Motion synchronization for the SHA/EMA hybrid actuation system by using an optimization algorithm

The current research develops a mathematical model and control strategies to address two major problems force fighting and precise position tracking for a hybrid actuation system composed of servo-hydraulic actuator and electro-mechanical actuator (SHA/EMA). The force fighting and desired position tracking are two essential problems of the SHA/EMA actuation system for a large civil aircraft. The trajectory-based fractional order proportional integral derivative (FOPID) control for the SHA/EMA actuation system is proposed, tuned with the help of the particle swarm optimization (PSO) technique and implemented with the support of the FOMCON toolbox in Matlab. The experiments are performed under different external aerodynamic loads that the aircraft usually experiences during flight operations. The results show that the proposed method shows better results for tracking performance, force fighting and load rejection ability

A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems

Currently, grid-connected Photovoltaic (PV) systems are widely encouraged to meet increasing energy demands. However, there are many urgent issues to tackle that are associated with PV systems. Among them, partial shading is the most severe issue as it reduces efficiency. To achieve maximum power, PV system utilizes the maximum power point-tracking (MPPT) algorithms. This paper proposed a two-level converter system for optimizing the PV power and injecting that power into the grid network. The boost converter is used to regulate the MPPT algorithm. To make the grid-tied PV system operate under non-uniform weather conditions, dragonfly optimization algorithm (DOA)-based MPPT was put forward and applied due to its ability to trace the global peak and its higher efficiency and shorter response time. Furthermore, in order to validate the overall performance of the proposed technique, comparative analysis of DOA with adaptive cuckoo search optimization (ACSO) algorithm, fruit fly optimization algorithm combined with general regression neural network (FFO-GRNN), improved particle swarm optimization (IPSO), and PSO and Perturb and Observe (P&O) algorithm were presented by using Matlab/Simulink. Subsequently, a voltage source inverter (VSI) was utilized to regulate the active and reactive power injected into the grid with high efficiency and minimum total harmonic distortion (THD). The instantaneous reactive power was adjusted to zero for maintaining the unity power factor. The results obtained through Matlab/Simulink demonstrated that power injected into the grid is approximately constant when using the DOA MPPT algorithm. Hence, the grid-tied PV system’s overall performance under partial shading was found to be highly satisfactory and acceptable

Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems

DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions (PFs) representing information from different frequency bands, then selects the PFs that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%

Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems

DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions (PFs) representing information from different frequency bands, then selects the PFs that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%

An Android-based Portable Smart Cane for Visually Impaired People

In today’s encouraging world of technology, mobile applications are a speedily increasing segment of the worldwide mobile market. An android operating system is the highest accepted and extremely developing open platform for mobile application development. Due to the rise of the impaired people population and there are limited technological-based facilities, we want to leverage technology to develop an Information & Communication Technologies (ICT) based smart portable cane for visually impaired people using android application. We have created an information-based probabilistic relative model amongst the key indicators and sequenced their data gathering priority and precedence. The device is developed and tested with blind people that gives better results for reliability, user friendly, portability, less weight, and economical so that everyone can easily purchase, mount, and conFigure to walk more confidently and perform a necessary operation such as obstacle detection in the range of 5 feet with varying buzzer frequency after every 12 inches to give batter understanding of distance to obstacle also the facility to operate mobile from the mounted device such as sending a message to caretakers, dialing a call, help message, SMS read and open Google maps to navigate by a single click on the mounted buttons on a white cane that wirelessly communicates through Bluetooth transceiver