An adaptive power system stabilizer (APSS) employing a new self-optimizing pole shifting control strategy and its application to a power system are described in this paper. Based on an identified model of the system, the control is computed by an algorithm which shifts the closed-loop poles of the system to some optimal locations inside the unit circle in the z-domain to minimize a given performance criterion. With the self-optimization property, outside intervention in the controller design procedure is minimized, thus simplifying the tuning procedure during commissioning. Also, a new method of calculating the variable forgetting factor in real-time parameter identification is discussed. Studies show that the proposed APSS can provide good damping of the power system over a wide operating range and significantly improve the dynamic performance of the system. >

An adaptive power system stabilizer based on the self-optimizing pole shifting control strategy

A fuzzy-logic-based adaptive power system stabilizer (PSS) is proposed in this paper. The parameters of the fuzzy-logic-based PSS are tuned by neural networks online. The system is divided into two subsystems, a recursive least square identifier with a variable forgetting factor for the generator and a fuzzy-logic-based adaptive controller to damp oscillations. The effectiveness of the proposed PSS in increasing the damping of local and inter-area modes of oscillation is demonstrated in a one-machine-infinite-bus system; a two-area, 4-machine, 13-bus system, and a 16-machine, 68-bus system.

An online adaptive neuro-fuzzy power system stabilizer for multimachine systems

Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms

A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system.

A fuzzy logic based power system stabilizer with learning ability

Inter-area low frequency electromagnetic oscillation (LFEO) in inter-connected power systems is one of the major barriers to improve the dynamic performance of power systems, since they could either limit the amount of power transfer on the tie-lines or endanger the dynamic stability of the power systems. With the high penetration of wind and photovoltaic (PV) power plants in power systems, LFEO is becoming a problem worth more attention in renewable energy age and must be better solved in order to accept more wind and PV power in power systems. Wide-area damping control (WADC) has gained popularity in suppression of inter-area LFEO recently. Many control strategies have been developed on WADC design. This paper aims to provide a general review on practical issues, typical WADC off-line and adaptive design methods and engineering cases regarding WADC implementations. These practical issues include design flow, mode analysis, choice of actuator and feedback signal, system identification and the effect of time delay. In terms of designing WADC controllers, both offline and adaptive methods have been reviewed and compared. Finally, WADC engineering cases all around the world are summarized.

A review on wide-area damping control to restrain inter-area low frequency oscillation for large-scale power systems with increasing renewable generation

An adaptive self-optimising pole shifting (PS) control algorithm which combines the advantages of the minimum variance (MV) and the pole assignment (PA) control algorithms, but avoids their drawbacks, is presented in the paper. With the properties of the PA control, the proposed algorithm can guarantee the stability of the closed-loop system on the basis of correct identified parameters, whether the controlled system is a minimum phase system or not. Under the control limit constraints, this algorithm optimises a given performance index which reflects the characteristics of the MV control. By means of a self-optimisation technique, outside intervention in the controller design procedure is minimised, thus simplifying the control tuning during commissioning. Simulation studies demonstrating the application of the proposed algorithm to linear discrete and nonlinear continuous systems are also presented.

Y.H. Qin

Papers

An adaptive power system stabilizer based on the self-optimizing pole shifting control strategy

Adaptive self-optimising pole shifting control algorithm