Centralized Control Architecture Research Articles

An assessment of nature-inspired metaheuristic algorithms for resilient controller placement in software-defined networks

Software-defined Networking (SDN) offers flexibility and programmability, making it a desirable option for modern network architecture. SDN provides numerous benefits to network administrators due to its centralized control architecture. This allows network administrators to manage and configure the network from a single location, making it easier to manage and automate network tasks. In a network of multiple controllers, the control plane’s resiliency may impact the overall system’s performance. In a controller failure scenario, switches must be reassigned to other active controllers with adequate capacity. Thus, we define a resilient controller placement (RCP) as an optimization problem. The aim is to design physically distributed and redundant controllers to manage switches with varying resilience levels. The propagation latency may increase due to the reassignment, increasing the network cost. The objective is to determine the number of controllers required, their positions, and the allocation of the network nodes to a particular controller while reducing the average propagation latency and cost in meeting the capacity constraint of the controller. Due to the wide area network (WAN) structure, four nature-inspired metaheuristic algorithms are proposed namely, simulated annealing (RCP-SA), moth-flame optimization algorithm (RCP-MFO), particle swarm optimization (RCP-PSO), and grey wolf optimization algorithm (RCP-GWO). These algorithms are evaluated on three network datasets to determine the optimum controller number and their positions. The experimental results show that RCP-GWO performs better than RCP-SA, RCP-MFO, RCP-PSO, and the Random methods.

An Enhanced Voltage Control Method for Fair Active Power Curtailment of Rooftop PV Systems in LV Distribution Networks

Solutions based on active power curtailment (APC) of photovoltaic inverters (PVIs) have been effectively used to alleviate overvoltages in low-voltage distribution networks (LVDNs) due to real power injection supplied by rooftop photovoltaic (PV) systems. However, volt–watt control methods presently available can lead to unfair APC among the PVIs, penalizing financially customers located far away from the distribution transformer of the LVDN. This paper proposes an innovative real-time voltage control method to address the unfair APC issue in LVDNs with rooftop PV systems. The proposed method is based on a centralized control architecture to calculate two fair curtailment factors for all PVIs of the LVDN. Additionally, to explore the extended communication infrastructure brought by centralized control architecture, this paper proposes a novel simplified method to compute the voltage sensitivity matrix (VSM) for applications on unbalanced three-phase LVDNs. Performance tests were conducted on a set of LVDNs, evaluating the effectiveness and fairness of APC quantitatively using Jain’s Fairness Index (JFI) as a metric. The results confirmed the effectiveness of the proposed control method for mitigating the overvoltage issue, ensuring fairness by presenting average JFI values close to one (the maximum allowed value). Furthermore, in comparison to conventional VWC strategies, penalties arising from the local dependence of PVIs on APC were eliminated for both curtailment factors. Finally, an accuracy test was performed on the proposed VSM model, based on voltage estimation through the linearized model of LVDN at a specific operating point. The findings demonstrated outstanding accuracy, with an error of less than 0.2%, compared to voltage estimation via power flow.

Application of Big Data Analysis and Intelligent Algorithm in Power System Operation Optimization

The power communication system provides powerful technical support for realizing the intelligent operation and information management of the power grid and improving the operation efficiency and power supply quality of the power grid. Quantum key distribution (QKD) is considered one of the most promising technologies for commercialization. QKD uses a single photon to encrypt data to produce a more secure and reliable password. This paper intends to study the hierarchical, centralized control architecture of power dispatching based on quantum essential supply (QKD). The performance indexes of MDI-QKD under symmetric and asymmetric conditions were studied by local optimization. The optimal key formation rate of the algorithm is analyzed. From the perspective of quantum critical utilization, a quantum key utilization scheme for grid backbone dispatching service is proposed. The dynamic adjustment test of multi-node time slot and service key update rate is carried out. Experiments show that the MDI scheme can effectively improve the effectiveness of a multi-node QKD system. Thus, the security of data transmission of the core business of power dispatching data networks can be ensured to the greatest extent. AMDI can effectively reduce the transmission timeout of low-priority data streams because the delay of high-priority data streams reaches the proportion. It can be an excellent solution to the power system and the password requirements.

Decentralized Power Sharing with Frequency Decoupling for a Fuel Cell-Battery DC Shipboard Power System

The maritime industry is under increasing pressure to reduce its carbon footprint by adopting new energy generation and storage technologies in shipboard power systems (SPS). Fuel cells (FCs) show great potential as primary power sources when hybridized with energy storage systems (ESS). Integrating different technologies in future SPS requires the coordination of power generation and storage modules, which can be facilitated by DC technology with power electronics interfaces. However, studies on FC integration have primarily focused on small-scale applications with centralized control architectures. There has been little research on the modular control of multiple FC and battery modules in SPS. This study proposes a decentralized droop-based power sharing approach with load frequency decoupling to efficiently utilize power system modules based on their dynamic capabilities. The proposed strategy further incorporates decentralized voltage regulation and state-of-charge (SoC) management functions. The methodology was applied to a short-sea cargo vessel with an FC-battery DC power system. The results indicate that the mission load profile can be satisfied while limiting fluctuations in the FC output power. Moreover, the proposed strategy achieves the same voltage regulation performance as a centralized proportional-integral (PI) controller and can be easily tuned to achieve load frequency decoupling with the desired time constant. Finally, a comparative analysis shows how the trade-off between the dynamic operation of the FC and the discharge depth of the ESS is affected by the choice of time constant.

An Enhanced Distributed Control Architecture of Multiple Three-Phase PMSG for Improving Redundancy

Multiple three-phase permanent magnet synchronous generator (MTP-PMSG) has excellent fault-tolerant features in theory. However, the commonly used control architectures, such as the centralized control architecture with only one central controller and the distributed control architecture where the interconnected communication cables between controllers may fail, limit the fault tolerance of MTP-PMSG. Therefore, this paper proposes an enhanced distributed control (EDC) architecture without interconnected communication cables of MTP-PMSG for improving redundancy. The sensorless control and harmonic current suppression algorithm are considered and implemented in the EDC architecture. First, this paper analyzes the magnetic field coupling between the winding sets of MTP-PMSG, and the variation law of equivalent inductance of the winding sets under the condition of arbitrary current sharing is obtained. Then, an enhanced distributed sensorless control (EDSC) method in EDC architecture is proposed based on the equivalent inductance. Furthermore, this paper proposes a model-free predictive harmonic current (MFPHC) suppression method to solve the MTP-PMSG's inherent harmonic problem in the EDC architecture. The effectiveness of the proposed methods is verified by the comparative simulations and experiments in a dual three-phase PMSG (DTP-PMSG) with arbitrary current sharing of winding sets.

Three Dimensional Formation Control of Unmanned Aerial Vehicles in Obstacle Environments

Today, the use of small unmanned aerial vehicles (UAVs) has increased due to technological advances. There has been an interest in using multiple UAVs instead of a single UAV to accomplish a given mission. This is because there are many scenarios where the capabilities of a single UAV are inadequate due to certain constraints (battery capacity, time in the air). For this reason, swarm UAV studies have increased. A swarm UAV consists of a large number of UAVs cooperating to accomplish a specific mission.
 
 This study shows three-dimensional formation control of a swarm UAV system in an obstacle environment. A centralized control architecture is used in this process. All task assignments are made from a centralized system. The Artificial potential fields method creates the formation at the target point by avoiding obstacles. The study was carried out using the Robot Operating System (ROS). The methods were tested in Webots simulation environment. Crazyflie robots were used in the experiments. 
 
 In the simulation environment, square, star and v formations were first tested in two dimensions. Then, as an example of three-dimensional formation, the cubic and pyramid formations were created and observed.

Method of aeroengine fault diagnosis based on a Fingerprint map

The electrification and intelligentization trends of aeroengine systems present new challenges for the diagnostics of traditionally centralized control architectures. Scientific and practical fault diagnosis is of great significance for reducing aeroengine failures, improving operation efficiency, and ensuring safety. To realize accurate fault diagnosis, an aeroengine fault diagnosis method based on an improved fingerprint map is proposed in this paper. Based on aeroengine data analysis, ISTOA-SVM diagnostic model that embeds improved fingerprint map data as a priori knowledge is adopted to identify the fault sets. Data based on the statistics of the fingerprint map are processed with a ratio coefficient, and the feature parameters are appended through Floyd-principal component analysis (Floyd-PCA) to increase the identification data dimension. On the other hand, to improve the diagnostic performance of the classifier, the sooty terns optimization algorithm algorithm combined with chaotic maps, a variable helix mechanism and a separating operator is designed, and the ISTOA-SVM is constructed. The fault diagnosis method was validated by using a real-world dataset of aeroengines. Comparison experiments show that the proposed method is superior in performance over the state-of-the-art approaches in diagnostic precision under the constraint of limited data. The ISTOA-SVM addresses the issue that similar features may be confused in traditional fingerprint maps. The results are presented through 5 practical cases. The study demonstrates that the proposed method achieves superior performance and can be applied to aeroengine fault diagnosis.

Coordinated AGC control strategy for an interconnected multi-source power system based on distributed model predictive control algorithm

The erratic and random characteristics of wind power and wind-thermal replacement significantly degrade the performance of AGC in an interconnected, multi-source power system. For the lack of cooperation between wind power and thermal plants in AGC of interconnected power system as well as the heavy computational burden and inflexible information interaction of centralized AGC architecture, a novel coordinated AGC control strategy for an interconnected multi-source power system based on distributed model predictive control (DMPC) algorithm is proposed in this research. Under the DMPC architecture, the dimension of centralized AGC problem is reduced in each subsystem, and the overall AGC performance can be enhanced through inter-area communication between subsystems. In the meantime, based on the proposed coordinated control strategy, the active AGC response capability of wind farms and energy storage in the interconnected system is exploited to realize the dynamic cooperation between the wind generation and thermal AGC plants, and the overall AGC control performance can be further improved. In this paper, local DMPC controllers are deployed in each subsystem to address the drawbacks of a centralized control architecture by exchanging forecast and state measurement information with neighboring subsystems. In addition, considering the current operating status of multiple kinds of energy sources with different features, a fuzzy-based coordinated control strategy is designed for the purpose of dynamically allocating the AGC demand inside the wind-storage system, and the wind farm’s reliability for AGC response in diverse operation scenarios can be guaranteed. Finally, comparative analysis with existing works has been conducted on a three-area power system, and numerical results demonstrate that the proposed coordinated AGC control strategy has better performance in AGC performance and the dynamic cooperation can be achieved between wind power and thermal plants in AGC response through the designed wind-storage system and coordinated DMPC AGC control strategy.

Non Linear Control System for Humanoid Robot to Perform Body Language Movements.

In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve this, the use of closed-loop control techniques that consider the complex nonlinear dynamics and disturbances inherent in these systems is required. In this paper, an implementation of a nonlinear controller for the tracking of trajectories and a profile of speeds that execute the movements of the arms and head of a humanoid robot based on the mathematical model is proposed. First, the design and implementation of the arms and head are initially presented, then the mathematical model via kinematic and dynamic analysis was performed. With the above, the design of nonlinear controllers such as nonlinear proportional derivative control with gravity compensation, Backstepping control, Sliding Mode control and the application of each of them to the robotic system are presented. A comparative analysis based on a frequency analysis, the efficiency in polynomial trajectories and the implementation requirements allowed selecting the non-linear Backstepping control technique to be implemented. Then, for the implementation, a centralized control architecture is considered, which uses a central microcontroller in the external loop and an internal microcontroller (as internal loop) for each of the actuators. With the above, the selected controller was validated through experiments performed in real time on the implemented humanoid robot, demonstrating proper path tracking of established trajectories for performing body language movements.

PWM Based Fixed Frequency Equivalent SM Controller for Stability of DC Microgrid System

DC microgrids are localized and independent power distribution networks which show high efficiency when batteries and renewable sources are interconnected with the system. This paper addresses the stability of the dc microgrid through a decentralized control scheme. A centralized control architecture can improve the stability but reliability is compromised if the central controller fails. Droop control is commonly used to address the stability problem based on techniques through linear controllers. However, the Droop controller requires a tradeoff between voltage regulation and droop gain. Further, the global stability of the systems cannot be ensured through linear control techniques. Additionally, for different operating requirements and load conditions, it is difficult to optimize the parameters of these controllers. To address limitations, a PWM Based fixed frequency equivalent sliding mode (SM) control technique is proposed for dc microgrid stability. SM controllers show high robust performance. To formulate the problem, system equations are modeled and the operation of the system under SM is verified for existence and stability conditions. To examine the transient performance, the responses for critically damped and underdamped are investigated and presented. The results of detailed experiments simulations are presented which show the efficiency of the proposed control method.

Resistive Shaping of Interconnected Low-Voltage Microgrids Operating Under Distorted Voltages

Beyond the impacting presence of nonlinear loads, low-voltage microgrids also experience low energy efficiency and resonance phenomena when operating interconnected to a distribution grid that suffers from distorted voltages. This article proposes a model-free control strategy capable of coordinating inverters existing within a dispatchable microgrid, allowing to operate it as a single-controllable entity that behaves like a resistor at selected harmonic frequencies. Such resistive shaping uses a centralized control architecture to steer inverters to distributively compensate reactive and harmonic currents, supporting active current sharing. Consequently, the microgrid point-of-common-coupling operates with a high power factor when the grid imposes distorted voltages. Additionally, if resonant components exist, the strategy supports harmonic resonance damping, which minimizes deterioration of voltage quality. For instance, comparative results show that, for the considered scenario, the proposed resistive shaping damps resonances up to 50% better than a previous approach that compensates harmonics using sinusoidal current synthesis. Simulation results carried out on a three-phase low-voltage microgrid testbench, considering three inverters, demonstrate the abovementioned capabilities of the proposed approach. Experimental results based on a single-phase microgrid prototype comprising two inverters with two linear loads and one nonlinear load validate the applicability of the method to real-life implementations.

Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of network topology have been generated to observe the effectiveness of proposed algorithms on different network architectures. The results reveal that RF performs better than KNN in a single topology, and both have close performance in other topologies.

Towards Blockchain-Based Secure Storage and Trusted Data Sharing Scheme for IoT Environment

Nowadays, cloud-based storage systems play a vital role in IoT data storage, processing, and sharing. Despite its contribution, the current cloud-based architecture may cause severe data leakage or jeopardize user privacy. Meanwhile, the cloud-based architecture heavily relies on a trusted third-party auditor (TPA) and runs in a centralized control manner. However, the TPA may not be a completely trustworthy entity, and a single point of failure might cause the centralized system to collapse. Fortunately, with the advent of blockchain technology, the decentralized storage model has gained popularity. A decentralized storage system successfully eradicates the rule of TPA, solves the problem of a single point of failure, and has many advantages over a centralized control architecture, such as low storage prices and high throughput. This study offers a blockchain-based decentralized distributed storage and sharing scheme that provides end-to-end encryption and fine-grained access control. In our proposed IoTChain model, fine-grained permission is based on attribute-based access control (A-BAC) policy by employing the Ethereum blockchain as an auditable access control layer. Smart contracts are tailored for the IoTChain model, which combines the Ethereum blockchain and the interplanetary file system (IPFS). We used an advanced encryption standard (AES) for encryption and the elliptic curve Diffie-Hellman key exchange protocol for secret key sharing between data owners and users. Also, the proof-of-work (PoW) consensus mechanism is replaced with a proof-of-authority (PoA) to minimize system transaction cost and boost system throughput. Additionally, our solution has been tested on the Ethereum official test network Rinkeby, and the results demonstrate that our approach is realistic and economical on the IoT data.

Power Grid Resource Integration of Enterprise Middle Station based on Analytic Hierarchy Process

With the transformation from smart grid to power Internet of Things, new power businesses such as power grid automation and power quality monitoring are constantly emerging. The load environment of power grid is changeable. In order to meet the needs of multi-service, the integrated access scheme for power grid resources in power enterprises is gradually diversified, which brings challenges to the unified management and control of power grid communication network. In this paper, SDN technology is used to improve the operation and maintenance management and control of power communication network, which aims at the integration scheme of power grid resources in power enterprises. Based on the controller cluster technology, combined with the new power business requirements, this paper designs a software-defined network centralized control architecture of the new business of power communication network. The architecture realizes the operation and maintenance management of network resources under the centralized control architecture of typical enterprise scenarios, such as power grid enterprises. The convergence speed is improved by 27%. The minimum value of iterative convergence is 31% better than that of other methods. The system requirement is reduced by 13.5%, which is helpful to improve the efficiency of node dynamic allocation and ensure the need of large-capacity data transmission of smart grid. The research in this paper can realize the two-way interaction, real-time expansion and unified deployment of power business in the future, and promote the intensive and lean development of power communication network.

Distributed data backup and recovery for software‐defined wide area network controllers

AbstractSoftware‐defined wide area network (SD‐WAN) is a new type of network architecture that has developed rapidly in recent years. SD‐WAN inherits the centralized control architecture of software‐defined networking (SDN), but supports more diverse access methods and equipment types and covers a wider area. It is also associated with greater uncertainty in the network environment. These characteristics make the fault management of the SD‐WAN controller more challenging, so that the existing SDN‐based data backup methods cannot adapt to SD‐WAN scenarios. This article proposes an SD‐WAN‐oriented distributed data backup and recovery method (DDBR) based on an improved secret sharing algorithm. To deploy this method, we design an online‐offline dual backup framework based on the data freshness requirements of the controller. Under this framework, dynamic data of the controller is divided into different shares, and then stored into the storage of switches. When the controller fails, data recovery can be performed on the backup controller quickly, which greatly improves the network availability. The outstanding feature of the proposed DDBR method is that it ensures the integrity and confidentiality of the backup data in an unreliable network environment, even when some of the storage nodes fail. Theoretical analysis and evaluation results on file backup example show that the proposed solution has significant advantages over existing methods in terms of backup data storage size, communication overhead, backup and recovery success rate.

A traffic anomaly detection scheme for non-directional denial of service attacks in software-defined optical network

As a promising centralized control architecture, software-defined network (SDN) has been widely used and developed in the field of optical access network. Though, its centralized control architecture has many advantages, it is also hindered by various security threats. Among all the threats, the attack, Denial-of-Service (DoS) is the most severe attack into the software defined optical network (SDON). In spite of, so many developments in tools and technology, there are few effective schemes to detect denial of service attacks in SDON. In our research work, we proposed a traffic anomaly detection scheme by analyzing and defining the specific security threat non-directional denial of service attack (ND-DoS) faced by the SDON. In this scheme, we first designed the function construction of the controller and the extension of the OpenFlow protocol, and then used the adaptive threshold detection algorithm based on time sliding window (TSW-ATD) and the repeated flow detection algorithm (RFD) to complete the first detection and re-detection detection of abnormal traffic, and finally designed a general formulaic measurement method. The proposed scheme is verified by simulation experiments. The experimental results show that compared with the existing related solutions, the forwarding success rate of this scheme is increased by about 29.4%, the data processing rate in the unit window is increased by about 39.3%, and the CPU occupancy rate is reduced by about 17.5%. Therefore, this scheme can effectively deal with DoS attacks in SDON with a higher detection rate and lower resource overhead.

Joint Radio Resource Allocation for Decoupled Control and Data Planes in Densely Deployed Coordinated WLANs

While Wireless Local Area Network (WLAN) gains growing popularity during the last two decades, it faces several new challenges in meeting the requirements of emerging applications due to spectrum shortage, complicated network management and inefficient radio resource allocation (RRA) in densely deployed scenarios. The operating band has evolved from microwave band (a.k.a. sub-6 GHz) to millimeter-wave band (mmWave for short) or even multi-band to provide higher data rates. However, the existing distributed WLAN architecture works in different bands separately and doesn't support the coordination among access points (APs), making it difficult for efficient network management and improved quality of service (QoS) provisioning. In overcoming these challenges, a centralized control architecture for WLAN with decoupled control and data planes can be used to orchestrate the RRA within the whole network while achieving improved communication performance. Therefore, a new control/data plane decoupled WLAN architecture is designed in this paper to realize efficient network management and optimized RRA by separating the control plane and data plane into sub-6 GHz and mmWave respectively. Then, we propose the sub-6 GHz assisted mmWave beamforming protocol, which can significantly reduce the overhead of beamforming training (BFT). Last, we investigate joint RRA for the control/data plane decoupled WLAN since the control plane affects the RRA in the data plane. Performance analysis and simulation show that the joint RRA mechanism in the control/data plane decoupled network can significantly improve the number of successfully scheduled users and the sum-rate compared with the traditional RRA.

Over-frequency support in large-scale photovoltaic power plants using non-conventional control architectures

Large scale photovoltaic power plants must provide a frequency regulation service, which is defined in the grid codes. This service has commonly required a response time between 15 and 30 s. But some countries are now introducing more strict regulations and requiring response times below 2 s. The typical centralized control architecture of photovoltaic power plants for frequency regulation can present undesired oscillatory responses (or even become unstable) when tuning the controller to achieve these small time response requirements. The present article proposes an alternative solution based on a hierarchical control architecture. In the proposed solution, inverter controllers apply a local frequency regulation action and the central controller corrects active power errors at the point of connection, which can be caused by power losses or lack of irradiance in some inverters. Simulation models are used to study and test the response of this new control approach. The proposed hierarchical control architecture is compared with a fully centralized and a fully decentralized archirectures. Results show that the hierarchical control architecture is not only capable to obtain a fast and accurate response, but also is robust against communication failures. The proposed hierarchical control architecture advantages could be extrapolated to other services. So, further research is proposed to confirm this hypothesis.

Cyber-security of centralized, decentralized, and distributed control-detector architectures for nonlinear processes

Decentralized and distributed control systems provide an efficient solution to many challenges of controlling large-scale industrial processes. With the expansion in communication networks, vulnerability to cyber intrusions also increases. This work investigates the effect of different types of standard cyber-attacks on the operation of nonlinear processes under centralized, decentralized, and distributed model predictive control (MPC) systems. The robustness of the decentralized control architecture over distributed and centralized control architectures is analyzed. Moreover, a machine-learning-based detector is trained using sensor data to monitor the cyber security of the overall system. Specifically, detectors built using feed-forward neural networks are used to detect the presence of an attack or identify the subsystem being attacked. A nonlinear chemical process example is simulated to demonstrate the robustness of decentralized control architectures and the effectiveness of the neural-network detection scheme in maintaining the closed-loop stability of the system.

A multi-objective operation strategy optimization for ice storage systems based on decentralized control structure

Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy. Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.