Xiaohui Yuan

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.

Sliding mode controller of hydraulic generator regulating system based on the input/output feedback linearization method

An input/output feedback linearization method based sliding mode control strategy is proposed for the hydraulic generator regulating system (HGRS) with external disturbance and system uncertainties to enhance its response. Based on the input/output feedback linearization method, the relationship between reference output and control output is established. Then a sliding mode controller is designed to reject the influence of external disturbance and system uncertainties on the system performance and compelled the current dynamic output exponentially stabilized at their reference states. In order to eliminate the inherent harmful chattering phenomenon of sliding mode controller, a high-slope saturation function is used to replace the discontinuous sign function in the sliding mode manifold design. Several simulations with respect to the dynamic analysis of HGRS system without controller, fixed point stabilization, periodic orbit tracking and robustness test against random noises have been carried out to test the effectiveness of the proposed controller technique. The results show that the proposed sliding mode controller improves the nonlinear HGRS system performance with an accurate precision and a shorter time in all cases. The nonlinear hydraulic generator regulating system (HGRS) model is studied.This kind of HGRS system considers the external disturbances and system uncertainty.The input/output feedback linearization method is used to establish the relationship.Sliding mode controller (SMC) is applied to adjust the orbit output of HGRS system.PID controller is employed to the adjustment process in HGRS system as a comparison.

An improved gravitational search algorithm for solving short-term economic/environmental hydrothermal scheduling

This paper proposes an improved gravitational search algorithm (IGSA) to find the optimum solution for short-term economic/environmental hydrothermal scheduling (SEEHTS), which considers minimizing fuel cost as well as minimizing pollutant emission. In order to improve the performance of GSA, this paper firstly uses particle memory character and population social information to update velocity. Secondly, a chaotic mutation operator is embedded into GSA and a selection-operator-based greedy rule is adopted to update population. When dealing with the constraints of the SEEHTS, a modification strategy by dividing the violation water volume into several parts and randomly selecting intervals to adjust the water discharge gradually is proposed to handle the water dynamic balance constraints. Meanwhile, a new symmetrical adjusting strategy is adopted to handle reservoir storage constraints. Furthermore, the priority index strategy based on thermal power output is applied to handle system load balance constraints. To test the performance of the proposed method, simulation results have been compared with those obtained by particle swarm optimization, evolutionary programming and differential evolution reported in literature. The results show that the proposed IGSA provides the optimum solution with less fuel cost and smaller emission. So it demonstrates that IGSA is effective for solving SEEHTS problem.

Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.

Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm

Nonlinear Muskingum model is a popular approach widely used for flood routing in hydraulic engineering. An improved backtracking search algorithm (BSA) is proposed to estimate the parameters of nonlinear Muskingum model. The orthogonal designed initialization population strategy and chaotic sequences are introduced to improve the exploration and exploitation ability of BSA. At the same time, a selection strategy based individual feasibility violation is developed to ensure that the computed outflows are non-negative in the evolutionary process. Finally, three examples are employed to demonstrate the performance of the improved BSA. The comparison between the results of routing outflows and those of Wilcoxon signed ranks test shows that the improved BSA outperforms particle swarm optimization, genetic algorithm, differential evolution and other algorithms reported in the literature in terms of solution quality. Therefore, it is reasonable to draw the conclusion that the proposed BSA is a satisfactory and efficient choice for parameter estimation of nonlinear Muskingum model.

Parameter Identification of Chaotic and Hyper-Chaotic Systems Using Synchronization-Based Parameter Observer

A technique is introduced for identifying uncertain and/or unknown parameters of chaotic and hyper-chaotic systems via using a synchronization-based parameter observer. The proposed technique is based on designing a state feedback controller and then solving for the unknown parameters using some simple parameter adaptive laws that require access to some or all of the states depending on the dynamical model of the systems. Two detailed cases utilizing chaotic and hyper-chaotic systems are used to exemplify the proposed observer when partial identification of the unknown parameters is considered, where the results demonstrated the effectiveness of the proposed method via comparing it with other methods reported in the literature, Furthermore, the feasibility of the designed observer on unknown-structure model and complex-variable model are verified through the theoretical analyses and simulation results, and the advantages and limitations of the synchronization-based parameter observer approach are discussed in detail.

Multiscaling Analysis of Monthly Runoff Series Using Improved MF-DFA Approach

An improved multifractal detrended fluctuation analysis(MF-DFA) method is applied to analyze the long-term monthly runoff records of a hydrological station in the Yangtze River with seasonal trend eliminated, through which the long-range correlation and the multifractal characteristics have been found. The multifractal spectrum has been fitted by a generalized expression of the multiplicative cascade model, and the results show that the monthly runoff series has strong multifractal characteristics. Comparing the results for the original runoff series with those of shuffled and surrogate series, it concludes that the multifractal characteristics of the monthly runoff time series is due to the broadness of both the probability density function and long-range correlation, and the broadness of the probability density function is dominant.

Multi-objective Artificial Physical Optimization Algorithm for Daily Economic Environmental Dispatch of Hydrothermal Systems

Abstract This article formulates the daily economic/environmental hydrothermal scheduling problem as a multi-objective optimization problem. By introducing non-dominated sorting and crowding distance, the multi-objective artificial physical optimization algorithm is proposed to solve the daily economic/environmental hydrothermal scheduling problem. To enhance the performance of the proposed algorithm, new velocity update equation, which takes advantage of the individual memory and population information, is utilized. To overcome the drawback of premature convergence, a chaotic mutation is adopted in the multi-objective artificial physical optimization algorithm. Especially for handling the equality constraints of daily economic/environmental hydrothermal scheduling, novel heuristic strategies are developed to repair the infeasible solutions. To demonstrate the effectiveness of the multi-objective artificial physical optimization algorithm for solving daily economic/environmental hydrothermal scheduling, the proposed method is implemented on a hydrothermal system and the numerical results are compared with several optimization approaches. It demonstrates that the proposed multi-objective artificial physical optimization algorithm is competent as an alternative for the daily economic/environmental hydrothermal scheduling problem.

Lockage scheduling of Three Gorges-Gezhouba dams by hybrid of chaotic particle swarm optimization and heuristic-adjusted strategies

A model of lockage scheduling of Three Gorges-Gezhouba Dams (LSTGD) was established.LSTGD is separated into three sub-problems and solved by different methods.Chaotic embedded particle swarm optimization is proposed to solve LSTGD.Heuristic-adjusted strategies are proposed to improve the merit of the solutions. This paper establishes a mathematical model for lockage scheduling of Three Gorges-Gezhouba Dams (LSTGD) problem based on the requirement of scheduling procedure in Three Gorges-Gezhouba Dams (TG-GD) system. The lockage scheduling is separated into three sub-problems: lockage assignment (LA), timetable optimization (TO) and ship dispatch (SD) problem. We propose chaotic embedded particle swarm optimization algorithm to solve the LSTGD problem. Meanwhile, three different chaotic maps are studied and the results are compared to evaluate the effect of different maps on PSO. In addition, heuristic-adjusted strategies are proposed based on the analysis of scheduling regulation to enhance the performance of the final solution. Finally, the proposed method is tested with the real historical execution data of lockage scheduling system in Three Gorges-Gezhouba Dams. The results show that the proposed method is efficient for solving the LSTGD problem.

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.