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In this study, we performed a detailed simulation of the PIDRN controller associated with a three-phase converter, taking into account different initial battery charging conditions. After introducing the concept of PIDRN and explaining the operation of the three-phase converter, we proceeded to model the system, defining the necessary parameters. We then configured several simulations, varying the initial charging conditions of the battery, and analyzed the numerical results obtained. This comparative analysis revealed variable system performance depending on the initial battery charge level, highlighting advantages and disadvantages in each case. In particular, we found that the PIDRN controller proves to be an optimal choice for this type of converter, thanks to its ability to effectively regulate voltage and current under varying battery charging conditions. We discussed the implications of these findings. In conclusion, this study provides an in-depth overview of the performance of the PIDRN controller in a three-phase converter context and highlights the importance of taking into account the initial battery conditions in the design and optimization of energy control.

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.

This paper seeks to improve mobile communications performance by integrating artificial intelligence (AI) techniques. The study is centered on device-to-device (D2D) communication, which has emerged as a significant aspect in the 5th generation of mobile networks (5G) and is expected to be extended to the 6th generation (6G). Based on the fuzzy system, the proposed solution tackles the critical issue of energy harvesting in D2D communication. The solution presents a support system that selects the best relay for the transmitter-receiver communication to minimize energy consumption and maximize the network’s lifetime. Minimizing energy consumption optimizes the network’s lifetime, thus providing a reliable and efficient communication system. The approach taken in this study offers a novel perspective in addressing the energy harvesting challenge in D2D communication. It is expected to have a significant impact on the performance of mobile communications.

Isolated microgrids (MGs) face challenges in performance stability and active/reactive power sharing as a result of frequency/voltage deviations and mismatched line impedance issues. In this paper, a consensus-based multiagent system (MAS) is proposed as a solution to restore voltage/frequency deviations and enable true power sharing. The invention of an Intelligent Distributed Secondary Control Scheme (IDSCS) can efficiently achieve hoped-for outcomes. The proposed IDSCS features estimation and compensation sublayers. For the estimation sublayer, discrete dynamic consensus algorithm-based state estimators are presented to collect average information of frequency, voltage, and reactive power. Each DG is viewed as an agent sharing information with its immediate neighbors through a sparse cyber communication network. In the compensation sublayer, online tuned proportional integral (PI) controllers using artificial neural networks (ANNs) are proposed as an intelligent voltage and frequency compensators. This combination uses the simplicity of the PI controller mathematical formula and ANN’s ability to deal with parameter variations and nonlinearity. Due to the global nature of the frequency parameter, the active power-sharing compensator is unnecessary. For compensating reactive power deviations, ANNs-based reactive power controllers are proposed. Furthermore, at the primary control level, the proposed strategy employs discrete-time proportional resonant (PR) controllers in a stationary reference frame, eliminating the need for any αβ/dq or dq/αβ transformations. Distributed implementation of the proposed method guarantees system scalability without MG topology or demand pattern expertise. The control scheme was validated using hypothetical MAS in MATLAB Simulink platform. The simulation findings indicate the proposed MG system can effectively distribute power among the DGs while maintaining voltage and frequency stable.

Solar energy provides one of the most favorable options regarding the transition to clean energy sources. The parameters of a photovoltaic (PV) system play determine its performance under various scenarios. The PV model parameter estimation is an example of nonlinear planning. This study proposes a novel use of the established Smell Agent Optimizer (SAO) algorithm to anticipate the undefined parameters of the PV model's single-diode and two-diode equivalent circuits. This study aims to create a precise PV model that can accurately characterize its performance under changing operational conditions. The desired objective function is defined as the square of the mean squared error between the model's current-voltage curve and the measured curve. [ABSTRACT FROM AUTHOR]

Air quality forecasting is a critical environmental challenge with significant implications for public health and urban planning. Conventional machine learning models, although quite effective, require data collection, which can be hampered by issues relating to privacy and data security. Federated Learning (FL) overcomes these limitations by enabling model training across decentralized data sources without compromising data privacy. This study describes a federated learning approach to predict the Air Quality Index (AQI) based on data from several Internet of Things (IoT) sensors located in different urban locations. The proposed approach trains a model using data from different sensors while preserving the privacy of each data source. The model uses local computational resources at the sensor level during the initial data processing and training, sharing only the model updates to the central location. The results show that the performance of the proposed FL model is comparable to a centralized model and ensures better data privacy with reduced data transmission requirements. This study opens new doors to real-time, scalable, and efficient air quality monitoring systems. The proposed method is quite significant for smart city initiatives and environmental monitoring, as it provides a solid framework for using IoT technology while preserving privacy. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenge of real-time identification of tomato leaf diseases using edge computing. Traditional plant disease detection methods rely on centralized cloud-based solutions that suffer from latency issues and require substantial bandwidth, making them less viable for real-time applications in remote or bandwidth-constrained environments. In response to these limitations, this study proposes an on-the-edge processing framework employing Convolutional Neural Networks (CNNs) to identify tomato diseases. This approach brings computation closer to the data source, reducing latency and conserving bandwidth. This study evaluates various pre-trained models, including MobileNetV2, InceptionV3, ResNet50, and VGG19 against a custom CNN, training and validating them on a comprehensive dataset of tomato leaf images. MobileNetV2 demonstrated exceptional performance, achieving an accuracy of 98.99%. The results highlight the potential of edge AI to revolutionize disease detection in agricultural settings, offering a scalable, efficient, and responsive solution that can be integrated into broader smart farming systems. This approach not only improves disease detection accuracy but can also provide actionable insights and timely alerts to farmers, ultimately contributing to increased crop yields and food security. [ABSTRACT FROM AUTHOR]

The robustness and longevity of agricultural robots, specifically those utilizing caterpillar tracks for coconut harvesting, are based on understanding their strain, stress, and load thresholds. This study delves into the deformation characteristics of caterpillar track systems, pinpointing critical structural vulnerabilities and potential points of failure. Through a meticulous analysis of the maximum allowed strain and stress thresholds, this study unravels crucial insights to enhance performance and reliability in coconut field operations. Leveraging the power of ANSYS for structural analysis and simulation under varied constraints, this study aims to fortify the structural integrity of agricultural robots. By offering valuable insights and solutions, this study paves the way for advancements in agricultural robotics technology, ensuring that these machines can endure rigorous tasks while maintaining peak functionality. [ABSTRACT FROM AUTHOR]

Combining Intelligent Reflecting Surfaces (IRSs) with Non-Orthogonal Multiple Access (NOMA) effectively enhances communication. This study introduces a NOMA-assisted Downlink Transmission (DT) system, emphasizing Energy Efficiency (EE) optimization. EE, crucial in Wireless Communications (WCs), measures data transmission relative to energy consumption. This study focuses on a Deep Deterministic Policy Gradient (DDPG) algorithm that intelligently adjusts IRS phase-shift matrices and access point beamforming in NOMA DT. Beamforming directs signals to users for optimal strength and quality, while phase shift control enhances signal coverage and quality. Strategic IRS placement improves user signal transmissions. The simulation results demonstrate significantly improved EE compared to other algorithms, such as Deep Q Network (DQN) and Proximal Policy Optimization (PPO), showcasing the effectiveness of the combined IRS and NOMA approach in enhancing communication systems' EE. [ABSTRACT FROM AUTHOR]

With the dramatic increase in mobile users and wireless devices accessing the network, the performance of 5G wireless communication systems is severely challenged. Reconfigurable Intelligent Surface (RIS) has received much attention as one of the promising technologies for 6G due to its ease of deployment, low power consumption, and low price. This study aims to improve accuracy, reliability, and the capacity to estimate channel characteristics between transmitter and receiver. However, this is practically challenging for the following reasons. Due to the lack of active components for baseband signal processing, low-cost passive RIS elements can only reflect incident signals but without the capability to transmit/receive pilot signals for channel estimation as active transceivers in conventional wireless communication systems. This study presents different channel estimation methods for RIS-MIMO systems that use deep learning techniques. [ABSTRACT FROM AUTHOR]

Power transformers are a fundamental component of the modern power distribution network. The fault-free operation of step-up and step-down transformers is of prime importance to the continuous supply of electrical energy to the consumers. To ensure such efficient operation, power distribution companies carry out routine maintenance of distribution transformers through preplanned schedules. The efficacy of such maintenance depends on a proper understanding of the transformer and its components and efficient prediction of faults in these components. There are several components whose condition can be studied to predict transformer failures and therefore the overall health of a transformer. These include transformer windings, insulations, transformer oil, core insulations, and ferromagnetic cores. This work develops a new, simplified fuzzy logic–based method to predict the health of a transformer by taking into account the state of several individual components. Case studies are used to demonstrate the efficacy of the developed method.

Usually, electric vehicle systems are based on various modules that should ensure the high power and stability of the vehicle on the track. The majority of these components are linked to the charging mechanism. In this regard, dynamic wireless power transfer is a practical method to solve electric vehicle range anxiety and reduce the cost of onboard batteries. Wireless recharging has long been common with pure electric vehicles and is designed to allow charging even when the vehicle is in motion. However, it is difficult to analyze this method since its operating philosophy is complex, particularly with the existence of several variables and parameters. Also, the state of the vehicle, whether it is in motion or not, defines several parameters such as the vehicle speed as well as the sizes and dimensions of the coil receivers. This paper presents a novel method to improve the performance of the dynamic wireless recharge system. In the proposed system, receiver coils have been added to maximize charging power by offering a dynamic mathematical model that can describe and measure source-to-vehicle power transmission even though it is in motion. In the proposed mathematical model, all physical parameters describing the model were presented and discussed. The results showed the effectiveness of the proposed model. Also, the experimental tests confirmed the validity of the simulation results obtained by providing two coil receivers under the vehicle.

Organizing automobiles in a city is challenging due to the sensitive data that need to be disclosed. Information that can be utilized to identify a car and provide some useful characteristics about it is among the large amount of data that can be collected from an automobile. This operation will be easier if the vehicles are placed on a specific platform based on the smart city concept. Even if sensors and cameras are installed around the roads and the city, having the vehicle information will be more useful. The current study tries to demonstrate how it is feasible to improve vehicle autonomy by initially enhancing the vehicle's energetic performance, based on the smart city idea. Intelligent control topology serves as the foundation for the exposed energy management protocol. The suggested concept is created and the associated results are displayed using the Matlab Simulink platform. [ABSTRACT FROM AUTHOR]