1. The impact of plug-in hybrid vehicles in low-voltage distribution systems using a Monte Carlo simulationEvica Smilkoska, Vasko Zdraveski, Jovica Vuletić, Jordančo Angelov, Mirko Todorovski, 2023, original scientific article Abstract: The growing presence and randomness of renewable-based Distributed Generation, such as solar, photovoltaic, and wind power, and heavy Plug-in Hybrid Electric Vehicle loads in residential distribution grids result in both a higher degree of imbalance and a wide range of voltage fluctuations. When increasing the number of Plug-in Hybrid Electric Vehicles that are simultaneously charged, the additional unpredicted load may cause several problems to the current grid in terms of voltage deviations, thermal overloads, power losses, increased aging of transformers and lines, decreased quality of supply, and power outages. This paper proposes an approach that models Plug-in Hybrid Electric Vehicles’ behaviour and performs power flow analysis on CIGRE low voltage benchmark grid to investigate the impact on the current distribution grid.
Keywords: plug-in hybrid electric vehicle, power quality, non-deterministic approach, voltage deviations, power losses, distribution systems
Published in DKUM: 11.10.2023; Views: 464; Downloads: 6
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2. Predictions of experimentally observed stochastic ground vibrations induced by blastingSrđan Kostić, Matjaž Perc, Nebojša Vasović, Slobodan Trajković, 2013, original scientific article Abstract: In the present paper, we investigate the blast induced ground motion recorded at the limestone quarry “Suva Vrela” near Kosjerić, which is located in the western part of Serbia. We examine the recorded signals by means of surrogate data methods and a determinism test, in order to determine whether the recorded ground velocity is stochastic or deterministic in nature. Longitudinal, transversal and the vertical ground motion component are analyzed at three monitoring points that are located at different distances from the blasting source. The analysis reveals that the recordings belong to a class of stationary linear stochastic processes with Gaussian inputs, which could be distorted by a monotonic, instantaneous, time-independent nonlinear function. Low determinism factors obtained with the determinism test further confirm the stochastic nature of the recordings. Guided by the outcome of time series analysis, we propose an improved prediction model for the peak particle velocity based on a neural network. We show that, while conventional predictors fail to provide acceptable prediction accuracy, the neural network model with four main blast parameters as input, namely total charge, maximum charge per delay, distance from the blasting source to the measuring point, and hole depth, delivers significantly more accurate predictions that may be applicable on site. We also perform a sensitivity analysis, which reveals that the distance from the blasting source has the strongest influence on the final value of the peak particle velocity. This is in full agreement with previous observations and theory, thus additionally validating our methodology and main conclusions.
Keywords: blasting, vibrations, surrogate data, deterministic chaos, stochasticity
Published in DKUM: 19.06.2017; Views: 1069; Downloads: 342
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3. Web application for hierarchical organizational structure optimization : human resource management case studyDavorin Kofjač, Blaž Bavec, Andrej Škraba, 2015, original scientific article Abstract: Background and Purpose: In a complex strictly hierarchical organizational structure, undesired oscillations may occur, which have not yet been adequately addressed. Therefore, parameter values, which define fluctuations and transitions from one state to another, need to be optimized to prevent oscillations and to keep parameter values between lower and upper bounds. The objective was to develop a simulation model of hierarchical organizational structure as a web application to help in solving the aforementioned problem.
Design/Methodology/Approach: The hierarchical structure was modeled according to the principles of System Dynamics. The problem of the undesired oscillatory behavior was addressed with deterministic finite automata, while the flow parameter values were optimized with genetic algorithms. These principles were implemented as a web application with JavaScript/ECMAScript.
Results: Genetic algorithms were tested against well-known instances of problems for which the optimal analytical values were found. Deterministic finite automata was verified and validated via a three-state hierarchical organizational model, successfully preventing the oscillatory behavior of the structure.
Conclusion: The results indicate that the hierarchical organizational model, genetic algorithms and deterministic finite automata have been successfully implemented with JavaScript as a web application that can be used on mobile devices. The objective of the paper was to optimize the flow parameter values in the hierarchical organizational model with genetic algorithms and finite automata. The web application was successfully used on a three-state hierarchical organizational structure, where the optimal flow parameter values were determined and undesired oscillatory behavior was prevented. Therefore, we have provided a decision support system for determination of quality restructuring strategies.
Keywords: hierarchical organizational structure, genetic algorithms, deterministic finite automata, system dynamics, optimization, human resources
Published in DKUM: 04.04.2017; Views: 1917; Downloads: 155
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5. Segmenting risks in risk managementBorut Jereb, 2009, original scientific article Abstract: The paper describes a segmentation of risks to make each risk segment more manageable. The proposed approach is primarily intended to improve the confidentiality of risk simulations. The description of the approach is based on a logistics business process system which requires that its input is represented as a process graph. Each process is defined in terms of input and output; input comprises general input as well as risks; output comprises general output as well as impacts. The model takes into consideration internalas well as external input and output. Parameters can be used to define individual processes. Processes include functions that calculate new values of parameters and output on the bases of given input. Based on given tolerance levels for risks, impacts and process parameters, the model determines whether these levels are acceptable. The model assumes that parameters and functions are non-deterministic, i.e. parameters and functions may change in time. Although the approach is described on a very general level, each segment can be further subdivided into subsegments in order to include more characteristics of observed risks.
Keywords: risk, impact, segmentation, risk management, process parameters, logistics, model, simulation tools, non-deterministic
Published in DKUM: 05.06.2012; Views: 2069; Downloads: 53
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