MVMO for Optimal Reconfiguration in Smart Distribution Systems

Rueda, J.L.; Loor, Rosanna; Erlich, I.

doi:10.1016/j.ifacol.2015.12.390

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Its novel feature is the use of a special mapping function for mutating the new offspring, on the basis of the mean and variance of the n-best population attained so far. Due to its good performance in terms of convergence and quality of solutions, MVMO is used to reactive power management of offshore wind power plants (Theologi et al, 2017), reconfiguration of distribution systems (Rueda et al, 2015b), and identification of model parameters for real-time digital simulation (Gbadamosi et al, 2017). The flow chart of MVMO is shown in Figure 6.…”

Section: Mean-variance Mapping Optimization (Mvmo)mentioning

confidence: 99%

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

et al. 2020

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Unlike synchronous generators, wind turbines cannot directly respond to large disturbances, which may cause transient instability, due to their power electronic-based interface and maximum power control strategy. To effectively monitor the influence of wind turbines, this paper proposes an approach that combines decision trees (DTs), and a newly developed variant of the Mean-Variance Mapping Optimization (MVMO) algorithm, to simultaneously tackle the problem of selecting the key variables that properly reflect the transient stability performance of a system dominated by wind power, and designing the DTs for reliable online assessment of transient stability. The notion of key variables refers to the set of variables that are closely related to the modified power system transient stability performance as a consequence of the replacement of conventional power plants by wind generators. The selection of key variables is formulated as a non-linear optimization problem with weight factors as decision variables and is tackled by MVMO. A weight factor is assigned to each key variable candidate, and its value is considered to reflect the degree of influence of the key variable candidate on the splitting property and estimation accuracy of the DTs. The samples of the key variable candidates and the initialized weight factors are used to build the first group of DTs. Then, MVMO iteratively evolves the weight factors according to its special mapping function with minimizing DTs' estimation error. According to the final list of optimized weight factors, system operators can select a reduced set of variables with the largest weight factors as key variables, depending on the resulting accuracy of the DTs. Meanwhile, DTs built by using key variables are considered as the optimal performance trees for transient stability estimation. In this way, the selection of key variables and the development of DTs are made jointly and automatically, without the interference of the users of the DTs. Test results Wang et al. MVMO Based Transient Stability Assessment on the modified IEEE 9 bus system and a synthetic model of a real power system show that the proposed method can correctly identify the set of key variables related to wind turbine dynamics, as well as its ability to provide a reliable estimation of the transient stability margin.

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Section: Mean-variance Mapping Optimization (Mvmo)mentioning

confidence: 99%

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

et al. 2020

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“…MVMO has been used to solve computationally expensive problems proposed in the competitions CEC2014 [5], CEC2015 [6], and CEC2016 [7], showing an outstanding performance in terms of convergence rate and quality of solutions found within the allowed number of function evaluations. This success has motivated further developments to adapt MVMO for smart applications in power systems, such as online optimal reactive power management of offshore wind power plants [8], [9], online optimal reconfiguration of distribution systems [10], and identification of model parameters for real-time digital simulation [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Single Parent-Offspring MVMO for Solving CEC2018 Computationally Expensive Problems

Rueda

Erlich

2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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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.

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An Application of MVMO Based Adaptive PID Controller for Process with Variable Delay

Salazar

Herrera

Camacho

2020

Advances in Intelligent Systems and Computing

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MVMO for Optimal Reconfiguration in Smart Distribution Systems

Cited by 7 publications

References 16 publications

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

Hybrid Single Parent-Offspring MVMO for Solving CEC2018 Computationally Expensive Problems

An Application of MVMO Based Adaptive PID Controller for Process with Variable Delay

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