Hardware Testbed Research Articles

Towards Resiliency Enhancement of Network of Grid-Forming and Grid-Following Inverters

This paper proposes an autonomous control scheme to mitigate voltage-frequency excursions observed in a network of grid forming (GFM) and grid following (GFL) inverters in a power electronics dominated grid (PEDG). The proposed control scheme leverages GFL inverter's ability to dynamically adjust their power set-points as well as change their operation mode on-the-fly to enhance the PEDG resiliency. A supervisory controller (SC) comprising of a Droop-ΔP estimator coupled with an optimal power allocator (OPA) module dynamically adjusts the power injections from GFL inverters to maintain the power balance and restore frequency in response to disturbances. Moreover, the proposed self-ranking based coordinated mode selection (SR-CMS) algorithm dynamically reconfigures the inverter's operation mode (either GFM or GFL) to enhance the PEDG resiliency in response to events and ensure GFM inverter allocation in grid clusters. Several case studies are performed to validate the feasibility, performance, and robustness of proposed autonomous control. Finally, the proposed scheme is experimentally validated on a small-scale hardware testbed.

Detecting system defects of digital devices in the course of malfunction imitation using fuzzing

Aim. The paper proposes approaches to the organisation of testing of digital systems through malfunction imitation for the purpose of ensuring compliance of international and Russian failure and fault resistance standards for the purpose of efficient (in terms of time) detection of software defects as part of mass production of products. The paper proposes a structure and operating algorithm of a hardware and software test bed for malfunction simulation intended for testing a system’s devices. The test bed collects and processes data for fuzzing, hardware error identification, as well as defines the scope of testing.Methods. The paper used basic systems approach, classical methods of the probability theory and mathematical statistics, decision theory, methods of hardware and software testing and development, mathematical theory of fuzzy sets and fuzzy logic.Results. Malfunction simulation algorithms were developed for the purpose of testing hardware and software systems using fuzzing that ensure probabilistic estimation of the termination time of testing with a specified accuracy.Conclusions. The above set of algorithms allows detecting system defects in the process of software and hardware integration into a single system that cause new malfunctions (emergence) that cannot be taken into consideration at the design stage.

Exploring Networked Airborne Computing: A Comprehensive Approach with Advanced Simulator and Hardware Testbed

In recent years, networked airborne computing (NAC) has emerged as a promising paradigm because it can leverage the collaborative capabilities of unmanned aerial vehicles (UAVs) for distributed computing tasks. Despite the burgeoning interests in NAC and UAV-based computing, many existing studies depend on over-simplified simulations for performance evaluation. This reliance has led to a gap in our understanding of NAC’s true potential and challenges. To fill this gap, this paper presents a comprehensive approach: the creation of a realistic simulator and a novel hardware testbed. The simulator, developed using ROS and Gazebo, emulates networked UAVs, focusing on resource-sharing and distributed computing capabilities. This tool offers a cost-effective, scalable, and adaptable environment, making it ideal for preliminary investigations across a myriad of real-world scenarios. In parallel, our hardware testbed comprises multiple quadrotors, each equipped with a Pixhawk control unit, a Raspberry Pi computing module, a real-time kinematic (RTK) positioning system, and multiple communication units. Through extensive simulations and hardware tests, we delve into the key determinants of NAC performance, such as computation task size, number of UAVs, communication quality, and UAV mobility. Our findings not only underscore the inherent challenges in optimizing NAC performance but also provide pivotal insights for future enhancements. These insights encompass refining the simulator, reducing computation overheads, and equipping the hardware testbed with cutting-edge communication devices.

Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking.

Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.

Modeling and Control of Variable Speed Drive Based Loads for Grid Primary Frequency Support

The amount of electricity generation from renewable energy resources (RES) has been increasing significantly all over the globe. However, traditional power grid management is challenged when a large amount of intermittent and unpredictable RES-based generation units are integrated into the power network. This can lead to more severe grid frequency fluctuation events. In this paper, a variable speed drive (VSD) based motor load is utilized as a frequency responsive load to support grid frequency stability. A primary frequency control scheme is proposed and applied to the VSD-based motor load, which incorporates the sophisticated rotating speed feedback controller. Additionally, the proposed frequency responsive VSD-based load is modeled and simplified. As a result, a droop-like response can be achieved with multiple VSD load units. The effectiveness of the proposed model and control scheme is evaluated by experimental studies performed in a multi-converter-based hardware testbed (HTB).

PAGroup: Privacy-aware grouping framework for high-performance federated learning

Federated Learning is designed for multiple mobile devices to collaboratively train an artificial intelligence model while preserving data privacy. Instead of collecting the raw training data from mobile devices to the cloud, Federated Learning coordinates a group of devices to train a shared model in a distributed manner with the training data located on the devices. However, unbalanced data distribution and heterogeneous hardware configurations across different devices badly hurts the performance of collaborative model and severely impacts the overall training progress. Thus, a framework that can well balance the model accuracy and the training progress is urgently required.In this paper, we propose PAGroup, a privacy-aware grouping framework for high-performance Federated Learning. PAGroup intelligently divides the participating clients into different groups through carefully analyzing the privacy requirement of the training data and the solid social relationship of the participating clients. After that, PAGroup conducts data shaping and capability-ware training in order to improve the model performance while accelerating the overall training process. We evaluate the performance of PAGroup with both simulation and hardware testbed. The evaluation results show that PAGroup improves model accuracy up to 21%. Meanwhile, it decreases 81% communication overhead, 29% computation cost and 84% wall-clock time at best comparing with the baselines.

A machine learning assisted optical multistage interconnection network: Performance analysis and hardware demonstration

AbstractIntegration of the machine learning (ML) technique in all‐optical networks can enhance the effectiveness of resource utilization, quality of service assurances, and scalability in optical networks. All‐optical multistage interconnection networks (MINs) are implicitly designed to withstand the increasing high‐volume traffic demands at data centers. However, the contention resolution mechanism in MINs becomes a bottleneck in handling such data traffic. In this paper, a select list of ML algorithms replaces the traditional electronic signal processing methods used to resolve contention in MIN. The suitability of these algorithms in improving the performance of the entire network is assessed in terms of injection rate, average latency, and latency distribution. Our findings showed that the ML module is recommended for improving the performance of the network. The improved performance and traffic grooming capabilities of the module are also validated by using a hardware testbed.

A Digital Lock-In Amplifier Assisted Active Islanding Detection Technique for DC Microgrids

Islanding detection is crucial in both AC and DC grids, failure of which might result in unstable operating points. This is a key impediment to increase renewable energy integration. Islanding detection in the DC grid is relatively a new concept as compared to AC grid islanding. In the DC grid, islanding detection majorly depends upon threshold settings with respect to under/over voltage limits (which fail during zero power mismatch (ZPM) conditions). Some active islanding techniques focus on disturbance injection, but the larger disturbance in those techniques causes unstable operations in the grid. This research proposes an impedance-based active islanding detection scheme to address the shortcomings of existing techniques. The digital Lock-In Amplifier (D-LIA) and sensors in PV are used to accomplish the approach. The proposed scheme can detect islanding during the ZPM scenario without any degradation of power quality. This scheme can discriminate islanding conditions efficiently with a disturbance injection of lower magnitude. The proposed active islanding detection technique is modeled in MATLAB/Simulink, with experimental validation on a DC Microgrids hardware test bed with the dSPACE (DSP-1104) controller.

Autonomous Wireless Technology Detection in Seamless IoT Applications

The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. The proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.

Dynamic Model and Converter-Based Emulator of a Data Center Power Distribution System

Data centers have become a widespread power electronics (PE) load, which has significant impact on the power grid. In order to investigate the data center load characteristics, this article proposes a complete dynamic model for a typical data center ac power distribution system. A generalized model with mode transition is proposed to coordinate different power stages in the data center power system. Meanwhile, to help evaluate the grid dynamic performance and transient stability, an all-in-one load data center power emulator is developed on a reconfigurable PE converter-based hardware testbed (HTB). The dynamic power model is digitized and simplified to be implemented in two local voltage source inverters on the HTB. This proposed data center power emulator has been verified experimentally in a regional network. Dynamic performances during voltage sag events and server load variations are emulated and discussed. The article details the design, development, and verification of the data center model and power emulator. The proposed model and emulator provide an effective, easy-to-use tool to better design data centers and study the interaction with the power system.

PkCOs: Synchronization of Packet-Coupled Oscillators in Blast Wave Monitoring Networks

Blast waves with a large amount of energy, from the use of explosive weapons, is a major cause of traumatic brain injury in armed and security forces. The monitoring of blast waves is required for defence and civil applications. The utilization of wireless sensing technology to monitor blast waves has shown great advantages, such as easy deployment and flexibility. However, due to drifting embedded clock frequency, the establishment of a common timescale among distributed blast monitoring sensors has been a challenge, which may lead to a network failing to estimate the precise acoustic source location. This work adopts a packet-coupled oscillators (PkCOs) protocol to synchronize drifting clocks in a wireless blast wave monitoring network. In order to address packet collisions caused by the concurrent transmission, an anti-phase synchronization solution is utilized to maintain clock synchronization, and the corresponding superframe structure is developed to allow the hybrid transmission of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sync</i> packet and the blast wave monitoring data. As a network scales up and the hop distance grows, the packet exchange lag increases during a superframe. This, along with the drifting clock frequency, leads to the degradation of synchronization performance while the clock frequency is usually assumed to be zero and nondrifting. Thus, a compensation strategy is proposed to eliminate the joint impacts and to improve synchronization precision. The theoretical performance analysis of the PkCOs algorithm in the network is presented along with verification by simulation means. Finally, the performance of the PkCOs synchronization protocol is evaluated on an IEEE 802.15.4 hardware testbed. The experimental results show that the PkCOs algorithm provides an alternative clock synchronization solution for blast wave monitoring networks.

Time-Division ISAC Enabled Connected Automated Vehicles Cooperation Algorithm Design and Performance Evaluation

To overcome the bottleneck of unreliable environment sensing caused by sensor failure and obstacle blockage, the cooperation among connected automated vehicles (CAVs) is crucial for the reliable and efficient raw sensing data sharing in order to guarantee the driving safety. Empowered by the narrow beamwidth and high data rate abilities, the millimeter wave (mmWave) communication technology can substantially improve the environment sensing ability among multiple CAVs. In this paper, a mmWave enabled CAVs cooperation algorithm is designed based on the proposed time-division integrated sensing and communication (TD-ISAC) system for raw sensing data sharing among CAVs. Considering various computing abilities at vehicle and infrastructure, a closed-form solution to the V2V or V2V/V2I cooperative communication mode selection is theoretically achieved based on response delay analysis to guarantee the timeliness of raw sensing data sharing. And the age of information based system status update algorithm is proposed for the V2V/V2I collaborative communication mode. The feasibility of the proposed TD-ISAC system is verified by simulation and hardware testbed results. Based on simulation results, the proposed communication mode selection algorithm can effectively minimize the response time delay in different conditions. The mmWave enabled TD-ISAC hardware testbed is developed and the position error of target detection can be reduced by 18.5 % using the sensing data fusion from two vehicles, while the communication throughput remains over 2.2 Gbps.

Grid‐forming inverter control design for PV sources considering DC‐link dynamics

AbstractA grid‐forming inverter in an inverter‐dominated grid should operate as a dispatchable voltage source, which is difficult to achieve when the inverter is interfaced with nonlinear dc sources such as photovoltaic (PV) arrays. This paper presents a new grid‐forming controller which considers the PV source dynamics and limitations and maintains dc‐link stability under transient and overload conditions. A single‐loop voltage controller without ac current feedback is used, and the synchronization is achieved using dc voltage‐frequency droop. Additional dc current‐virtual impedance feedforward compensation is included to improve the ac output passivity. The controller limits the operation of the PV source inverter in the linear portion of its characteristic by regulating its modulation index, thus preventing dc voltage collapse. The proposed controller is implemented and tested on a controller‐in‐the‐loop simulation platform. The simulation results show that the controller shares power in proportion to the dc source capacities of parallel inverters, effectively limits the output current during faults, and limits dc‐link voltage drop when the inverter is overloaded. The controller is also validated on a hardware testbed.

Real-Time Power Management for Microgrids With Dynamic Boundaries and Multiple Source Locations

The dynamic boundary concept enables more flexible and efficient operation of microgrids with distributed energy resources (DER) that are intermittent in nature. As the integration of renewables continues to accelerate, an adaptive power management module that enables dynamic boundary operations in microgrids with an increasing number of source locations is essential for the fast and low-cost deployment of microgrid controllers. The power management module introduced in this paper is capable of handling the increased complexity in topological variations and transitions stemming from the dynamic boundaries and multiple source locations. This includes real-time operation of multiple islands with dynamic boundaries, initiation of topological transitions (merging and separation of islands), and automatic source coordination for power sharing and frequency regulation. All functions in the power management module are designed to be automatically adaptable to arbitrary microgrids with non-meshed topologies so that the deployment of the controller at new microgrid sites can be expedited with a reduced cost. The module has been implemented on NI&#x2019;s CompactRIO system as an essential part of an MG controller and tested on a converter-based hardware testbed (HTB). Testing results validated the effectiveness of the algorithms under various operating conditions.

Fault-Resilience for Bandwidth Management in Industrial Software-Defined Networks

Industrial Cyber-Physical Systems (ICPS) expect assurances of timely delivery of data even during the occurrence of distinct faults. It is a challenge to manage the required bandwidth by providing resilience to link failures and dynamically changing bandwidth requirements. In this paper, we address the aforementioned challenge by exploring Software-Defined Networks (SDN). We present a framework coined SDN-RMbw (Software-Defined Networking Resilience Management for Bandwidth), which is a contract-based framework, where the components are bound to bandwidth contracts and a resilience manager. The bandwidth contracts state the bandwidth requirements of traffic flows. With each such contract, a monitor is associated, which is responsible to detect two events, run-time changes and link failures. Directly after receiving the event trigger reports from the monitor, new routes are calculated by a path-finding algorithm. Based on newly calculated routes, an observer detects whether the contract requirements are still satisfied, or the contract gets violated (termed as fault). To provide resilience to such faults in the network, a resilience manager integrated with control logic decides and executes a suitable response strategy. The proposed SDN-based framework aims at providing fault-resilience as well as adapting to different network-state changes. The proposed framework is evaluated using a Ryu SDN controller on a hardware testbed. Our results show that the proposed framework provides enhanced network resilience as compared to baseline mechanisms and improves the success rate up to 21% and bandwidth up to 111 Mbps under distinct network scenarios. Furthermore, extensive experimental emulations on the Mininet tool depicts the scalability of the proposed framework.

Stand‐alone PV water pumping system based on high‐gain resonant inverter fed induction motor serving two‐head for permanent water supply

SummaryA vast community of developing countries, like India, is still deprived of a proper water supply system. Such communities often depend on hand‐pumps, ponds, and ravines for their daily water needs. Government‐subsidized PV‐based water pumps have emerged as a boon for such communities. But intermittent nature of solar irradiation and unavailability at night time poses a big challenge. To mitigate this challenge, the present article proposes a stand‐alone PV‐based induction motor‐driven water pumping system serving a dual‐head, that is, an overhead storage tank and ground level supply. A single‐stage three‐phase high‐gain resonant inverter (HGRI) is proposed for driving the induction motor. A back‐ended wide bandgap (WBG) device‐based boost converter is used to extract maximum power from PV by employing the MPPT algorithm. HGRI injects sinusoidal current to the induction motor while operating it through PV power matched modified V/f control algorithm, thus minimizing the torque ripple. HGRI coupled with boost converter provides a high‐gain voltage boost at the output; enabling wide input voltage operation, which increases the operability of the system in high as well as low irradiation level. The practicality of the system is validated on a laboratory‐developed hardware test bed with a 1.5HP induction motor.

Empirical Evaluation of Diagnostic Algorithm Performance Using a Generic Framework

A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison. In this paper we introduce a framework, called DXF, that provides a common language to represent the system description, sensor data and the fault diagnosis results; a run-time architecture to execute the diagnosis algorithms under identical conditions and collect the diagnosis results; and an evaluation component that can compute performance metrics from the diagnosis results to compare the algorithms. We have used DXF to perform an empirical evaluation of 13 diagnostic algorithms on a hardware testbed (ADAPT) at NASA Ames Research Center and on a set of synthetic circuits typically used as benchmarks in the model-based diagnosis community. Based on these empirical data we analyze the performance of each algorithm and suggest directions for future development.

Primary Frequency Control Using Motor Drives for Short Term Grid Disturbances

Industrial motor systems make up a quarter of all electric sales in the United States. Variable speed drives (VSDs) can provide energy efficiency savings to the customer by regulating motor speed based on specific and varying needs. In addition to the benefits provided to the customer, VSDs can provide support to the grid through ancillary services. The Center for Ultra-Wide-Area Resilient Electric Energy Transmission Networks (CURENT) developed a power electronics converter-based grid emulator to allow testing of various power system architectures and demonstration of key technologies in monitoring, control, actuation, and visualization. This paper proposes using an active front-end VSD's connected motor load to provide frequency regulation to a large scale power grid. Each part of the emulator is described including motor and power electronics model and control. The proposed frequency regulation is implemented in VSDs and modeled in both a transmission system in EMTDC/PSCAD and verified on CURENT's hardware testbed.

Inter-UAV Routing Scheme Testbeds

With the development of more advanced and efficient control algorithms and communication architectures, UAVs and networks thereof (swarms) now find applications in nearly all possible environments and scenarios. There exist numerous schemes which accommodate routing for such networks, many of which are specifically designed for distinct use-cases. Validation and evaluation of routing schemes is implemented for the most part using simulation software. This approach is however incapable of considering real-life noise, radio propagation models, channel bit error rate and signal-to-noise ratio. Most importantly, existing frameworks or simulation software cannot sense physical-layer related information regarding power consumption which an increasing number of routing protocols utilize as a metric. The work presented in this paper contributes to the analysis of already existing routing scheme evaluation frameworks and testbeds and proposes an efficient, universal and standardized hardware testbed. Additionally, three interface modes aimed at evaluation under different scenarios are provided.

Proportional–Integral Synchronization for Nonidentical Wireless Packet-Coupled Oscillators With Delays

Precise timing among wireless sensor nodes is a key enabling technology for time-sensitive industrial wireless sensor networks (WSNs). However, the accuracy of timing is degraded by manufacturing tolerance, aging of crystal oscillators, and communication delays. This article develops a framework of packet-coupled oscillators (PkCOs) to characterize the dynamics of communication and time synchronization of clocks in WSNs. The nonidentical clock is derived to describe the embedded clock's behavior accurately. A proportional-integral (PI) packet coupling scheme is proposed for synchronizing networked embedded clocks, while, scheduling wireless Sync packets to different slots for transmission. It also possesses the feature of automatically eliminating the effects of unknown processing delay, which further improves the synchronization performance. The rigorous theoretical analysis of PI-based PkCOs is presented via studying a closed-loop time synchronization system. The performance of PI-based PkCOs is evaluated on a hardware testbed of IEEE 802.15.4 WSN. The experimental results show that the precision of the proportional-integral PkCOs protocol is as high as 60 μs (i.e., 2 ticks) for 32.768 kHz crystal oscillator-based clocks.