right) \) :

Acceleration to the charge Qi in nth dimension

a :

A constant

b :

A unit vector which insures the full observability of the system

C L :

The cost of communication links/metre

C pmu :

The cost of PMU

C sw :

The cost of communication switch and router/unit

\( \varepsilon \) :

Constant of small positive value

\( E_{i}^{n} \left( t \right) \) :

Generated electric field by charge Qi

\( F_{i}^{n} \left( t \right) \) :

Electrostatic forces that acts on charge Qi

fiti :

The best fitness value of charge Qi at any time instant t

iter:

The current iteration

itermax :

The maximum number of iteration

K(t):

The Coulomb constant at time instant t

K 0 :

An initial value of K

L i :

The length of communication link i in metre

M :

The number of communication links

Mi(t):

The mass of charge Qi at time instant t

N :

Buses number

\( p_{i}^{n} \) :

He position of the individual best fitness value of a charge i at a time instant

Q i :

The charge i

R ij :

The distance between two charges

Sw:

The number of switches and routers

X :

The vector of the binary decision variable vector

\( V_{i}^{n} \left( t \right) \) :

The velocity of the charge Qi in nth dimension

x i :

Feasibility of PMUs on the ith bus

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Authors and Affiliations

  1. Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt

    Abdelazeem A. Abdelsalam

  2. Faculty of Engineering & Technology, Future University in Egypt, Cairo, Egypt

    Almoataz Y. Abdelaziz

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  1. Abdelazeem A. Abdelsalam
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  2. Almoataz Y. Abdelaziz
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Correspondence to Abdelazeem A. Abdelsalam.

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Abdelsalam, A.A., Abdelaziz, A.Y. Minimizing the Cost of Wide Area Monitoring Systems by Optimal Allocation of PMUs and Their Communication Infrastructure. Arab J Sci Eng 45, 6453–6466 (2020). https://doi.org/10.1007/s13369-020-04473-y

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