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In large-scale power system, low frequency oscillation is becoming a serious threat to the power system security. Online identification is used to monitor the low frequency oscillations and control the system damping. This paper presents a method to identify the characteristic of low frequency oscillations, and describe the online implementation strategy. The simulation result of real power grid validates the effectivity of the presented method.

Power System Stabilizer (PSS) is widely applied in power systems as an effective measure to damping the low frequency oscillations. The control effectiveness of PSS is fundamentally determined by appropriate parameters. The conventional method for setting the parameters of PSS only takes a single oscillation mode into account, which may have sub optimal damping performance in other modes. As a further study of the classical phase compensation method for PSS tuning, this paper presents a technique for parameters optimization based on genetic algorithms, taking several oscillation modes into consideration. The simulation results on a four-machine two-area power system demonstrate the effectiveness of PSS designed by the proposed method.

An unified power flow controller (UPFC) is a multi-input and multi-output (MIMO) multivariable controller and dynamic interactions are found among its multiple control functions. Focus of study in this paper is to investigate two cases of UPFC control interactions: (1) interactions caused by inappropriate setting of UPFC controller's parameters; (2) interactions irrelevant with setting of UPFC controller's parameters, the case of inherent interactions of UPFC control. The work presented in this paper also reveals that with variations of power system and UPFC operating conditions, controllable parameter region of UPFC controllers moves in its parameter space. At two different operating conditions, there could be no union of the corresponding controllable parameter regions. Hence in this paper, a novel variable-parameter control for UPFC based on artificial neural network (ANN) is proposed, in conjunction with a decoupling design of multi-variable UPFC control that is also based on an ANN decoupling control design