Novel Filters Based Operational Scheme for Five-Level Diode-Clamped Inverters in Microgrid

Abstract

This paper introduces a new operational scheme for several multilevel (five-level diode-clamped) inverters in the microgrid. The presented operational scheme has a central structure (different from the distributed controllers for droop schemes) to alleviate the drawback of the conventional droop control for microgrid operation. The contribution of this paper is focused on the suggested operational scheme, which has two contributory components. The first component is the novel smooth variable structure filters (SVSF), which are formulated to estimate the disturbances of voltage harmonics and voltage unbalance. The second component is an efficient adaptive sliding mode controller to operate these inverters. This proposed operational achieves equal power sharing among working inverters, compensating for voltage unbalance at the point of common coupling (PCC) at loads, and mitigation for voltage harmonics at the PCC. More importantly, the propounded operational scheme efficiently stabilizes voltage with the variation of balanced, unbalanced, and non-linear loads in addition to the seamless transient and steady-state performance for injected power by inverters. The performance of the suggested operational scheme is justified by simulation results.

Sliding innovation filter to estimate power converters of electric vehicles

“…Filters play a crucial role in a wide range of estimation applications [1][2][3][4][5][6][7][8][9] by extracting meaningful information from signals and minimizing the impact of uncertainties, disruptions, and noise. The main objective of filters is to enhance the overall dynamics performance of the system [10][11][12][13][14][15][16][17][18][19][20] by improving the system controller. However, there are various challenges to achieving optimal performance due to the presence of several obstacles, such as limited measured signals, non-measured or hidden states, and disturbances and noise.…”

Sliding innovation filter for micorgrid application

“…Filters cater for sensor reading restrictions and uncertainties to enhance system state assessment accuracy and reliability [5][6][7][8][9][10][11][12][13][14]. Filters minimize sensor data noise, enhancing estimated information and issue and diagnostic discoveries [15][16][17][18][19][20][21][22][23][24][25]. The Kalman filter (KF) estimates a system's state using poor or noisy data.…”

Estimating the complex maneuvering of a UGV using IMM-SIF

“…When the approximated data is used in conjunction with the controller, the system's reaction is improved. Filters can enhance efficiency and functionality in fault and diagnostic applications by minimizing the impact of noise on the estimation process [11][12][13][14][15][16][17][18][19][20][21]. When it comes to filtering estimating methods, there are two primary schools of thought.…”

“…𝑥 ̂𝑗,𝑘|𝑘 = 𝑥 ̂𝑗,𝑘|𝑘−1 + 𝐾 𝑗,𝑘 (𝑧 𝑘 − 𝐻 𝑘 𝑥 ̂𝑗,𝑘|𝑘−1 ) (𝑃 𝑗,𝑘|𝑘 = (𝐼 − 𝐾 𝑗,𝑘 )𝑃 𝑗,𝑘|𝑘−1 −𝐻 𝑘 𝑥 ̂𝑗,𝑘|𝑘−1 )(𝑧 𝑘 −𝐻 𝑘 𝑥 ̂𝑗,𝑘|𝑘−1 ) 𝑇 𝑆 𝑗,𝑘|k−1 ) √|2𝜋𝑆 𝑗,𝑘|k−1 |(11) …”