Topology Control Research Articles

A Transmission-Reliable Topology Control Framework Based on Deep Reinforcement Learning for UWSNs

This paper focuses on the topology optimization problem to decrease transmission delay and prolong network lifetime on the premise of reliable transmission in Underwater Wireless Sensor Networks (UWSNs). This is extremely challenging owing to dynamic ocean current movement, harsh underwater communication channel and increasingly demanding application requirements. With the development of software defined network architectures, the centralized topology control (TC) strategy with a global perspective in UWSNs is expected to become a more effective way to tackle the above challenges compared with the existing distributed and heuristic TC strategies involving local network state information. Therefore, we first transform the topology optimization problem of UWSNs into an integer nonlinear programming (INLP) model and design a centralized TC framework to solve the INLP model. In this framework a TC center is built to periodically generate the network topology for UWSNs according to the current network state information. Further, an efficient topology generation algorithm based on deep reinforcement learning (TGA-DRL) is proposed in the TC center. In TGA-DRL, to reduce computing overhead and improve operational efficiency, we formulate an action-space narrowed Markov decision process suitable for network topology generation and solve it with the aid of the rainbow algorithm which is a deep reinforcement learning model. Finally, the performance of our centralized TC framework is verified in terms of the node out-degree, algorithm convergence and optimization effect.

Energy-Efficient Topology Control Mechanism for IoT-Oriented Software-Defined WSNs

In time-varying software-defined wireless sensor networks (SDWSNs) for Internet-of-Things (IoT) applications, the topology may change due to the interference or abnormal events, thus leading to network performance degradation. In this paper, an energy-efficient topology control (TC) mechanism applied for IoT-oriented SDWSNs is proposed to maximize the network energy efficiency (EE) during the dynamic topology maintenance. First, a hierarchical SDWSN architecture consisting of the cluster-based sensing network and the programmable relay network is presented. Second, two TC algorithms based on the link EE are proposed to apply in the cluster and relay sub-networks of SDWSN, respectively. In the cluster sub-network, the proposed distributed TC algorithm enables the link interference mitigation by employing power control and rate allocation in each cluster. In the relay sub-network, the proposed centralized TC algorithm first utilizes a specified model to construct the original topology. During the dynamic topology maintenance, the proposed centralized TC algorithm is realized by the value-iteration learning method based on a Markov decision process (MDP) model, upon which the state-transition probability (STP) of the relay sub-network is obtained, where the relay-network state is composed of the link, the queue, and the residual energy ratio states for all nodes in the relay sub-network. Finally, simulation results show that both two TC algorithms can improve the corresponding sub-network EE of time-varying SDWSN.

Design and control of a novel topology for multilevel inverters using high frequency link

The requirement of more than one source in multilevel inverters is an issue to be solved for applications with a single DC source. One solution to this problem is to obtain the required voltages using High Frequency Link (HFL). With the traditional use structure of HFL, only one DC source is generated from each diode bridge unit. In this study, voltage generation is diversified as full and half by adding only one switching device and one diode to this circuit. With the proposed modified HFL circuit topology, 35 levels were obtained from 15 level generating circuits. It was compared with some recently published papers using HFL and a satisfactory result was observed in terms of total standing voltage (TSV), although the use of switching devices was reasonable. Proposed topology with different frequency and amplitude outputs has been verified both in simulation environment and experimentally using FPGA board. In terms of the economic dependability of the proposed inverter, calculations for the TSV, cost per level factor, and efficiency have been performed. According to the results obtained, it has been observed that the total harmonic distortion of the output waveform of the proposed topology is 2.5%.

A Novel Co-phase Traction Power Supply System in Electrified Railway

The electrified railway has power quality problems such as three phases seriously unbalanced, large harmonics, and reactive power. In addition, the neutral section exists at each power supply section, which restricts the development of the high-speed and heavy-haul railway. This paper proposes a novel co-phase traction power supply system (TPSS) based on a YNvd balanced transformer, which can not only solve the above problems but also save one single-phase coupling step-down transformer, thus reducing the cost and space of the whole system. In addition, the voltage level of the converter switching device can be reduced by the rational design of the transformer tap position, which enhances the reliability and feasibility of the system. The compensation principle and control strategy of the co-phase TPSS based on the novel YNvd balanced transformer are analyzed in detail in this paper, and the compensation current detection and integrated power flow controller (IPFC) control methods are given. Simulation and analysis show that the proposed topology structure and control method are correct and feasible.

A Hybrid Submodule Three-Phase Multiplexing Arm Modular Multilevel Converter With Wide Operation Range and DC-Fault Blocking Capability

Three-phase multiplexing arm modular multilevel converter (TPMA-MMC) possesses lower cost, smaller volume, and higher efficiency compared with MMC. However, the limited operation range limits its popularity. Hence, in this article, it redefines arm submodule (SM) configuration type, where the conventional arm is configured with full-bridge submodules (FBSMs), and the multiplexing arm is formed by half-bridge submodules (HBSMs). Meanwhile, a new energy balance principle and modulation scheme are proposed to expand TPMA-MMC’s operation range. Furthermore, the DC-fault blocking schemes could be implemented to realize DC-fault tolerance reliably without the multiplexing arm blocking. Compared with hybrid MMC(H-MMC), it could reduce by 33.33% in submodules a least, which leads lower footprint and operation loss. Due to reregulating the energy flowing path of multiplexing arm, and rearranging the chain-link voltage of multiplexing and conventional arm, it could reduce the required SM capacitance and further decrease 65% energy storage requirement, resulting in improving the power density. Finally, the topology control scheme and DC-fault blocking scheme are verified by simulation and scale-down prototype experimental results.

Horse Herd Optimized Intelligent Controller for Sustainable PV Interface Grid-Connected System: A Qualitative Approach

The need for energy is always increasing as civilization evolves. Renewable energy sources are crucial for meeting energy demands as conventional fuel resources are slowly running out. Researchers are working to extract the most amount of power possible from renewable resources. Numerous resources are in demand, including solar, wind, biomass, tidal, and geothermal resources. Solar energy outperformed all the aforementioned resources in terms of efficiency, cleanliness, and pollution freeness. Intermittency, however, is the resource’s main shortcoming. Maximum power point tracking algorithm (MPPT) integration is required for the system to achieve continuous optimum power by overcoming the feature of intermittency. However, generating electrical energy from solar energy has presented a significant problem in ensuring the output power’s quality within a reasonable range. Total harmonic distortion (THD), a phenomenon, may have an impact on the power quality. Depending on the properties of the load, variables like power factor, voltage sag/swell, frequency, and unbalancing may occur. The quality of power and its criterion exhibits a non-linear connection. The article’s primary objective is to analyze the PV interface grid-linked system’s qualitative and quantitative performance. With respect to varying solar irradiation conditions, partial shading conditions, and solar power quality within the acceptable dimension, a novel intelligent multiple-objective horse herd optimization (HHO)-based adaptive fractional order PID (HHO-AFOPID) controller is used to achieve this goal. Adaptive fractional order PID (AFOPID), conventional FOPID, and PID controllers were used to evaluate the performance of the suggested controller, which was then validated using a commercially available PV panel in MATLAB/Simulink by varying the productivity of non-conventional resources, the inverter’s level of uncertainty, and the potential at the grid’s end. In order to realize the features of the system, sensitivity examination is also carried out for solar energy’s sensitive parameters. The stability analysis of the proposed control topology is also carried out in terms of the integral absolute error (IAE) and integral time absolute error (ITAE). The examination of the sensitivity of variations in solar radiation in kilowatt per square meter per day is based on the total net present cost (TNPC) and levelized cost of energy (LCOE), as optimal dimension and energy cost are both aspects of priority. The suggested control methodology is an approach for the qualitative and quantitative performance analysis of a PV interface grid-oriented system.

Reasonable thickness determination for implicit porous sheet structure using persistent homology

Porous structures are widely used in various industries because of their excellent properties. Porous surfaces have no thickness and should be thickened to sheet structures for further fabrication. For porous surfaces with implicit representation, sheet structures can be generated by extracting the region between two iso-surfaces. However, too small sheet thickness leads to extra small holes emerging on the sheet structure, and too large sheet thickness makes the pores closed. In this study, we propose a novel method for determining the sheet thickness for porous surfaces represented by implicit functions. Specifically, after calculating the persistent diagram (PD) of the scalar field dictated by the implicit function, the points in the PD are clustering into three clusters. Using these clusters, a topology control method is developed, which allows users to determine how many pores can be closed. Moreover, a reasonable thickness can be determined, by which all of the pores keep open, and the extra small holes are eliminated. Experimental results show the effectiveness of the developed method.

Cascaded dual fuzzy logic controller for stable microgrid operation mitigating effects of natural uncertainty in solar and wind energy sources

In a microgrid system, the perturbations due to variable solar irradiance, fluctuating wind speed, and even the sudden fluctuation in load lead to the deviation in system voltage, frequency, DC-Link voltage, etc., causing instability. Thus, this paper proposes a robust and adaptive energy management system using the cascaded dual-fuzzy logic-based control approach to mitigate such issues. Besides, the system considers the auto-tuned PI-based voltage regulator with the reference voltage 415 V for inverter control and nine controllable/non-controllable system parameters for the proposed cascaded control approach. The proposed algorithm can control the battery operation, mode of microgrid, and load management to improve stability. It is analyzed for experimental conditions like reduced renewable output, microgrid transition, and unhealthy grid/microgrid conditions. In addition, the proposed method has been compared with the existing control topologies by assessing load voltage, frequency, and DC-Link voltage. The proposed approach has been validated for stable microgrid operation using MATLAB-Software. As per observations, it maintains the limits of 0.25% frequency deviation, 0.57% voltage deviation, and 0.877% DC link voltage deviation, which are acceptable as per IEEE 1547 standard, IEEE 2030 standard, and Indian standard CERC 2011 for the concerned system parameters.

The Upper Bounds of Cellular Vehicle-to-Vehicle Communication Latency for Platoon-Based Autonomous Driving

Cellular vehicle-to-vehicle (V2V) communications can support advanced cooperative driving applications such as vehicle platooning and extended sensing. As the safety critical applications require ultra-low communication latency and deterministic service guarantee, it is vital to characterize the latency upper bound of cellular V2V communications. However, the contention-based Medium Access Control (MAC) and dynamic vehicular network topology brings many challenges to model the upper bound of cellular V2V communication latency and assess the link capability for quality of service (QoS) guarantee. In this paper, we are motivated to reduce the research gap by modelling the latency upper bound of cellular V2V with network calculus. Based on the theoretical model, the probability distribution of the delay upper bound can be obtained under the given task features and environment conditions. Moreover, we propose an intelligent scheme to reduce upper bound of end-to-end latency in vehicular platoon scenario by adaptively adjusting the V2V communication parameters. In the proposed scheme, a deep reinforcement learning model is trained and implemented to control the time slot selection probability and the number of time slots in each frame. The proposed approaches and the V2V latency upper bound are evaluated by simulation experiments. Simulation results indicate that our network calculus based analytical approach is effective in terms of the latency upper bound estimations. In addition, with fast iterative convergence, the proposed intelligent scheme can significantly reduce the latency by about 80% compared with the conventional V2V communication protocols.

An efficient controller design of a PV integrated single inductor multiple output DC-DC converter for dynamic voltage and low power applications.

In this article, a Single Inductor Multiple Output (SIMO) DC-DC boost converter for driving independent three outputs of dynamic voltage and low power is proposed. Compared with the traditional SIMO DC-DC converter, the proposed work accomplishes (i) independent control of power at each output, (ii) small switch count, (iii) relatively better scalability with increasing output channel, (iv) fast response time, and (v) relatively better efficiency. The core aim of the current article is to implement an efficient controller design of the SIMO DC converter circuit operating in continuous conduction mode for dynamic voltage applications. The SIMO circuit comprises a photovoltaic (PV) array as a renewable energy harvesting source at the input. A hysteretic controller based on direct control with seamless transition control topology is implemented in this model for SIMO operation. This scheme is popular due to its less cost, simple, easy-to-use design architecture, and fast speed of close loop control. The configuration of the solar array is designed to deliver maximum power on the input of the SIMO DC-DC converter. For tracking the maximum power point, incremental conductance with integral control configuration is applied on the PV array with fast speed and efficiency. The MATLAB/Simulink environment is utilized for model configuration. The simulation results show that the proposed SIMO configuration with the PV array provided 100.5 W and 21.7 W for 1000 W/m2 and 250 W/m2, respectively.

A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs).

WSN is one of the most efficient technologies in intelligent communication and because of its advantages, this technology has been utilized in various applications. By using WSNs, different types of data can be collected and analyzed in wide environments. The high variety of applications and types of data available in this network can cause several challenges about heterogeneous data routing. This research, presents a Fuzzy Model for Content-Centric Routing (FMCCR) in WSN to solve these challenges. The performance of FMCCR is based on two basic steps: "topology control", and "data transmission through content-centric and fuzzy logic-based routing algorithm". In the first step of FMCCR, the network topology is constructed. In the second step of the proposed method, data transmission paths are determined based on network topology and content type, and data transmission is performed. The performance of FMCCR has been evaluated in a simulation environment and the results have been compared with previous algorithms. The results show that FMCCR reduce energy consumption and improve the traffic load distribution in the network in addition to increasing the network lifetime. According to the results, FMCCR can increase network lifetime at least 10.74% and at the same time, deliver at least 88.1% more packets through the network, compared to previous methods. These results, prove the efficiency of the proposed method for using in real-world scenarios.

An improved fault control strategy for virtual synchronous generator with the coordination of STATCOM during unbalanced fault

AbstractThe virtual synchronous generator (VSG) is a good solution for stabilizing the power system with high penetration of renewable energy. However, in case of serious unbalanced voltage disturbance/fault, the conventional VSG may lose voltage, inertia, and damping support characteristics to the grid and even can cause disconnection of renewable energy. This paper proposes an improved fault control strategy for VSG with the coordination of STATCOM. The proposed method can provide sufficient voltage support while keeping continuous system inertia and damping support under severe unbalanced fault. In the paper, an improved VSG and STATCOM control topology based on positive and negative sequence current control are first proposed so as to keep the damping and inertia support to grid during the grid fault. Secondly, the voltage support control method for the VSG with improved topology during unbalanced fault is introduced, which can achieve multiple control objectives, in terms of voltage support, current limitation, and active power output simultaneously. Then a coordination control scheme of improved VSG and STATCOM is developed so as to optimize the maximum control objectives in all possible scenarios, especially in the case of severe unbalanced fault. Finally, the effectiveness of the method is verified by using the MATLAB/SIMULINK simulation platform.

Improvement of Power Quality Using Reduced Switch Count Eleven-Level Inverter in Smart Grid

This article proposed a hybrid control topology to cascaded multilevel-inverter (CMLI) using 11-level for the grid-tied photovoltaic generation system. The proposed hybrid technique combines an archerfish hunting optimiser (AHO) and Spike Neural Network (SNN); hence, it is called the AHO-SNN technique. The proposed control technique is to maintain the regulation of power or maximal energy conversion of the solar subsystem and reduce total harmonic distortion (THD). Here, the AHO is utilised to construct the optimum control signal dataset. The best control signals are estimated using a data set performed by SNN. The resultant control signals will regulate insulated-gate-bi-polar-switches (IGBT) of Cascaded MLI. The proposed AHO-SNN control topology determines the converter switching states by constructing the operating modes of the generation system. The system parameter changes and outside disturbances are optimally minimised with this control strategy. The proposed AHO-SNN control is done in MATLAB platform, and it evaluated their performance by using existing methods, like War Strategy Optimization Algorithm (WSO) Fuzzy Wavelet Neural Network (FWNN), Side-Blotched Lizard Algorithm (SBLA), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Memetic Fire-Fly Algorithm (MFA). The result shows that the proposed approach based on THD is less than existing approaches.

A time-varying acoustic channel-aware topology control mechanism for cooperative underwater sonar detection network

Topology control plays a vital role in cooperative underwater sonar detection networks (USDNs) to ensure detection applications. By incorporating spatio-temporal uncertainties in acoustic channel and limited resources in underwater networks, a large number of node deployment schemes have been proposed to overcome these difficulties through topology control. However, the channel uncertainty has not been solved fundamentally. In order to effectively deal with the time-space-frequency uncertainty of the acoustic channel, this paper proposes a time-varying acoustic channel-aware (TVAC) topology control mechanism to deploy sonar nodes in USDNs. Initially, a time-varying acoustic channel state information (CSI) estimation algorithm and a prediction algorithm are proposed by applying three sub-modules: a feature stitching deep neural network-based temperature prediction model that we designed, a deep neural network-based sound velocity estimation model, and a BELLHOP-based propagation loss calculation model. After that, a TVAC topology control mechanism is adopted to adaptively deploy nodes based on the CSI estimation and prediction results. Using the improved weighted set covering algorithm, the current CSI is adopted to determine the working modes of sonar nodes, and the predicted CSI is adopted to obtain the cycle of topology update. What is more, we develop an offline–online CSI prediction model to improve the accuracy and stability of node working modes management. Simulation results show that compared with other topology control schemes, TVAC can obtain superior coverage performance, full connectivity, and prolong the network lifetime with guaranteed coverage and connectivity.

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments.

Realization and Supervision of an Intelligent Energy Distribution System with a New Combination Topology of Fuzzy and PID Controllers

Realization and Supervision of an Intelligent Energy Distribution System with a New Combination Topology of Fuzzy and PID Controllers

Design of supervisory controllers to reduce energy consumption in plant-wide biological wastewater treatment plants with feedback signals from non-ideal sensors

In this research, proportional-integral (PI) and Fuzzy logic control (FLC) strategies are designed to control wastewater treatment processes at the whole plant level. Two control combination strategies are implemented to minimize the operational cost index (OCI) and effluent quality index (EQI). The benchmark simulation model (BSM2-P) is used as a working platform. In close resemblance to practical operations, the sensors are considered as non-ideal with delay and also having measurement noise. Binary-level control topology is designed in which the lower-level PI controller is used to control the dissolved oxygen (DO) levels in the second aerobic reactor by manipulating the oxygen mass transfer coefficient (KLa) of the last three aerobic reactors and FLC is designed at the supervisory level based on Ammonia levels. Ammonia in the second aerobic reactor is taken as a feedback signal for FLC to compute the DO setpoints. In comparison with non-supervisory control structures, operational cost is improved in PI-Fuzzy by 6.5%. Compared to the open-loop control, the PI controller improves effluent quality by 2.1%. Nevertheless, the combination of PI and fuzzy control shows high greenhouse gas production rates.

Design and Experimental Investigation of Temperature Control for a 10 kW SOFC System Based on an Artificial Neuronal Network

In this work the authors designed and experimentally evaluated different controller topologies for fuel cell operation (SOFC) of a reversible solid oxide cell (rSOC) system. Aim of the controller is to operate the SOFC system autonomously at a constant maximum temperature for maximum efficiency. The controller design incorporates an artificial neuronal network (ANN) for real time state predictions. The training data for the ANN was generated by a Digital Twin of this system. The generated training data consists of about 16,000 different steady state operating conditions.

Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network

An enhanced self-assembling network routing algorithm is proposed for the problem of weak connectivity of communication networks caused by factors such as movement or environmental interference in the construction and operation, and the maintenance of construction robot clusters. Firstly, the dynamic forwarding probability is calculated based on the contribution of nodes joining routing paths to network connectivity, and the robust connectivity of the network is achieved by introducing the connectivity feedback mechanism; secondly, the influence of link quality evaluation index Q balanced hop count, residual energy, and load on link stability is used to select appropriate neighbors for nodes as the subsequent hop nodes; finally, the dynamic characteristics of nodes are combined with the topology control technology to eliminate low-quality links and optimize the topology by link maintenance time prediction and to set robot node priority. The simulation results show that the proposed algorithm can guarantee a network connectivity rate above 97% under heavy load, reduce the end-to-end delay, and improve the network survival time, providing a theoretical basis for achieving stable and reliable interconnection between building robot nodes.

Design and Experimental Validation of a Switchable Frequency Selective Surface with Incorporated Control Network.

Tunable/switchable devices are more and more required in modern communication systems. However, the realization of the tuning requires the presence of active devices, which in turn must be biased. The current paper comes up with a solution for designing and experimentally validating such a switchable Frequency Selective Surface. Two different metallic structures are simulated and measured, having incorporated the same topology control network (CN). In this scenario, the main innovation of this paper is the presence of the feeding part, namely the control network. In this work, the main FSS structure is flanked by three parallel CN microstrip lines and several via holes that allow biasing the active elements, namely PIN diodes. The switchability of the proposed structure is achieved through PIN diodes, whose bias determines the values of the elements in the equivalent circuit. At different biases, the response of the FSS changes accordingly. From all possible values of the bias, the extreme cases when the diodes act as (almost) short- and open-circuits are considered in the submitted manuscript for the sake of brevity. These cases are modeled by the main and cut-slot structures, respectively. The proposed structures have been evaluated using electromagnetic simulation and implemented on an FR4 substrate having a thickness of 1.58 mm. With the periodicity of the square-shaped unit cell of 18 mm edge length, different filtering bands are obtained below 12 GHz. Another novelty that has received very little consideration in the existing literature is the use of a finite array of unit cells instead of an infinite one. And finally, tests in an anechoic chamber have proved that there is a good agreement between practical and simulation results and also demonstrated the proper performance of the devices for wide angular incidence for both TE and TM polarizations.