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1

Prediction of flyrock distance induced by blasting using particle swarm optimization and multiple regression analysis: an engineering perspective

Chen Yong, Wang Minghua, Yin Heng, Zhang Tianbao

Acta Geophysica

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2024

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Vol. 72, no. 1

287--301

EN

Flyrock is one of the major safety hazards induced by blasting operations. However, few studies were for predicting blasting-induced flyrock distance from the perspective of engineers. The present paper attempts to provide an engineer-friendly equation predicting blasting-induced flyrock distance. Data used in the present study contains s seven blasting parameters including borehole diameter, blasthole length, powder factor, stemming length, maximum charge per delay, burden, and flyrock distance is obtained. Data is inputted into Random Forest for feature selection. The selected features are formulated as two candidate equations, including Multiple Linear Regression (MLR) equation and Multiple Nonlinear Regression (MNR) equation. Those two candidates are respectively referred by Particle Swarm Optimization for searching optimum values for the coefficients of selected features. It is proved that MLR equation has better accuracy. MLR equation is compared with two empirical equations and the MLR equation based on least squares method. It is found that the coefficient of correlation of the proposed MLR equation reaches 0.918, which is the highest compared with the scores of other three equations. The present study utilizes feature selection process to screen inputs, which effectively excludes irrelevant parameters from being considered. Plus the contribution of Particle Swarm Optimization, the accuracy of the obtained equation can be guaranteed.

2

A tuning of PID power controller using particle swarm optimization for an electro-surgical unit

Mohammed Mohammed Jamal, Abed Jameel Kadhim, Mahdi Ali Jafer, Hussein Basheer M.

Przegląd Elektrotechniczny

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2023

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R. 99, nr 10

23--29

EN

Electro surgical unit is a popular modern device. It has been used in operating rooms for cutting, fulguration and coagulation of human tissues. ESU generates high frequency alternating current to prevent the stimulation of nerves and muscles. The objective of this article was to improve the performance of an ESU by controlling its output power under the variation of tissue impedance using proportional integral derivative controller based on particle swarm optimization to achieve minimum overshoots and fast dynamic response. The controller was simulated in MATLAB/SIMULINK to demonstrate the superiority of the suggested method. A comparative analysis was presented with ESU utilizing manual tuning process. The results showed that the proposed controller offered best performance utilizing manual tuning method. Moreover, both of the tuning methods presented better results from open-loop controller as a result of which charring of tissues could be eliminated and clinical operations could be made more efficient.

PL

Aparat elektrochirurgiczny to popularne nowoczesne urządzenie. Stosowano go na salach operacyjnych do cięcia, nakłuwania i koagulacji tkanek ludzkich. ESU generuje prąd zmienny o wysokiej częstotliwości, aby zapobiec stymulacji nerwów i mięśni. Celem tego artykułu była poprawa wydajności ESU poprzez sterowanie jego mocą wyjściową przy zmianie impedancji tkanki przy użyciu proporcjonalnego całkowego regulatora pochodnego opartego na optymalizacji roju cząstek w celu uzyskania minimalnych przeregulowań i szybkiej odpowiedzi dynamicznej. W celu wykazania wyższości zaproponowanej metody przeprowadzono symulację sterownika w programie MATLAB/SIMULINK. Przedstawiono analizę porównawczą z ESU z wykorzystaniem procesu strojenia ręcznego. Wyniki pokazały, że proponowany regulator oferował najlepszą wydajność przy zastosowaniu metody strojenia ręcznego. Co więcej, obie metody strojenia dały lepsze wyniki w przypadku sterowania z otwartą pętlą, w wyniku czego można było wyeliminować zwęglanie tkanek i zwiększyć efektywność operacji klinicznych.

3

Finite element model updating of steel-concrete composite bridge: A study case of the Ruri bridge in Vietnam

Nguyen Duc Cong, Salamak Marek, Katunin Andrzej, Gerges Michael, Abdel-Maguid Mohamed

Archives of Civil Engineering

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2023

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Vol. 69, nr 3

425--443

EN

The study presents the finite element (FE) model update of the existing simple-spans steel-concrete composite bridge structure using a particle swarm optimisation (PSO) and genetic algorithm (GA) approaches. The Wireless Structural Testing System (STS-WiFi) of Bridge Diagnostic, Inc. from the USA, implemented various types of sensors including: LVDT displacement sensors, intelligent strain transducers, and accelerometers that the static and dynamic historical behaviors of the bridge structure have been recorded in the field testing. One part of all field data sets has been used to calibrate the cross-sectional stiffness properties of steel girders and material of steel beams and concrete deck in the structural members including 16 master and slave variables, and that the PSO and GA optimisation methods in the MATLAB software have been developed with the new innovative tools to interface with the analytical results of the FE model in the ANSYS APDL software automatically. The vibration analysis from the dynamic responses of the structure have been conducted to extract four natural frequencies from experimental data that have been compared with the numerical natural frequencies in the FE model of the bridge through the minimum objective function of percent error to be less than 10%. In order to identify the experimental mode shapes of the structure more accurately and reliably, the discrete-time state-space model using the subspace method (N4SID) and fast Fourier transform (FFT) in MATLAB software have been applied to determine the experimental natural frequencies in which were compared with the computed natural frequencies. The main goal of the innovative approach is to determine the representative FE model of the actual bridge in which it is applied to various truck load configurations according to bridge design codes and standards. The improved methods in this document have been successfully applied to the Vietnamese steel-concrete composite bridge in which the load rating factors (RF) of the AASHTO design standards have been calculated to predict load limits, so the final updated FE model of the existing bridge is well rated with all RF values greater than 1.0. The presented approaches show great performance and the potential to implement them in industrial conditions.

4

Development of hybrid performance‑based optimization algorithm for structures equipped with vibration damper devices

Ayyash Al‑jbury Najad Amer, Hejazi Farzad

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 2

art. no. e123, 2023

EN

Nowadays, various types of vibration damping systems are being implemented in different buildings to diminish seismic effects on structures. However, engineers are faced with the challenging task of developing an optimum design for structures utilizing a proper type of damping device based on new techniques such as the performance-based design method. Therefore, this research was aimed at developing a multi-objective optimization algorithm by hybridizing the particle swarm optimization (PSO) and gravitational search algorithm (GSA) to obtain an optimum design for structures equipped with vibration damper devices based on the performance-based design method. Then, the developed hybrid algorithm (PSOGSA) would be capable of optimizing the damping system simultaneously with the optimized details of the structural sections, including the steel rebars, by satisfying all the design criteria. For this purpose, a special process for the design of structures equipped with vibration damper devices according to the performance-based design method was developed by considering of a wide range of vibration damping systems. The proposed PSOGSA optimization framework was then implemented to design a 12-storey reinforced concrete structure equipped with different types of dampers to minimize the structural weight while satisfying all the prescribed performance-based design acceptance criteria. The results indicated that the proposed optimization method was able to successfully optimize the details of the structural members as well as the type and properties of the damper, which significantly improved the structural response in terms of the formation of plastic hinges and the structural movements.

5

Developing numerical equality to regional intensity-duration-frequency curves using evolutionary algorithms and multi-gene genetic programming

Citakoglu Hatice, Demir Vahdettin

Acta Geophysica

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2023

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Vol. 71, no. 1

469--488

EN

This study aims to carry out regional intensity−duration−frequency (IDF) equality using the relationship with IDF obtained from point frequency analysis. Eleven empirical equations used in the literature for seven climate regions of Turkey were calibrated using particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques and the obtained results were compared. In addition, in this study, new regional IDF equations were obtained for each region utilizing Multi-Gene Genetic Programming (MGGP) method. Finally, Kruskal–Wallis (KW) test was applied to the IDF values obtained from the methods and the observed values. As a result of the study, it was observed that the coefficients of 11 empirical equations calibrated with PSO, and GA techniques were different from each other. The mean absolute error (MAE), root mean square error (RMSE), mean absolute relative error (MARE), coefficient of determination (R2 ), and Taylor diagram were used to evaluate the performances of PSO, GA, and MGGP techniques. According to the performance criteria, it has been determined that the IDF equations obtained by the MGGP method for the Eastern Anatolia, Aegean, Southeastern Anatolia, and Central Anatolia regions are more successful than the empirical equations calibrated with the PSO and GA method. The empirical IDF equations produced with PSO and the IDF equations acquired with MGGP have similar findings in the Mediterranean, Black Sea, and Marmara. In addition, the KW test results showed that the data of all models were from the same population.

6

Research on the influence of airflow resistance of layered porous structures on the sound absorption coefficient

Flach Artur

Vibrations in Physical Systems

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2022

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Vol. 33, nr 3

art. no. 2022311

EN

The paper presents the research on the influence of airflow resistance on the sound absorption coefficient of layered porous structures. For the calculation of the sound absorption coefficient, the models of layered sound-absorbing structures were developed with the use of numerical computational models. Using the developed models, optimization was carried out to maximize the average sound absorption coefficient of the structures for a given frequency range. As a result of the research, the dependence of the change in airflow resistance for the successive layers of the material was determined. The results of the work will be particularly useful in the design of wedges used in anechoic chambers.

7

Enhancing energy efficiency for optimal multiple photovoltaic distributed generators integration using inertia weight control strategies in PSO algorithms

Zellagui Mohamed, Lasmari Adel, Alaboudy Kasem, Settoul Samir, Hassan Heba Ahmed

Polityka Energetyczna

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2022

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T. 25, z. 1

59--88

EN

Recently, interest in incorporating distributed generators (DGs) into electrical distribution networks has significantly increased throughout the globe due to the technological advancements that have led to lowering the cost of electricity, reducing power losses, enhancing power system reliability, and improving the voltage profile. These benefits can be maximized if the optimal allocation and sizing of DGs into a radial distribution system (RDS) are properly designed and developed. Getting the optimal location and size of DG units to be installed into an existing RDS depends on the various constraints, which are sometimes overlapping or contradicting. In the last decade,meta-heuristic search and optimization algorithms have been frequently developed to handle the constraints and obtain the optimal DG location and size. This paper proposes an efficient optimization technique to optimally allocate multiple DG units into a RDS. The proposed optimization method considers the integration of solar photovoltaic (PV) based DG units in power distribution networks. It is based on multi-objective function (MOF) that aims to maximize the net saving level (NSL), voltage deviation level (VDL), active power loss level (APLL), environmental pollution reduction level (EPRL), and short circuit level (SCL). The proposed algorithms using various strategies of inertia weight particle swarm optimization (PSO) are applied on the standard IEEE 69-bus system and a real 205-bus Algerian distribution system. The proposed approach and design of such a complicated multi-objective functions are ultimately to make considerable improvements in the technical, economic, and environmental aspects of power distribution networks. It was found that EIW-PSO is the best applied algorithm as it achieves the maximum targets on various quantities; it gives 75.8359%, 28.9642%, and 64.2829% for the APLL, EPRL, and VDL, respectively, with DG units’ installation in the IEEE 69-bus test system. For the same number of DG units, EIW-PSO gives remarkable improved performance with the Adrar City 205-bus test system; numerically, it shows 72.3080%, 22.2027%, and 63.6963% for the APLL, EPRL, and VDL, respectively. The simulation results of this study prove that the proposed algorithms exhibit higher capability and efficiency in fixing the optimum DG settings.

PL

Ostatnio zainteresowanie włączeniem generatorów rozproszonych do sieci dystrybucji energii elektrycznej znacznie wzrosło na całym świecie ze względu na postęp technologiczny, który doprowadził do obniżenia kosztów energii elektrycznej, zmniejszenia strat mocy, zwiększenia niezawodności systemu elektroenergetycznego i poprawy profilu napięcia. Korzyści te można zmaksymalizować, jeśli opracuje się i zaprojektuje optymalną alokację i wielkość generatorów rozproszonych w promieniowym systemie dystrybucji. Uzyskanie optymalnej lokalizacji i wielkości jednostek generatorów rozproszonych, które mają być zainstalowane w istniejącym promieniowym systemie dystrybucji, zależy od różnych ograniczeń, które czasami nakładają się lub są sprzeczne. Aby poradzić sobie z ograniczeniami i uzyskać optymalną lokalizację i rozmiar generatora rozproszonego, w ostatniej dekadzie często opracowywano metaheurystyczne algorytmy wyszukiwania i optymalizacji. W niniejszym artykule zaproponowano skuteczną technikę optymalizacji, aby przydzielić wiele jednostek generatorów rozproszonych do promieniowego systemu dystrybucji. Zaproponowana metoda optymalizacji uwzględnia integrację jednostek generatorów rozproszonych opartych na ogniwach fotowoltaicznych w sieciach dystrybucji energii. Opiera się na funkcji wielokryterialnej, która ma na celu maksymalizację poziomu oszczędności netto, poziomu odchylenia napięcia, poziomu utraty mocy czynnej, poziomu redukcji zanieczyszczenia środowiska i poziomu zwarcia. Zaproponowane algorytmy wykorzystujące różne strategie optymalizacji roju cząstek o masie bezwładności (PSO) są stosowane w standardowym systemie IEEE 69-autobus oraz w rzeczywistym algierskim systemie dystrybucji autobusu 205. Proponowane podejście i projekt tak skomplikowanych, wielozadaniowych funkcji ma ostatecznie doprowadzić do znacznej poprawy technicznych, ekonomicznych i środowiskowych aspektów sieci dystrybucyjnych. Stwierdzono, że algorytm EIW-PSO jest najlepszy do zastosowania w systemie testowym IEEE 69-bus, ponieważ osiąga maksymalne cele dla różnych wielkości: 75,8359%, 28,9642% i 64,2829% odpowiednio dla utraty mocy czynnej, poziomu redukcji zanieczyszczenia środowiska i poziomu odchylenia napięcia w procesie instalacji jednostek rozproszonych. Dla tej samej liczby generatorów rozproszonych, EIW-PSO zapewnia znacznie lepszą wydajność w testach autobusów 205 w mieście Adrar; liczbowo: 72,3080%, 22,2027% i 63,6963% odpowiednio dla utraty mocy czynnej, poziomu redukcji zanieczyszczenia środowiska i poziomu odchylenia napięcia. Wyniki symulacji tego badania dowodzą, że zaproponowane algorytmy wykazują większą zdolność i skuteczność w ustalaniu optymalnych ustawień generatorów rozproszonych.

8

The application of different optimization techniques and Artificial Neural Networks (ANN) for coal-consumption forecasting: a case study

Seker Mustafa, Unal Kartal Neslihan, Karadirek Selin, Gulludag Cevdet Bertan

Gospodarka Surowcami Mineralnymi

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2022

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T. 38, z. 2

77--112

EN

The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world’s energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S-curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S-curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S-curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R2 = 0.9881, RE = 0.011, RMSE = 1.079, MAE = 1.3584, and STD = 1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.

PL

Zapotrzebowanie na energię w skali globalnej rośnie z dnia na dzień. W przeciwieństwie do odnawialnych źródeł energii, paliwa kopalne mają ograniczone rezerwy i zaspokajają większość światowego zapotrzebowania na energię pomimo ich niekorzystnego wpływu na środowisko. Niniejsze opracowanie przedstawia nową strategię prognozowania, w tym oparte na optymalizacji podejście oparte na krzywej S dla zużycia węgla w Turcji. W tym podejściu algorytmy optymalizacji genetycznej (GA) i optymalizacji roju cząstek (PSO), optymalizacja Gray Wolf (GWO) i algorytm optymalizacji wielorybów (WOA) należą do metaheurystycznych technik optymalizacji stosowanych do określenia optymalnych parametrów krzywej S. Ponadto algorytmy te oraz sztuczna sieć neuronowa (SSN) zostały również wykorzystane do oszacowania zużycia węgla. Przy ocenie zużycia węgla za pomocą SSN jako parametry wejściowe wykorzystuje się parametry energetyczne i ekonomiczne, takie jak moc zainstalowana, produkcja brutto, zużycie energii elektrycznej netto, import, eksport i energia ludności. W modelowaniu SSN wykorzystano strukturę Feed Forward Multilayer Perceptron Network, a do uczenia sieci wykorzystano propagację wsteczną Levenberg-Marquardt. Krzywe S zostały obliczone za pomocą optymalizacji, a ich skuteczność w przewidywaniu zużycia węgla została oceniona statystycznie. Wyniki pokazują, że podejście oparte na optymalizacji opartej na krzywej S zapewnia większą dokładność niż SSN w rozwiązaniu przedstawionego problemu. Wyniki statystyczne obliczone przez GWO mają wyższą dokładność niż PSO, WOA i GA z R2 = 0,9881, RE = 0,011, RMSE = 1,079, MAE = 1,3584 i STD = 1,5187. Nowość tego badania, prezentowana metodyka nie wymaga dodatkowych parametrów wejściowych do analizy. Dzięki temu może być z łatwością wykorzystany z dużą dokładnością do oszacowania zużycia węgla w innych krajach o tendencji wzrostowej zużycia węgla, takich jak Turcja.

9

Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator

Belkebir Amel, Bourek Yacine, Benguesmia Hani

Diagnostyka

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2022

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Vol. 23, No. 3

art. no 2022309

EN

This paper proposes training an artificial neural network (ANN) by a particle swarm optimization (PSO) technique to predict the flashover voltage of outdoor insulators. The analysis follows a series of real-world tests on high-voltage insulators to form a database for implementing artificial intelligence concepts. These tests are performed in various degrees of artificial contamination (distilled brine). Each contamination level shows the amount of contamination in milliliters per area of the isolator. The acquisition database provides values of flashover voltage corresponding to their electrical conductivity in each isolation zone and different degrees of artificial contamination. The results show that ANN trained by PSO can not only provide better prediction results, but also reduce the amount of computation efforts. It is also a more powerful model because: it does not get stuck in a local optimum. In addition, it also has the advantages of simple logic, simple implementation, and underlying intelligence. Compared to the results obtained by practical tests, the results obtained present that the PSO-ANN technique is very effective to predict flashover of high-voltage polluted insulators.

10

Cat swarm optimization algorithm tuned multilayer perceptron for stock price prediction

Chandar S. Kumar, Punjabi Hitesh

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

145--160

EN

Due to the nonlinear and dynamic nature of stock data, prediction is one of the mostchallenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of thehybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired bythe behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variablesfor the proposed model. The model’s performance is validated using historical data notused for training. The model’s prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model’s results are compared with other models optimized byvarious bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the bestperformance compared to other models taken for analysis.

11

A hybrid optimized resource allocation modelfor multi-cloud environment using batand particle swarm optimization algorithms

Selvapandian D., Santhosh R.

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

87--103

EN

In cloud computing, scheduling and resource allocation are the major factors that definethe overall quality of services. An efficient resource allocation module is required in cloudcomputing since resource allocation in a single cloud environment is a complex process.Whereas resource allocation in a multi-cloud environment further increases the complexityof allocation procedures. Earlier, resources from the multi-cloud environment were allocated based on task requirements. However, it is essential to analyze the present resourceavailability status and resource capability before allocating to the requested tasks. So, inthis research work, a hybrid optimized resource allocation model is presented using bat optimization algorithm and particle swarm optimization algorithm to allocate the resourceconsidering the resource status, distance, bandwidth, and task requirements. Proposedmodel performance is evaluated through simulation and compared with conventional optimization algorithms. For a set of 500 tasks, the proposed approach allocates resourcesin 47 s, with a minimum energy consumption of 200 kWh. Compared to conventionalapproaches, the performance of the proposed model is much better in terms of deadlinemissed tasks, resource requirement, energy consumption, and allocation time.

12

Power quality management in electrical grid using SCANN controller-based UPQC

Varadharajan Balaji, Subramanian Chitra

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 1

art. no. e140257

EN

The electrical grid integration takes great attention because of the increasing population in the nonlinear load connected to the power distribution system. This manuscript deals with the power quality issues and mitigations associated with the electrical grid. The proposed single comprehensive artificial neural network (SCANN) controller with unified power quality conditioner (UPQC) is modelled in MATLAB Simulink environment. It provides series and shunt compensation that helps mitigate voltage and current distortion at the end of the distribution system. Initially, four proportional integral (PI) controllers are used to control the UPQC. Later the trained SCANN controller replaces four PI Controllers for better control action. PI and SCANN controllers’ simulation results are compared to find the optimal solutions. A prototype model of SCANN controller is constructed and tested. The test results show that the SCANN based UPQC maintains grid voltage and current magnitude within permissible limits under fluctuating conditions.

13

Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization

Diwan Supriya P., Deshpande Shraddha S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 4

art. no. e140696

EN

As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm

14

A parallel improved PSO algorithm with genetic operators for 2D inversion of resistivity data

Abril Jorge L., Vasconcelos Marcos A., Barboza Francisco M., Mojica Oscar F.

Acta Geophysica

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2022

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Vol. 70, no 3

1137--1154

EN

In this paper, an improved particle swarm optimization technique known as elitist-mutated particle swarm optimization (EMPSO) was applied in the 2D electrical resistivity imaging, a complex and highly nonlinear optimization problem. The EMPSO enables better exploration of the search space, by replacing particles with a worse performance by the best particle of the swarm mutated in random positions. Nevertheless, this technique, as any other based on a population of models, costs much computation time in solving optimization problems with a large number of unknown parameters. We addressed this problem by developing a parallel version of the EMPSO that supports pure MPI and hybrid MPI-OpenMP modes, and we named as parallel elitist-mutated PSO (PEMPSO). The solution to the inverse problem is based on minimizing an objective function with a regularization term to create a mathematically stable solution. Total variation and global smoothness regularizations were used in the inversion of synthetic data obtained from simple models and a set of real data of a highly complex geological/geotechnical nature. By virtue of the features of the synthetic models and the geology of the local where the data were acquired, the inversions with total variation regularization provided the best outcomes. Additionally, we have improved the execution time significantly with our parallel solution (the pure MPI model turned out to be better than the hybrid model) in comparison with the sequential version. Cumulative frequency distribution of errors between modeled and observed apparent resistivity data for all experiments was used to validate the PEMPSO technique for estimating resistivity.

15

Optimal power system stabilizer design using craziness particle swarm optimization in Sulselrabar system

Djalal Muhammad Ruswandi, Saini Makmur, Yunus A. M. Shiddiq, Kitta Ikhlas

Przegląd Elektrotechniczny

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2021

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R. 97, nr 10

76--81

EN

Power System Stabilizer (PSS) is a supplementary control that provides additional control actions on the excitation side of the generator. In this study a Craziness Particle Swarm Optimization (CRPSO) based tuning method is proposed to optimize the PSS parameters. CRPSO is a development of the conventional PSO method, where in conventional PSO there is a tendency to achieve premature convergence. This condition causes the solution obtained to be the optimum local. With optimal PSS parameters, the optimal PSS performance is obtained. The combination of PSS and excitation is used to reduce the oscillation that occurs in the system. In this research a case study of load addition and load shedding is used. From the simulation results, it is found that system performance is more optimal using CRPSO than using conventional PSO. System performance is shown by the response of the generator speed and rotor angle which results in a small overshoot and a faster settling time when there is an increase in load and also load shedding. Increased system performance is also viewed from the negative system eigenvalue, negative eigenvalue indicates the system is stable.

PL

Stabilizator systemu zasilania (PSS) jest dodatkowym sterowaniem, które zapewnia dodatkowe działania sterujące po stronie wzbudzenia generatora. W tym badaniu zaproponowano metodę strojenia opartą na Craziness Particle Swarm Optimization (CRPSO) w celu optymalizacji parametrów PSS. CRPSO jest rozwinięciem tradycyjnej metody PSO, gdzie w konwencjonalnym PSO istnieje tendencja do osiągnięcia przedwczesnej konwergencji. Stan ten powoduje, że otrzymane rozwiązanie jest optymalne miejscowo. Przy optymalnych parametrach PSS uzyskuje się optymalną wydajność PSS. Połączenie PSS i wzbudzenia służy do zmniejszenia oscylacji występujących w systemie. W tym badaniu wykorzystano studium przypadku dodawania i odciążania. Z symulacji wynika, że wydajność systemu jest bardziej optymalna przy użyciu CRPSO niż przy użyciu konwencjonalnego PSO. Wydajność systemu jest pokazana przez reakcję prędkości generatora i kąta wirnika, co skutkuje niewielkim przeregulowaniem i szybszym czasem ustalania, gdy występuje wzrost obciążenia, a także zmniejszenie obciążenia. Zwiększona wydajność systemu jest również postrzegana z ujemnej wartości własnej systemu, ujemna wartość własna wskazuje, że system jest stabilny.

16

Structural design optimization of steel beamsand frames with web-tapered members usingthe PSO-FEM algorithm

Sych Piotr, Słoński Marek

Computer Assisted Methods in Engineering and Science

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2021

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Vol. 28, no. 1

39--55

EN

This paper presents an algorithm for structural design optimization of steel beams andframes with web-tapered members using the particle swarm optimization (PSO) algorithmand the finite element method (FEM). The design optimization is done in accordancewith Eurocode 3 (EC 3) for the minimum mass. The proposed algorithm is more flexibleand efficient than traditional design methods based on a trial and error approach. Theeffectiveness of the presented PSO-FEM algorithm is evaluated on examples of the sizeoptimization of web-tapered members cross-section. The results show that the PSO-FEM algorithm is feasible and effective for finding useful designs.

17

High quality repetitive control system for a grid-tied converter under distorted grid voltage conditions – design and implementation

Ufnalski Bartlomiej, Straś Andrzej, Grzesiak Lech M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 2

art. no. e136739

EN

The paper features a grid-tied converter with a repetitive current controller. Our goal here is to demonstrate the complete design workflow for a repetitive controller, including phase lead, filtering and conditional learning. All key parameters, i.e., controller gain, filter and fractional phase lead, are designed in a single optimization procedure, which is a novel approach. The description of the design and optimization process, as well as experimental verification of the entire control system, are the most important contributions of the paper. Additionally, one more novelty in the context of power converters is verified in the physical system – a conditional learning algorithm to improve transient states to abrupt reference and disturbance changes. The resulting control system is tested experimentally in a 10 kW converter.

18

Direct least squares and derivative-free optimisation techniques for determining mine-induced horizontal ground displacement

Rusek Janusz, Tajduś Krzysztof

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 1

art. no. e135840

EN

The paper presents the results of analyses concerning a new approach to approximating trajectory of mining-induced horizontal displacements. Analyses aimed at finding the most effective method of fitting data to the trajectory of mining-induced horizontal displacements. Two variants were made. In the first, the direct least square fitting (DLSF) method was applied based on the minimization of the objective function defined in the form of an algebraic distance. In the second, the effectiveness of differential-free optimization methods (DFO) was verified. As part of this study, the following methods were tested: genetic algorithms (GA), differential evolution (DE) and particle swarm optimization (PSO). The data for the analysis were measurements of on the ground surface caused by the mining progressive work at face no. 698 of the German Prospel-Haniel mine. The results obtained were compared in terms of the fitting quality, the stability of the results and the time needed to carry out the calculations. Finally, it was found that the direct least square fitting (DLSF) approach is the most effective for the analyzed registration data base. In the authors’ opinion, this is dictated by the angular range in which the measurements within a given measuring point oscillated.

19

Economic dispatch in power system networks including renewable energy resources using various optimization techniques

Hafiz Abrar Mohamed, Abdelrahman M. Ezzat, Temraz Hesham

Archives of Electrical Engineering

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2021

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Vol. 70, nr 3

643--655

EN

Economic dispatch (ED) is an essential part of any power system network. ED is how to schedule the real power outputs from the available generators to get the minimum cost while satisfying all constraints of the network. Moreover, it may be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED, e.g., Newton-Raphson method Lambda-Iterative technique, Gaussian-Seidel method, etc. All these traditional methods need the generators’ incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and solar are a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, it is attempted to enhance the operation of electrical power system networks via economic dispatch. An ED problem is solved using various techniques, e.g., Particle Swarm Optimization (PSO) technique and Sine-Cosine Algorithm (SCA). Afterwards, the results are compared. Moreover, case studies are executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses are performed on MATLAB software.

20

Coordinated control strategy for microgrid stability maintenance under isolated island operation

Wu Pan, Xu Xiaowei

Archives of Electrical Engineering

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2021

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Vol. 70, nr 2

285--295

EN

In this study, the inverter in a microgrid was adjusted by the particle swarm optimization (PSO) based coordinated control strategy to ensure the stability of the isolated island operation. The simulation results showed that the voltage at the inverter port reduced instantaneously, and the voltage unbalance degree of its port and the port of point of common coupling (PCC) exceeded the normal standard when the microgrid entered the isolated island mode. After using the coordinated control strategy, the voltage rapidly recovered, and the voltage unbalance degree rapidly reduced to the normal level. The coordinated control strategy is better than the normal control strategy.