Protection of Sensitive Loads in Distribution Systems Using a BSFCL-DVR System

Firouzi, Mehdi; Mobayen, Saleh; Kartijkolaie, Hossein Shahbabaei; Nasiri, Mojtaba; Chen, Chih‐Chiang

doi:10.3390/s21051615

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…Around the world, every city has distinctive qualities that set it apart from other cities. But the ability of sustainable cities (SCs) to meet present-day demands without jeopardizing the needs of the future is what distinguishes them from communities that exhaust their energies/resources [ 1 , 2 , 3 ]. While every SC may have something distinctive to offer, it is the ability to balance social, economic, and environmental considerations that allows an SC to endure and prosper over time.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Functionality of a Grid-Connected Photovoltaic System in a Distant Egyptian Region Using an Optimized Dynamic Voltage Restorer: Application of Artificial Rabbits Optimization

Ibrahim

Alkuhayli

Béroual

et al. 2023

Sensors

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Photovoltaic (PV) systems are crucial to the production of electricity for a newly established community in Egypt, especially in grid-tied systems. Power quality (PQ) issues appear as a result of PV connection with the power grid (PG). PQ problems cause the PG to experience faults and harmonics, which affect consumers. A series compensator dynamic voltage restorer (DVR) is the most affordable option for resolving the abovementioned PQ problems. To address PQ difficulties, this paper describes a grid-tied PV combined with a DVR that uses a rotating dq reference frame (dqRF) controller. The main goal of this study is to apply and construct an effective PI controller for a DVR to mitigate PQ problems. The artificial rabbits optimization (ARO) is used to obtain the best tune of the PI controller. The obtained results are compared with five optimization techniques (L-SHADE, CMAES, WOA, PSO, and GWO) to show its impact and effectiveness. Additionally, Lyapunov’s function is used to analyze and evaluate the proposed controller stability. Also, a mathematical analysis of the investigated PV, boost converter, and rotating dqRF control is performed. Two fault test scenarios are examined to confirm the efficacy of the suggested control approach. The parameters’ (voltage, current, and power) waveforms for the suggested system are improved, and the system is kept running continuously under fault periods, which improves the performance of the system. Moreover, the findings demonstrate that the presented design successfully keeps the voltage at the required level with low THD% values at the load side according to the IEEE standards and displays a clear enhancement in voltage waveforms. The MATLAB/SIMULINK software is used to confirm the proposed system’s performance.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Functionality of a Grid-Connected Photovoltaic System in a Distant Egyptian Region Using an Optimized Dynamic Voltage Restorer: Application of Artificial Rabbits Optimization

Ibrahim

Alkuhayli

Béroual

et al. 2023

Sensors

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“…Lithium-ion batteries are used as the power supply system of electric vehicles, and incorporated bridge type superconducting fault current limit and dynamic voltage restorer are used to improve the power distribution system. 7,8 The irreversible process can lead to continuous capacity fading and eventually battery failure. Failure to take safety measures such as replacement or maintenance in time can have catastrophic consequences.…”

Section: Introductionmentioning

confidence: 99%

A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery

Gao

Liu

Zhu

et al. 2022

Measurement and Control

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For accelerating the technology development and facilitating the reliable operation of lithium-ion batteries, accurate prediction for battery remaining useful life (RUL) are both critical. In this paper, a 1D CNN-BiLSTM method is proposed to extract the RUL prediction of lithium-ion battery of Electric Vehicles (EVs). By using one dimensional convolutional neural network (1D CNN) and bidirectional long short-term memory (BiLSTM) neural network simultaneously, selecting the ELU activation function to apply to the convolutional layer, a hybrid neural network is proposed to improve the accuracy and stability of lithium-ion battery RUL prediction. The 1D CNN is used to fully mine the deep features of lithium-ion SOH data, while the BiLSTM is adopted to study the deep features in two directions, and the RUL prediction of lithium-ion battery is output through dense layer. To verify the effectiveness of the proposed method, the battery data of the National Aeronautics and Space Administration (NASA) are utilized to make some comparisons among the RNN model, LSTM model, BiLSTM model and hybrid neural network model. The results show that the hybrid one has higher generalization ability and prediction accuracy than the others.

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“…The suggested observer-based control scheme needs only Cartesian position measurement. [28][29][30][31][32][33][34][35] The contributions of this paper lie in twofold. Firstly, proposing a new Enhanced Fruit fly optimization (EFFO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

Abed

Rashid

Abedi

et al. 2022

Measurement and Control

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This work proposes a new kind of trajectory tracking controller for the differential drive mobile robot (DDMR), namely, the nonlinear neural network fractional-order proportional integral derivative (NNFOPID) controller. The suggested controller’s coefficients comprise integral, proportional, and derivative gains as well as derivative and integral powers. The adjustment of these coefficients turns the design of the proposed NNFOPID control further problematic than the conventional proportional-integral-derivative control. To handle this issue, an Enhanced Fruit Fly Swarm Optimization algorithm has been developed and proposed in this work to tune the NNFOPID’s parameters. The enhancement achieved on the standard fruit fly optimization technique lies in the increased uncertainty in the values of the initialized coefficients to convey a broader search space. subsequently, the search range is varied throughout the updating stage by beginning with a big radius and declines gradually during the course of the searching stage. The proposed NNFOPID controller has been validated its ability to track specific three types of continuous trajectories (circle, line, and lemniscate) while minimizing the mean square error and the control energy. Demonstrations have been run under MATLAB environment and revealed the practicality of the designed NNFOPID motion controller, where its performance has been compared with that of a nonlinear Neural Network Proportional Integral Derivative controller on the tracking of one of the aforementioned trajectories of the DDMR.

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Protection of Sensitive Loads in Distribution Systems Using a BSFCL-DVR System

Cited by 6 publications

References 44 publications

Enhancing the Functionality of a Grid-Connected Photovoltaic System in a Distant Egyptian Region Using an Optimized Dynamic Voltage Restorer: Application of Artificial Rabbits Optimization

Enhancing the Functionality of a Grid-Connected Photovoltaic System in a Distant Egyptian Region Using an Optimized Dynamic Voltage Restorer: Application of Artificial Rabbits Optimization

A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

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