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1

Smart control of energy storage system in residential photovoltaic systems for economic and technical efficiency

Kaczorowska Dominika, Rezmer Jacek, Janik Przemysław, Sikorski Tomasz

Archives of Electrical Engineering

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2023

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Vol. 72, nr 1

81--102

EN

In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus, a new unconventional case was considered in which technical and economic objectives were combined, leading to an optimum benefit of €255.17 with 560 discharge cycles per year. Further research of the algorithm will focus on the development of a fitness function coupled to the power system model.

2

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.

3

Green last-mile route planning for efficient e-commerce distribution

Kunnapapdeelert Siwaporn, Johnson James Vincent, Phalitnonkiat Passarin

Engineering Management in Production and Services

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2022

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Vol. 14, no. 1

1--12

EN

This study aims to design vehicle routes based on cost minimisation and the minimisation of greenhouse gasses (GHG) emissions to help companies solve the vehicle routing problem with pickup and delivery (VRPPD) via particle swarm optimisation (PSO). An effective metaheuristics search technique called particle swarm optimisation (PSO) was applied to design the optimal route for these problems. Simulated data from Li and Lim (2001) were used to evaluate the PSO performance for solving green vehicle routing problems with pickup and delivery (Green VRPPD). The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to customers living in a specific area called a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed. PSO has been confirmed for solving VRPPD effectively. The study compared the results based on the use of two different objective functions with fuel consumption from diesel and liquefied petroleum gas (LPG). It indicates that solving VRPPD by considering the emissions of direct greenhouse gases as an objective function provides cleaner routes, rather than considering total cost as the objective function for all test cases. However, as Green VRPPD requires more vehicles and longer travel distances, this requires a greater total cost than considering the total cost as the objective function. Considering the types of fuels used, it is obvious that LPG is more environmentally friendly than diesel by up to 53.61 %. This paper should be of interest to a broad readership, including those concerned with vehicle routing problems, transportation, logistics, and environmental management. The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed.

4

Seismic bearing capacity of shallow strip footing embedded in slope resting on two-layered soil

Debnath Litan

Studia Geotechnica et Mechanica

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2021

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Vol. 43, nr 3

285--306

EN

In this paper, the limit equilibrium method with the pseudo-static approach is developed in the evaluation of the influence of slope on the bearing capacity of a shallow foundation. Particle swarm optimisation (PSO) technique is applied to optimise the solution. Minimum bearing capacity coefficients of shallow foundation near slopes are presented in the form of a design table for practical use in geotechnical engineering. It has been shown that the seismic bearing capacity coefficients reduce considerably with an increase in seismic coefficient. Besides, the magnitude of bearing capacity coefficients decreases further with an increase in slope inclination.

5

Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform

George S. Thomas, Subathra M. S. P., Sairamya N. J., Susmitha L., Joel Premkumar M.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

709--728

EN

Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive neural activity which can be diagnosed by inspecting the electroencephalography (EEG) signals visually. The manual inspection of EEG signals is subjected to human error and is a tedious process. Further, an accurate diagnosis of generalized and focal epileptic seizures from normal EEG signals is vital for the supervision of pertinent treatment, life advancement of the subjects, and reduction in cost for the subjects. Hence the development of automatic detection of generalized and focal epileptic seizures from normal EEG signals is important. An approach based on tunable-Q wavelet transform (TQWT), entropies, Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) is proposed in this work for detection of epileptic seizures and its types. Two EEG databases namely, Karunya Institute of Technology and Sciences (KITS) EEG database and Temple University Hospital (TUH) database consisting of normal, generalized and focal EEG signals is used in this work to analyze the performance of the proposed approach. Initially, the EEG signals are decomposed into sub-bands using TQWT and the non-linear features like log energy entropy, Shannon entropy and Stein's unbiased risk estimate (SURE) entropy is computed from each sub-band. The informative features from the computed feature vectors are selected using PSO and fed into ANN for the classification of EEG signals. The proposed algorithm for KITS database achieved a maximum accuracy of 100% for four experimental cases namely, (i) normal-focal, (ii) normal-generalised, (iii) normal-focal + generalised and (iv) normal-focal-generalised. The TUH database achieved an accuracy of 95.1%, 97.4%, 96.2% and 88.8% for the four experimental cases. The proposed approach is promising and able to discriminate the epileptic seizure types with satisfactory classification performance.

6

An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

Alihodzic Adis, Delalic Sead, Gusic Dzenan

Annals of Computer Science and Information Systems

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2020

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Vol. 21

207--214

EN

To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.

7

Finding optimal location of FACTS device for dynamic Reactive Power compensation using Genetic Algorithm and Particle Swarm Optimisation (PSO)

Pavleka Josip, Nikolovski Srete, Stojkov Marinko

Przegląd Elektrotechniczny

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2019

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R. 95, nr 8

86--91

EN

This paper presents advantage of using a FACTS device for dynamic Reactive Power compensation. Simulation model was built in MATLAB Simulink software to prove mathematical constraints. Determination of the most favourable location and size of the compensation devices from the aspect of losses, power quality, costs are calculated as a fitness function developed by genetic algorithm. Optimisation was done by Particle swarm optimization (PSO). Finally, cut convergence time and significant potential of usage such type of PSO optimisation method for determination of future investments are shown. This algorithm is tested to determine optimal location of FACTS device in railway application, instead of the methods and algorithms in transmission or distribution power system used until now.

PL

W artykule zaprezentowano korzyści ze stosowanie FACTS do dynamicznej kompensacji mocy biernej. Symulacje miały na celu określenie najlepszego położenia i roz,miaru urządzeń kompensujących z punktu widzenia jakości energii i kosztów. Zastosowano algorytm genetyczny PSO do optymalizacji i analizy przyszłych inwestycji.

8

Swarm algorithms for NLP : the case of limited training data

Tambouratzis George, Vassiliou Marina

Journal of Artificial Intelligence and Soft Computing Research

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2019

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Vol. 9, No. 3

219--234

EN

The present article describes a novel phrasing model which can be used for segmenting sentences of unconstrained text into syntactically-defined phrases. This model is based on the notion of attraction and repulsion forces between adjacent words. Each of these forces is weighed appropriately by system parameters, the values of which are optimised via particle swarm optimisation. This approach is designed to be language-independent and is tested here for different languages. The phrasing model’s performance is assessed per se, by calculating the segmentation accuracy against a golden segmentation. Operational testing also involves integrating the model to a phrase-based Machine Translation (MT) system and measuring the translation quality when the phrasing model is used to segment input text into phrases. Experiments show that the performance of this approach is comparable to other leading segmentation methods and that it exceeds that of baseline systems.

9

Sposób wyboru optymalnej trajektorii lotu manewru antykolizyjnego realizowanego w otoczeniu ruchomych przeszkód

Graffstein J.

Pomiary Automatyka Robotyka

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2018

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R. 22, nr 4

5--11

PL

Do rozwiązania problemu unikania przeszkód przez poruszający się samolot w przestrzeni powietrznej niezbędne jest wykrycie zagrożenia kolizji oraz wykonanie bezpiecznego manewru w celu ominięcia zagrażających przeszkód. W pracy przedstawiono sposób wykrywania niebezpieczeństwa zderzenia z przeszkodą dla przypadku, gdy w otoczeniu samolotu znajduje się wiele ruchomych obiektów. Zaproponowano sposób wyboru optymalnej trajektorii manewru antykolizyjnego, i potwierdzono jej wykonalność. Wybór trajektorii przeprowadzono rozwiązując zagadnienie optymalizacji metodą roju cząstek (PSO). W tym celu zaproponowano postać funkcji celu i przedstawiono wyniki analizy jej przebiegu dla różnych współczynników wagowych. Wykonane symulacje lotu wzdłuż optymalnej trajektorii manewru antykolizyjnego potwierdziły wykonalność takiego manewru.

EN

For solving the airplane to obstacle collision avoidance problem two methods are necessary: one, for detecting a collision threat, and the other one, for synthesizing a safe manoeuvre avoiding threating obstacles. In the article a method for detecting a threat of collision to obstacle was presented for the case of many obstacles moving within the neighbourhood of the airplane. Methods for optimal anti collision trajectory synthesis and for proving the workability of such a result were proposed too. A solution of an optimisation problem, obtained by the Swarm of Particles Optimization was used for trajectory synthesis. A form of quality index was proposed for this task and the analyses of its behaviour for several values of weighting factors were presented. Results of simulations of flight along an optimal, anti collision manoeuvre trajectory proved that such a manoeuvre is workable.

10

Coordinated control of phase shifting transformers in the power system

Korab R., Owczarek R., Połomski M.

Acta Energetica

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2017

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nr 3

97--103

EN

In response to the growing problem of unscheduled flows, more and more transmission system operators in Europe provide their systems with phase shifting transformers (PST). However, the operations of several PSTs deployed close to each other must be coordinated for them to be effective and to avoid their harmful interactions. Coordination of a group of such devices leads to a problem of multidimensional optimisation. This paper presents a method of optimal PST setting based on the particle swarm optimisation (PSO) algorithm. As an optimisation criterion the minimization of unscheduled flow through the given system has been applied. The impact of the number of particles in the swarm and their maximum permissible velocity on the optimisation algorithm’s efficiency was analysed. Results are presented for a 118-node test grid.

PL

W odpowiedzi na rosnący problem przepływów nieplanowych coraz większa liczba operatorów systemów przesyłowych w Europie wyposaża swoje systemy w przesuwniki fazowe (PST). Jednakże użycie kilku PST zainstalowanych geograficznie lub elektrycznie blisko siebie musi być skoordynowane w celu skutecznego wykorzystania tych urządzeń i uniknięcia ich niekorzystnych interakcji. Koordynacja grupy takich urządzeń prowadzi do problemu optymalizacji wielowymiarowej. W artykule przedstawiono metodę optymalizacji nastaw PST opartą na algorytmie roju cząstek (PSO). Jako kryterium optymalizacji zastosowano minimalizację przepływu nieplanowego przez dany system. Przeanalizowano wpływ liczby cząstek roju oraz ich maksymalnej dozwolonej prędkości na efektywność algorytmu optymalizacji. Przedstawiono wyniki dla sieci testowej zawierającej 118 węzłów.

11

Efficiency Improvement of Axial Flux PM Motor Using Particle Swarm Optimisation

Cvetkovski G., Petkovska L.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 2b

38--41

EN

In this paper a particle swarm based optimal design of axial field permanent magnet motor (AFPMM) is proposed. This approach employs a particle-swarm-optimization (PSO) technique to search for optimal design solution of an AFPMM based on the efficiency value of the motor. A comparative analysis of the optimised solution and the prototype is presented and it is based on the values of the optimised objective function, on the values of the optimisation parameters, and on a set of electric and magnetic parameters of the motor.

PL

W artykule opisano wykorzystanie metody optymalizacji roju cząstek w projektowaniu maszyny synchronicznej z magnesami trwałymi o strumieniu osiowym (ang. AFPMM). Dokonano analizy porównawczej wyznaczonych optymalizacji oraz przedstawiono prototyp oparty na wartościach parametrów optymalizacji oraz parametrach elektrycznych i magnetycznych maszyny.

12

Porównanie skuteczności wybranych dwu metod optymalizacji

Grzyb A., Kuczek T.

Pomiary Automatyka Kontrola

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2011

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R. 57, nr 11

1421-1424

PL

W artykule zaprezentowano dwa stosunkowo nowe algorytmy stosowane do optymalizacji bez ograniczeń funkcji jednej lub wielu zmiennych. Są to algorytmy: ewolucji różnicowej oraz roju cząstek. Przedstawiono w skrócie cechy charakterystyczne algorytmów, najważniejsze informacje dotyczące zasad ich działania. Ponadto opisano sposób ich badania, mający na celu ocenę skuteczności tych algorytmów. Zamieszczono wyniki badań dotyczące kilku wybranych funkcji testowych oraz sformułowano uwagi dotyczące porównania skuteczności badanych metod.

EN

The paper presents two relatively new algorithms used for optimisation without limitations of single- or multi-variable functions. They are algorithms of differential evolution and particle swarm optimisation. The paper describes characteristic features of the two algorithms and provides vital information about their functioning. Moreover, the paper presents methods used to estimate the algorithm effectiveness. The comparison of efficiency was conducted on the basis of several specially selected test functions. The functions can be found in [5]. The optimum point is known for these functions. For each of the functions, numerous optimisations using various sequences of pseudorandom numbers were conducted [1]. The examination results for a few test function are given and the effectiveness of the tested methods is discussed. The algorithm of the differential evolution method is more reliable than that of the particle swarm method because the latter is often ineffective with multi-variable functions.