Stability Improvement of DFIG-Based Wind Farm Integrated Power System Using ANFIS Controlled STATCOM

Abstract

The stability of the control grid is a critical prerequisite for a safe and efficient power system service. A thorough knowledge of the effects of the power system volatility is essential for the effective study and control of power systems. This paper presents the simulation outcome of a multimachine power network implemented by a wind farm (WF) utilizing a static synchronous compensator (STATCOM) for better stability control objectives. A similarly aggregated double-fed induction generator (DFIG) powered by a gearbox analogy with an equally aggregated wind turbine (WT) determines the operating output of the wind farm. A proportional–integral–derivative controller (PID)-based damping controller, PID including Fuzzy Logic Controller (FLC), and an adaptive network-based fuzzy inference system (ANFIS) controller of the proposed SATCOM are intended to add sufficient damping properties to the dominating modes of the examined system during diverse working circumstances. To assess the feasibility of the suggested control schemes, a frequency-domain method concentrated on a linearized mathematical structure layout utilizing a time-domain strategy centered on a nonlinear configuration of the device that is subjected to severe fault on the attached bus was carried out consistently. A STATCOM damping controller is configured using the ANFIS method to apply appropriate damping properties to the device’s decisive modes being evaluated under various test conditions. From the findings of the comparative simulation, it can be inferred that the suggested STATCOM along with the planned ANFIS is seen as comparable to STATCOM with PID and STATCOM with PID plus FLC to increase the stability of the studied device.

Power Grid Dynamic Performance Enhancement via STATCOM Data-Driven Control

“…Therefore, based solely on measurement data to generate P, the coefficients of a stabilizing controller can be obtained without the need for an explicit mathematical model. In other words, the controller given by C(σ)w = 0 can be realized out of the numerical solution of C in (11).…”

“…After examining (11), one can conclude that there are several solutions that will deliver convenient stabilization controllers for a certain plant. However, regarding electric power systems, there might be a particular interest in finding solutions that exhibit particular requirements, for instance, the regulation of certain variables despite of disturbances.…”

“…As an example, in this section, a general convenient controller structure is proposed. The associated gains can be implicit in C, and thus they can be numerically calculated after solving (11).…”

“…Then, the matrix inequality shown in (11) is implemented using MATLAB and external optimization tools, in this case we used Yalmip. To set up (11), it is necessary to define the adequate sizes of Psi (in this case N = 2 and q = 6) and V (with the same dimension as P. It thus remains to define the matrix C that contains the parameters of the candidate controller. For ease of implementation, we chose a PI configuration as the candidate controller with the following equations:…”

“…For example, in [10], a control scheme based on the fundamentals of a STATCOM is employed in photovoltaic (PV) power plants to reduce power oscillations in power grids, due to an inverter being able to act as a STATCOM; this enables the mitigation of POD during transient events. In [11], a STATCOM is also used in wind power plants to enhance the dynamic performance of the main oscillating modes; in that work, an adaptive network-based fuzzy inference system (ANFIS) controller is employed. Another proposal focused on POD can be found in [12], where a STATCOM is equipped with energy storage so that the combination of real and reactive power injection offers good robustness during the dynamic response of the power system.…”