Multi-objective optimal power flow solutions using a constraint handling technique of evolutionary algorithms

Biswas, Partha Pratim; Suganthan, Ponnuthurai Nagaratnam; Mallipeddi, Rammohan; Amaratunga, G.A.J.

doi:10.1007/s00500-019-04077-1

Cited by 84 publications

(40 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the lower and upper bounds of the control variables, the initial solution p is randomly selected. After that, all the variables will be eventually updated according to Equation (12). On the basis of the fitness value of the objective function, the best and worst solutions are determined [21].…”

Section: Maximized (Or Minimized) Value Of Objective Function M(z)mentioning

confidence: 99%

“…, n). If z i, j, k represents the value of jth variable for the kth candidate in ith iteration; that value is updated according to Equation (12).…”

Section: Maximized (Or Minimized) Value Of Objective Function M(z)mentioning

confidence: 99%

“…In (12), z i,j,B and z i,j,W are the best candidate and worst candidate value of variable j, respectively. The updated value of z i,j,k is z i+1,j,k and throughout the ith iteration, α i,j,1 and α i,j,2 are two random numbers for the jth variable within [0, 1].…”

Section: Maximized (Or Minimized) Value Of Objective Function M(z)mentioning

confidence: 99%

“…iv. Modify the solutions using the best and the worst solutions Equation (12). v. If the modified solution is found to be better than the previous one, move to step vi, otherwise jump to step vii.…”

Section: Initialize the Population With Control Variables And Set Maximum Iteration Countmentioning

confidence: 99%

“…The OPF results and the optimal control variable settings of the J-PPS3 algorithm are compared with DA, GWO, Jaya, J-PPS1 and J-PPS2 in Table 5. Table 6 displays the comparison of numerical outcomes of DA, GWO, Jaya, J-PPS1, J-PPS2 and J-PPS3 algorithms and the reported results [10,12] for the IEEE 57-bus system. Figure 6 displays the convergence characteristics of DA, GWO, Jaya, J-PPS1, J-PPS2 and J-PPS3 algorithms.…”

Section: Case 3: Opf No Dg In Ieee 57-bus Test Systemmentioning

confidence: 99%

See 4 more Smart Citations

A Hybrid Jaya–Powell’s Pattern Search Algorithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation

et al. 2021

View full text Add to dashboard Cite

A new hybrid meta-heuristic approach Jaya–PPS, which is the combination of the Jaya algorithm and Powell’s Pattern Search method, is proposed in this paper to solve the optimal power flow (OPF) problem for minimization of fuel cost, emission and real power losses and total voltage deviation simultaneously. The recently developed Jaya algorithm has been applied for the exploration of search space, while the excellent local search capability of the PPS (Powell’s Pattern Search) method has been used for exploitation purposes. Integration of the local search procedure into the classical Jaya algorithm was carried out in three different ways, which resulted in three versions, namely, J-PPS1, J-PPS2 and J-PPS3. These three versions of the proposed hybrid Jaya–PPS approach were developed and implemented to solve the OPF problem in the standard IEEE 30-bus and IEEE 57-bus systems integrated with distributed generating units optimizing four objective functions simultaneously and IEEE 118-bus system for fuel cost minimization. The obtained results of the three versions are compared to the Dragonfly Algorithm, Grey Wolf Optimization Algorithm, Jaya Algorithm and already published results using other methods. A comparison of the results clearly demonstrates the superiority of the proposed J–PPS3 algorithm over different algorithms/versions and the reported methods.

show abstract

Section: Maximized (Or Minimized) Value Of Objective Function M(z)mentioning

confidence: 99%

“…, n). If z i, j, k represents the value of jth variable for the kth candidate in ith iteration; that value is updated according to Equation (12).…”

Section: Maximized (Or Minimized) Value Of Objective Function M(z)mentioning

confidence: 99%

Section: Maximized (Or Minimized) Value Of Objective Function M(z)mentioning

confidence: 99%

Section: Initialize the Population With Control Variables And Set Maximum Iteration Countmentioning

confidence: 99%

Section: Case 3: Opf No Dg In Ieee 57-bus Test Systemmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Jaya–Powell’s Pattern Search Algorithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation

et al. 2021

View full text Add to dashboard Cite

show abstract

Optimal control method for power system reactive power management considering real‐time uncertainties

Anandam

Shunmugalatha

Babulal³

2022

Optim Control Appl Methods

View full text Add to dashboard Cite

This article investigates multi-objective reactive power management while accounting for real-time generation and load uncertainties. The primary goal of this research is to investigate the effects of uncertainties on reactive power dispatch, while taking into account multiple objectives at the same time and keeping operational constraints in mind. The optimal reactive power dispatch (ORPD) problem is solved using an "indicator-based evolutionary technique" called "ISDE+," which considers two different goal functions: real power loss minimization and voltage deviation minimization. IEEE 30 and IEEE 57 bus test systems are taken into account in the simulation. ISDE+ generates a set of non-dominated optimal solutions, from which the best compromise solution is extracted using the "S" shaped fuzzy membership function. A hyper-volume indicator is used to evaluate the optimizer's performance. In addition, this work proposes a stochastic ORPD problem for tri-objective optimization with load demand and wind power uncertainty. The Weibull probability distribution function was used to model stochastic wind power using real-time wind speed data gathered from the National Institute of Wind Energy. Furthermore, to obtain a generalized wind speed set, a scenario-based reduction technique based on the Python programming language is developed. The outcomes of simulations in deterministic and stochastic instances were studied and compared using ISDE+, which outperforms other methods documented in the literature.

show abstract

Multi-objective Adaptive Guided Differential Evolution for Multi-objective Optimal Power Flow Incorporating Wind-Solar-Small Hydro-Tidal Energy Sources

Kahraman

Duman

2022

Studies in Computational Intelligence

View full text Add to dashboard Cite

Multi-objective optimal power flow solutions using a constraint handling technique of evolutionary algorithms

Cited by 84 publications

References 77 publications

A Hybrid Jaya–Powell’s Pattern Search Algorithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation

A Hybrid Jaya–Powell’s Pattern Search Algorithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation

Optimal control method for power system reactive power management considering real‐time uncertainties

Multi-objective Adaptive Guided Differential Evolution for Multi-objective Optimal Power Flow Incorporating Wind-Solar-Small Hydro-Tidal Energy Sources

Contact Info

Product

Resources

About