A Robust Control Approach for Primary Frequency Regulation through Variable Speed Wind Turbines

Abstract

MemberThis paper presents a robust control approach to enhance the participation of Variable Speed Wind Turbines (VSWTs) in the primary frequency regulation during network disturbances. The proposed control system utilizes an H∞ Linear Matrix Inequality (H∞ LMI) based scheme to improve the closed-loop performance. In order to demonstrate the effectiveness of the proposed control scheme, it is compared with two classical control systems: the Inertial Control (IC) and the Modified Inertial Control (MIC). Several simulations on a multi-machine test system were performed in Matlab/Simulink environment. The H∞ LMI controller optimizes the trade-off between frequency deviation smoothing and wind turbine (WT) speed deviation after the disturbance. Results show a considerable improvement in frequency deviation smoothing, optimal speed recovery and power injection, during a sudden variation in the system load, compared with the two traditional approaches. A desirable robust performance was also obtained with the proposed scheme after variations in the system parameters.

Operation Strategy for a Power Grid Supplied by 100% Renewable Energy at a Cold Region in Japan

“…In case of isolated operation, wind turbines can support frequency by emulating conventional synchronous generators and by injecting reactive power to the system [23,24]. In this paper, only the normal condition case shown in Figure 3 is considered.…”

Model predictive based load frequency control_design concerning wind turbines

“…(13) gives the optimal sequence of control signal over the horizon N while respecting the given constraints of Eq. (14).…”

“…On the other hand, the frequency deviation and wind turbine rotational speed are used as feedback for the closed loop control system. Both of the reference and measured area control error ACE i , (ACE ref,i = 0 HZ) and both of the reference and measured wind rotational speed (w ref,i = w opt ) are fed to the model predictive controller MPC i,2 of the wind turbine unit in order to obtain the signal DV qr,i (q-axis component of the deviation of the rotor voltage, which fed to the wind turbine), and the second controller MPC i,1 is fed by only by the reference and measured area control error ACE i , (ACE ref,i = 0 HZ) in order to obtain the supplementary control action DP ci which is added to the negative frequency feedback signal to give the signal which feeds the governor giving the governor valve position which supplies the turbine to give the mechanical power change DP mi which is affected by the load change DP Li , the tie-line power change DP tie,i and the active power change of wind turbine DP e (which multiplied by a gain equal to 0.5 to make a correlation Table 3 Wind turbine parameters and operating point [14]. between power system and wind turbine bases) giving the input of the rotating mass and load block to provide the actual frequency deviation Df.…”

“…Fig. 1 shows a simplified model of DFIG based wind turbine for frequency response [14]. This simplified model can be described by the following equations:…”

Decentralized model predictive‐based load‐frequency control in an interconnected power system concerning wind turbines

“…In addition, the tie-line flow deviation is added to the frequency deviation in the supplementary feedback loop to give the area control error ACE i . Also, each area has its own aggregated WT model (which consists of 200 WT units of 2 MW rated VSWTs [18]) which is fed by the frequency deviation and produces its electrical power deviation which is multiplied by a gain equal to 0.5 (to make a correlation between power system and WT bases), and used to adjust the total power change P mi as shown in the figure. The used practical two-area power system had the nominal parameters [1] listed in Table III.…”