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This paper presents a distributed adaptive neural tracking consensus control strategy for a class of stochastic nonlinear multiagent systems with whole state time delays, input and output constrains. The considered systems are involved in the existence of whole state delays and stochastic disturbances, which makes the controller design more difficult and complex. Firstly, time delays are related to unknown dynamic interactions with the whole states of the agent systems, and novel Lyapunov-Krasovskii functionals are constructed. Secondly, the smooth asymmetric saturation nonlinearity is given based on Gaussian error function, output constraints are achieved via barrier Lyapunov functions, and neural networks are utilized to deal with the completely unknown nonlinearities and stochastic disturbances. Then, based on Lyapunov stability theory, a delay-independent adaptive controller is developed via Lyapunov-Krasovskii functionals and backstepping technique, and it reduces the complexity of learning parameters. It is proved that the proposed approximation-based controller can guarantee that all closed-loop signals are cooperatively semi-globally uniformly ultimately bounded (CSGUUB), and the tracking errors between the followers and the leaders eventually converge to a small neighbourhood around the origin. Finally, simulation studies are carried out, and the simulation results verify the correctness and effectiveness of the proposed strategy.

The grid connection of wind farms affects the load flow of a power system and may introduce an unexpected threat to the power system small signal stability. This study presents an application of probabilistic eigenvalue sensitivity indices (PSIs) for optimal allocation of wind farm in a power system. For this purpose, firstly, probabilistic sensitivity indices are used to identify the strongly correlated electromechanical eigenvalues of generators and to facilitate the wind farm site selection. Two different cases are studied, wind farm replacing synchronous generators or being directly added to the power system. Afterwards, the influence of the wind farm location on power system small signal stability is examined. For this purpose, an index is proposed to measure the small signal stability of the system and is used for optimal allocation of the wind farm. Two examples, i.e. a 3-machine 9-bus and an 8-machine 24-bus power system, with a wind farm integrated based on PSIs are presented to demonstrate the proposed approach by employing the eigenvalue analysis and a dynamic time-domain simulation. Finally, the results are compared with the traditional optimal allocation of a wind farm for power loss reduction.

The well-developed unified power flow controller (UPFC) has demonstrated its capability in providing voltage support and improving power system stability. The objective of this paper is to demonstrate the capability of the UPFC in mitigating oscillations in a wind farm integrated power system by employing eigenvalue analysis and dynamic time-domain simulation approaches. For this purpose, a power oscillation damping controller (PODC) of the UPFC is designed for damping oscillations caused by disturbances in a given interconnected power system, including the change in tie-line power, the changes of wind power outputs, and others. Simulations are carried out for two sample power systems, i.e., a four-machine system and an eight-machine system, for demonstration. Numerous eigenvalue analysis and dynamic time-domain simulation results confirm that the UPFC equipped with the designed PODC can effectively suppress oscillations of power systems under various disturbance scenarios.