Two-level Control Structure Research Articles

Modular Model Predictive Control of a 15-kW, Kilo-to-Mega-Hertz Variable-Frequency Critical-Soft-Switching Nonisolated Grid-Tied Inverter With High Efficiency

A variable-frequency critical-soft-switching (VF-CSS) model predictive control method is developed combining with a modified nonisolated inverter to improve the efficiency. A two-level control structure is developed, including 1) the central-level grid-side inductor current control and zero-sequence voltage control; and 2) the local-level per-phase power module control of VF-CSS model predictive controller with state estimator. Two VF-CSS controllers are proposed, including variable-continuous-frequency critical-soft-switching controller and variable-discrete-frequency critical-soft-switching, to achieve critical soft-switching operation for high efficiency. The combination of MPC and VF-CSS guarantees a complete critical soft-switching operation at full period range of varying frequency and especially during transient. The state estimator provides sampling noise rejection and accurate switch-side inductor current estimations for MPC and VF-CSS. Also, with the help of state estimator, the current sensor cost is reduced by 50%. Meanwhile, the estimator contributes to a more accurate switch-side inductor current reading for MPC implementation with variable continuous frequency controller, especially when the critical soft switching requires huge inductor current ripple. With the proposed control strategies and topology, medium-frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 200 kHz, 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> H) and high-frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 1 MHz, 4.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> H) test benches are validated experimentally. The efficiency is above 99% at a rated power of 15 kW. A power density of more than 10.4 kW/L is achieved.

A Fully Distributed Economic Dispatch Method in DC Microgrid Based on Consensus Algorithm

Uncertainties of intermittent renewables and complexities of load demands bring challenges to the optimal operation of the microgrid. This paper proposes a fully distributed method based on consensus algorithm for solving the economic dispatch problem (EDP) of DC microgrid. The proposed distributed method is performed using multiagent system. It only needs information from neighboring agents and does not need a central controller. A two-level control structure is adopted to realize the economic dispatch and voltage regulation simultaneously. The upper level obtains the optimal power reference of dispatchable agents and maintains the power balance of the system. The lower level performs an improved droop control to track the power reference and regulate the average bus voltage for the microgrid. The proposed method’s properties and convergence are analyzed using eigenvalue perturbation theory to obtain a faster convergence speed. By designing the adjacency matrix appropriately, the proposed method can ensure the system stable when the communication topology changes. Finally, several simulations are designed to verify the effectiveness of this novel control method.

Designing Traffic Management Strategies for a Heterogeneous Traffic Network

This paper focuses on modeling and controlling a non-signalized heterogeneous traffic network consisting of Human-Driven Vehicles (HDVs) and Autonomous Vehicles (AVs) at a macroscopic level. To describe the traffic network's behavior, we introduce an extended heterogeneous METANET model wherein each vehicle class's density and velocity dynamics are described. To develop traffic control policies, we propose a two-level control structure. At the higher level, a decentralized Extremum-Seeking (ES) control approach is designed to determine an optimal density of human and autonomous vehicles so that the average flow within the cell is maximized. At the lower level, a filtered feedback linearization control approach is designed to determine the velocity of autonomous and human-driven vehicles such that the density of each type of vehicle reaches the values set by the upper-level macroscopic controller. Numerical simulation demonstrates the effectiveness of the proposed approach in managing the traffic flow of a heterogeneous system.

Intelligent scheduling and flexible operation for the commercial-scale coal-fired power plant integrated with post-combustion carbon capture

This paper develops a two-level control structure for commercial-scale coal-fired power plant integrated with post-combustion carbon capture (CFPP-PCC). In high-level scheduler, an economic objective function with the consideration of grid demand, price conditions and system constraints is developed. Deep belief network is utilized to develop the steady-state model for CFPP-PCC process. Intelligent scheduling of electricity generation and capture level is then implemented using Bayesian optimization. Given the optimal setpoints, model predictive controller (MPC) is applied to achieve flexible operation in the low-level supervisory control. The simulation results show that the proposed control structure specifies an economically attractive operation profile for the CFPP-PCC process.

An autonomous hybrid DC microgrid with ANN-fuzzy and adaptive terminal sliding mode multi-level control structure

Conventional fuels for power systems are now plaguing the environment more than ever before. Efforts to curb the environmental impacts of fossil fuels are now driving modern microgrids (MGs) to utilize green energy sources. In this regard, DC microgrids are well known for their versatility. Considering the rising significance of DC-MGs in future, this paper presents an aggregated DC microgrid model comprising of wind resource, photovoltaics, fuel cell, battery, and a supercapacitor. The model also includes DC–DC power converters and a two-level control structure. Firstly, the high-level of the proposed control structure constitutes an artificial neural network for maximum power point tracking and an intelligent energy management powered by a fuzzy logic controller. At this control level, the unified operation of the fuel cell and storage components under various dynamic conditions is ensured. Furthermore, the low-level control structure is based on an adaptive terminal sliding mode non-linear controller that derives the control signal for the converters. Finally, results showed that the energy management system along with the adaptive terminal sliding mode control successfully regulated the DC bus voltage by keeping the sources within their operational constraints. Moreover, the proposed non-linear controller demonstrated model’s robustness, successfully converged the sliding surface to zero in finite time, ensured global asymptotic stability and effectively dealt with the model uncertainties. All the simulation leading to these results were performed in MATLAB/Simulink and further evaluation was carried out by hardware in loop experiments using C2000 Delfino microcontroller F28397D launchpad.

Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion battery and supercapacitor (SC) as the EV’s energy sources, known as a hybrid energy storage system (HESS), is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from the vehicular network could not directly be used to improve HESS, a two-level control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in level two control structure. The ANN provides a direct prediction that could approximate the future power demand prediction with the assumption that the vehicular network scenario is deployed to obtain surrounding information. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3%, respectively, compared to the battery-only case.

Receding Horizon Reference Governor for Implementable and Optimal Powertrain-Aware Eco-Driving

This paper presents an adaptive, two-level control structure that makes it possible to implement a numerical optimization algorithm for eco-driving in real time. The reference governor in the higher level is characterized by a low, flexible sampling rate and adopts a receding horizon for preview and optimization. The optimization algorithm thereby finds the energy-minimizing solution, based on Pontryagin’s minimum principle (PMP), for traveling the selected route segments without colliding with the preceding vehicle. The tracking control in the lower level compensates for errors due to modeling inaccuracies and unmodeled disturbances. The hierarchical structure can accommodate different types of numerical solvers and control schemes that apply to various vehicle powertrain configurations. A large-scale simulation study using real-world route data with high-fidelity powertrain models validates the proposed control structure and its online implementation.

Distributed Control With Virtual Capacitance for the Voltage Restorations, State of Charge Balancing, and Load Allocations of Heterogeneous Energy Storages in a DC Datacenter Microgrid

This paper proposes a distributed coordination control strategy for load sharing and energy balancing between heterogeneous energy storages. These control objectives are satisfied through a two-level control structure. At the primary level, the decentralized virtual impedance control, without the requirement of communication links, allocates the low frequency component of the loads to batteries, while the high frequency component is allocated to ultracapacitors. Distributed control strategy, introduced at the secondary level over a sparse communication network, achieves battery state of charge balancing and regulation of the local bus voltages. Two sets of data are exchanged via the communication links, the local bus voltages, and state of charges of batteries. The distributed controller for the restoration of local bus voltages implements an average consensus protocol, while the controller for energy balancing uses a cooperative protocol. In addition, the ultracapacitor voltages are locally restored at a slower time-scale. The proposed control strategy is resilient to communication link failures and features plug-and-play capability. Presented results demonstrate performance of the proposed control strategy for an islanded 380 VDC datacenter with variable loads. Different operating conditions are verified through the RTDS Technologies real-time digital power system simulator using switching converter models and nonlinear battery models.

Fragmentation-Based Distributed Control System for Software-Defined Wireless Sensor Networks

Software-defined wireless sensor networks (WSNs) are a new and emerging network paradigm that seeks to address the impending issues in WSNs. It is formed by applying software-defined networking to WSNs whose basic tenet is the centralization of control intelligence of the network. The centralization of the controller rouses many challenges such as security, reliability, scalability, and performance. A distributed control system is proposed in this paper to address issues arising from and pertaining to the centralized controller. Fragmentation is proposed as a method of distribution, which entails a two-level control structure consisting of local controllers closer to the infrastructure elements and a global controller, which has a global view of the entire network. A distributed controller system brings several advantages and the experiments carried out show that it performs better than a central controller. Furthermore, the results also show that fragmentation improves the performance and thus have a potential to have major impact in the Internet of things.

Characterisation, control, and energy management of electrified turbocharged diesel engines

The electrification of engine components offers significant opportunities for fuel efficiency improvements. The electrified turbocharger is one of the most attractive options since it recovers part of the engine exhaust gas mechanical energy to assist boosting. Therefore, the engine can be downsized through improved transient responsiveness. In the electrified turbocharger, an electric machine is mounted on the turbine shaft and changes the air system dynamics, so characterisation of the new layout is essential. A systematic control solution is required to manage energy flows in the hybrid system. In this paper, a framework for characterisation, control, and energy management for an electrified turbocharged diesel engine is proposed. The impacts of the electric machine on fuel economy and air system variables are analysed. Based on the characterisation, a two-level control structure is proposed. A real-time energy management strategy is employed as the supervisory level controller to generate the optimal values of critical variables, while a model-based multi-variable controller is designed as the low level controller to track the values. The two controllers work together in a cascade to address both fuel economy optimisation and battery state-of-charge maintenance. The proposed control strategy is validated on a high fidelity physical engine model. The tracking performance shows the proposed framework is a promising solution in regulating the behavior of electrified engines.

Two-Level Control of Doubly Fed Induction Generator Using Flatness-Based Approach

In this study, implementation of flatness-based control on doubly fed induction generator (DFIG) is investigated. Flatness-based approach is well adopted to control systems in two-level control structure where the tasks of planning, trajectory generation, and tracking the desired trajectories can be easily separated. As a result, the system is decoupled into two trivial linear systems. While in vector control the trajectories are found using PI controllers, in the proposed control structure the trajectories are computed directly, through algebraic equations. Desired active and reactive power are used to develop the set points for trajectory generation. Tracking of the desired trajectories is guaranteed through feedback linearization. The two-level control structure enables active power control and the opportunity for wind plants to contribute in frequency regulation. The proposed control shows promising results in tracking the desired reactive power and the active power across a variety of scenarios and in providing ancillary service to support the frequency regulation of the grid.

Operating Point Optimization of a Hydrogen Fueled Hybrid Solid Oxide Fuel Cell-Steam Turbine (SOFC-ST) Plant

This paper presents a hydrogen powered hybrid solid oxide fuel cell-steam turbine (SOFC-ST) system and studies its optimal operating conditions. This type of installation can be very appropriate to complement the intermittent generation of renewable energies, such as wind generation. A dynamic model of an alternative hybrid SOFC-ST configuration that is especially suited to work with hydrogen is developed. The proposed system recuperates the waste heat of the high temperature fuel cell, to feed a bottoming cycle (BC) based on a steam turbine (ST). In order to optimize the behavior and performance of the system, a two-level control structure is proposed. Two controllers have been implemented for the stack temperature and fuel utilization factor. An upper supervisor generates optimal set-points in order to reach a maximal hydrogen efficiency. The simulation results obtained show that the proposed system allows one to reach high efficiencies at rated power levels.

Wide-Area Measurements-Based Two-Level Control Design Considering Signal Transmission Delay

In this paper the design of power system stabilizers for small-signal stability using phasor measurements is considered. An approach based on optimal control with structural constraints is proposed to design a two-level control structure. Time delays are included in the design. The method is combined with order reduction to ensure faster convergence of the design algorithm and to facilitate the choice of the weighting matrices for damping interarea modes. The control scheme is discussed and modal analysis and time-domain simulations of two Brazilian equivalent systems are performed to assess the control performance. The robustness to loss of communication links, topological changes, and variations of time delay is evaluated.

Predictive control applied to heat-exchanger networks

This paper discusses the online optimization and control of a heat-exchanger network (HEN) through a two-level control structure. The low level is a constrained model predictive control (MPC) and the high level is a supervisory online optimiser. Since MPC is a multivariable control technique capable of handling control-input constraints, it is neither necessary to define a variable-pairing approach nor to include individual loop-protections to avoid close-loop saturations. The proposed MPC algorithm uses an approximate linear model of the system to perform the output predictions and to account for the constraints. On the other hand, the supervisory program, based on a rigorous model, computes desired values to key manipulated variables of MPC, leading to minimum utility consumption. The coordination between the supervisory program and MPC is achieved through the definition of an extended cost-function that enables the controller to drive the system to the optimal operating condition. The proposed method was successfully tested by rigorous simulation of a typical HEN of the process industry.

Computer Simulation of Flood Operation in Multireservoir Systems

In this article, a summary of experience with the development of methods for controlling multiplereservoir systems during flood periods, gained through computer simulation, is presented. Several decision mechanisms based on online forecasting and optimization are taken into consideration. Particular attention is focused on a two-level control structure with periodic coordination. The advantages of applying parallel global algorithms to determine the optimal decisions regarding outflows from the reservoirs are presented. Advantages of developing sophisticated control structures, as well as decision mechanisms with respect to the available quality of inflow forecasting and delays involved by data transmission and computation time, are considered. Finally, the results of simulations of online management in the case study of two reservoir systems located in the southern part of Poland are described and discussed.

A hierarchical physical model-based approach to predictive control of a thermal power plant for ef”cient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat ‘ow on the boiler-side, while condenser vacuum was ‘uctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range. These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Co-ordinated hierarchical control strategy for multi-infeed HVDC systems

A co-ordinated hierarchical control strategy for multi-infeed HVDC systems is presented. The control law of the HVDC system's transfer power can be derived using optimal control theory with the HVDC system being treated as a variable admittance connected at the commutation busbars. A two-level hierarchical control structure is assumed for the rectifier and inverter side AC system, respectively. The control of constituent HVDC subsystems can be better co-ordinated by the consideration of the AC commutation voltage included in the optimised performance index, and the performance of both rectifier and inverter side AC systems is considered.

FAST AND SLOW GENERATION OF A MULTILEVEL CONTROL FOR THE WOOD DRYING PROCESS

ABSTRACT The dynamic characteristics of the wood drying process were represented by an actuator network and an internal model. These two subsystems are composed of a set of ordinary differential, partial differential and algebraic equations with adjusting operating functions. The formulation of the operating functions is based on interaction between air and the lumber board in a semi-industrial drying kiln. Using this internal model, it was possible to establish a state observer, a state predictor, and a reference model for which the boundary conditions were generated by the actuator network. Simulation results were obtained with a C++ software package. In order to implement the optimization techniques to the wood drying process, the two-level control structure was proposed and tested for a minimum drying time as a performance criterion. Numerical results confirm that the internal model and its operating functions were properly chosen.

A Novel Hierarchical Approach to Plant-Wide Control of a Thermal Power Unit

Abstract This paper presents a novel hierarchical approach to plant-wide control by making effective use of a physical plant model. It incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level non-linear model predictive controller (NMPC). The lower-level PI loops help stabilise the unstable dmm-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested on a simulation of a 200 MW oil-fired power plant at Ballylumford in N. Ireland. Impressive results have been obtained during large system disturbances and extremely high rate of load changes. These results compare favourably with those from a non-linear, state-space generalised predictive control (GPC) method designed under similar conditions.

Multilevel intelligent fuzzy control of oversaturated urban traffic networks

Some drawbacks of existing automated urban traffic control systems are discussed. It is shown that most of them are based on deterministic algorithms and have a centralized structure. As a result, qualitative and quantitative complexity of transport processes are not taken into account effectively. To overcome this inconvenience, an approach for implementing distributed intelligence systems is proposed. It is based on multilevel and artificial intelligence techniques. Fuzzy sets theory is chosen as a tool for distributed intelligence control systems design and a specific method is presented. It is shown, however, that this method is not suitable for real time control of large scale systems and an alternative method, leading to a closed-loop fuzzy control law, is proposed. It is based on decomposition of the system into subsystems and presentation as a hierarchical two-level control structure. In this case, the controls have two components—local (calculated by the lower level) and global (calculated by the...