Enhancing Cybersecurity in Distributed Microgrids: A Review of Communication Protocols and Standards.

The effective operation of distributed energy sources relies significantly on the communication systems employed in microgrids. This article explores the fundamental communication requirements, structures, and protocols necessary to establish a secure connection in microgrids. This article examines the present difficulties facing, and progress in, smart microgrid communication technologies, including wired and wireless networks. Furthermore, it evaluates the incorporation of diverse security methods. This article showcases a case study that illustrates the implementation of a distributed cyber-security communication system in a microgrid setting. The study concludes by emphasizing the ongoing research endeavors and suggesting potential future research paths in the field of microgrid communications.

Real-time control of urban drainage systems using neuro-evolution.

With climate change and urbanization, existing urban drainage systems are being stressed beyond their design capacity in many parts of the world. Real-time control (RTC) can improve the performance of these systems and reduce the need for system upgrades. However, developing optimal control policies for RTC is a challenging research area due to computational demands, high uncertainties and system dynamics. This study presents a new RTC method using neuro-evolution for controlling combined sewer overflow (CSO) in urban drainage systems. Neuro-evolution is an approach to neural network research by evolutionary algorithms. Neuro-evolution realizes RTC by training the control policy in advance, thus avoiding the online optimization process in the application period. The simulation results of the benchmark Astlingen network indicate that the trained control policy outperforms the equal filling degree strategy in terms of CSO volume reduction and robustness in the face of tank level uncertainty. The performance analysis of the typical CSO events shows that the control policy mainly makes positive contributions during 'small' CSO events rather than 'large' ones. In particular, the effectiveness of the control policy in 'small' CSO events is more prominent in the initial phase of the events compared with the final phase. This work stands to support a foundation for future studies in the control of urban water systems based on neuro-evolution.

Smart roofs featuring predictive control: An upgrade for mitigating precipitation extreme-induced pluvial floods.

In the face of escalating urban pluvial floods exacerbated by climate change, conventional roof systems fall short of effectively managing precipitation extremes. This paper introduces a smart predictive solution: the Smart Internal Drainage Roof (SIDR) system, which leverages forecasted data to enhance the mitigation of pluvial floods in Central Business District (CBD) areas. Unlike traditional approaches, SIDRs utilize a synergistic combination of Rule-based Control (RBC) and Model Predictive Control (MPC) algorithms, tailored to optimize the operational efficiency of both grey and green roofs. Within the examined 1.3 km2 area in Beijing, China, SIDRs, covering 11% of the site, decreased total flooded areas by 30%-50% and eliminated 60%-100% of high-risk zones during three actual events. Moreover, SIDRs streamlined outflow processes without extending discharge time and reduced flood duration at a high-risk underpass by more than half. The SIDR's distinct features, including a high control resolution of 5 min, integration with existing waterproofs, and advanced 2D dynamic runoff visualization, position it as a scalable and cost-efficient upgrade in urban flood resilience strategies.

Tunable linear feedback control of urban drainage systems using models defined purely from data.

Real-time and model-predictive control promises to make urban drainage systems (UDS) adaptive, coordinated, and dynamically optimal. Though early implementations are promising, existing control algorithms have drawbacks in computational expense, trust, system-level coordination, and labor cost. Linear feedback control has distinct advantages in computational expense, interpretation, and coordination. However, current methods for building linear feedback controllers require calibrated software models. Here we present an automated method for generating tunable linear feedback controllers that require only system response data. The controller design consists of three main steps: (1) estimating the network connectivity using tools for causal inference, (2) identifying a linear, time-invariant (LTI) dynamical system which approximates the network, and (3) designing and tuning a feedback controller based on the LTI urban drainage system approximation. The flooding safety, erosion prevention, and water treatment performance of the method are evaluated across 190 design storms on a separated sewer model. Strong results suggest that the system knowledge required for generating effective, safe, and tunable controllers for UDS is surprisingly basic. This method allows near-turnkey synthesis of controllers solely from sensor data or reduction of process-based models.

Alternating-Current Microgrid Testbed Built with Low-Cost Modular Hardware.

With the growing popularity of microgrids for alternative energy management, there is demand for tools that allow us to study the effect of microgrids in distributed power systems. Popular methods involve software simulation and prototype validation with physical hardware. Simulations often do not capture the complex interactions, and combinations of software simulations with hardware testbeds promise to give a more accurate picture. These testbeds, however, usually aim at the validation of hardware for industrial-scale use, which makes them expensive and not readily accessible. To fill the gap between full-scale hardware and software simulation, we propose a modular lab-scale grid model at a 1:100 power scale over residential single-phase networks with 12 V AC and 60 Hz grid voltage. We present different modules-power sources, inverters, demanders, grid monitors, and grid-to-grid bridges-that can be assembled into distributed grids of almost arbitrary complexity. The model voltage poses no electrical hazards, and microgrids can readily be assembled with an open power line model. Unlike a prior DC-based grid testbed, the proposed AC model allows us to examine additional aspects, such as frequency, phase, active and apparent power, and reactive loads. Grid metrics, including the discretely sampled voltage and current waveforms, can be collected and sent to higher-tier grid management systems. We integrated the modules with Beagle Bone micro-PCs, which in turn connect any such microgrid with an emulation platform built on CORE (Common Open Research Emulator) and the Gridlab-D power simulator, thereby allowing hybrid software/hardware simulations. Our grid modules were shown to fully operate in this environment. Through the CORE system, multitiered control and even remote grid management is possible. However, we also found that the AC waveform poses design challenges that require us to balance accurate emulation (most notably with respect to harmonic distortion) with per-module costs.

Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System.

In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The "virtual stiffness" method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations.

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks.

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.

Real time control of nature-based solutions: Towards Smart Solutions and Digital Twins in Rangsit Area, Thailand.

The intensity and frequency of hydro-meteorological hazards have increased due to fast-growing urbanisation activities and climate change. Hybrid approaches that combine grey infrastructure and Nature-Based Solutions (NBSs) have been applied as an adaptive and resilient strategy to cope with climate change uncertainties and incorporate other co-benefits. This research aims to investigate the feasibility of Real Time Control (RTC) for NBS operation in order to reduce flooding and improve their effectiveness. The study area is the irrigation and drainage system of the Rangsit Area in Thailand. The results show that during the normal flood events, the RTC system effectively reduces water level at the Western Raphiphat Canal Station compared to the system without RTC or with additional storage. Moreover, the RTC system facilitates achieving the required minimum volume and increasing the volume in the retentions. These findings highlight the potential of using RTC to improve the irrigation and drainage system operation as well as NBS implementation to reduce flooding. The RTC system can also assists in equitable water distribution between Klongs and retention areas, while also increasing the water storage in the retention areas. This additional water storage can be utilized for agricultural purposes, providing further benefits. These results represent an essential starting point for the development of Smart Solutions and Digital Twins in utilizing Real-Time Control for flood reduction and water allocation in the Rangsit Area in Thailand.

Towards Tomography-Based Real-Time Control of Multiphase Flows: A Proof of Concept in Inline Fluid Separation.

The performance of multiphase flow processes is often determined by the distribution of phases inside the equipment. However, controllers in the field are typically implemented based on flow variables, which are simpler to measure, but indirectly connected to performance (e.g., pressure). Tomography has been used in the study of the distribution of phases of multiphase flows for decades, but only recently, the temporal resolution of the technique was sufficient for real-time reconstructions of the flow. Due to the strong connection between the performance and distribution of phases, it is expected that the introduction of tomography to the real-time control of multiphase flows will lead to substantial improvements in the system performance in relation to the current controllers in the field. This paper uses a gas-liquid inline swirl separator to analyze the possibilities and limitations of tomography-based real-time control of multiphase flow processes. Experiments were performed in the separator using a wire-mesh sensor (WMS) and a high-speed camera to show that multiphase flows have two components in their dynamics: one intrinsic to its nonlinear physics, occurring independent of external process disturbances, and one due to process disturbances (e.g., changes in the flow rates of the installation). Moreover, it is shown that the intrinsic dynamics propagate from upstream to inside the separator and can be used in predictive and feedforward control strategies. In addition to the WMS experiments, a proportional-integral feedback controller based on electrical resistance tomography (ERT) was implemented in the separator, with successful results in relation to the control of the distribution of phases and impact on the performance of the process: the capture of gas was increased from 76% to 93% of the total gas with the tomography-based controller. The results obtained with the inline swirl separator are extended in the perspective of the tomography-based control of quasi-1D multiphase flows.

Evaluation of uncertain signals' impact on deep reinforcement learning-based real-time control strategy of urban drainage systems.

Real-time control (RTC) is a recognized technology to enhance the efficiency of urban drainage systems (UDS). Deep reinforcement learning (DRL) has recently provided a new solution for RTC. However, the practice of DRL-based RTC has been impeded by different sources of uncertainties. The present study aimed to evaluate the impact caused by the uncertainties on DRL-based RTC to promote its application. The impact of uncertainties in the measurement of water level signals was evaluated through large-scale simulation experiments and quantified using measures of statistical dispersion of control performance distribution and relative change of control performance compared to the baseline scenario with no uncertainty. Results show that the statistical dispersion of DRL-based RTC was reduced by 15.48%-81.93% concerning random and systematic uncertainties compared to the conventional rule-based control (RBC) strategy. The findings indicated that DRL-based RTC is robust and could be reliably applied to safety-critical real-world UDS.

An integrated continuous downstream process with real-time control: A case study with periodic countercurrent chromatography and continuous virus inactivation.

Integrated continuous downstream processes with process analytical technology offer a promising opportunity to reduce production costs and increase process flexibility and adaptability. In this case study, an integrated continuous process was used to purify a recombinant protein on laboratory scale in a two-system setup that can be used as a general downstream setup offering multiproduct and multipurpose manufacturing capabilities. The process consisted of continuous solvent/detergent virus inactivation followed by periodic countercurrent chromatography in the capture step, and a final chromatographic polishing step. A real-time controller was implemented to ensure stable operation by adapting the downstream process to external changes. A concentration disturbance was introduced to test the controller. After the disturbance was applied, the product output recovered within 6 h, showing the effectiveness of the controller. In a comparison of the process with and without the controller, the product output per cycle increased by 27%, the resin utilization increased from 71.4% to 87.9%, and the specific buffer consumption was decreased by 21% with the controller, while maintaining a similar yield and purity as in the process without the disturbance. In addition, the integrated continuous process outperformed the batch process, increasing the productivity by 95% and the yield by 28%.

Is flow control in a space-constrained drainage network effective? A performance assessment for combined sewer overflow reduction.

Recurring combined sewer overflows (CSOs) can have a significant impact on the ecological condition of receiving water bodies. There are several structural measures, like adding retention basins and switching to low impact development solutions, that have been proposed to reduce the number of sewage overflows. Besides, several flow control strategies have been discussed in scientific literature that take advantage of the space within urban drainage networks, which is assumed to be adequate, for temporary storage. The adequacy of such storage space, however, is not a universally valid assumption as a large fraction of drainage networks frequently operate close to their design discharge. In this paper, we investigate the efficacy of flow control for a space-constrained drainage network. We employ a low-cost, heuristic real time control strategy with the use of flow control devices (FCDs) and available in-sewer space to reduce the magnitude of CSOs. We consider the performance of the proposed control strategy and discuss the effect of FCD location on CSO reduction. Our results, based on over 300 rainfall-event simulations, show that the flow control strategy using limited sewer capacity is more efficient during relatively small rainfall events, where the CSO is large enough to enable reduction using the chosen control rules. The CSO is reduced, to varying degrees, for around 80% of rainfall events with peak intensity between 10 and 20 mm h-1. For larger rainfall events, the flow control is more unstable in response to abrupt water release during control operation, which seems to be unavoidable because of the water accumulation effect and the transition to pressurized pipe flow in space-constrained networks. We also found that the flow control performance is highly sensitive to the FCD location - as it depends on the interplay of the peak rainfall intensity and the water level condition immediately upstream of the FCD. The efficacy of a location for flow control is determined by the unfilled capacity (i.e., effective in-sewer storage potential) in the pipe upstream of the FCD during the rainfall peak; furthermore, the location also has to be resistant to the water accumulation effect. Using our analysis, we substantiate two anticipated caveats to flow control strategies when the storage space is limited in a drainage network: diminished performance in CSO reduction and the appearance of additional control-related challenges, which are otherwise mitigated in more spacious networks.

MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides.

Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox.

MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides.

Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox. MaxQuant.Live builds on the fast application programming interface of quadrupole Orbitrap mass analyzers to control data acquisition in real-time (freely available at www.maxquant.live). Its graphical user interface enables advanced data acquisition strategies, such as in-depth characterization of peptides of interest. Online recalibration in mass, retention time, and intensity dimensions extends this concept to more than 25,000 peptides per run. Our "global targeting" strategy combines the best of targeted and shotgun approaches.

A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control.

Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm.

Traffic congestion is a major problem in today's society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.

A smart predictive framework for system-level stormwater management optimization.

This study presents a global real-time control (RTC) approach for sustainable and adaptive management of stormwater. A network of inter-connected devices are assumed to dynamically generate the required set-points for the system actuators at the remote control center where global optimization algorithms calculate real-time operational decision-making target values. These target values activate the local controllers to manipulate the spatially distributed detention basin's outlets enabling a smart catchment scale optimal control. A real world watershed with four outlets to a nearby watercourse is chosen to test the applicability and efficiency of the proposed dynamic control approach, based on model simulation results. Results show that the proposed autonomous control approach has the ability to enhance the global performance of the stormwater management system in terms of quality and quantity to balance the network flow dynamics and environmental demands, while reducing the potential for erosion of receiving water bodies. Climate change is specifically discussed as a challenge for the designed control framework. Although, the performance criteria are shown to be affected by the increased rainfall intensities compared to actual rainfall scenarios, the proposed methodology still improves the peak flow reduction and detention time of water, at global scale, up to 54% and 14 h respectively under climate change conditions.

Integrated pollution-based real-time control of sanitation systems.

An integrated pollution-based real-time control (RTC) approach is proposed for a sewer network (SN) integrated with wastewater treatment plants (WWTPs) in a sanitation system (SS) to mitigate the impacts of pollution from combined sewer overflows (CSOs) on ecosystems. To obtain the optimal solution for the SS while considering both quantity and quality dynamics for multiple objectives, model predictive control (MPC) is selected as the optimal control method. To integrate SN and WWTP management, a feedback coordination algorithm is developed. A closed-loop virtual-reality simulator is used to assess the results of the optimal management approach achieved by applying MPC. The Badalona SS (Spain) provides a pilot case study to assess the efficacy and applicability of the proposed approach. A comparison with local rule-based and volume-based control strategies currently in use indicates that the proposed integrated pollution-based RTC approach can reduce the pollutant loads released to the receiving environment.

Advanced nitrogen removal without addition of external carbon source in an anaerobic/aerobic/anoxic sequencing batch reactor.

Advanced nitrogen removal without the addition of external carbon source is challenging in the conventional biological nitrogen removal processes. This study presented a novel anaerobic/aerobic/anoxic sequencing batch reactor (A/O/A SBR) based on endogenous nitrate (NO3--N) respiration to enhance nitrogen removal. The mean effluent total nitrogen (TN) in the A/O/A SBR could be reduced to as low as 3.5 mg/L, when the average influent TN and chemical oxygen demand (COD) were 52.7 and 235.4 mg/L, respectively. This advanced nitrogen removal was attributed to the post-denitrification, since 82.7% of TN removal was achieved in the post-anoxic stage. The post-denitrification rate with nitrite (NO2--N, 0.59 mg NO2--N/gMLVSS/h) was higher than that with NO3--N (0.35 mg NO3--N/gMLVSS/h). Therefore, the post-anoxic time could be further optimized by achieving denitrification via NO2--N. The A/O/A SBR has good potential in achieving advanced nitrogen removal, especially in nitrogen-sensitive rural areas.

A Sensorless and Low-Gain Brushless DC Motor Controller Using a Simplified Dynamic Force Compensator for Robot Arm Application.

Robot arms used for service applications require safe human-machine interactions; therefore, the control gain of such robot arms must be minimized to limit the force output during operation, which slows the response of the control system. To improve cost efficiency, low-resolution sensors can be used to reduce cost because the robot arms do not require high precision of position sensing. However, low-resolution sensors slow the response of closed-loop control systems, leading to low accuracy. Focusing on safety and cost reduction, this study proposed a low-gain, sensorless Brushless DC motor control architecture, which performed position and torque control using only Hall-effect sensors and a current sensor. Low-pass filters were added in servo controllers to solve the sensing problems of undersampling and noise. To improve the control system's excessively slow response, we added a dynamic force compensator in the current controllers, simplified the system model, and conducted tuning experiments to expedite the calculation of dynamic force. These approaches achieved real-time current compensation, and accelerated control response and accuracy. Finally, a seven-axis robot arm was used in our experiments and analyses to verify the effectiveness of the simplified dynamic force compensators. Specifically, these experiments examined whether the sensorless drivers and compensators could achieve the required response and accuracy while reducing the control system's cost.