Adaptive-Observer-Based Data Driven Voltage Control in Islanded-Mode of Distributed Energy Resource Systems

Abstract

In this paper, an adaptive observer based data driven control scheme is proposed for the voltage control of dispatchable distributed energy resource (DER) systems which work in islanded operation. In the design procedure of the proposed control scheme, we utilize the novel transformation and linearization technique for the islanded DER system dynamics, which is proper for the proposed data driven control algorithm. Moreover, the pseudo partial derivative (PPD) parameter matrix can be estimated online by multiple adaptive observers. Then, the adaptive constrained controller is designed only based on the online identification results derived from the input/output (I/O) data of the controlled DER system. It is theoretically proven that all the signals in the closed-loop control system are uniformly ultimately bounded based on the Lyapunov stability analysis approach. In addition, the results of the simulation comparison are given to verify the voltage control effect of the proposed control scheme.

Optimal Data-Driven Control of an LCC HVDC System for Real-Time Grid Frequency Regulation

“…5(a) and (b) show the magnitudes of the HSVs and the corresponding normalized cumulative energy [26], respectively, for 1 ≤ r ≤ p, when H is established with an arbitrarily large value of p for the test condition of the HVDC-linked grids, discussed in Section IV-A (see Table 1). For r = 16, H in (19) captures over 99.9% of the total energy contained in H. This indicates that a 16 th -order data-driven model can successfully reflect the operating characteristics of the HVDC-linked grids. Note that the physics-based modeling approach, discussed in Appendix, results in a 204 th -order small-signal model.…”

“…The SVD algorithm [26] is then applied to H, as shown in (18) and (19), so that only the dominant temporal pattern in Y δ k is considered to construct a low-order dynamic model of the HVDC-linked grids. This mitigates the effects of sensing noises and reduces the model construction time, thereby facilitating practical implementation of the optimal SFC scheme, as discussed in Section III-C.…”

“…In other words, S contains the r (< p) large HSVs to be retained, whereas S t includes the (p − r) small HSVs to be truncated. The truncated Hankel matrix H = L S R * in (19) can still capture the dominant dynamics in H. Fig. 5(a) and (b) show the magnitudes of the HSVs and the corresponding normalized cumulative energy [26], respectively, for 1 ≤ r ≤ p, when H is established with an arbitrarily large value of p for the test condition of the HVDC-linked grids, discussed in Section IV-A (see Table 1).…”

“…Due to recent advances in sensing and data technologies, increasing amounts of grid operating data, for example, on frequency, net load, and WF power generation [16], [17] can be collected and processed in real time, resolving the challenges associated with physics-model-based control. For example, in [18] and [19], model-free adaptive control (MFAC) was applied to ac-dc inter-linking converters and inverter-based generators, respect-ively. In [20] and [21], reinforcement learning (RL) was used for FR in isolatedand interconnected-grids, respectively.…”

Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

“…Data-driven control (DDC) [1] relies on input/output (I/O) data of control systems and does not need to consider mechanism models of systems. After several years of development, some data-driven control techniques have been investigated, such as, proportional-integral derivative (PID) [2], fuzzy logic control [3], unfalsified control (UC) [4,5], model free adaptive control (MFAC) [6][7][8][9][10][11][12], iterative learning control (ILC) [13][14][15], iterative feedback tuning (IFT) [16,17], some control algorithms based on neural network [18][19][20][21][22][23] and so on.…”