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An optimized FO-PID controller and predictive current control of the APF connected AWPS for power quality improvement

Hamouda Noureddine, Babes Badreddine, Kahla Sami, Hamouda Cherif, Uwe Rädel

Przegląd Elektrotechniczny

2023

R. 99, nr 5

102--107

This paper deals with the design of a new topology of arc welding power supply (AWPS) based on an active power filter (APF) suitable to feed an arc welding machine. The principal task of this welding topology is to ensure a good welding process and ensure a unity power factor correction (PF), on the grid side, an active power filter (APF) is connected in parallel between the three-phase full bridge diode rectifier and the grid. The utility of the APF is to provide a unity power factor correction of arc welding power supply (AWPS). The predictive current control (PCC) and self-tuning filter (STF) are used to control the APF. On the arc welding machine side, an optimized FO-PID controller is used to control the welding current and voltage, by controlling the full bridge buck circuit, which offers many exceptional features, like a rapid response to load and grid voltage variations, and inherent short-circuit current limit which results in an improved welding performance and weld bead quality. the performance of the proposed configuration is examined in regards to its power quality given by reading the total harmonic distortion (THD) of the grid current is 3.47%, ensuring the best regulation of the DC-link voltage of APF, and providing a good tuning of the welding voltage and current. The parameters of the FO-PID controller are extracted by a Grey Wolf Optimization approach (GWO). Simulation results are noticed as satisfactory and prove the stability, accuracy, and dynamic response of the synthesized optimized regulating system.

Artykuł dotyczy zaprojektowania nowej topologii zasilania spawarki łukowej (AWPS) opartej na aktywnym filtrze mocy (APF) odpowiednim do zasilania spawarki łukowej. Podstawowym zadaniem tej topologii spawania jest zapewnienie dobrego procesu spawania i zapewnienie jednościowej korekcji współczynnika mocy (PF), po stronie sieci aktywny filtr mocy (APF) jest włączony równolegle pomiędzy trójfazowy prostownik diodowy pełnomostkowy i siatkę. Użyteczność APF polega na zapewnieniu jednościowej korekcji współczynnika mocy zasilacza do spawania łukowego (AWPS). Predykcyjne sterowanie prądem (PCC) i samodostrajający się filtr (STF) służą do sterowania APF. Po stronie spawarki łukowej zoptymalizowany kontroler FO-PID służy do sterowania prądem i napięciem spawania poprzez sterowanie obwodem pełnego mostka buck, który oferuje wiele wyjątkowych funkcji, takich jak szybka reakcja na zmiany obciążenia i napięcia sieci oraz nieodłączne ograniczenie prądu zwarciowego, co skutkuje lepszą wydajnością spawania i jakością spoiny. wydajność proponowanej konfiguracji jest badana pod kątem jakości zasilania podanej przez odczyt całkowitego zniekształcenia harmonicznego (THD) prądu sieciowego wynoszącego 3,47%, co zapewnia najlepszą regulację napięcia obwodu pośredniego APF i zapewnia dobre dostrojenie napięcia i prądu spawania. Parametry regulatora FO-PID są pozyskiwane metodą optymalizacji Gray Wolf (GWO). Wyniki symulacji oceniane są jako zadowalające i świadczą o stabilności, dokładności i dynamicznej odpowiedzi zsyntetyzowanego zoptymalizowanego układu regulacyjnego.

Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation

He Ping, Dong Jie, Wu Xiaopeng, Yun Lei, Yang Hua

Archives of Electrical Engineering

2023

Vol. 72, nr 3

613--628

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.

Rolling Bearing Fault Diagnosis Method Based on Improved Variational Mode Decomposition and Information Entropy

Ge Liang, Fan Wen, Xiao Xiaoting, Gan Fangji, Lai Xin, Deng Hongxia, Huang Qi

Engineering Transactions

2022

Vol. 70, iss. 1

23--51

Due to the complex randomness and nonlinearity of rolling bearing vibration signal, it is challenging to extract fault features effectively. By analyzing the vibration mechanism of rolling bearing, it is found that the vibration signal of local damage defects of rolling bearing has the characteristics of periodic impact and amplitude modulation. The variational mode decomposition (VMD) algorithm has a good advantage in dealing with nonlinear and nonstationary signals and decomposing a signal into different modes. However, VMD has the problem of parameter selection, which directly affects the performance of VMD processing, and causes mode aliasing. Therefore, a rolling bearing fault diagnosis method based on improved VMD is proposed. A new fitness function combining differential evolution (DE) algorithm with gray wolf optimization (GWO) algorithm is proposed to form a new hybrid optimization algorithm, named DEGWO. The simulation results show that the improved VMD method based on DEGWO can adaptively remove the noise according to the characteristics of the signal and restore the original characteristics of the vibration signal. Finally, in order to verify the advantages of the research, the information entropy is extracted from the data of 1000 samples in the bearing database of Case Western Reserve University as the feature set, which is input into support vector machine (SVM) for fault diagnosis test. The results show that the diagnostic accuracy of this method is 96.5%, which effectively improved the accuracy of rolling bearing fault diagnosis.