In this paper the design, manufacturing and automation of a micro hydroelectric power plant (MHPP) prototype has been carried out. The experimental setup consists of three 1 kW synchronous generators (SGs) working in synchronization with each other and with the grid, three Pelton turbines with a single nozzle manufactured using a 3D printer, a water tank with a capacity of approximately one ton, a 5.5 kW centrifugal pump providing appropriate flow and head conditions and an 11 kW driver controlling the speed of this pump. The mechanical and electrical structure of the system and its working scenario are designed to be the closest to a real MHPP. S7-1200 PLC (Programmable Logic Controller) is used in order to control the voltage and frequency values of synchronous generators according to the load as well as for other control processes. In this study, PID control method is preferred for frequency and voltage control. It is possible to control and monitor the whole system through SCADA (Supervisory Control and Data Acquisition) screens. The results have been evaluated by obtaining frequency-time, voltage-time, active power-valve opening, excitation current-reactive power graphs of synchronous generators under different load conditions and in single, local and synchronous operating modes.
Renewable energy sources, as well as the studies being conducted regarding these energy sources, are becoming increasingly important for our world. In this manuscript, the daily energy production level of a small (15 MW) run-of-river hydropower plant (RRHPP) was estimated using the artificial neural network (ANN) model. In this context, the model utilized both meteorological data and HPP-related data. The input parameters of the artificial neural network included the daily total precipitation, daily mean temperature, daily mean water vapour pressure, daily mean relative humidity, and the daily mean river water elevation at the hydropower plant, while the only output parameter consisted of the total daily energy production. For the ANN, data from the four years between 2017 and 2020 were used for training purposes, while data from the first eight months of 2021 were used for testing purposes. Ten different ANN networks were tested. A comparison of the ANN data with the real data indicated that the model provided satisfying results. The minimum error rate was 0.13%, the maximum error rate was 9.13%, and the mean error rate was 3.13%. Furthermore, six different algorithms were compared with each other. It was observed that the best results were obtained from the Levenberg-Marquardt algorithm.This study demonstrated that the ANN can estimate the daily energy production of a run-of-river HPP with high accuracy and that this model can potentially contribute to studies investigating the potential of renewable energies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.