This paper proposes a novel voltage estimation scheme for the modular multilevel converter (MMC) based on model predictive control (MPC). The developed strategy is presented by combining a disturbance observer (DOB) with an adaptive neural network (ANN) for voltage estimation in the MMC. Firstly, the ac-side and dc bus voltages are estimated as the disturbance items of the DOB which acts as the cost function during each control cycle and ensures the minimal computational cost. Then, the submodule (SM) capacitor voltage estimation is achieved based on the ANN with the estimated ac-side and dc bus voltages. The proposed method requires only one current sensor per arm and has a simple structure with three weights to be adjusted. Comprehensive simulation studies and experiments are presented to demonstrate its effectiveness and feasibility. The results indicate that the proposed method has a high accuracy, a fast dynamic response, and no effects on the original MPC performance.

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.

The power electronic transformer (PET) has recently emerged as a type of power converter. It features the basic functions of power conversion and isolation as well as additional functions related to power quality control. A novel PET for a distribution grid called a flexible power distribution unit is proposed in this paper, and the energy exchange mechanism between the network and the load is revealed. A 30 kW 600 VAC/220 VAC/110 VDC medium-frequency isolated prototype is developed and demonstrated. This paper also presents key control strategies of the PET for electrical distribution grid applications, especially under grid voltage disturbance conditions. Moreover, stability issues related to the grid-connected three-phase PET are discussed and verified with an impedance-based analysis. The PET prototype is tested, and it passes the voltage-disturbance ride-through function. The experimental results verify the power quality control abilities of the PET.

This article investigates the sampled-data disturbance rejection control problem for a class of non-integral-chain systems with mismatched uncertainties. Aiming to reject the adverse effects caused by general mismatched uncertainties via digital control strategy, a new generalized discrete-time extended state observer is first proposed to estimate the lumped disturbances in the sampling point. A disturbance rejection control law is then constructed in a sampled-data form, which will lead to easier implementation in practices. By carefully selecting the control gains and a sampling period sufficiently small to restrain the state growth under a zero-order-holder input, the bounded-input bounded-output stability of the hybrid closed-loop system and the disturbance rejection ability are delicately proved even the controller is dormant within two neighbor sampling points. Numerical simulation results demonstrate the feasibility and efficacy of the proposed method.

In the field of construction engineering, utilizing information technology to empower and upgrade traditional construction methods has become an inevitable trend. Intelligent construction sites aim to enhance project management, quality, and safety levels through three layers—digitalization, networking, and intelligentization—by leveraging advanced information technologies such as Building Information Modeling (BIM), the Internet of Things (IoT), intelligent equipment, and big data. To promote the application of intelligent construction site technology, this paper takes intelligent construction sites as the research object, with the analysis of application benefits as the primary research focus. It systematically examines the definition and connotation of intelligent construction sites, reviews the current research status of intelligent construction sites and benefit evaluation theory, and proposes an intelligent construction site application benefit system across five dimensions: economy, product, organization, management, and strategy. Existing benefit assessment studies predominantly adopt single-dimension evaluation approaches, lacking integrated frameworks that combine quantitative and qualitative analysis. Return on Investment (ROI) and Analytic Hierarchy Process (AHP) analysis models are employed to calculate and evaluate the direct and indirect benefits, respectively. Validation was conducted through an actual project, and the results demonstrate that the application of intelligent construction sites yields an ROI of 102.7% based on discounted cash flow analysis (8% social discount rate), with an expert scoring of 9.42 for indirect benefits. The analysis models indicate positive benefits associated with intelligent construction site implementation. While direct causal attribution requires controlled comparison, the observed improvements are consistent with theoretical expectations and industry benchmarks for intelligent construction site adoption. This study verifies the availability of the evaluation system through its application to an actual project. It is hoped that this research will provide a reference for decision-making regarding the application and promotion of intelligent construction sites.