Mean-Variance Mapping Optimization (MVMO) belongs to the family of evolutionary algorithms, and has proven to be competitive in solving computationally expensive problems proposed in the Icompetitions CEC2014, CEC2015, and CEC2016. MVMO can tackle such problems by evolving a set of solutions (population based approach) or a single solution (single parent-offspring approach). The evolutionary mechanism of MVMO performs within a normalized search space in the range [0, 1]. The power of MVMO stems from its ability - based on statistical analysis of the evolving solution based on a mapping function - to adaptively shift the search priority from exploration to exploitation. This paper introduces a newly defined mapping function as well as a new rule for using an embedded local search strategy, and presents several tests conducted by using the test bed of the CEC2018 competition. Numerical results indicate significant improvements on the results obtained in CEC2016 competition.

The MVMO algorithm (Mean-Variance Mapping Optimization) has two main features: i) normalized search range for each dimension (associated to each optimization variable); ii) use of a mapping function to generate a new value of a selected optimization variable based on the mean and variance derived from the best solutions achieved so far. The current version of MVMO offers several alternatives. The single parent-offspring version is designed for use in case the evaluation budget is small and the optimization task is not too challenging. The population based MVMO requires more function evaluations, but the results are usually better. Both variants of MVMO can be improved considerably if additionally separate local search algorithms are incorporated. In this case, MVMO is basically responsible for the initial global search. This paper presents the results of a study on the use of the hybrid version of MVMO, called MVMO-PH (population based, hybrid), to solve the IEEE-CEC 2018 test suite for single objective optimization with continuous (real-number) decision variables. Additionally, two new mapping functions representing the unique feature of MVMO are presented.

Mean-Variance Mapping Optimization (MVMO) is a recent addition to the heuristic optimization field. The main traits of its evolutionary mechanism reside in the adoption of a single parent-offspring pair approach along with a normalized range of the search space for all optimization variables as well as in the use of a special mapping function, which accounts for the actual mean and variance of the normalized optimization variables for mutation operation. MVMO is also open to further extensions and hybridization with other approaches. Along this spirit, this paper surveys the performance of MVMO when executed in its pure algorithmic procedure and when hybridized to include a local search strategy. Numerical experiments are conducted on the IEEE-CEC 2015 optimization test bed on bound constrained single-objective computationally expensive numerical optimization. Remarkably, MVMO proves effective at solving different complex problems within a reduced number of allowed function evaluations.

Mean-variance mapping optimization (MVMO) is an emerging evolutionary algorithm, which adopts a single-solution based approach and performs evolutionary operations within a normalized range of the search for all optimization variables. MVMO uses a special mapping function for mutation operation, which allows a controlled shift from exploration priority at early stages of the search process to exploitation at later stages. Recently, the MVMO has been extended to a population-based and hybrid variant denoted as MVMO-SH, which includes strategies for local search and multi-parent crossover. This paper provides an study on the performance of MVMO-SH on the IEEE-CEC 2015 competition test suite on learning-based real-parameter single objective optimization. Experimental results evidence the effectiveness of MVMO-SH for successfully solving different optimization problems with different mathematical properties and dimensionality.

Mean-Variance Mapping Optimization (MVMO) constitutes an emerging heuristic optimization algorithm, whose evolutionary mechanism adopts a single parent-offspring pair approach along with a normalized range of the search space for all optimization variables. Besides, MVMO is characterized by an archive of n-best solutions from which the unique mapping function defined by the mean and variance of the optimization variables is derived. The algorithm proceeds by projecting randomly selected variables onto the corresponding mapping function that guides the solution towards the best set achieved so far. Despite the orientation on the best solution the algorithm keeps on searching globally. This paper provides an evaluation of the performance of MVMO when applied for the solution of computationally expensive optimization problems. Experimental tests, conducted on the IEEE-CEC 2014 optimization test bed, highlight the capability of the MVMO to successfully tackle different complex problems within a reduced number of allowed function evaluations.

This paper provides a survey on the performance of the hybrid variant of the Mean-Variance Mapping Optimization (MVMO-SH) when applied for solving the IEEE-CEC 2014 competition test suite on Single Objective RealParameter Numerical Optimization. MVMO-SH adopts a swarm intelligence scheme, where each particle is characterized by its own solution archive and mapping function. Besides, multi-parent crossover is incorporated into the offspring creation stage in order to force the particles with worst fitness to explore other sub-regions of the search space. In addition, MVMO-SH can be customized to perform with an embedded local search strategy. Experimental results demonstrate the search ability of MVMO-SH for effectively tackling a variety of problems with different dimensions and mathematical properties.

Mean-Variance Mapping Optimization (MVMO) is a recent addition to the emerging field of heuristic optimization algorithms, which has been quite successful in solving a variety of power system optimization problems. This paper introduces a hybrid variant of MVMO (MVMO-SH) for solving the IEEECEC 2013 competition test suite. MVMO-SH is based on a swarm scheme of MVMO with embedded local search and multi-parent crossover strategies to increase search diversity and solution quality. Numerical results attest to the promising prospect of MVMO-SH to become a general purpose optimization algorithm.

A methodology for parameter estimation of equivalent wind power plant is proposed in this paper. The parameter algorithm is formulated as an optimization problem which miminize the output between the real system and the mathematical model. The parameters are updated using Newton-Raphson method which depends on the information of trajectory sensitivity functions. A generic equivalent of wind power plant was used as a structure of the model, which is valid for Double-Fed Induction Generator (DFIG) and full Converter Based Wind Turbine. In addition, the procedure to obtain of the trajectory sensitivity functions of the model is presented in this paper. A discussion of the estimated results is also analysed. In general, the convergence happened in a few seconds for most of the cases studied.

This paper investigates harmonics performance and common mode voltage (CMV) of three-level neutral point clamped (NPC) back-to-back voltage source converter (VSC) using standard pulse width modulation (PWM) techniques for the application of doubly fed induction generator (DFIG) based wind turbine (WT) and provides a comparison with two-level back-to-back VSC. These two converter topologies are reviewed and the principle of switching instants generation methods is explained. Based on carrier based technique, phase disposition (PD) method and phase opposition disposition (POD) method for the three-level NPC converter are applied. Results show different level of harmonics performance for each different PWM type, and POD method for the three-level NPC converter produced the highest reduction of CMV. These results help in the intelligent choice of the appropriate PWM type for the DFIG application with reduction in CMV while remaining less harmonics distortion.

Based on swarm intelligence principles and an enhanced mapping scheme, the extension of the original single-particle mean-variance mapping optimization (MVMO) to its swarm variant (MVMOS) is investigated in this paper. Numerical experiments and comparisons with other heuristic optimization methods, which were conducted on several composition test functions, demonstrate the feasibility and effectiveness of MVMOSwhen solving multimodal optimization problems. Sensitivity analysis of the algorithm parameters highlights its robust performance.

This paper provides an overview of the current state of the technology of offshore wind-based power generation and the technological challenges with emphasis on the electrical parts. First, a brief review of the core control functions, their correlation with operational behavior, and the grid-supporting capability of the machine during normal operation as well as during contingency situations are provided. This is followed by the discussion of basic considerations in wind farm collector design, including topology, grounding options, and outlay of the offshore substation. Then, issues related to offshore turbine foundation and typical dimensions of the offshore substation platform are discussed. The platform is designed to accommodate the main and grounding transformers, the switch gear, and other assorted accessories. Next, options for the transmission link from the offshore plant to the grid onshore are reviewed. Finally, a discussion of issues related to grid integration together with currently applicable special grid code requirements concludes the paper.

Abstract—Vulnerability assessment is one of the main tasks in a Self-Healing Grid structure, since it has the function of detecting the necessity of performing global control actions in real time. Due to the short-time requirements of real time applications, the eligible vulnerability assessment methods have to consider the improvement of calculation time. Although there are several methods capable of performing quick assessment, these techniques are not fast enough to analyze real large power systems in real time. Based on the fact that vulnerability begins to develop in specific regions of the system exhibiting coherent dynamics, large interconnected power systems can be reduced through dynamic equivalence in order to reduce the calculation time. A dynamic equivalent should provide simplicity and accuracy sufficient for system dynamic simulation studies. Since the parameters of the dynamic equivalent cannot be easily derived from the mathematical models of generators and their control systems, numerical identification methods are needed. Such an identification task can be tackled as an optimization problem. This paper introduces a novel heuristic optimization algorithm, namely, the Mean-Variance Mapping Optimization (MVMO), which provides excellent performance in terms of convergence behavior and accuracy of the identified parameters. The identification procedure and the level of accuracy that can be reached are demonstrated using the Ecuadorian-Colombian interconnected system in order to obtain a dynamic equivalent representing the Colombian grid. Keywords-dynamic equivalent; heuristic optimization; Mean-Variance Mapping Optimization; smart grid; vulnerability I.

A new stochastic optimization algorithm referred to by the authors as the `Mean-Variance Optimization' (MVO) algorithm is presented in this paper. MVO falls into the category of the so-called “population-based stochastic optimization technique.” The uniqueness of the MVO algorithm is based on the strategic transformation used for mutating the offspring based on mean-variance of the n-best dynamic population. The mapping function used transforms the uniformly distributed random variation into a new one characterized by the variance and mean of the n-best population attained so far. The searching space within the algorithm is restricted to the range - zero to one - which does not change after applying the transformation. Therefore the variables are treated always in this band but the function evaluation is carried out in the problem range. The performance of MVO algorithm has been demonstrated on standard benchmark optimization functions. It is shown that MVO algorithm finds the near optimal solution and is simple to implement. The features of MVO make it a potentially an attractive algorithm for solving many real-world optimization problems.

Most power systems optimization problems have to be solved under uncertainty. The scenarios used for modeling the uncertainties should be able to represent their stochastic nature. If this requires huge sampling, particle swarm optimization (PSO) based scenario reduction technique can be a good option to approximate the initial scenario distribution. This paper proposes a multi-stage model for the optimal operation of a wind integrated power system. A parameter free self learning particle swarm optimization algorithm has been used to solve the deterministic and stochastic models. The robustness of the solution procedure has been verified by the effective utilization of the various generation units.