Factual power loss reduction by dynamic membrane evolutionary algorithm

Received Apr 4, 2020 Revised Dec 1, 2020 Accepted Feb 22, 2021 This paper presents Dynamic Membrane Evolutionary Algorithm (DMEA) has been applied to solve optimal reactive power problem. Proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. All elementary membranes are amalgamated into one membrane in the computing procedure. Furthermore, integrated membrane are alienated into the elementary membranes 1, 2,_ m. In particle swarm optimization (PSO) C1, C2 (acceleration constants) are vital parameters to augment the exploration ability of PSO in the period of the optimization procedure. In this work, Gaussian probability distribution is initiated to engender the accelerating coefficients of PSO. Proposed Dynamic Membrane Evolutionary Algorithm (DMEA) has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show the projected algorithm reduced the real power loss comprehensively.


INTRODUCTION
Reactive power problem plays a key role in secure and economic operations of power system. Optimal reactive power problem has been solved by variety of types of methods [1]- [6]. Nevertheless numerous scientific difficulties are found while solving problem due to an assortment of constraints. Evolutionary techniques [7]- [16] are applied to solve the reactive power problem, but the main problem is many algorithms get stuck in local optimal solution & failed to balance the Exploration & Exploitation during the search of global solution. In this paper, Dynamic Membrane Evolutionary Algorithm (DMEA) has been applied to solve optimal reactive power problem. Proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. In this work, composition of the dynamic membrane algorithm along with the fusion, division rules are utilized to solve the optimal reactive power problem. In skin membrane 0, elementary membranes 1, 2,_, m are embedded in the structure and it contains set of evolutionary, communication rules, multi-set of objects . All elementary membranes are amalgamated into one membrane in the computing procedure. Furthermore, integrated membrane are alienated into the elementary membranes 1, 2,_, m. In particle swarm optimization (PSO), 1 , 2 (acceleration constants) are vital parameters to augment the exploration ability of PSO in the period of the optimization procedure. Conversely, dissimilar optimization problems has altered values for the acceleration constants, it will not be an effortless assignment to choose the optimal values. In this work, Gaussian probability distribution is initiated to engender the accelerating coefficients of PSO. Particle swarm optimization (PSO) based on Gaussian distribution will be employed concurrently in area from 1 to m. The Proposed Dynamic Membrane Evolutionary Algorithm (DMEA) has been tested in standard IEEE 14,30,57,118,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.

PROBLEM FORMULATION
Objective of the problem is to reduce the true power loss as (1).

DYNAMIC MEMBRANE EVOLUTIONARY ALGORITHM
In membrane computing, P systems with dynamic membranes are a very blistering research topic and the analogous membrane algorithms have been used extensively to solve various types of optimization problems [17]. In this work, composition of the dynamic membrane algorithm along with the fusion, division rules are utilized to solve the optimal reactive power problem. In skin membrane 0, elementary membranes 1, 2,_, m are embedded in the structure and it contains set of evolutionary, communication rules, multi-set of objects. All elementary membranes are amalgamated into one membrane in the computing procedure. Furthermore, integrated membrane are alienated into the elementary membranes 1, 2,.., m. Proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. a. One level membrane structure has been specified b. In particle swarm optimization (PSO), 1 , 2 (acceleration constants) are vital parameters to augment the exploration ability of PSO in the period of the optimization procedure. Conversely, dissimilar optimization problems has altered values for the acceleration constants, it will not be an effortless assignment to choose the optimal values. In this work, Gaussian probability distribution is initiated to engender the accelerating coefficients of PSO. Particle swarm optimization (PSO) based on Gaussian distribution will be employed concurrently in area from 1 to m. , c. Execute the integration process, all elementary membranes are amalgamated into one elementary membrane one and all elementary membranes strings are go into the membrane . d. In m one membrane, adaptive differential evolution is utilized to modernize the strings object. In this work self-adaptive method is used to control the parameters CR and F.
− Engender the preliminary population − For each individual in the population ; Engender three arbitrary different integers r 1 , r 2 and r 3 ∈ {1,2, . . , N} and Engender an arbitrary integer J random ∈ {1,2, . , n} When , ′ infringe the boundary constraint, and then the violated variable value is brought back by, e. By using fitness function compute the fitness of each string f. Employ the contact rules, a replica of the most excellent strings are chosen in the membrane m one which will be sent to the skin membrane, and the present most excellent strings are accumulated in the skin membrane. g. Once the end condition is met, subsequently output the results; otherwise go to Step h. h. With the m elementary membranes m one Membrane is alienated into the identical structure. At present most excellent strings and − 1strings with the poor fitness will be send to every elementary membrane in roll by the send-in contact rules, and then go back to Step b. i. End condition is the utmost number of iterations. Algorithm will end if the utmost number of iterations is reached, and output the results.

SIMULATION RESULTS
At first in standard IEEE 14 bus system the validity of the proposed Dynamic Membrane Evolutionary Algorithm (DMEA) has been tested, Table 1 shows the constraints of control variables Table 2 shows the limits of reactive power generators and comparison results are presented in Table 3.  Then the proposed Dynamic Membrane Evolutionary Algorithm (DMEA) has been tested, in IEEE 30 Bus system. Table 4 shows the constraints of control variables, Table 5 shows the limits of reactive power generators and comparison results are presented in Table 6.
Then the proposed Dynamic Membrane Evolutionary Algorithm (DMEA) has been tested, in IEEE 57 Bus system. Table 7 shows the constraints of control variables, Table 8 shows the limits of reactive power generators and comparison results are presented in Table 9.
Then the proposed Dynamic Membrane Evolutionary Algorithm (DMEA) has been tested, in IEEE 118 Bus system. Table 10 shows the constraints of control variables and comparison results are presented in Table 11.
Then IEEE 300 bus system [19] is used as test system to validate the performance of the Dynamic Membrane Evolutionary Algorithm (DMEA). Table 12 shows the comparison of real power loss obtained after optimization.

CONCLUSION
In this work Dynamic Membrane Evolutionary Algorithm (DMEA) successfully solved the optimal reactive power problem. Proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. In this paper, composition of the dynamic membrane algorithm along with the fusion, division rules are utilized to solve the optimal reactive power problem. In this work, Gaussian probability distribution is initiated to engender the accelerating coefficients of PSO. Proposed Dynamic Membrane Evolutionary Algorithm (DMEA) has been tested in standard IEEE 14, 30, 57,118,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.