W
Wenlong Zhu
Researcher at Huazhong University of Science and Technology
Publications - 13
Citations - 468
Wenlong Zhu is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Fault (power engineering) & Extreme learning machine. The author has an hindex of 10, co-authored 13 publications receiving 375 citations.
Papers
More filters
Journal ArticleDOI
An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis
TL;DR: In this article, an adaptive fast ensemble EEMD (AFEEMD) method combined with complementary ensemble EMD (CEEMD), where the two critical parameters are respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials.
Journal ArticleDOI
An adaptively fast fuzzy fractional order PID control for pumped storage hydro unit using improved gravitational search algorithm
TL;DR: In this article, an adaptive fast fuzzy fractional order PID (AFFFOPID) control method for pumped storage hydro unit (PSHU) is proposed, which is based on the standard gravitational search algorithm accelerates convergence speed with a combination of the Pbest-Gbest-guided strategy and adaptive elastic-ball method.
Journal ArticleDOI
A novel KICA–PCA fault detection model for condition process of hydroelectric generating unit
TL;DR: In this article, a fault detection model based on kernel independent component analysis and principal component analysis (KICA-PCA) monitoring model for condition process of HGU is presented, where each of the condition processes is equivalent to a multivariate statistical process monitoring (MSPM).
Journal ArticleDOI
Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings
TL;DR: D dependent feature vector (DFV) is proposed to denote the fault symptom attributes of the six REB faults in this paper, and this is a self-adaptive fault representation method which describes each fault sample according to its own characteristics.
Journal ArticleDOI
Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit
TL;DR: 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 in this nonlinear multi-mode MPC scheme.