John N. Jiang

Nonlinear Dual-Mode Control of Variable-Speed Wind Turbines With Doubly Fed Induction Generators

This paper presents a feedback/feedforward nonlinear controller for variable-speed wind turbines with doubly fed induction generators. By appropriately adjusting the rotor voltages and the blade pitch angle, the controller simultaneously enables: 1) control of the active power in both the maximum power tracking and power regulation modes; 2) seamless switching between the two modes; and 3) control of the reactive power so that a desirable power factor is maintained. Unlike many existing designs, the controller is developed based on original, nonlinear, electromechanically-coupled models of wind turbines, without attempting approximate linearization. Its development consists of three steps: 1) employ feedback linearization to exactly cancel some of the nonlinearities and perform arbitrary pole placement; 2) design a speed controller that makes the rotor angular velocity track a desired reference whenever possible; and 3) introduce a Lyapunov-like function and present a gradient-based approach for minimizing this function. The effectiveness of the controller is demonstrated through simulation of a wind turbine operating under several scenarios.

An Approximate Wind Turbine Control System Model for Wind Farm Power Control

Wind farm power control is key to reliable large-scale wind integration. The design of a sophisticated wind farm controller, however, is challenging partly because there is a lack of models that appropriately simplify the complex overall dynamics of a large number of wind turbine control systems (WTCSs). In this paper, using system identification approaches, we develop a simple approximate model that attempts to mimic the active and reactive power dynamics of two generic WTCS models under normal operating conditions: an analytical model described by nonlinear differential equations, and an empirical one by input-output measurement data. The approximate model contains two parts-one for active power and one for reactive-each of which is a third-order system that would have been linear if not for a static nonlinearity. For each generic model, we also provide an identification scheme that sequentially determines the approximate model parameters. Finally, we show via simulation that, despite its structural simplicity, the approximate model is accurate and versatile, capable of closely imitating several different analytical and empirical WTCS models from the literature and from real data. The results suggest that the approximate model may be used to facilitate research on wind farm power control.

Voltage stability and sensitivity analysis of grid-connected photovoltaic systems

Integration of significant amount of solar power challenges the power system stability operation. This paper presents analyses on the static and transient voltage characteristics at the point of common coupling of a grid-connected photovoltaic system. The static voltage response, known as a PV curve, for the photovoltaic system is analyzed. The voltage transient behaviors caused by the disturbance of parameters in photovoltaic generation system are also studied. In particular, we try to find out the impact of system parameters, such as temperature, solar irradiance, and load changes on the voltage stability. In addition, a method of voltage stability sensitivity analysis is presented for comparison between the impacts of different parameters on voltage stability.

Model predictive and adaptive wind farm power control

This paper introduces a wind farm controller that enables the power output of a wind farm to accurately and smoothly track a desired reference from a power grid operator. Developed based on a model of wind turbine control systems we recently proposed, the wind farm controller consists of an outer control loop and an inner one. The outer loop contains a model predictive controller, which uses various forecasts and feedbacks to iteratively compute a set of desired power trajectories, so that the deterministic tracking accuracy of the wind farm power output on a receding horizon is optimized. In contrast, the inner loop contains an adaptive controller, which uses estimated wind speed characteristics to adaptively tune a set of proportional controller gains, so that the stochastic smoothness of the wind farm power output on a shorter timescale is optimized. The paper also provides a series of simulation studies that illustrate the salient features of the wind farm controller.

A refined Differential Current Protection Method in the FACTS-compensated line

This paper proposes a refined Differential Current Protection Method (DCRM) to solve problems associated with malfunction or error in relay response caused by operation of FACTS devices. The FACTS devices of interest include STATCOM, SSSC and UPFC. Analytical models and methods are presented to analyze impacts of those compensation devices on the transmission system for these devices. A variety of faults cases are studied with the proposed methods and simulation results are provided as well.

Calibration of a Wind Farm Wind Speed Model With Incomplete Wind Data

Publicly available information today on wind and wind generation is incomplete for deep understanding of their characteristics. The owner or operator of a wind farm typically only releases minutes-level turbine wind speed data. Although seconds-level wind power data may be obtained from the SCADA system, they only reflect aggregated performance at the wind farm level and thus cannot be used to obtain the covariance/correlation of wind speeds experienced by individual turbines at the seconds-level, which is a key to in-depth understanding of the characteristics of wind for wind farm control studies. To address this issue, this paper presents an empirical framework that uses incomplete wind data to calibrate a wind farm wind speed model. First, two such models are constructed from an ARIMA state-space model. Next, Monte Carlo filters and likelihood function maximization are incorporated to calibrate the models, reconstruct the unobserved states, and estimate the parameters. Finally, we test the model using synthetic wind data and demonstrate its effectiveness in statistically recovering missing information in the data.

Impact of Grid Structure on Dynamics of Interconnected Generators

Many important issues of power system dynamics are often studied by looking at the dynamic responses of generators connected through the power grid, taking their interactions into consideration. In this paper, we present a specific study on the significant impact of grid structure on the power system dynamics with quantitative evidence. Specifically, it is found that the impact of grid structure heavily depends on two types of grid-structure-related variables: 1) electrical distances among the generator internal buses, and 2) electrical distances between the generator internal buses and the faulted bus. Furthermore, with the examples of coherency identification problem, the significance of grid structure is demonstrated by the strong agreement of identification results between using a set of criteria that only takes into account the grid-structure-related variables and dynamic simulations.

Voltage/pitch control for maximization and regulation of active/reactive powers in wind turbines with uncertainties

This paper addresses the problem of controlling a variable-speed wind turbine with a Doubly Fed Induction Generator (DFIG), modeled as an electromechanically-coupled nonlinear system with rotor voltages and blade pitch angle as its inputs, active and reactive powers as its outputs, and most of the aerodynamic and mechanical parameters as its uncertainties. Using a blend of linear and nonlinear control strategies (including feedback linearization, pole placement, uncertainty estimation, and gradient-based potential function minimization) as well as time-scale separation in the dynamics, we develop a controller that is capable of maximizing the active power in the Maximum Power Tracking (MPT) mode, regulating the active power in the Power Regulation (PR) mode, seamlessly switching between the two modes, and simultaneously adjusting the reactive power to achieve a desired power factor. The controller consists of four cascaded components, uses realistic feedback signals, and operates without knowledge of the Cp-surface, air density, friction coefficient, and wind speed. Finally, we show the effectiveness of the controller via simulation with a realistic wind profile.

Fast Assessment of Frequency Response of Cold Load Pickup in Power System Restoration

In this paper, we introduce an approach for a fast assessment of the dynamic characteristics of system frequency during the period of cold load pickup following a large-scale blackout. The approach is developed based on a load model and a model of generation control system: the load model considers the unique time-dependent characteristic of inrush power surges during the period of cold load pickup, while the model of generation control system mimics the characteristics of various governors that are crucial to the determination of the key variables including initial rate of frequency change, frequency nadir and settling frequency. With the proposed linearization and estimation methods, the model of generation control system can be quickly constructed to offer a great amount of flexibility and simplicity for the study of the impact of cold load on frequency response. The proposed approach allows system operators to quickly evaluate many different options for system restoration. The application of the proposed approach is demonstrated in a case study based on the IEEE 39-bus system. The differences between the results obtained using the proposed approach and those obtained from complete PSS/E simulation are very small, which suggests that the linearized model of generation control system is fast, and accurate enough to address the major concerns of cold load pickup in system restoration.

Cluster analysis of wind turbines of large wind farm

Understanding the dynamics of the power output of a wind farm is important to the integration of large scale wind energy into the power system. In a large complex dynamic engineering system, such as a wind farm, clustering is an effective way to reduce the model complexity and improve the understanding of its local dynamics.