A computer program called HYDRA, that calculates the optimal design parameters of hydropower plants, is described here.
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90 xiii LIST OF TABLES TABLES Table 4.1 Types and Province of Sample HEPP Projects............................24 Table 4.2 Types and Province of Testing HEPP Projects............................28 Table 4.3 Ranges of Parameters.............................................................33 Table 4.4 Normalization Procedure of Parameters....................................35 Table 5.1 Analyzed Data Set...................................................................40 Table 5.2 Cost Table of HEPP Project -1..................................................42 Table 5.3 Highly Correlated Variable Pairs and Correlation Coefficient........56 Table 5.4 Parameters of Testing Projects................................................77 Table 5.5 Results of Regression Model Cost Estimation.............................78 Table 5.6 Results of First Neural Network Model Cost Estimation...............79 Table 5.7 Results of Second Neural Network Model Cost Estimation..........80 Table 5.8 Results of Third Neural Network Model Cost Estimation.............81 Table 5.9 Results of NNM and RM Cost Estimations..................................83 Table B.1 Representation of Parameters – Column Matches....................102 Table B.2 Representation of Parameters – Column Matches....................109 xiv LIST OF FIGURES FIGURES Figure 3.1 Views of Atatürk Dam in Turkey...............................................12 Figure 3.2 Tazimina Project in Alaska Example of Run-off River HEPP.........15 Figure 3.3 Components of a HEPP Project.................................................17 Figure 3.4 Typical Cross Sections of Channels...........................................19 Figure 3.5 General Arrangement of The Headpond....................................20 Figure 4.1 Discharge Sustainability Graph of Project 11.............................30 Figure 5.1 Typical Neural Network Architecture Described by Kim et.al (2004)………………………………………………………………49 Figure 5.2 Worksheet Example of Minitab.................................................53 Figure 5.3 Selecting Correlation From Stat Menu.......................................54 Figure 5.4 Selecting Variables in Correlation Dialog Box.............................55 Figure 5.5 Selecting Regression From stat Menu.......................................60 Figure 5.6 Selecting Dependent Variable in Regression Dialog Box.............61 Figure 5.7 Selecting Independent Variable in Regression Dialog Box...........62 Figure 5.8 Regression Analysis Results.....................................................63 Figure 5.9 Information Window in Neural Power.......................................67 xv Figure 5.10 Data Files Module in Neural Power...........................................68 Figure 5.11 Independent Variables of Modeling Projects..............................69 Figure 5.12 Dependent Variables of Modeling Projects.................................69 Figure 5.13 Learning Settlements Window of Learning Module.....................70 Figure 5.14 Learning Configuration Window of Learning Module..................71 Figure 5.15 Layer Properties Window of Learning Settlements.....................72 Figure 5.16 Settlement Arrangements of The Model....................................73 Figure 5.17 View Monitor Window of Learning Settlements..........................75 Figure 5.18 An Example of RMSE Behaviour During Analysis........................75 Figure
SCADA is the software through which almost all the operations of starting/ stopping the generation units, monitoring of various parameters and controlling of
HYPER simulates the daily performance, investment, operation and maintenance costs, and economic profit of a RoR hydropower plant.
For estimating I would suggest Buildern. Saves time and gives more accurate estimates compared to using different systems. You can save, update,
You pull last night's inflow report, cross-reference it against a market forecast open in another tab, update the dispatch spreadsheet, check environmental constraints from a separate document, and try to build a plan that holds, usually before the morning meeting, usually working with data that's already a few hours old by the time the plan is done. A price window opens at 14:00 that wasn't there when you filed the morning plan. Each of those events means starting over, or deciding it's not worth it and living with a plan you know isn't optimal. When inflow forecasts, market signals, and compliance constraints are connected in a single planning engine, you can make better decisions faster, without spending the first half of your shift reconciling conflicting inputs. The practical meaning is this: your forecasting data, your market data, and your compliance constraints all need to feed into the same planning engine at the same time.