Electronic Load Controller based on Dimmer Circuit and Stepper Motor for 5 kW Micro-Hydro Power Plant

This paper presents the results of research on a new schematic generator load controller simulation, namely an electronic load controller based on a dimmer circuit and a stepper motor for a small-scale 5 kW micro-hydro. The load controller is built from a dimmer circuit and a stepper motor with program control using Matlab software, and the PPI 8255 interface device. Using a dimmer circuit built from diac, triac, a variable resistor (pot), and capacitor components. As well as using a 28BYJ-48 stepper motor. Simulation is made to determine the performance of the load controller in controlling the distribution of power to the ballast load when the generator supplies power to consumers less than the full load of the generator. By using the simulation data of 45 variations of the consumer load sample, the result is that there are only 4 samples where the load controller is not working well. For the 4 samples, the generator was loaded beyond its full load tolerance limit (full load tolerance of 5kW ± 5 %). Overall, based on the simulation results, it can be said that the generator load controllers tested in this study have good performance. HIGHLIGHTS Methods and power electronic configurations used in electronic load controllers for micro-hydropower plants from 1980 until now. In the future, the use of low-power micro hydro and pico hydro power plants is also in great demand, especially for rural areas The utilization of micro-hydro in Indonesia, especially low-power micro-hydro, has been utilized by the community in Bulukumba Regency, South Sulawesi Province. A 5 kW generator has been operated in Katimbang Village, Borong Rappoa District, Kindang Regency, Bulukumba Regency, South Sulawesi Province to distribute electrical energy to 15 households This research proposes a new electronic load control scheme for a simple and inexpensive 5 kW micro-hydro power plant, namely a dimmer circuit and stepper motor-based generator load controller. With the consideration that the main components that make up this controller are not expensive GRAPHICAL ABSTRACT

Parameters Identification for Three Groups of Squirrel Induction Motor

This paper presents a developed method to calculate the parameters for thirty-three squirrel cage induction motors operating at three-phase ac voltage of 380 volts. These motors are the total product of an Egyptian factory holding a license from SIEMENS international company to fabricate all parts of these motors. The parameters of all mentioned motors are computed based on the proposed method. Then, the performance characteristics of these motors are investigated at full-load using the conventional equivalent circuit in order to validate the proposed method. The obtained curves achieve significant convergence with the full-load values provided by the data sheets of investigated motors. This confirms the validity of the proposed method.

Prediction of combined cycle power plant electrical output power using machine learning regression algorithms

In order to monitor the performance and related efficiency of a combined cycle power plant (CCPP), in addition to the best utilization of its power output, it is vital to predict its full load electrical power output. In this paper, the full load electrical power output of CCPP was predicted employing practically efficient machine learning algorithms, including linear regression, ridge regression, lasso regression, elastic net regression, random forest regression, and gradient boost regression. The original data came from an actual confidential power plant, which was working on a full load for 6 years, with four major features: ambient temperature, relative humidity, atmospheric pressure, and exhaust vacuum, and one target (electrical power output per hour). Different regression performance measures were used, including R2 (coefficient of determination), MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error). Research results revealed that the gradient boost regression model outperformed other models with and without using the dimensionality reduction technique (PCA) with the highest R2 of 0.912 and 0.872, respectively, and had the lowest MAPE of 0.872 % and 1.039 %, respectively. Moreover, prediction performance dropped slightly after using the dimensionality reduction technique almost in all regression algorithms used. The novelty in this work is summarized in predicting electrical power output in a CCPP based on a few features using simpler algorithms than reported deep learning and neural networks algorithms combined. That means a lower cost and less complicated procedure as per each, however, resulting in practically accepted results according to the evaluation metrics used.

Modelling an Electrically Turbocharged Engine and Predicting the Performance Under Steady-State Engine

This paper discusses the evaluation of the energy recovery potential of turboshaft separated (decoupled) electric turbocharger and its boosting capability in a spark-ignition engine through simulation-based work and comparing it to a conventional turbocharged engine in terms of fuel consumption. The main objective of this study is to evaluate the amount of energy that can be recovered over a steady state full-load operating conditions and boosting capabilities from a decoupled electric turbocharger of an SI engine using a 1-D engine simulation software. The electric turbocharged system includes two motors and a battery pack to store the recovered electrical energy. Gt-Power engine simulation software was used to model both engines and utilizes each of the components described earlier. The conventional turbocharged engine is first simulated to obtain its performance characteristics. An electric turbocharger is then modelled by separating the turbine from the compressor. The turbine is connected to the generator and battery, whereas the compressor is connected to the motor. This electrically turbocharged engine was modelled at full load and controlled to produce the same brake power (kW) and brake torque (Nm) properties as the similarly sized conventional turbocharged engine. This step was necessary to investigate the effect an electrical turbocharger without a wastegate has on the engine’s BSFC and determine the energy that can be recovered by the electrical boosting components, and cycle-averaged fuel consumption was evaluated. The evaluation of energy recovered from the electrically turbocharged engine from the analysis can assessed in full-load steady state conditions that can be useful for research in part-load and transient studies involving the decoupled electrical turbocharger. The study revealed that a maximum of 21.6 kW of electrical power can be recovered from the decoupled electrical turbocharger system, whereas 2.6% increase in fuel consumption can be observed at 5000 rpm engine speed.

Fuzzy Classification Based Driving Distance Estimation for Electric Vehicles

Electric vehicle technology is an essential research field for improving full-electric vehicle (FEVs) capabilities. Different subsystem parameters in the FEVs should be monitored on a regular basis. The better these subsystems are used, the better the FEVs' performance, life, and range become. Nowadays, estimation of the state of charge (SoC) of the batteries and the driving distance is the area not been standardized sufficiently. In this study, a novel fuzzy classification method (FCM) is proposed to make the exact driving distance estimation of FEVs. The proposed FCM considers the consumed power and parameters of the battery under dynamic conditions. A test location was selected for the proposed FCM and tested under 3 different test conditions, namely, no-load, half-load and full-load conditions. Also, the performance of FCM is studied under several slope conditions, and the result shows that if the battery voltage decreases then the power consumed by the vehicle is improved in the uphill travel and the battery voltage is normal and the power consumption of the vehicle is decreased in the downhill drive. Finally, the drive distance of the proposed FCM is determined. HIGHLIGHTS Fuzzy classification based driving distance estimation for full-electric vehicle is proposed Parameters of battery and power consumption has been considered under dynamic condition CAN communication is established between different subsystems of electric vehicle Three test conditions (no-load, half load, and full load) have been considered

Numerical simulation of gas flow in nozzle channels with a central body

The development of a reusable launch system is one of the most promising directions in the development of cosmonautics. World rocket companies are striving to create a space vehicle with improved tactical and technical characteristics. The main task is to ensure the most efficient flight mode of the reusable spacecraft from the point of view of the full load of the injection system. Since the main costs are spent on transporting the fuel of the rocket and its own design, the developers strive to fully load such a system not only when sending into orbit, but also when returning back. The implementation of the idea of a reusable spacecraft entails large energy losses, which suggests the idea of creating a single-stage reusable launch system.