High penetration of wind power in the modern power system renders traditional automatic generation control (AGC) methods more challenging, due to the uncertainty of the external environment, less reserve power, and small inertia constant of the power system. An improved AGC method named predictive optimal 2-degree-of-freedom proportion integral differential (PO-2-DOF-PID) is proposed in this paper, which wind farm will participate in the load frequency control process. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of wind power is built. Then, predictive optimal 2-degree-of-freedom PID controller is presented to improve the system robustness considering system uncertainties. The objective function is designed based on the wind speed and whether wind farm takes part in AGC or not. The controller solves the optimization problem through the predictive theory while taking into account given constraints. In order to obtain the predictive sequence of output of the whole system, the characteristic of the 2-DOF-PID controller is integrated in the system model. A three interconnected power system is introduced as an example to test the feasibility and effectiveness of the proposed method. When considering the penetration of wind power, two cases of high wind speed and low wind speed are analyzed. The simulation results indicate that the proposed method can effectively deal with the negative influence caused by wind power when wind power participates in AGC.

In order to mitigate the negative influence of geographical dispersity of modern power system and the penetration of renewable energy, a system compensation based model predictive AGC method is proposed in this paper. A compensation unit is introduced in AGC system to reduce the influence of PV power output fluctuation. Furthermore, because the distributed power system is geographically dispersed, which leads to some defects such as time delay and packet dropouts, a selection and optimization algorithm is presented to obtain the appropriate variable when delays exist. Finally, a two-area interconnected power grid with PV systems is shown as an example to testify the feasibility and effectiveness of the proposed method. The simulation results indicate that the system controlled by the proposed method has the expected dynamic performance.

With the emergence of the concept of smart grid, the networked automatic generation control (AGC) method has been more and more important for secondary frequency control due to its characteristics such as openness and flexibility. However, the networked AGC system also presents some defects such as time delays and packet dropouts. The existence of time delays makes the traditional AGC strategies more challenging. A novel AGC method is proposed in this paper to mitigate the negative effects of time delays. Firstly, a multiarea power system model is built under the consideration of two-channel time delays: from controller to actuator and from sensor to controller. More practically, the difference of delays between areas is also exhibited in the model. Thus, from the predictive characteristics of model predictive control (MPC), a method of selection with optimization is presented to obtain the appropriate control variable when delays exist. Furthermore, three cases, (a) no processing for delay, (b) control sequence selection, (c) control sequence selection with optimization, are analyzed. The frequency and area control error (ACE) performance are evaluated with step load perturbation and random load perturbation. The simulation results indicate that the system controlled by the proposed method has desired dynamic performances. Consequently, the feasibility and effectiveness of the proposed method are verified.

This paper proposes an entropy-based Markov (EMC) chain fusion technique to solve the problem that the sample set is incompletion in fault diagnostic field. Firstly, the concept about probability Petri net is defined. It can calculate the fault occurred probability from incidence matrix based on the complemental information. Secondly, probability Petri net diagnostic model is designed from diagnostic rules that obtained by Skowron default rule generation method after the sample set is reduced by rough set theory. And in order to simplify the framework of the diagnostic model, Petri net model is designed as distributed form. Finally, depending on the diagnosis of distributed diagnostic model, EMC technique will be used to obtain consensus output if the places that represent fault in the model have several tokens. The diagnostic result is the consensus output that with the maximum of posterior probability after normalized treatment. The design is described by an example about rotating machinery fault diagnosis, and is proved availability by test sample set.

This paper proposes a selectivity criterion to choose appropriate information fusion method for the fusion of multiarguments to acquire logical consensus output. The research theory can improve the cognitive veracity of the object based on the redundancy of information, and can resolve the problem of incompletion about detected data. Firstly, the characteristic of D-S evidence theory is analyzed. It indicates that D-S theory will be fit for the instance that combined argument has analogical estimation to the object. Secondly, a selectivity criterion of D-S evidence theory is deduced. The fusion result will be more rationalized by the treatment of D-S theory if the relation of combined arguments is accord with the restriction of the criterion. Finally, an instance about fault diagnosis is analyzed. A logical diagnostic result can be acquired by the use of D-S evidence theory selectivity criterion. It is proved that the researched theory is available.