Hybrid Intelligence Methods For Forecasting And Operation In Hydropower System

With rapid development of hydropower construction and project scale widened constantly, the scale and complexity of problem is becoming bigger and bigger in hydropower system and posses properties of multi-dimension, non-linear, non-convex and etc. This is a reason that results are not ideal through the traditional method and single intelligence method to solve, and the idea of hybrid algorithms is very important way for improving effective and efficient algorithms. Therefore, how to efficiently utilize the advantages of various algorithms to develop more efficient algorithm is very significant to improve hydrological forecasting accuracy, economical and effective operation of hydropower system. In this dissertation, first, the conceptual rainfall-runoff (CRR) model parameters calibration and uncertainty analysis, the long-term forecasting and operation of hydropower system are reviewed in detail. Then, based on the project background of hydropower station(s) in Hunan Shuangpai reservoir, Yunan Lancangjiang River basin and Guizhou Wujiang River basin, the hybrid intelligence methods are studied for CRR model parameters calibration and uncertainty analysis combined with case of project application. The dissertation studies long-term forecasting and optimization operation of hydropower system using Artificial intelligence techniques such as genetic algorithms (GA), chaos system, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models, support vector machine (SVM) method and hybrid intelligence optimization methods. The major research work is outlines as follows:(1) The hybrid metaheruistic genetic algorithm with fuzzy multiobjective optimization is presented for solving the Xinanjiang model parameters based on 48 historical floods from hydrological telemetry system with one hour routing period for 7 years (2000-2006) in Shuangpai Reservoir. The proposed method takes advantages of the ergodic and stochastic properties of chaotic variables, an annealing chaotic mutation operation which is employed to replace standard mutation operator in the evolutionary process of GA and the local optimal search capability of SA method. The three statistical ratios of acceptable criteria relative to the peak discharge, peak time and total runoff volume among the calibrated and validated historical flood events, are used to evaluate the parameter calibration performance for rainfall-runoff model by fuzzy multiobjective optimization from actual application. The results of calibration and validation indicate that the proposed method can obtain better model parameters for short-term flood forecasting.(2) While application Xinanjiang model to simulate hydrograph, the "best" parameter set calibrated may be not unique and uncertain because of model limitation, more parameters and limited information. Considering previously parameter optimization of Xinanjiang model, there is only a unique "best" parameter set to be found and it doesn't describe uncertainty of parameter. There is a certain one-sidedness and limitation for Xinanjiang model used. Aiming at this problem, this paper presents using SCEM-UA algorithm based Markov Chain Monte Carlo (MCMC) methods for optimization and uncertainty assessment of Xinanjiang model parameters with different routing periods for Shuangpai basion. The results demonstrate that SCEM-UA algorithm is well suited to infer the posterior distribution of Xinanjiang model parameters. The results of calibration and validation indicate that it is a feasible and effective for optimization and uncertainty assessment of Xinanjiang model parameters.(3) Developing a hydrological forecasting model to apply Artificial intelligence (AT) technology which include artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method. The main purpose is to investigate the performance of several AI methods for forecasting monthly discharge time series. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. The autoregressive moving-average (ARMA) model is also employed as reference benchmark. The results obtained in this study indicate that the AI methods are powerful tools to model the discharge time series and can give good prediction performance than traditional time series approaches through 52 years discharges series in Manwan hydropower and 54 years discharges series in Hongjiadu hydropower. The results indicate that the best performance can be obtained by ANFIS, GP and SVM in terms of different evaluation criteria. The results of the study are highly encouraging and suggest that ANFIS, GP and SVM approaches are promising in modeling monthly discharge time series comparation with the ANN and ARMA.(4) By use of the properties of ergodicity, randomicity, and regularity of chaos, a chaos genetic algorithm (CGA) based float encoding is proposed to solve optimal operation of hydropower reservoir. CGA adopts chaos optimization of the initialization to improve species quality and utilizes annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. Comparison of results among the dynamic programming, the standard GA and CGA showed that CGA can significantly reduce the overall optimization time and improve the convergence quality through complex function optimization, hydropower station optimization operation with typical annual runoff, hydropower reservoir optimization operation with a series of monthly inflow and cascaded hydropower optimization operation. The analysis results show that CGA has obvious advantages in convergence speed and solution quality. Thus, the proposed approach is feasible and effective in optimal operations of complex reservoir systems.Finally, a summary is given and some problems to be further studied are discussed.