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1

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Zheng Wenfang, Wang Tieying

Archives of Electrical Engineering

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2024

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Vol. 73, nr 2

481--498

EN

This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

2

Metody śledzenia punktu MPP modułu fotowoltaicznego

Drałus Grzegorz

Przegląd Elektrotechniczny

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2023

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R. 99, nr 9

26--30

PL

W pracy przedstawiono metody śledzenia punktu mocy maksymalnej (MPPT) modułu fotowoltaicznego (PV) pracującego w warunkach zmiennego promieniowania słonecznego i temperatury otoczenia. Do wyznaczania położenia punktu mocy maksymalnej (MPP) dla modułu PV wykorzystano metody inteligencji obliczeniowej. Aby zoptymalizować jakość śledzenia tego punktu, opracowano metodę sterowania ślizgowego przekształtnikiem DC/DC która wykorzystuje możliwość regulacji prądu przekształtnika. Opracowane metody z powodzeniem wykorzystano w badaniu symulacyjnym metod MPPT modułu PV w warunkach szybkich zmian wartości natężenia promieniowania słonecznego i temperatury modułu.

EN

This paper presents methods for tracking the maximum power point (MPPT) of a photovoltaic (PV) module operating under varying solar radiation and ambient temperature conditions. Computational intelligence methods were used to determine the position of the PV module's maximum power point (MPP). To optimize the quality of MPP tracking, a sliding control method for the DC/DC converter was developed that takes advantage of the converter's current control capability. The developed methods were successfully used in a simulation study of the MPPT methods of the PV module under conditions of rapid changes in the solar irradiance and temperature of the module.

3

Fuzzy-based PSO algorithm for transmission line Losses minimization with UPFC

Hussein Shaimaa A., Mahmood Dhari Y., Numan Ali H.

Przegląd Elektrotechniczny

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2023

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R. 99, nr 5

139--144

EN

In this paper, one of the essential types of flexible alternating current transmission systems (FACTS), the Unified Power Flow Controller(UPFC), was used to reduce the losses in transmission lines. A particle swarm optimization (PSO)-based modified fuzzy logic (FL)controller with UPFC was proposed to obtain the optimal location of UPFC and optimum parameters of the normalized fuzzy controller to achieve the objective function of the research and compare the results with the PI-controller. The Newton-Raphson method was employed to perform load flow analysis by MATLAB code/M-file. The proposed method was tested in the IEEE-14bus system, and the results showed that the PSO-FL minimized losses better than PSO-PI.

PL

W artykule wykorzystano jeden z podstawowych typów elastycznych systemów przesyłowych prądu przemiennego (FACTS), czyli Unified Power Flow Controller (UPFC), w celu zmniejszenia strat w liniach przesyłowych. W celu uzyskania optymalnej lokalizacji UPFC i optymalnych parametrów znormalizowanego kontrolera rozmytego w celu osiągnięcia celu badań i porównania wyników z PI zaproponowano zmodyfikowany sterownik z logiką rozmytą (FL) oparty na optymalizacji roju cząstek (PSO) z UPFC. -kontroler. Do wykonania analizy przepływu obciążenia za pomocą kodu MATLAB/pliku M zastosowano metodę Newtona-Raphsona. Zaproponowana metoda została przetestowana w systemie IEEE-14bus, a wyniki wykazały, że PSO-FL minimalizuje straty lepiej niż PSO-PI.

4

Design and Analysis of a Low-profile Microstrip Antenna for 5G Applications using AI-based PSO Approach

Kundu Krishanu, Bhattacharya Ankan, Mohammed Firdous Hussain, Pathak Narendra Nath

Journal of Telecommunications and Information Technology

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2023

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nr 3

68--73

EN

Microstrip antennas are high gain aerials for low-profile wireless applications working with frequencies over 100 MHz. This paper presents a study and design of a low cost slotted-type microstrip patch antenna that can be used in 5G millimeter wave applications. This research focuses on the effect of ground slots and patch slots which, in turn, affect different antenna parameters, such as return loss, VSWR, gain, radiation pattern, and axial ratio. The working frequency range varies from 24 to 28 GHz, thus falling within 5G specifications. A subset of artificial intelligence (AI) known as particle swarm optimization (PSO) is used to approximatively solve issues involving maximization and minimization of numerical values, being highly challenging or even impossible to solve in a precise manner. Here, we have designed and analyzed a low-profile printed microstrip antenna for 5G applications using the AI-based PSO approach. The novelty of the research is mainly in the design approach, compactness of size and antenna applicability. The antenna was simulated with the use of HFSS simulation software.

5

Feature optimization using a two-tier hybrid optimizer in an Internet of Things network

Agrawal Akhileshwar Prasad, Singh Nanhay

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 2

313--326

EN

The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.

6

Optimization of fractional order PI controller using metaheuristics algorithms applied to multilevel inverter for grid connected PV

Boucheriette Wafa, Mechgoug Raihane, Benguesmia Hani

Diagnostyka

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2023

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Vol. 24, No. 3

art. no. 2023311

EN

Due to its multiple advantages in industrial and grid-connected applications, Multi-Level Inverters (MLIs) have increased in popularity in recent years. To improve the efficiency of a grid-connected PV system's integrated multi-level inverter fractional order PI (FOPI) controllers are used to describe the control process. The control system is made up of three control loops based on FOPI controllers: one for controlling the intermediate circuit voltage (Vdc) and the other two for controlling the direct and quadratic currents (Id, Iq) supplied by the multilevel inverter. The proposed controller parameters (Kp, KI, λ) must be selected in order to increase the efficiency of the multi-level inverter while decreasing the total harmonic distortion (THD) of the output current of the inverter as well as voltage. For this we used three meta-heuristic algorithms (PSO, ABC, GWO). The performance of the three controllers PSO-FOPI, ABC-FOPI and GWO-FOPI controller is compared. The findings showed that GWO-FOPI performs better than the other PSO-FOPI and ABC-FOPI in accuracy and total harmonic distortion THD term. The simulation will be conducted using Matlab/Simulink.

7

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.

8

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.

9

Optimization of distributed generation placement using particle swarm optimization method at Sampang feeder

Sulistyowati Riny, Sujono Hari Agus, Cipta Yudha

Przegląd Elektrotechniczny

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2022

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R. 98, nr 11

92--96

EN

Placement of Distributed Generation in a distribution network has increased power losses due to improper DG connection. One technique to reduce power losses is to determine the exact location of the DG connection through optimization. Optimizing the placement of DG connection locations at the substation aims to minimize power losses in the distribution system of the substation. This research was conducted at the substation in Blegah Sampang Regency. The initial power flow obtained in the distribution system power losses of 1560 KW. To optimize the DG connection, the Particle Swarm Optimization (PSO) method is used. This PSO method was chosen because it can provide more stable results according to the inputted fitness. After applying the PSO method to the distribution network system of the substation in Sampang, the minimum power loss in the distribution system reaches 906 KW. The most optimal DG placement is on Bus 5 and the average power losses are reduced by 58%. Uses are duly drawn.

PL

Umieszczenie generacji rozproszonej w sieci dystrybucyjnej spowodowało wzrost strat mocy z powodu niewłaściwego połączenia DG. Jedną z technik zmniejszania strat mocy jest określenie dokładnej lokalizacji połączenia DG poprzez optymalizację. Optymalizacja rozmieszczenia lokalizacji przyłączy DG w podstacji ma na celu zminimalizowanie strat mocy w systemie dystrybucyjnym podstacji. Badania te przeprowadzono w podstacji w Blegah Sampang Regency. Początkowy przepływ mocy uzyskany w systemie rozdzielczym straty mocy 1560 KW. Aby zoptymalizować połączenie DG, stosuje się metodę optymalizacji roju cząstek (PSO). Ta metoda PSO została wybrana, ponieważ może zapewnić bardziej stabilne wyniki w zależności od wprowadzonej sprawności. Po zastosowaniu metody PSO w systemie sieci dystrybucyjnej stacji w Sampang minimalna strata mocy w systemie dystrybucyjnym sięga 906 KW. Najbardziej optymalne umieszczenie DG znajduje się na magistrali 5, a średnie straty mocy są zmniejszone o 58%. Zastosowania są należycie sporządzone.

10

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.

11

Optimizing the Bit-flipping Method for Decoding Low-density Parity-check Codes in Wireless Networks by Using the Artificial Spider Algorithm

Ghaffoori Ali Jasim, Abdul-Adheem Wameedh Riyadh

International Journal of Electronics and Telecommunications

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2022

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Vol. 68, No. 1

109--114

EN

In this paper, the performance of Low-Density Parity-Check (LDPC) codes is improved, which leads to reduce the complexity of hard-decision Bit-Flipping (BF) decoding by utilizing the Artificial Spider Algorithm (ASA). The ASA is used to solve the optimization problem of decoding thresholds. Two decoding thresholds are used to flip multiple bits in each round of iteration to reduce the probability of errors and accelerate decoding convergence speed while improving decoding performance. These errors occur every time the bits are flipped. Then, the BF algorithm with a low-complexity optimizer only requires real number operations before iteration and logical operations in each iteration. The ASA is better than the optimized decoding scheme that uses the Particle Swarm Optimization (PSO) algorithm. The proposed scheme can improve the performance of wireless network applications with good proficiency and results. Simulation results show that the ASA-based algorithm for solving highly nonlinear unconstrained problems exhibits fast decoding convergence speed and excellent decoding performance. Thus, it is suitable for applications in broadband wireless networks.

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

Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction

Pitchandi Perumal

Advances in Science and Technology. Research Journal

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2022

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Vol. 16, no 3

269--281

EN

In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information. These repositories include personal, historic, cultural, and business event images. Image mining is a limited field in research where most techniques look at processing images instead of mining. Very limited tools are found for mining these images, specifically 3D (Three Dimensional) images. Open source image datasets are not structured making it difficult for query based retrievals. Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D. This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors. Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects. Corresponding cultural 3D images are grouped for a 3D reconstruction engine’s optimized performance. Spatial clustering techniques based on density like PVDBSCAN (Particle Varied Density Based Spatial Clustering of Applications with Noise) eliminate image outliers. Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers. Clustering based on Information theory is also used in this work to identify cultural object’s image views which are then reconstructed using 3D motions. The proposed scheme is benchmarked with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to prove the proposed scheme’s efficiency. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.

15

Particle swarm optimization algorithm for solar pv system under partial shading

Abdulla Fawaz S., Hamoodi Ali N., Kheder Abdulaziz M.

Przegląd Elektrotechniczny

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2021

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R. 97, nr 10

87--90

EN

Conventional maximum power point tracking (MPPT) has several demits such as steady-state oscillation and the inability to distinguish between multipacks generated under partial shading conditions (PSC). This paper studies the compression between the conventional Perturb and Observe (P&O) algorithm and the Particle Swarm Optimization ( PSO) algorithm to track global peak (GP) . Matlab Simulink carried out under PSC, the result shows that the PSO algorithm is successful to capture GP with 98.6% efficiency and the P&O algorithm is failed to capture the GP.

PL

Konwencjonalne śledzenie punktu maksymalnej mocy (MPPT) ma kilka wad, takich jak oscylacja stanu ustalonego i niemożność rozróżnienia opakowań zbiorczych generowanych w warunkach częściowego zacienienia (PSC). Ten artykuł bada kompresję pomiędzy konwencjonalnym algorytmem Perturb and Observe (P&O) a algorytmem Particle Swarm Optimization (PSO) w celu śledzenia globalnego piku (GP). Matlab Simulink przeprowadzony w ramach PSC, wynik pokazuje, że algorytm PSO z powodzeniem wychwytuje GP z wydajnością 98,6%, a algorytm P&O nie jest w stanie wychwycić GP.

16

Modeling and scheduling home appliances using nature inspired algorithms for demand response purpose

Haroun Isra, Shareef Hussain, Ibrahim Ahmad Asrul, Khalid Saifulnizam, Idris Abdelrahman Omer

Przegląd Elektrotechniczny

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2021

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R. 97, nr 4

60--66

EN

Demand response (DR) refers to programs used in endeavors to reduce overall power consumption, manage consumption peak hour shifting, and reduce demand on service providers or utilities using different methods. This paper proposes a home appliance scheduler suitable for DR applications. In the proposed method, a controller controls thermal and shiftable loads, where thermal loads are empirical models that consider different factors. They produce the load profile of the home in consideration of different input parameters, e.g., setpoints and user tolerance ranges, and various factors, e.g., the room’s physical structure and the external environment. A scheduler uses the controller to implement load shifting using the whale optimization algorithm, particle swarm optimization, and gray wolf optimization (GWO) algorithms for three different occupancy and price schemes. Acceptable results were obtained by applying the models using various outer temperatures and user tolerance ranges. The results also demonstrate cost reduction of 38.59% with GWO for the first occupancy scheme.

PL

Demand Response (DR) oznacza programy do redukcji poboru mocy, doboru czasu pracy, odbiorników energii elektrycznej. W artykule zaproponowano program użycia urządzeń domowych spełniający wymagania DR z uwzględnieniem termicznych warunków pracy. . Zaproponowano algorytmy optymalizacji.

17

An inventory model to study the effect of the probabilistic rate of carbon emission on the profit earned by a supplier

Bhattacharjee Nabajyoti, Sen Nabendu

Operations Research and Decisions

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2021

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Vol. 31, No. 4

5--33

EN

Sensitivity analysis of parameters is usually more important than the optimal solution when it comes to linear programming. Nevertheless, in the analysis of traditional sensitivities for a coefficient, a range of changes is found to maintain the optimal solution. These changes can be functional constraints in the coefficients, such as good values or technical coefficients, of the objective function. When real-world problems are highly inaccurate due to limited data and limited information, the method of grey systems is used to perform the needed optimisation. Several algorithms for solving grey linear programming have been developed to entertain involved inaccuracies in the model parameters; these methods are complex and require much computational time. In this paper, the sensitivity of a series of grey linear programming problems is analysed by using the definitions and operators of grey numbers. Also, uncertainties in parameters are preserved in the solutions obtained from the sensitivity analysis. To evaluate the efficiency and importance of the developed method, an applied numerical example is solved.

18

Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images

Shehab Jinan N., Abdulkadhim Hussein A.

International Journal of Electronics and Telecommunications

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2021

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Vol. 67, No. 3

403--408

EN

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time.

19

Optimal placement and sizing of FACTS devices based on Autonomous Groups Particle Swarm Optimization technique

Shehata Ahmed A., Refaat Ahmed, Ahmed Mamdouh K., Korovkin Nikolay V.

Archives of Electrical Engineering

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2021

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Vol. 70, nr 1

161--172

EN

This paper presents the application of Flexible Alternating Current Transmission System (FACTS) devices based on heuristic algorithms in power systems. The work proposes the Autonomous Groups Particle Swarm Optimization (AGPSO) approach fort he optimal placement and sizing of the Static Var Compensator (SVC) to minimize thetotal active power losses in transmission lines. A comparative study is conducted with other heuristic optimization algorithms such as Particle Swarm Optimization (PSO), Time-varying Acceleration Coefficients PSO (TACPSO), Improved PSO (IPSO), Modified PSO(MPSO), and Moth-Flam Optimization (MFO) algorithms to confirm the efficacy of the proposed algorithm. Computer simulations have been carried out on MATLAB with the MATPOWER additional package to evaluate the performance of the AGPSO algorithm on the IEEE 14 and 30 bus systems. The simulation results show that the proposed algorith moffers the best performance among all algorithms with the lowest active power losses and the highest convergence rate.

20

Genetic algorithm-PID controller for model order reduction pantographcatenary system

Al-Awad Nasir A., Abboud Izz K., Al-Rawi Muaayed F.

Applied Computer Science

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2021

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Vol. 17, no 2

28--39

EN

Controlling the contact force between the pantograph and the catenary has come to be a requirement for improving the performances and affectivity of high-speed train systems Indeed, these performances can also significantly be decreased due to the fact of the catenary equal stiffness variation. In addition, the contact force can also additionally differ and ought to end up null, which may additionally purpose the loss of contact. Then, in this paper, we current an active manipulate of the minimize order model of pantograph-catenary system. The proposed manipulate approach implements an optimization technique, like particle swarm (PSO), the usage of a frequent approximation of the catenary equal stiffness. All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. Then, the usage of Genetic Algorithm with Proportional-Integral-derivative (G.A-PID) as proposed controller appeared optimum response where, the contacts force consequences to be virtually equal to its steady reference. Finally it seems the advantageous of suggestion approach in contrast with classical manipulate strategies like, Internal mode control(IMC) method, linear quadratic regulator (LQR). The outcomes via the use of MATLAB simulation, suggests (G.A-PID) offers better transient specifications in contrast with classical manipulate.