Scientists and researchers are exploring different methods of generating and delivering electrical energy in an economical and reliable way, enabling them to generate electricity focusing on renewable energy resources. All of these possess the natural property of self-changing behavior, so the connection of these separate independent controllable units to the grid leads to uncertainties. This creates an imbalance in active power and reactive power. In order to control the active and reactive power in wind turbine generators with adjustable speed, various control strategies are used to allay voltage and current variations. This research work is focused on the design and implementation of effective control strategies for doubly fed induction generator (DFIG) to control its active and reactive power. A DFIG system with its control strategies is simulated on MATLAB software. To augment the transient stability of DFIG, the simulation results for the active and reactive power of conventional controllers are compared with three types of feed forward neural network controllers, i.e., probabilistic feedforward neural network (PFFNN), multi-layer perceptron feedforward neural network (MLPFFN) and radial basic function feedforward neural network (RBFFN) for optimum performance. Conclusive outcomes clearly manifest the superior robustness of the RBFNN controller over other controllers in terms of rise time, settling time and overshoot value.

The use of wireless communications for real-time control applications poses several problems related to the comparatively low reliability of the communication channels. This paper is concerned with adaptive and predictive application-level strategies for ameliorating the effects of packet losses and burst errors in industrial sampled-data Distributed Control Systems (DCSs), which are implemented via one or more wireless and/or wired links, possibly spanning multiple hops. The paper describes an adaptive compensator that reconstructs the best estimates (in a least squares sense) of a sequence of one or more missing sensor node data packets in the controller node. At each sample time, the controller node calculates the current control, and a prediction of future controls to apply over a short time horizon; these controls are forwarded to the actuator node every sample time step. A simple design method for a digital Proportional Integral Derivative (PID)-like adaptive controller is also described for use in the controller node. Together these mechanisms give robustness to packet losses around the control loop; in addition, the majority of the computational overhead resides in the controller node. An implementation of the proposed techniques is applied to a case study using a Hardware in the Loop (HIL) test facility, and favorable results (in terms of both performance and computational overheads) are found when compared to an existing robust control method for a DCS experiencing artificially induced burst errors.

This paper covers the application of the cross-correlation method for measuring the velocity of solid particles. Capacitive electrodes are used as primary sensors to measure the time required by the solid particles to cover a known distance between the electrodes. The capacitive variations of both the electrode sensors are stored in computer synchronously for offline estimation of the velocity of the solid particles. Single glass marble is used as a solid particle to conduct the performance in the lab for its good signal to noise ratio (SNR) and vivid graphs. Matlab R2018a is used as a programming tool to perform the cross-correlation algorithm. The distance between the sensors is adjusted optimally. The results were realistic to ensure the correctness of the system.

The precise and accurate measurement of flow rate in the batch flow of the solid particles is of primary importance in many process industries for the improvement of the efficiency of the system. Many techniques developed for the measurement of mass flow rate. The capacitive sensors has a significance of being non-invasive, higher accuracy and low cost for mass flow measurement despite the fact that many factors adversely affect the performance- including non-uniform flow, multiphase flow, temperature, pressure, and moisture in the solid particles. This paper covers preliminary investigations of the offline estimation of mass flow concentration based upon the calibration of capacitance electrodes to quantify the mass of dielectric as a function of capacitance variation between the electrodes.

Gas-solid flow is used in the chemical industry, food industry, pharmaceuticals, vehicles, and power generation. The calculation of flow has aroused great interest in contemporary industry. In recent decades, researchers have been seeking to build an effective system to monitor and calculate gas-solid flow. Attempts have been extended from computational modeling to the creation of flow pattern visualization methods and mass flow (MFR) quantification. MFR is usually studied by volume flow concentration (VFC) and velocity distribution of solid particles. A non-invasive device is used for testing MFR, in which electronic and mechanical sensors are used to balance the shortcomings related to each other. This study investigates the simulation of flow patterns to demonstrate the behavior of solid particles as they pass through the channel. The particles are allowed to slide longitudinally in the insulated tending channel. This slippage is due to the influence of natural gravity. Electronic sensor components are used to measure the velocity distribution and concentration of volumetric flow. The load cell is used as an auxiliary sensor for measuring MFR. In addition, ANSYS fluent is used to analyze streaming queries. The experimental results are related to evaluating the accuracy and relative error of the data collected from various sensors under different conditions. However, the simulation results can help explain the movement of the gas-solid mixture and can understand the cause of pipeline blockage during the slow movement of solid particles.

The complex multiphase gas-solid flow has always been a point of attraction for researchers over the past decade to explore the sensing techniques to sense and measure the mass flow. Weather dilute or dense phase flow, the gas-solid flow measurement generally requires velocity profile and volumetric concentration measurement to find the mass flow rate. The nature of the solids, the environmental factors-specially moisture adversely affects the sensor readings-specifically when it is non-invasive capacitive sensors. Gas-solid flow finds its applications in power plants, food, chemical, automobile, and pharmaceutical industries. This paper aims to explore the evolution of a novel approach of using load cell in conjunction with capacitive electrodes for calculating the mass flow rate of the solids.