Decentralized nonlinear control for power systems using normal forms and detailed models

This paper proposes a decentralized method for nonlinear control of oscillatory dynamics in power systems. The method is applicable for ensuring both transient stability as well as small-signal stability. The method uses an optimal control law which has been derived in the general framework of nonlinear control using normal forms. The model used to derive the control law is the detailed subtransient model of synchronous machines as recommended by IEEE. Minimal approximations have been made in either the derivation or the application of the control law. The developed method also requires the application of dynamic state estimation technique. As the employed control and estimation schemes only need local measurements, the method remains completely decentralized. The method has been demonstrated as an effective tool to prevent blackouts by simulating a major disturbance in a benchmark power system model and its subsequent control using the proposed method

An extended linear quadratic regulator for LTI systems with exogenous inputs

This paper proposes a cost effective control law for a linear time invariant (LTI) system having an extra set of exogenous inputs (or external disturbances) besides the traditional set of control inputs. No assumption is made with regard to a priori knowledge of the modeling equations for the exogenous inputs. The problem of optimal control for such a system is defined in the standard framework of linear quadratic control and an extended linear quadratic regulator (ELQR) is proposed as the solution to the problem. The ELQR approach is demonstrated through an example and is shown to be significantly more cost effective than currently available approaches for linear quadratic control

Decentralized robust dynamic state estimation in power systems using instrument transformers

This paper proposes a decentralized method for estimation of dynamic states of a power system. The method remains robust to time-synchronization errors and high noise-levels in measurements. Robustness of the method has been achieved by incorporating internal angle in the dynamic model used for estimation and by decoupling the estimation process into two stages with continuous updation of measurement-noise variances. Additionally, the proposed estimation method does not need measurements obtained from phasor measurement units (PMUs); instead, it just requires analogue measurements of voltages and currents directly acquired from instrument transformers. This is achieved through statistical signal processing of analogue voltages and currents to obtain their magnitudes and frequencies, followed by application of unscented Kalman filtering for nonlinear estimation. The robustness and feasibility of the method have been demonstrated on a benchmark power system model

An extended linear quadratic regulator and its application for control of power system dynamics

This paper presents a generalized solution to the problem of optimal control of systems having an extra set of exogenous inputs besides control inputs. The solution is derived in the framework of linear quadratic control and it is termedextended linear quadratic regulator (ELQR)'. The ELQR is applied for control of unstable or poorly damped oscillatory dynamics occurring in a power system and is shown to be significantly more cost effective than the classical power system stabilizer (PSS) based approach

A Model Predictive Approach for Enhancing Transient Stability of Grid-Forming Converters

A model-based approach for controlling post-fault transient stability of grid-forming (GFM) converter energy resources is designed and analyzed. This proposed controller is activated when the converter enters into the saturated current operation mode. It aims at mitigating the issues arising from insufficient post-fault deceleration due to current saturation and thus improving the transient stability of the GFM Inverter Based Resources (IBRs). The considered approach conveniently modifies the post-fault trajectory of GFM IBRs by introducing appropriate corrective phase angle jumps and power references. These corrections are optimised following a model predictive approach (the model referring to post-fault dynamics of GFM IBRs in both saturated and normal operation modes). While constructing the proposed controller, the situation for GFM IBRs to enter into the saturated operation mode are identified. The effectiveness of this transient stability enhancement approach by means of dynamic simulations under various grid conditions is tested and discussed. The results demonstrate much better transient stability performance.Comment: 14 pages, 19 figure

Estimation of inherent governor dead-band and regulation using unscented Kalman filter

The inclusion of the governor droop and dead-band in dynamic models helps to reproduce the measured frequency response accurately and is a key aspect of model validation. Often, accurate and detailed turbine-governor information are not available for various units in an area control centre. The uncertainty in the droop also arise from the nonlinearity due to the governor valves. The droop and deadband are required to tune the secondary frequency bias factors, and to determine the primary frequency reserve. Earlier research on droop estimation did not adequately take into account the effect of dead-band and other nonlinearities. In this paper, unscented Kalman filter is used in conjunction with continuously available measurements to estimate the governor droop and the dead-band width. The effectiveness of the approach is demonstrated through simulation

Deep Learning Based Forecasting-Aided State Estimation in Active Distribution Networks

Operating an active distribution network (ADN) in the absence of enough measurements, the presence of distributed energy resources, and poor knowledge of responsive demand behaviour is a huge challenge. This paper introduces systematic modelling of demand response behaviour which is then included in Forecasting Aided State Estimation (FASE) for better control of the network. There are several innovative elements in tuning parameters of FASE-based, demand profiling, and aggregation. The comprehensive case studies for three UK representative demand scenarios in 2023, 2035, and 2050 demonstrated the effectiveness of the proposed approach