Chu Zhang

Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit

This paper proposes a nonlinear multi-mode intelligent model predictive control (MPC) strategy for hydroelectric generating unit (HGU). In this multi-mode MPC scheme, excitation MPC mode and integrated MPC mode work for excitation control process and load scheduling condition, respectively. Every control mode is built on the basis of a tree-seed algorithm based model predictive control (TSA-MPC) scheme, which introduces a newly proposed tree-seed algorithm (TSA) and the stability-guaranteed measures into rolling optimization mechanism of nonlinear MPC (NMPC) to replace the existing complex numerical differential geometric solutions. Simulation experiments of the proposed multi-mode MPC and the comparative methods are undertaken under diverse operating conditions in a HGU control system as case studies. Experimental results indicate the superiority in voltage regulation and damping performance as well as the effectiveness of the comprehensive control of turbine governing and generator excitation.

A parameter adaptive identification method for a pumped storage hydro unit regulation system model using an improved gravitational search algorithm

With increasing wind farm, solar farm, and pump storage plant integrations, intense frequency fluctuation of the pumped storage hydro unit (PSHU) under the no-load running condition, which is caused by its operation along the S-shaped curve, has been noted and researched. So, parameter identification of the PSHU regulation system (PSHURS) is crucial in precise modeling of the PSHU and can provide support for the optimized control and stability analysis of the power system. In this paper, a parameter adaptive identification method together with an improved gravitational search algorithm (IGSA) is proposed and applied to solve the identification problem for a PSHURS under the no-load condition. The IGSA, which is based on the standard gravitational search algorithm, accelerates convergence speed with a combination of the Pbest-Gbest-guided search strategy and the adaptive elastic-ball method and improves the local optimal with the added chemotaxis operator of the bacterial foraging algorithm. Furthermore, for the parameter adaptive identification method, the parameter performance evaluator is employed to devise the moving step of the agent of the chemotaxis operator. The illustrative experiment for parameter identification of the PSHURS is used to verify the feasibility and effectiveness of the proposed method. Comparison with other methods clearly shows that the adaptive parameter identification method along with the IGSA perform best for all identification indicators.

Modeling and Combined Application of Orthogonal Chaotic NSGA-II and Improved TOPSIS to Optimize a Conceptual Hydrological Model

Conceptual rainfall-runoff modelling is a widely-used approach for rainfall-runoff simulation in streamflow forecasting. The objective of this paper is to introduce an improved non-dominated sorting genetic algorithm-II (NSGA-II) for multi-objective automatic calibration of a hydrologic model. The orthogonal design based initialization technique is exploited to produce a more uniformly-distributed initial population. At the same time, a chaotic crossover operator as well as a chaotic mutation operator are presented to avoid trapping into local minima and to obtain high quality solutions. Finally, a multi-criteria decision-making (MCDM) approach combing Shannon entropy weighting method and an improved technique for order preference by similarity to ideal solution (ITOPSIS) based on projection is introduced to prioritize the Pareto optimal solutions and select the comprehensive optimal solution as a follow-up step. Hydrological data from two river basins named the Leaf and Muma River basins are exploited to test the ability of the orthogonal chaotic NSGA-II (OCNSGA-II) for solving the multi-objective HYMOD (MO-HYMOD) problem. The results demonstrate that the OCNSGA-II can obtain better-distributed Pareto optimal front and thus can be exploited as an effective alternative approach for the multi-objective automatic calibration of hydrologic model.

Fault diagnosis based on a novel weighted support vector data description with fuzzy adaptive threshold decision

The fault diagnosis of generator units is critical to guarantee the high efficiency of the electric system. However, detailed fault samples are difficult to obtain, and the distribution of fault samples usually shows the characteristics of unevenness and unbalance, which may lead to low fault diagnosis precision. Nevertheless, it has been seldom considered in the traditional classifier of fault diagnosis for generator units until now. In this paper, a novel fault classifier of weighted support vector data description (SVDD) with fuzzy adaptive threshold decision is proposed and applied in the fault diagnosis of generator units. To tackle the drawback that SVDD is sensitive to the distribution of samples, a novel SVDD model based on a complex weight is proposed. The complex weight is assigned with local density and size-based weight, while local density of each data point is obtained with the k-nearest neighbour approach and the size-based weight of each data point is computed according to the proportion of classes. Then the conventional SVDD is reformulated with the complex weights. Furthermore, new decision rules based on the relative distance and fuzzy adaptive threshold decision are applied to identify the class of testing samples. Finally, the proposed method is applied in the identification of several standard datasets, as well as the fault diagnosis for a turbo-generator unit. Experimental results and the engineering application reveal that the proposed method shows good performance in accuracy and universality, and is suitable for the fault diagnosis of generator units.