scholarly journals Unsupervised neural network for forecasting alarms in hydroelectric power plant

1997 ◽  
pp. 1298-1306
Author(s):  
P. Isasi-Viñuela ◽  
J. M. Molina-López ◽  
A. Sanchis de Miguel
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Hydroelectric Power Plant ◽  
Hydroelectric Power ◽  
Unsupervised Neural Network

scholarly journals A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

Forecasting ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 410-428
Author(s):  
Emanuele Ogliari ◽  
Alfredo Nespoli ◽  
Marco Mussetta ◽  
Silvia Pretto ◽  
Andrea Zimbardo ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Hybrid Method ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Hydroelectric Power Plant ◽  
Hydroelectric Power ◽  
Production Forecast ◽  
Decomposition Approach ◽  
Artificial Neural Network Ann

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

Research on the NNARX Model Identification of Hydroelectric Unit Based on Improved L-M Algorithm

2013 ◽  
Vol 871 ◽  
pp. 304-309 ◽  
Author(s):  
Zhi Huai Xiao ◽  
Zhou Peng An ◽  
Shu Qing Wang ◽  
Shi Qi Zeng
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Controller Design ◽  
Model Identification ◽  
Hydroelectric Power Plant ◽  
Identification Accuracy ◽  
Hydroelectric Power ◽  
Generalization Ability ◽  
Marquardt Algorithm ◽  
High Recognition Accuracy

Its hard to use traditional ways to set up a hydroelectric power plant model due to its complex, time-varying and nonlinear characteristics. This article uses the neural network autoregressive with exogenous input (NNARX) to identify and model hydro-turbine generating unit which is the key in the hydroelectric power plant modeling. The random guide vain signal is used to train NNARX in this paper and the other two working conditions are used to check its generalization ability. In order to improve identification accuracy, generalization performance and training speeds, an improved Levenberg-Marquardt algorithm is proposed in this article which is based on the L-M algorithm that widely used in artificial neural network weights adjustment. Simulation results indicate that NNARX model with improved L-M algorithm can reach high recognition accuracy and have good generalization ability. It can provide a good simulation model for intelligent controller design in the future.

Forecasting of Water Flow in a Hydroelectric Power Plant Using LSTM Recurrent Neural Network

2020 ◽  
Author(s):  
Arlindo Rodrigues Galvao Filho ◽  
Diogo Fernandes Costa Silva ◽  
Rafael Viana de Carvalho ◽  
Filipe de Souza Lima Ribeiro ◽  
Clarimar Jose Coelho
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Water Flow ◽  
Recurrent Neural Network ◽  
Hydroelectric Power Plant ◽  
Hydroelectric Power

Water Level Control of Small-scale Hydroelectric Power Plant

2002 ◽  
Vol 122 (6) ◽  
pp. 989-994
Author(s):  
Shinichiro Endo ◽  
Masami Konishi ◽  
Hirosuke Imabayashi ◽  
Hayami Sugiyama
Keyword(s):  
Power Plant ◽  
Water Level ◽  
Hydroelectric Power Plant ◽  
Small Scale ◽  
Hydroelectric Power ◽  
Level Control

Hydroelectric Power-Plant Simulator Implemented in Python

2021 ◽  
Author(s):  
Michal Kuchar ◽  
Adam Peichl ◽  
Milan Kucera ◽  
Jaromir Fiser ◽  
Pavel Kulik ◽  
...  
Keyword(s):  
Power Plant ◽  
Hydroelectric Power Plant ◽  
Hydroelectric Power

scholarly journals Dynamics of Lady’s slipper orchid (Cypripedium calceolus L.) population at Lake Kwiecko (West Pomerania)

2014 ◽  
Vol 36 (1) ◽  
pp. 53-60 ◽  
Author(s):  
Maciej Korczyński ◽  
Ewa Krasicka-Korczyńska
Keyword(s):  
Power Plant ◽  
Endangered Species ◽  
Rare Species ◽  
Negative Impact ◽  
Good Condition ◽  
Hydroelectric Power Plant ◽  
Field Observations ◽  
Hydroelectric Power ◽  
Cypripedium Calceolus ◽  
Slipper Orchid

Abstract Cypripedium calceolus is considered an endangered species in the territory of Poland. Population of this rare species, situated at Lake Kwiecko (Western Pomerania), was regularly monitored in the years 1986-2013. The studied population has been under the permanent influence of the nearby hydroelectric power plant for almost 45 years. The field observations showed that the power plant had no negative impact on the condition of Cypripedium calceolus population. An indication of its good condition was, among others, an increase in the size - from 150 to 350 specimens within the study period.

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