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Real power loss reduction by monitor lizard optimization algorithm based on class room learning

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Abstract

In this paper monitor lizard optimization algorithm based on class room learning algorithm (MLCL) is designed to solve the optimization problem. Key objectives are power loss reduction, voltage stability enhancement and voltage deviation minimization. When power loss reduction has been done in the electrical transmission network then it will economically improves the whole system. Equally system stability is based on the voltage stability amplification and voltage deviation minimization. In monitor lizard optimization algorithm (MLA) movement of the prey and environmental issues are considered in the modeling. The monitor lizard does the Brownian movement and Victim (prey) performs the levy movement. Population has been divided for exploration and exploitation. Monitor lizard is accountable for exploration and Victim (prey) is in charge for exploitation. Natural issues will have impact over the action of preying and that has been included in the modeling of the algorithm. Action of species (AS) has been considered and included as a factor in the modeling of the algorithm. Then class room learning algorithm (CLA) is designed which based on how the learner gains the knowledge form tutor and co learner. The designed algorithm possesses two segments—Tutor segment and learner segment. Mutation and crossover of differential evolution is added in the hybridized MLCL algorithm. At first the tutor segment is intermingled in the procedure such that learning about the victim (prey) information will be enhanced. By considering L (voltage stability)—index MLCL verified in IEEE 30-bus system. Then without L-index MLCL optimization algorithm is appraised in 30 bus test systems. Monitor lizard optimization algorithm based on class room based learning algorithm condensed the power loss competently with augmentation in voltage stability and minimization of voltage deviation.

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Abbreviations

OBF:

Minimization of the objective function

L and M :

Control and dependent variables of the optimal reactive power problem

r :

Consist of control variables

\(\left( {Q_{c} } \right)\) :

Reactive power compensators

T :

Dynamic tap setting of transformers

\(\left( {V_{g} } \right)\) :

Level of the voltage in the generation units

u :

Consist of dependent variables

\(PG_{slack}\) :

Slack generator

\(V_{L}\) :

Voltage on transmission lines

\(Q_{G}\) :

Generation unit’s reactive power

\(S_{L}\) :

Apparent power

NTL:

Number of transmission line indicated by conductance of the transmission line between the \(ith\;{\text{and}}\;jth\) buses, \(\O_{ij}\). Phase angle between buses i and j

\(V_{Lk}\) :

Load voltage in \(kth\) load bus

\(V_{Lk}^{\rm desired}\) :

Voltage desired at the \(k{\rm th}\) load bus

\(Q_{GK}\) :

Reactive power generated at \(k{\rm th}\) load bus generators

\(Q_{KG}^{\rm Lim}\) :

Reactive power limitation

\(N_{LB} \;{\text{and}}\;Ng\) :

Number load and generating units

Tt:

Transformer tap

Gen volt:

Generator voltage

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  1. Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India

    Lenin Kanagasabai

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Kanagasabai, L. Real power loss reduction by monitor lizard optimization algorithm based on class room learning. Energy Syst 13, 335–354 (2022). https://doi.org/10.1007/s12667-021-00481-5

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