L
Lin Jiang
Researcher at University of Liverpool
Publications - 465
Citations - 14528
Lin Jiang is an academic researcher from University of Liverpool. The author has contributed to research in topics: Electric power system & Computer science. The author has an hindex of 50, co-authored 389 publications receiving 10061 citations. Previous affiliations of Lin Jiang include University of Sheffield & Xiamen University.
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Delay-Dependent Stability for Load Frequency Control With Constant and Time-Varying Delays
TL;DR: In this article, the authors investigated the delay-dependent stability of load frequency control (LFC) schemes by using Lyaponuv-theory based delaydependent criterion and linear matrix inequalities (LMIs) techniques.
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Delay-Dependent Stability of Single-Loop Controlled Grid-Connected Inverters with LCL Filters
TL;DR: In this article, a systematic study on the relationship between the time delay and stability of single-loop controlled grid-connected inverters that employ inverter current feedback or grid current feedback (GCF) was carried out, and the ranges of time delay for system stability were analyzed and deduced in the continuous s-domain and discrete z-domain.
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An extended reciprocally convex matrix inequality for stability analysis of systems with time-varying delay
TL;DR: An extended reciprocally convex matrix inequality is developed, which reduces the estimation gap of the R CCL-based matrix inequality and reduces the number of decision variables of the recently proposed delay-dependent RCCL.
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Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers
TL;DR: In this article, a robust sliding-mode control using nonlinear perturbation observers for wind energy conversion systems (WECS), in which a doubly-fed induction generator (DFIG) is employed to achieve an optimal power extraction with an improved fault ride-through (FRT) capability.
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Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition
TL;DR: A novel bio-inspired optimization method developed by extending the original salp swarm algorithm with multiple independent salp chains, thus it can implement a wider exploration and a deeper exploitation under the memetic computing framework.