Decentralized Control Approach Research Articles

A bi-level inter-phase coordinated control method for voltage regulation in unbalanced LV distribution networks using PV-battery energy storage systems

Photovoltaic systems coupled with battery energy storage (PV-BES) can help to minimize the effects of variability in PV generation including voltage problems in low voltage distribution grids (LVDNs). Therefore, a joint centralized-decentralized method consisting of two control levels based on sensitivity analysis is proposed in this paper, which employs the BES charging and discharging control in the first level and reactive power capability of inverters in the second control level. When the BES charging and discharging control is insufficient for voltage regulation, the second level of the proposed method is triggered. For full utilization of the BES capacity and to ensure adequate capacity for the following day's voltage regulation needs, an enhancement algorithm is applied to the voltage to active power sensitivity coefficients. All of the voltage sensitivity coefficients are calculated by a central controller using an offline procedure and sent to the PV-BES inverters to determine the amount of active and reactive power in real-time according to the measured local voltage. Taking into account the inter-phase voltage effects, a request for Var compensation signal flow with inter-phase coordination constraints is utilized to properly coordinate the inverters in reactive power control. The proposed method can efficiently regulate voltage by preventing over and under-voltage problems, improving the average total voltage deviation (TVD), reducing the total reactive power absorption and injection by the inverters, and avoiding reduction in PV generation. To evaluate the performance of the proposed control method, it is compared with the commercially available inverter voltage control techniques in an unbalanced residential test feeder. Moreover, a comparison is conducted between the proposed method and an adaptive decentralized control approach presented in the literature. Furthermore, a three-phase Volt-Var strategy, with and without the inter-phase coordination constraints, is simulated that indicates the necessity of the constraints for proper coordination.

A decentralized control approach in hypergraph distributed optimization decomposition cases

We study the main decomposition approaches (primal, dual and primal–dual) for a distributed optimization problem from a dynamical system perspective where the couplings among the variables of the optimization problem are described by an undirected, unweighted hypergraph. We conduct stability analysis for the respective dynamical systems of the decomposition cases by using non linear decentralized control theoretic techniques and spectral properties of the respective communication matrices, i.e., the incidence and the Laplacian matrices of the hypergraph. Finally, we provide numerical simulations under a specific coalitional setting that demonstrate the superiority of the hypergraph compared to its respective graph analogue, the clique expansion graph, for the given decomposition algorithms in terms of convergence rate and information transmission efficiency.

Robust decentralized controller design for highly Maneuverable Aircraft

Large-scale control systems often encounter challenges due to their intricate control structures, leading to delays and uncertainties when traditional centralized control methods are implemented in engineering applications. In response to these challenges and with the aim of enhancing control efficiency, the exploration of decentralized control approaches has become increasingly prominent. This paper presents two decentralized control strategies: fixed structure control theory, which is built upon the foundational principles of traditional robust controller design, and the weighted model approximation method, which integrates weighted model reduction with optimization strategies. The fixed structure control theory predetermines architecture of the controllers, significantly alleviating the computational burden associated with the development of robust controllers. Conversely, the weighted model approximation method proposes a more intuitive and succinct pathway for controller design and engineering application. The efficacy of these strategies is evaluated through their application to the Highly Maneuverable Aircraft (HIMAT) system. The results demonstrate that, the fixed structure control theory generally exhibits good performance. However, in certain engineering scenarios, the conclusions derived from the weighted model approximation method for decentralized controller design are easier to comprehend and facilitate rapid application.

Stabilization of Distribution Grids With High Penetration of Renewables: The Path From Decentralized Control to a Centralized One

The integration of renewable energies and electric vehicle (EV) charging stations is challenging the electric grid in terms of resonances caused by complex interaction mechanisms involving power convertor control and electromagnetic transients of the grid. Very often, these problems have been tackled with decentralized control approaches, which have difficulties in taking all the interdependencies into consideration. This article proposes a different perspective: a centralized control approach through a smart transformer (ST).

Towards Scalable Dynamic Traffic Assignment With Streaming Agents: A Decentralized Control Approach Using Genetic Programming

Towards Scalable Dynamic Traffic Assignment With Streaming Agents: A Decentralized Control Approach Using Genetic Programming

Design and FPGA-in-loop based validation of predictive hierarchical control for islanded AC microgrid

This paper proposes a decentralized control approach for the flexible operation of an autonomous AC microgrid (MG). AC MG typically consists of two or more voltage source inverters (VSI), capable of simultaneously regulating the voltage at the point of common coupling (PCC) and meeting the local power demand. The classical linear control techniques attain these functions. However, they have many limitations, such as slow transient response, high sensitivity to the parameter variations, and inability to handle the system nonlinearities. This paper aims to address these issues by presenting an improved finite control set model predictive control (FCS-MPC) approach for inverter-based distributed generation (DG). The proposed scheme tracks the voltage trajectory using cost function (CF) over two-step prediction horizons. The proposed control method is employed for the AC MG having two parallel DGs. Droop control is responsible for the power-sharing between the parallel DGs regardless of impedance mismatch at the distribution level of AC MG. The decentralized secondary control is developed to eliminate the deviation in the voltage and frequency caused due to overlooking the primary control. The proposed control scheme has been validated through extensive simulations and real-time controller hardware in the loop tests using an FPGA ZYBO Z7 board. Moreover, the proposed methodology depicts the enhanced transient response, less computational burden than the classic MPC, and shows robustness to parametric uncertainties in terms of THD than hierarchical linear control. The simulations and experimental results visually represent research outcomes, exhibiting the THD of 0.98 % for different dynamic loads, which is within the limit of IEEE and IEC standards. Furthermore, the proposed controller’s mathematical stability is further supported by analysis based on the Lyapunov stability theory.

Co-Simulation of a Cellular Energy System

The concept of cellular energy systems of the German Association for Electrical, Electronic & Information Technologies (VDE) proposes sector coupled energy networks for energy transition based on cellular structures. Its decentralized control approach radically differs from that of existing networks. Deeply integrated information and communications technologies (ICT) open opportunities for increased resilience and optimizations. The exploration of this concept requires a comprehensive simulation tool. In this paper, we investigate simulation techniques for cellular energy systems and present a concept based on co-simulation. We combine simulation tools developed for different domains. A classical tool for studying physical aspects of energy systems (Modelica, TransiEnt library) is fused with a state-of-the-art communication networks simulator (OMNeT++) via the standardized functional mock-up interface (FMI). New components, such as cell managers, aggregators, and markets, are integrated via remote procedure calls. A special feature of our concept is that the communication simulator coordinates the co-simulation as a master and integrates other components via a proxy concept. Model consistency across different domains is achieved by a common description of the energy system. Evaluation proves the feasibility of the concept and shows simulation speeds about 20 times faster than real time for a cell with 111 households.

Reliability Analysis of Microgrids: Evaluation of Centralized and Decentralized Control Approaches

The transformation of traditional power systems into micro and smart grids has become necessary with recent advancements in technology. However, these new systems often have low inertia, which can lead to instability, decreased power quality, and potential blackouts. There are two methods for controlling microgrids: centralized and decentralized controllers. Each method provides different advantages and has some limitations. This paper focuses on the reliability of central and decentralized controlled microgrids. It reviews the reliability of microgrids using both centralized and decentralized controllers, and explains various methods and analysis that can be applied. Examples which analyses reliability assessment of microgrid central controller are given. Examples employed different methods such as the Monte Carlo simulation, the Markov Chain process, the fast decoupled load flow simulation method and hybrid methods. Then, centralized and decentralized microgrids reliability is analyzed and compared through an example with Markov Chain Modeling and reliability indices. It is understood that the microgrid central controller has to be survive all the time otherwise it is very likely to see the grid failure. Also, it is seen that decentralized architecture is crucial with regard to reliability because centrally controlled microgrids are prone to failures due to their single point of failure.

Decentralised fully probabilistic design for stochastic networks with multiplicative noise

In this paper, we present an innovative decentralised control framework, designed to address stochastic dynamic complex systems that are influenced by multiple multiplicative noise factors. Our advanced approach builds upon the foundation of conventional Decentralised Fully Probabilistic Design (DFPD) by refining the Riccati equation to accommodate multiple noise sources effectively. By embracing the inherent stochastic nature of complex systems, our methodology fully characterises their dynamic behaviours using probabilistic state–space models, delivering a comprehensive representation of subsystem components. Importantly, the DFPD approach also incorporates system and input constraints by characterising their corresponding ideal distributions, ensuring optimal functionality and performance while adhering to permissible boundaries. To further enhance system performance, we introduce a probabilistic message passing architecture that enables seamless communication between neighbouring subsystems and promotes harmonised decision-making among local nodes. To demonstrate the efficacy of our proposed framework, we employ a three-inverted pendulum system as a numerical example and compare its performance to that of the conventional DFPD. Through this comparison, we showcase the advantages of our novel decentralised control approach in handling complex systems with multiple noise factors.

Dynamic Motion Planning Model for Multirobot Using Graph Neural Network and Historical Information

In order to effectively improve the path‐finding capability of a multirobot system in a decentralized control approach, a dynamic motion planning model based on graph neural networks and historical information (GNNHIM) is proposed. Due to the limited sensing range of the robot's onboard sensors, GNNHIM uses convolutional neural networks to extract features from the heterogeneous environmental information collected and analyzes the motion trends of other robots in conjunction with the historical path information stored locally. After finishing feature fusion, each robot exchanges its feature vectors with other robots still within sensing range to obtain the next action by the graph neural network model processing. Herein, imitation learning is used to train robots to choose behavioral strategies to maximize team benefit. The experimental results show that GNNHIM can make path planning for multirobot systems more efficient and reliable, enabling all robots to reach their own goal within a limited time in 95.1% of the experimental dataset. Moreover, GNNHIM still has great generalization when applied to unseen scenarios and larger scale robot systems. GNNHIM in unseen scenarios has improved the success rate by 6%‐34% and reduced the detour percentage by 3.8% on average compared to the other scheduling model.

Decentralized secondary control for frequency restoration and power allocation in islanded AC microgrids

In this paper, we show that a fully decentralized simple control method can be used to solve the secondary frequency restoration and power allocation problem in AC microgrid (MG) systems. Compared to communication enabled control schemes, this paper makes a breakthrough of realizing frequency restoration under a decentralized secondary control by which no data exchanging process happens and thus, MG reliability and robustness can be enhanced. Different from most of existing decentralized control approaches, rigorous theoretical analysis proves that the proposed simple decentralized controller, which is easier for implementation, can still ensure the stability of the overall AC MG system, achieve frequency restoration and accurate optimal power sharing flexibly. In order to illustrate the proposed scheme, real-time tests are carried out in an islanded AC MG system built in the real-time simulator OPAL-RT. The results do show its effectiveness and also verify the established theoretical findings.

Control of Multi-Agent Systems with Distributed Delay

This study deals with the control of nonlinear multi-agent systems in which the interconnections among the agents can be modeled using distributed delay terms. It was shown that in the addressed multi-agent system class, the stabilization of the agent outputs can be performed with a prescribed precision. The proposed decentralized control approach combines passivity-based control, the synchronization of dynamic systems, and delay systems theory to achieve the control goal. As an application, the control of single-species migration networks with distributed delay was addressed. The setpoint control problem in such networks can be solved by extending the proposed passivity-based control with a suitably chosen feed-forward term. Simulation results are also provided to show the applicability of the proposed control strategy.

New Nonlinear Control Based on Polynomial Approach for Islanded DC Microgrid Robustness and Voltage Stability

This paper aims at developing a novel robust polynomial decentralized control approach for an islanded hybrid DC MicroGrid (MG) with saturation constraint. The investigated MG system consists of a PV unit, a battery and a SuperCapacitor (SC). Each source is controlled by a DC-DC power converter. The challenge is to design a new control approach to enhance the robustness and the stability of the DC MG subject to high nonlinear variations. The main nonlinear control objectives are to: 1) maintain the DC bus voltage to a reference value, 2) ensure the power balance in the system, and 3) warranty the system robustness against perturbations and parametric uncertainties. The Polynomial Control (PC) theory offers an adequate framework to meet the required performances. The resulting control algorithms are simple, and do not need important calculating resources. An extended version of the PC controller is also proposed to make the system Fault Tolerant (FT) w.r.t severe eventual faults, such as a short circuit case at the DC bus level. The design conditions of the developed polynomial controllers are solved using the Sum Of Squares (SOS) approach while verifying the saturation constraint. Stability conditions are ensured based on polynomial Lyapunov functions. Finally, simulations are carried out on MATLAB/Simulink to prove the pertinence and the robustness of the developed control architecture while considering variable loads and solar irradiance. Results are compared to model-based feedback linearization control and sliding mode control techniques.

Decentralized adaptive neuro-fuzzy dynamic surface control for maximum power point tracking of a photovoltaic system

The current work proposes a decentralized adaptive dynamic surface control approach for extracting the maximum power from a photovoltaic (PV) system and then regulating the required voltage for charging the battery. In this regard, two cascaded direct current-direct current (DC-DC) converters are utilized. The boost converter is interposed between the PV system and the load to help extract the maximum power. The buck-boost converter is then exploited to maintain the output voltage at a specified level which must meet the battery demand. Therefore, to handle the interactions between the cascaded converters, a decentralized control approach is developed. In the suggested approach, by introducing a nonlinear filter, an effective dynamic surface control (DSC) scheme is proposed with guaranteeing asymptotic tracking convergence. Further, by incorporating a nonlinear compensation term into the proposed control approach, the robustness of the resulting controller is improved. In addition, since the model of the converters is nonlinear with unknown uncertainties, the neuro-fuzzy system is used to estimate lumped uncertainties. The proposed control method has good attributes in terms of having a low tracking error, an excellent transition response, and a quick response to changes in atmospheric conditions. The stability of the whole control system is proved by the Lyapunov stability theorem. Finally, comprehensive simulation results are performed to validate the effectiveness of the suggested control approach.

Mitigation of Voltage Unbalance in Distribution Feeders Using Phase Switching Devices: A Decentralized Control Approach Based on Local Measurements

High penetration level of single-phase solar PVs in low voltage distribution systems has raised concern about the Voltage Unbalance (VU) level. Real-time phase reconfiguration using Dynamic Switching Devices (DSD) has been proposed as a cost-effective solution to mitigate the excessive level of Voltage Unbalance. DSDs are fast power electronics devices that could change the phase connection of a solar PV with minimum disturbances. Most of the proposed methodologies for controlling DSDs are based on a central controller and the assumption of availability of Advanced Metering Infrastructure (AMI) and a communication system. In this paper, we propose a distributed control methodology to control DSDs based on the measurement from the local node only and using the sensitivity of nodal voltage to power injection. Each DSD decides on phase swapping if it rectifies the unbalance index of the local node. We show that decreasing the Voltage Unbalance Factor (VUF) of individual nodes will improve the unbalance situation of the whole feeder. The proposed scheme does not require a communication system nor a central controller. The result of simulations confirms the efficacy of the proposed methodology.

Decentralized Formation and Constellation Stability Design Requirements Using Differential Mean Orbit Elements

Over the past few years, there has been an increased interest in large constellation architectures for different applications and services within the aerospace industry. Inherently, the growing attention to these complex systems comes with a desire for mission robustness and safety. Therefore, the aim of this paper is to enable a framework of necessary conditions to guarantee stability for formations with low levels of communication as well as low knowledge of control policies between users and formations with coordinated or uncoordinated impulsive maneuvers. A weighted formation barycenter and formation slots are presented to define the formation problem. Graph theory results are leveraged to demonstrate convergence on the consensus of the formation barycenter. Sufficient stability conditions are developed for different scenarios, and their performances are measured though a proposed formation error Lyapunov function. Analysis using the proposed Lyapunov formation error function shows that a democratic weighting scheme minimizes the formation error. An impulsive controller is presented to test the developed stability conditions. Simulations show the application of an impulsive decentralized control approach with sufficient stability conditions for different constellation scenarios; and it is validated that a democratic formation minimizes the formation error.

Distributed model predictive control of buildings and energy hubs

Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized due to the fact that energy demands are often assumed to be fixed quantities rather than controlled dynamic variables. The joint optimization of energy hubs and buildings’ energy management systems can result in higher energy savings. This paper investigates how different MPC strategies perform on energy management systems in buildings and energy hubs. We first discuss two MPC approaches; centralized and decentralized. While the centralized control strategy offers optimal performance, its implementation is computationally prohibitive and raises privacy concerns. On the other hand, the decentralized control approach, which offers ease of implementation, displays significantly lower performance. We propose a third strategy, distributed control based on dual decomposition, which has the advantages of both approaches. Numerical case studies and comparisons demonstrate that the performance of distributed control is close to the performance of the centralized case, while maintaining a significantly lower computational burden, especially in large-scale scenarios with many agents. Finally, we validate and verify the reliability of the proposed method through an experiment on a full-scale energy hub system in the NEST demonstrator in Dübendorf, Switzerland.

Cancellation of unknown multi-harmonic disturbances in multivariable flexible mechanical structures

Active vibration control of flexible mechanical structures is a challenging field of research in control engineering due to typically high state-space dimensions of the plant models and the resulting multi-modal dynamical behavior. This work deals with the cancellation of unknown multi-harmonic disturbances in such flexible mechanical structures with multiple inputs and multiple outputs. The proposed control scheme exploits the passivity property of the closed-loop system to ensure robustness to unknown and slowly varying plant parameters. The controller design follows a decentralized control approach, which features a high flexibility in terms of the number of available actuators. However, the unknown disturbance frequency is estimated in a centralized way. Here, the frequency estimator switches between individual outputs based on the estimated amplitudes of the compensation scheme. This leads to an improved rate of convergence compared to a fixed output frequency estimator while retaining the high flexibility of the controller design regarding the number of available actuators. In this work, the local exponential stability of the respective nonlinear time-varying switched error dynamics is proven. Furthermore, measurements from an experimental test rig for electromagnetic stabilization of thin steel strips demonstrate the effectiveness and robustness of the proposed control scheme.

Electromagnetic uncoordinated control of a ChipSats swarm using magnetorquers

A swarm of ChipSats equipped with miniature magnetorquers is considered, the electromagnetic interaction force is used for relative motion control at extremely short relative distances up to several centimetres. A ChipSat is a satellite printed on 3.5 × 3.5 cm circuit board with a set of sensors, solar panels, onboard computer, and communication system. Assuming the relative motion between each satellite is known, a Lyapunov-based control algorithm is proposed to achieve bounded relative trajectories. A centralized and a decentralized control approaches are implemented. For the centralized approach, a CubeSat is considered as the main satellite after swarm deployment, producing a high value magnetic dipole moment which interacts with the magnetic dipole of the surrounding ChipSats in order to stop relative drift. For the decentralized approach, the ChipSats are linked in interchangeable pairs in order to apply the electromagnetic control force and reduce the relative drift between the satellites to the vicinity of zero. Two different pairing selection methods are proposed in the paper, based on the closest satellite and on the satellite with highest relative drift. Both translational and rotational motion of satellites are considered. The magnetic dipole moments are used for angular velocity damping when orbit control is not required, and a repulsive collision avoidance electromagnetic control is applied when two ChipSats are within dangerously close proximity to each other. The proposed control schemes performance is studied numerically with different algorithm and ChipSats parameters. The influence of these parameters on the swarm separation is obtained using Monte-Carlo simulations.

Decentralized Phase Shedding with Low Power Mode for Multiphase Converter

With a multiphase converter, the phase-shedding function dedicated to maximizing the power efficiency, in a manner that is dependent on the load current, is always provided by a centralized controller that induces a Single Point of Failure (SPOF). The objective of this study is to obtain a decentralized control approach to implement this function by removing any SPOF. The method consists of using identical local controllers, each associated with a converter phase, that communicate with each other in a daisy-chain structure. Instead of measuring the global output current to determine the optimal number of active phases required, each local controller measures its own leg current and takes a local decision based on threshold crossing management and inter-controller communications. Functional simulations are carried out on a 5-leg 12 V/1.2 V 60 W multiphase converter supplying a modern microcontroller. They demonstrate that the number of active phases is well adjusted, in a dynamic manner, depending on the load current level. Specific events such as load current inrush or the start-up sequence are analyzed to guarantee optimal transient responses. A maximum power efficiency tracking ability is also demonstrated. Finally, it is shown that this control strategy allows phase shedding to be implemented using as many phases as desired, in a modular manner, thereby avoiding any centralized processing.