Prediction of double-regulated hydraulic turbine on-cam energy characteristics by artificial neural networks approach

Određivanje energetskih kombinatorskih karakteristika dvojno regulisane hidraulične turbine se zasniva na rezultatima opsežnih i skupih eksperimentalnih ispitivanja na modelu u laboratoriji i terenskih merenja na prototipu u hidroelektranama. Eksploatacioni dijagram se dobija na osnovu prostornih interpolacija reprezentativnih mernih tačaka koje pripadaju kombinatorskim krivama formiranih za različite brzinske faktore. U radu je dat akcenat na primeni savremene metode veštačkih neuronskih mreža u određivanju kombintorskih karakteristika turbine posebno u radnim režimima koji nisu mereni. Deo postojećih podataka o energetskim parametrima Kaplan turbine koji su dobijeni eksperimentalnim putem iskorišćeni su za obučavanje tri razvijena modela veštačkih neuronskih mreža. Analizom, testiranjem i validacijom dobijenih energetskih parametara turbine međusobnim upoređivanjem sa ostalim eksperimentalnim podacima razmatrana je pouzdanost primenjene metode.The determination of the energy characteristics of a double-regulated hydro turbine is based on numerous measuring points during extensive and expensive experimental model tests in the laboratory and on site prototype tests at the hydropower plant. By the spatial interpolation of representative measured points that belong to the so-called on-cam curves for different speed factors, the hill performance diagram is obtained. The focus of the paper is the contemporary method of artificial neural network models use for the prediction of turbine characteristics, especially in not measured operation modes. A part of the existing set of experimental data for the Kaplan turbine energy parameters is used to train three developed neural network models. The reliability of applied method is considered by analysing, testing and validating the predicted turbine energy parameters in comparison with the remaining data

Prediction of double-regulated hydraulic turbine on-cam energy characteristics by artificial neural networks approach

Određivanje energetskih kombinatorskih karakteristika dvojno regulisane hidraulične turbine se zasniva na rezultatima opsežnih i skupih eksperimentalnih ispitivanja na modelu u laboratoriji i terenskih merenja na prototipu u hidroelektranama. Eksploatacioni dijagram se dobija na osnovu prostornih interpolacija reprezentativnih mernih tačaka koje pripadaju kombinatorskim krivama formiranih za različite brzinske faktore. U radu je dat akcenat na primeni savremene metode veštačkih neuronskih mreža u određivanju kombintorskih karakteristika turbine posebno u radnim režimima koji nisu mereni. Deo postojećih podataka o energetskim parametrima Kaplan turbine koji su dobijeni eksperimentalnim putem iskorišćeni su za obučavanje tri razvijena modela veštačkih neuronskih mreža. Analizom, testiranjem i validacijom dobijenih energetskih parametara turbine međusobnim upoređivanjem sa ostalim eksperimentalnim podacima razmatrana je pouzdanost primenjene metode.The determination of the energy characteristics of a double-regulated hydro turbine is based on numerous measuring points during extensive and expensive experimental model tests in the laboratory and on site prototype tests at the hydropower plant. By the spatial interpolation of representative measured points that belong to the so-called on-cam curves for different speed factors, the hill performance diagram is obtained. The focus of the paper is the contemporary method of artificial neural network models use for the prediction of turbine characteristics, especially in not measured operation modes. A part of the existing set of experimental data for the Kaplan turbine energy parameters is used to train three developed neural network models. The reliability of applied method is considered by analysing, testing and validating the predicted turbine energy parameters in comparison with the remaining data

Improvement of hydroelectric power generation using pumped storage system

Hydroelectric power is a renewable source of energy. By principle, hydroelectric power generation relies on the law of conservation of energy where kinetic energy that resulted from the movement of the mass of water from the river is translated into electr icity, the quantum of which depends on systemic variables viz: plant efficiency, volumetric water flow through the turbine and the head of the water from the water surface to the turbine. Understanding the dynamics of these variables, and the correlation b etween them are core to proper planning and management of a hydroelectric power station. In this Study, simple mathematical methods that include linear programming and statistical analysis based on simulation techniques were used to evaluate vital parameters based on the data obtained from the Hydrologic units of the Shiroro Power Stations in Nigeria. The overall aim of the study is to idealize power generation at Shiroro dam in and out of raining season so as to ensure optimum generation of electricity all year round in order to achieve energy sufficiency in Nigeria. The result of the study is encouraging as it supports the viability of the pumped storage system for generating hydroelectric power all year round. The coupling of the hydroelectric power with pumped storage system if properly harnessed could be the needed panacea for the erratic power supply in Nigeria. Keywords: hydroelectric power, pumped storage, reservoir inflows, turbine, hydrological variables, simulation technique

Bibliography on the Electrical Aspects of Small Hydro Power Plants

This bibliography is designed to help the reader search for information on some of the electrical aspects of small hydro power plants. The bibliography is intended to help engineers and scientists who may be unfamiliar with this aspect of small hydro, university researchers who are interested in this field, manufacturers who want to learn more about these topics and librarians who provide information to their clients. Topics covered range from the small hydro economic analysis, control and governors, some aspects of hydropower development projects, modeling and simulation studies and future role of small hydro power plants. The references appearing throughout this bibliography do not represent all available material on a specific topic. The inclusion of references in the bibliography is based on several factors, including relevancy to the particular topic, frequency of citation in the professional literature and availability

Optimizing Hydroelectric Power Generation: The Case of Shiroro Dam

Abstract—Hydroelectric power, one of the most important sources of mass generation of electric power, is a renewable source of energy. The amount of electricity that can be produced by a hydro-electricity generating system depends on systemic variables viz; plant efficiency, volumetric water flow through the turbine and the head of the water from the water surface to the turbine. The availability of the Water in the reservoir is a function of some hydrological variables principal among which are rainfall, reservoir inflows and evaporation. Understanding the dynamics of these variables, and the correlation between them are core to proper planning and management of a hydroelectric power station. In this Study, simple mathematical methods that include linear programming and statistical analysis based on simulation techniques were used to evaluate vital parameters based on the hydrologic data obtained from the Hydrologic Units of the Shiroro Power Stations in Nigeria. The overall aim of the study is to idealize power generation at Shiroro dam in and out of rain season so as to ensure optimum generation of electricity all year round in order to achieve energy sufficiency in Nigeria

Fault detection, identification and accommodation techniques for unmanned airborne vehicles

Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures

Using a fuzzy inference system to control a pumped storage hydro plant

The paper discusses the development of a fuzzy inference system (FIS) based governor control for a pumped storage hydroelectric plant. The First Hydro Company's plant at Dinorwig in North Wales is the largest of its kind in Europe and is mainly used for frequency control of the UK electrical grid. In previous investigations, a detailed model of the plant was developed using MATLAB(R)/SIMULINK(R) and this is now being used to compare FIS governor operation with the proportional-integral-derivative (PID) controller currently used. The paper describes the development of an FIS governor, and shows that its response to a step increase in load is superior to the PID under certain conditions of load. The paper proceeds to discuss the implications of these results in view of the possible practical application of an FIS governor at the Dinorwig plant

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

Automated On-line Fault Prognosis for Wind Turbine Monitoring using SCADA data

Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A Supervisory Control and Data Acquisition (SCADA) system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT’s health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purpose; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This thesis develops an automated on-line fault prognosis system for WT monitoring using SCADA data, concentrating particularly on WT pitch system, which is known to be fault significant. A number of preliminary activities were carried out in this research. They included building a dedicated server, developing a data visualisation tool, reviewing the existing WT monitoring techniques and investigating the possible AI techniques along with some examples detailing applications of how they can be utilised in this research. The a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (APK-ANFIS) was selected to research in further because it has been shown to be interpretable and allows domain knowledge to be incorporated. A fault prognosis system using APK-ANFIS based on four critical WT pitch system features is proposed. The proposed approach has been applied to the pitch data of two different designs of 26 Alstom and 22 Mitsubishi WTs, with two different types of SCADA system, demonstrating the adaptability of APK-ANFIS for application to variety of technologies. After that, the Alstom results were compared to a prior general alarm approach to show the advantage of prognostic horizon. In addition, both results are evaluated using Confusion Matrix analysis and a comparison study of the two tests to draw conclusions, demonstrating that the proposed approach is effective