A MRAS Based Model Predictive Control for Multi-Leg Based Multi-Drive System Used in Hot Rolling Mill Applications

Abstract

In this paper, a multi-leg based multi-drive configuration is presented to be used in multi AC drive applications such as the hot rolling mills. The proposed configuration enjoys compactness, fewer semiconductors, and lower drive cost compared with conventional topology, making it a promising approach. In a conventional rolling mill stand, an active front-end (AFE) rectifier and two inverters are required for grid-side and motor-side connections. However, in the proposed configuration, all converters are unified using a multi-leg structure. To improve the operational performance of the drive, a model predictive control (MPC) is designed. In this approach, an individual cost function for every output, along with a comprehensive cost function containing all control objectives for the overall system, is defined. By obtaining the valid and invalid switching states of the proposed converter, the defined multi-objective cost function is minimized to find the most optimum switching states in each sampling time. In addition, to improve the robustness of the proposed multi-drive system, a model reference adaptive system (MRAS) is designed to estimate rotor speed and stator resistance. The high-performance capability of the proposed multi-drive system is evaluated in both steady and dynamic states using MATLAB/Simulink software and compared with a conventional configuration. According to simulation results, it is deduced that the independent control of the top and bottom motors in a rolling mill stand can be guaranteed using the proposed low-cost sensorless drive.

A Novel Simplified Implementation of Finite-Set Model Predictive Control for Power Converters

“…The FS-MPC is nowadays, in the academic literature, a very popular way to track current and power references, these types of controllers do not need to be tuned and, in general, have much faster dynamics than the response of linear controllers [24]. However, to properly control power converters a high sampling frequency is commonly employed [25]- [27], and additionally, FS-MPC requires to consider all possible states into the sampling time and select the one that minimizes the cost functional, aspects which lead to a high computational burden. Instead, in this section is proposed the key concept of the new NI-MPC method to improve this drawback.…”

“…Then, the first step is to determine the zone where the reference voltage is located (zone 0, I, II, … , V). Afterwards, it is verified if the voltage reference is inside or outside the central hexagon, FIGURE 3 (b), using the ratios defined in (27) in the latter case. The algorithm follows the sequence illustrated in FIGURE 4, in order to select the optimum state to apply, where the box "into the center hexagon?"…”

A novel multiport converter switching scheme to integrate multiple renewable energy sources

“…Although the presented methods in Jones et al 29 and Zhou et al 32 are only applicable for a five‐leg converter. In comparison with the presented method in Jones et al 29 that needs n SV modulators for generating the duty cycles of outputs, the proposed SSVM needs only one modulator. In the proposed SSVM, all feasible combinations of common vectors between different outputs are examined, and the best combination is adopted for improving the DC‐link voltage utilization. However, in Jones et al, 29 only some combinations are taken into account. In comparison with the carrier‐based PWM methods reported in previous studies, 24,28,33,37,38 the proposed SSVM enjoys the advantages of SVM method. Compared with Safaeian et al 4 and Zhou et al, 32 the proposed SSVM uses predefined offline DMs to find the common switching states and therefore, does not impose a heavy computation burden on the processor.…”

“…Many industrial applications in the energy sector, like adjustable speed drives (ASDs) and grid integration of renewable energy sources (RESs), need high‐performance control of several voltage‐source converters (VSCs) 1–4 . As connected sources/loads might experience different operating characteristics, the autonomous control of these sources/loads is an essential requirement that should be met by VSCs 5 .…”

“…In Zhou et al, 32 a model predictive control (MPC) in combination with the SV modulator‐based PWM is reported for supplying an induction motor in reconfiguration mode from back‐to‐back converter to a five‐leg converter in a fault condition. A model predictive‐based switching approach for a seven‐leg converter is developed in Safaeian et al 4 for independent control of the grid and two three‐phase induction motors used in hot rolling mill application. For this aim, an online optimization approach is exploited to find the best switching states in every sampling period.…”