Calculation of interval damping ratio under uncertain load in power system

Abstract

Abstract. The problem of small-signal stability considering load uncertainty in power system is investigated. Firstly, this paper shows attempts to create a nonlinear optimization model for solving the upper and lower limits of the oscillation mode's damping ratio under an interval load. Then, the effective successive linear programming (SLP) method is proposed to solve this problem. By using this method, the interval damping ratio and corresponding load states at its interval limits are obtained. Calculation results can be used to evaluate the influence of load variation on a certain mode and give useful information for improvement. Finally, the proposed method is validated on two test systems.

P1-TS fuzzy scheduling control system design using local pole placement and interval analysis

“…The P1-TS fuzzy system was used to approximate the parameters of controllers and pendulum model based on the two scheduling variables, rope length l and mass of a payload m. The planar model of a crane (Fig. 5) transferring a payload, which is assumed to be a point-mass suspended at the end of a massless rigid cable, is simplified to the first and second-order discrete-time transfer functions, which describe the relation between crane speed and input function (18), and sway angle of a payload and crane speed (19), where model's parameters vary in relation to the rope length l and mass of a payload m. The closed-loop control scheme (Fig. 6) is assumed as a set of linear controllers for crane position, speed and firstorder discrete-time controller of payload sway angle with parameters denoted k 1 , k 2 , q 0 , q 1 and s 0 .…”

“…Also, numerous authors adopted the interval mathematics [11,12] for robust control systems synthesis [13][14][15]. Interval analysis is implemented for modeling interval systems and designing robust controller according to the iterative procedures [16][17][18], Monte Carlo technique [19], or through applying the soft computing methods, e.g. evolutionary algorithm (EA) [20] and artificial neural network [21].…”

Interval arithmetic-based fuzzy discrete-time crane control scheme design

“…the performances of fuzzy logic-based control system satisfy desired conditions if the coefficients of closed-loop system characteristic equation lie within the coefficients intervals of desired polynomial (10) determined using the arithmetic operations on intervals [24,37]:…”

“…Interval analysis is implemented for modeling interval systems and designing robust controller according to the iterative procedures [35][36][37] or through applying soft computing methods, e.g. genetic algorithms [38][39] and artificial neural network [40].…”