Application of mean-variance mapping optimization for parameter identification in real-time digital simulation

Abstract

Abstract-This paper deals with the process of identifying the parameters of the dynamic equivalent (DE) load model of an active distribution system (ADN) simulated in RTDS using mean-variance mapping optimization (MVMO) algorithm. MVMO is an emerging variant of population-based, evolutionary optimization algorithm whose features include evolution of its solutions through a unique search mechanism within a normalized range of the sample space. Due to the prominent largescale integration of DG in low and medium voltage networks, it is important to develop equivalent models that are suitable for representing the resulting active distribution network in dynamic studies of large power systems. This would significantly reduce the computational demands and simulation time. Moreover, only a defined portion of a system is usually studied, which means that the external system can be substituted with DE thereby allowing the detailed modelling of the focus area. The IEEE 34-Bus distribution system was modified and used as the reference network where measurement data were gathered for identification of the parameters of its developed DE. An optimization-enabled simulation involving MATLAB, which host the MVMO algorithm and RTDS, which simulates the models was established. The reactions of the detailed network and the DE were compared upon subjecting them to different disturbances in the retained system. The effectiveness of the MVMO algorithm in identifying DE parameters based on its unique mapping function is reflected through the results of the response comparison.

Swarm‐based mean‐variance mapping optimization for optimal placement of energy storage with synthetic inertia control on a low inertia power grid

“…The MVMO is one of the population-based optimization techniques [29]. MVMO has been implemented to solve various problems, for example, optimal transmission expansion planning [30], optimal reactive power dispatch [26], placement and tuning of power system stabilizer (PSS) [25] and parameter identification [31].…”

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

“…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.…”

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

“…Two score measures, which are indicated in [7], were calculated in Matlab according to (9) and (10), respectively. The score measures were calculated for 10 repetitions of the application of MVMO-SHM to solve the IEEE-CEC 2018 expensive optimization test problems, for both, 10D and 30D.…”

“…total score (Score 2 for 10D + Score 2 for 30D), which can be in the order of 1E+04.   (10) where Dim can be 10D or 30D.…”

“…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].…”