Yanhe Xu

Design of a fractional-order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation

A pumped storage unit (PSU) is more difficult to control compared to a conventional hydropower generation unit due to the frequent switching of working conditions and the S-shaped characteristics of pump turbine. The traditional proportionalintegralderivative (PID) controller typically cannot easily provide high quality control. To overcome these difficulties, a fractional-order PID (FOPID) controller is designed for a PSU in this study. Although the FOPID controller is more effective compared to the traditional PID controller, it is more complex to optimize the parameters of this controller for a pump turbine governing system (PTGS). Thus, a gravitational search algorithm combined with the Cauchy and Gaussian mutation, named as CGGSA, is proposed and used to optimize the FOPID controller parameters. The experimental results indicate that the CGGSA has shown excellent optimization ability compared with some popular meta-heuristics on benchmark functions. Results have also proved that the FOPID-CGGSA controller shows significant advantages over other PID-type controllers with different optimization strategies. Meanwhile the optimally designed controller has shown great potential to improve the control quality of PTGS under multiple water heads.

A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions

This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods.

A state tendency measurement for a hydro-turbine generating unit based on aggregated EEMD and SVR

The reliable measurement of state tendency for a hydro-turbine generating unit (HGU) is significant in guaranteeing the security of the unit and promoting stability of the power system. For this purpose, an aggregated ensemble empirical mode decomposition (AEEMD) and optimized support vector regression (SVR)-based hybrid model is developed in this paper in order to enhance the measuring accuracy of state tendency for a HGU. First of all, the non-stationary time series of the state signal are decomposed into a collection of intrinsic mode functions (IMFs) by EEMD. Subsequently, to obtain the refactored intrinsic mode functions (RIMFs), the IMFs with different scales are aggregated with the proposed reconstruction strategy in consideration of the frequency and energy conditions. Later, the phase–space matrix in accordance with each RIMF is deduced by phase–space reconstruction and all the RIMFs are predicted through establishing homologous optimal SVR forecasting models with a grid search. Finally, the ultimate measuring values of state tendency can be determined through the accumulation of all the RIMF forecasting values. Furthermore, the effectiveness of the proposed method is validated in engineering experiments and comparative analyses.

Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks

Abstract Fault diagnosis of rolling bearing has been research focus to improve the productivity and guarantee the operation security. In general, traditional approaches need prior knowledge of possible features and a mass of labeled data. Due to the complexity of working conditions, it costs a lot of time to label the monitoring data. In this paper, Categorical Adversarial Autoencoder (CatAAE) is proposed for unsupervised fault diagnosis of rolling bearings. The model trains an autoencoder through an adversarial training process and imposes a prior distribution on the latent coding space. Then a classifier tries to cluster the input examples by balancing mutual information between examples and their predicted categorical class distribution. The latent coding space and training process are presented to investigate the advantage of proposed model. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method. The experimental results indicate that the proposed method achieved satisfactory performance and high clustering indicators with strong robustness when environmental noise and motor load changed.

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.

Synergetic governing controller design for the hydraulic turbine governing system with complex conduit system

Abstract The hydraulic turbine governing system plays an indispensable role in maintaining the stability of electrical power system. In this paper, synergetic control theory is introduced to enhance the regulating ability of the hydraulic turbine governing system. For the purpose of describing the characteristics of objective system and deducing the synergetic control rule, a nonlinear mathematic model of a hydraulic turbine governing system with long tail race and two surge tanks is established. Furthermore, the nonlinear characteristic of the hydraulic turbine is described by six variable partial derivatives. For further investigation, the hydraulic turbine governing system is conducted to running under load condition when its coaxial generator connects to an infinite bus. Simulation experiments have been made under both load disturbance and three-phase short circuit fault conditions to compare the dynamic performances of proposed synergetic governing controller and classic PID controller. The results indicate that the proposed synergetic governing controller is an effective alternative in normal condition and a superior one in emergency. Moreover, the robustness of synergetic governing controller has also been discussed at the end of this paper.