Model predictive control (MPC) is a powerful strategy for tackling multiobjective control challenges, but it often involves a laborious process of tuning weighting factors. This article proposes a gain-free MPC method for a recently developed nine-level T-type converter (9L-T2C), which offers advantages over traditional topologies, such as fewer components and improved efficiency. Drawing inspiration from Lyapunov's theory, this method avoids the use of weighting factors while effectively handling three targets, including current tracking, balancing of flying capacitors (FCs), and regulation of the neutral point (NP). Comparable with the traditional finite-control-set MPC (FCS-MPC), the proposed controller demonstrates high performance concerning all objectives. Additionally, it showcases superior resilience against model uncertainties when compared with the traditional approach. Experimental validation of the proposed MPC method is conducted in grid-connected operation under several conditions. The proposed method is subjected to a comparative analysis via the experimental implementation, where it is compared with a proportional-resonant (PR) controller and other state-of-the-art MPC methods. This analysis reveals the advantages of the proposed method, including eliminating the need for gains or weighting factors, improved robustness, and effective control of the FCs.

Rectangular Hollow Flange (RHF) steel beams may have openings drilled in their top surface to pass some plumbing and electrical wiring inside the RHF cavity. These openings affect the behavior of these steel beams and may reduce their resistance to bending moment. To investigate this effect, Finite Element Modeling (FEM) was used to simulate RHF steel beams with and without flange openings with several variables in geometric di mensions. The FEM results were examined using 8 experimental test specimens of RHF steel beams obtained from previous studies without flange openings. The results showed high accuracy in modeling these RHF steel beams in structural behavior and ultimate bending moment capacity. Current design codes were applied to predict the capacity of these RHF steel beams without flange openings, both from FEM results and experimental tests. The prediction values were always less than the ultimate capacity of RHF steel beams without flange openings. After verifying the results, 98 RHF steel beams with different flange opening diameters were modeled. The reduction ratios in ultimate capacity in steel beams with hollow flange openings are directly proportional to the opening diameter, hollow flange height, and steel yield stress, while they are inversely proportional to the thickness and width of the hollow flange and the steel beam web height. The reduction ratios in the ultimate capacity for RHF steel beams with flange openings are small in the compact category, and these ratios increase with the non compact and slender categories.

External loads applied to a box-section steel column before it is filled with concrete to increase its efficiency due to modifications in structural systems or design errors may reduce its ultimate capacity and change its structural behavior. To examine this effect, finite element modeling (FEM) has been used to simulate these columns under preloading at different ratios with many variables in the geometric dimensions of the columns. The FEM results have been investigated using 38 experimental specimens obtained from previous studies without preloading. The results demonstrated high accuracy in modeling these columns in structural behavior and ultimate load capacity. After verifying the results, 84 Concrete-Filled Steel Columns (CFSC) were modeled under different preload ratios. The results indicated that some variables have directly affected the value of the decrease in column capacity in terms of its height, wall thickness, yield stress, and preload ratios, while others were inversely proportional in terms of the cross-section dimensions and concrete strength. The preload effect ratio had two separate limits, where when it reached 70%, the maximum value of the decrease in column capacity was 10.90%. The value increased sharply reaching 19.90% when there was a preload equal to 80%. New equations have been proposed to predict the ultimate capacity of CFSC under preloading with suitable accuracy with a correlation coefficient of no less than 0.949.

A combined multi-stage algorithm is proposed and built to handle thirteen challenges along with electrical
constraints that face electric-supply restoration in smart grids. The challenges include maximization of the
number of recovered out-of-service healthy loads, avoiding in-service load shedding, occurrence of multi-simultaneous faults, minimization of total power loss, sequence consideration of commanded switches, minimization of number of switches receiving order, reducing restoration time, independency on system size and
achieving self-healing. The electrical constraints include branch current capacity, voltage limits, load priority
and system radiality. These challenges are discussed in five stages, each stage represents a step in dealing with
these challenges and electrical constraints. The proposed algorithm is aimed at minimizing the energy-not-supplied by minimizing number of out-of-service healthy downstream loads in a tree exposed to a permanent
single fault or multi-simultaneous faults without violating the electrical constraints. The proposed algorithm is
independent on distribution system size and its restoration time lies within 190–199 ms. The proposed algorithm
is tested under IEEE 16-bus, IEEE 33-bus and IEEE 69-bus distribution systems for all maneuvering fault processes. The proposed algorithm achieves 100 % self-healing capability under a single fault maneuvering processes against 95.8 %, 98.3 % and 97. 8 % satisfaction of self-healing condition under exposure to two, three and four multi-simultaneous faults, respectively. The proposed algorithm showed better performance compared to
other algorithms reported in the literature as regards as capability of self-healing, efficiency, restoration time and
number of considered challenges.