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Deploying emergency vehicles has become a key guarantee for power supply in post-disaster distribution networks on account of their flexibility, maneuverability, safety, and reliability. However, due to limitations in configuration, the continuous power supply capacity of existing electrical vehicles (EVs) is insufficient, making it difficult to meet the needs of energy transfer and flow regulation in post-disaster distribution networks. Therefore, in this study, we comprehensively considered the energy time-shift characteristics of EVs and the flexible control function of soft open points (SOPs), integrated their advantages, and designed an emergency vehicle with SOP (EV-SOP) and its management strategy for distribution network line faults. Firstly, we present the EV-SOP architecture and its mathematical model. Then, aiming to minimize the economic losses caused by power loss during line faults, an EV-SOP emergency management strategy based on data collection, scheduling judgment, and optimal modeling techniques is proposed. Finally, by taking the case study of an IEEE33-node distribution network with contact switches as an example, we validate the effectiveness and superiority of the EV-SOP and its emergency management strategy compared with traditional energy storage emergency vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wheel failure is one of the worst problems for a planetary rover working on Mars or the Moon, which may lead to the interruption of the exploration mission and even the loss of mobility. In this study, a driving test of a planetary rover prototype with a faulty drive wheel was conducted, and state analysis and dynamics modeling were carried out. The drag motion relationship between the faulty drive wheel and the normal wheels on the same suspension was established based on the targeted single wheel test (faulty wheel-soil bin). In order to maintain the subsequent basic detection capability of the planetary rover, an emergency control system is proposed that integrates the path planning strategy with faulty wheel priority and the motion control method of correcting heading and coordinating allocation. The experimental results and emergency strategies of this study on simulating Martian soil and terrain can provide researchers with ideas to solve such problems. [ABSTRACT FROM AUTHOR]

Based on the practical problem that excessive effluent pollutants caused by the impact of soybean wastewater on urban sewage treatment system, the effect of soybean wastewater on pollutants removal in sequencing batch reactor (SBR) and emergency control measures were investigated through single cycle instantaneous wastewater impact test. The results showed that with the increase of water quality impact load, COD removal rate increased initially and then decreased, while NH4+-N and TN removal rates increased gradually. The effect of water quality impact on TP removal was relatively small. When COD of the impact wastewater was 370 mg/L, the effluent NH4+-N and TN could not meet the first level class A standard of Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant (GB 18918-2002). As the water volume impact increased, the changes of COD and TN removal rates remained consistent with the water quality impact, and the effect of water volume impact on NH4+-N and TP removal was relatively small. As the impact of water quality and water volume increased, the protein(PN) and polysaccharide(PS) components in extracellular polymeric substances(EPS) were both affected. Regulating the aeration rate could be used as an effective emergency control measure for the impact of soybean products wastewater. When the COD of the impact wastewater was 370 mg/L and the aeration rate was 0.9 L/min, COD, NH4+-N, TN, and TP could all meet the discharge standards. The results could provide reference for coping with the impact of other loaded soybean products wastewater.

Copyright of Industrial Water Treatment is the property of CNOOC Tianjin Chemical Research & Design Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The outbreak of coronavirus (COVID-19) has forced governments around the world to limit the movement of people and prohibit cross-countries travel or activities. However, the policymakers have been mobilizing a lot of effort to organize the transactions of essential goods and services to tackle the pandemic outcomes. These worldwide efforts indicate the importance of establishing and maintaining accurate management of logistics operations either during the current crisis or the consecutive periods. Motivated by the vital importance of logistics operations during the speedy increase of COVID-19 across the globe, this study critically investigates the existing works closely related to it. The main objective is to identify and boost the understanding of the characteristics of the logistics that play a critical role during pandemics. The collection of the literature was performed employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. Moreover, a multilateral framework was used to classify the selected works. The findings of this study indicate that academic works have focused mostly on "post-event" (47.24%) management of logistics operations followed by the "pre-event" (32.76%) and least in the "Integrated" (20%.) methods. Moreover, the analysis of these results provided valuable insights that are discussed in detail. Furthermore, numerous key areas have been recognized that need proper attention to adjust the overall efficiency of the current logistics operations using intelligent solutions. We believe that the findings of this study would be useful to enhance the decision-makers. furthermore, maintaining efficient sustainable logistics systems during and after a crisis remains essential. [ABSTRACT FROM AUTHOR]

This paper proposes a synchrophasor measurement-assisted integrated scheme to enhance power grid stability via transient stability assessment and emergency control of power systems in real-time. The synchrophasor measurements are time-stamped network information available from Phasor Measurement Units (PMUs). The Transient Stability Assessment (TSA) is based on measured generator bus angles obtained directly by PMUs at the control center. These synchronized angle measurements are utilized to construct features, and a Random Forest classifier is implemented to infer the TSA in real-time. The TSA results are determined within the first three cycles following the fault clearance. For operating scenarios that are unstable, an effective emergency control scheme is developed to avoid system degradation. The control strategy utilizes wide-area measurements to formulate a proportional sharing principle-based methodology in real-time. The composite scheme estimates the location and magnitude of the emergency measures to enhance grid stability. The performance of the proposed composite scheme is tested on an IEEE 39-bus test system. The results highlight that the scheme is computationally efficient and robust to topological changes. [ABSTRACT FROM AUTHOR]

Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.

Deploying emergency vehicles has become a key guarantee for power supply in post-disaster distribution networks on account of their flexibility, maneuverability, safety, and reliability. However, due to limitations in configuration, the continuous power supply capacity of existing electrical vehicles (EVs) is insufficient, making it difficult to meet the needs of energy transfer and flow regulation in post-disaster distribution networks. Therefore, in this study, we comprehensively considered the energy time-shift characteristics of EVs and the flexible control function of soft open points (SOPs), integrated their advantages, and designed an emergency vehicle with SOP (EV-SOP) and its management strategy for distribution network line faults. Firstly, we present the EV-SOP architecture and its mathematical model. Then, aiming to minimize the economic losses caused by power loss during line faults, an EV-SOP emergency management strategy based on data collection, scheduling judgment, and optimal modeling techniques is proposed. Finally, by taking the case study of an IEEE33-node distribution network with contact switches as an example, we validate the effectiveness and superiority of the EV-SOP and its emergency management strategy compared with traditional energy storage emergency vehicles.

Wheel failure is one of the worst problems for a planetary rover working on Mars or the Moon, which may lead to the interruption of the exploration mission and even the loss of mobility. In this study, a driving test of a planetary rover prototype with a faulty drive wheel was conducted, and state analysis and dynamics modeling were carried out. The drag motion relationship between the faulty drive wheel and the normal wheels on the same suspension was established based on the targeted single wheel test (faulty wheel-soil bin). In order to maintain the subsequent basic detection capability of the planetary rover, an emergency control system is proposed that integrates the path planning strategy with faulty wheel priority and the motion control method of correcting heading and coordinating allocation. The experimental results and emergency strategies of this study on simulating Martian soil and terrain can provide researchers with ideas to solve such problems.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions' value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems. [ABSTRACT FROM AUTHOR]

Abstract Undervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a probabilistic energy and reserve co-dispatch (PERD) model to address the strong uncertainties in high-renewable power systems. The expected costs of potential renewable energy curtailment/load shedding are fully considered in this model, which avoids insufficient or excessive emergency control capacity to produce more economical reserve decisions than conventional chance-constrained dispatch methods. Furthermore, an analytical reformulation approach of PERD is proposed to make it tractable. We firstly develop an approximation technique with high precision to convert the integral terms in objective functions into analytical ones. Then, the calculation of probabilistic constraints is equivalently transformed into an unconstrained optimization problem by introducing value-at-risk (VaR) representation. Specifically, the VaR formulas can be computed by a computationally-cheap dichotomy search algorithm. Finally, the PERD model is transformed into a convex problem, which can be solved reliably and efficiently using off-the-shelf solvers. Case studies are performed on IEEE test systems and real provincial power grids in China to illustrate the scalability and efficiency of the proposed method.

Communication failures and transmission delays are two major issues associated with Wide-Area Damping Controllers (WADCs). While transmission delays have been extensively studied and various solutions have been proposed, little research has been done on communication failures and most of the proposed methods are based on preventive controls. However, in today’s liberalized electricity markets, preventive controls are no longer acceptable and the trend is to use emergency controls instead. This paper proposes a novel emergency control scheme to counteract the loss of remote signals related to the input and to the output of the WADC (i.e., sensor and actuator failures). The proposed scheme is based on a simple criterion, which overcomes the complexity of the previous methods. Modal analysis and time domain simulations are performed to verify the performance of the proposed method. The simulation results show that the proposed method performs well in handling communication failures and can maintain good damping performance. This research work is particularly important in view of the trend towards the wide-scale adoption of wide-area measurement technologies, while the vulnerability to cyber-attacks is increasing.

Abstract Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre‐defined data. The purpose of this study is to develop an adaptable algorithm for turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator “torque–load angle”. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre‐defined parameters of a power system model are required. The suggested algorithm was tested on the power system model NE39bus using Matlab/Simulink. The efficiency of the suggested algorithm is verified and demonstrated.

Undervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios. [ABSTRACT FROM AUTHOR]

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

This paper discusses the construction method of ventilation and prevention system based on the application practice of Zhuanlongwan Coal Mine. A precise perception technology for key parameters of ventilation network is proposed. Through the optimal arrangement of sensors and the use of the least monitoring points, the correlation parameter analysis of the whole mine ventilation resistance route is achieved; the remote software modules of the main ventilator, local ventilator, and ventilation facilities are developed by extracting the ventilation and prevention information of the safety monitoring system, and the modules such as beam pipe monitoring, dust monitoring, grouting monitoring, and personnel positioning system are integrated; the intelligent analysis technology for the state of the ventilation and prevention system is proposed, and the three-dimensional simulation model of the mine ventilation network is established, which achieves the real-time calculation of the ventilation network, the visual display and trend analysis of the ventilation and prevention parameters, and the diagnosis and early warning of ventilation anomalies. By organically integrating ventilation, gas, fire, dust and other ventilation and prevention safety information, a hierarchical management and control mode of mine ventilation and prevention system based on multi heterogeneous information integration is proposed, a mine intelligent ventilation and prevention platform is developed, and mine emergency management and control platform is invented. Through field practice, the relevant parameter judgment and control model are verified, which provides application experience for the construction of mine intelligent ventilation and prevention system.

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre‐defined data. The purpose of this study is to develop an adaptable algorithm for turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator "torque–load angle". The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre‐defined parameters of a power system model are required. The suggested algorithm was tested on the power system model NE39bus using Matlab/Simulink. The efficiency of the suggested algorithm is verified and demonstrated. [ABSTRACT FROM AUTHOR]

During the Lunar New Year of 2020, the Chinese government implemented a strict nationwide lockdown to prevent the spread of the COVID-19, which may have led to changes in the distribution of pollutants. To enhance our understanding of the three-dimensional evolution of pollutants under special control scenarios, this study used vehicle lidar to obtain vertical profiles of aerosols and ozone (O 3) in Wuhan during the pandemic. Combined with ground observations and WRF-Chem model simulation results, we explored the evolution characteristics of pollutants. The results showed that the fine particulate matter (PM 2.5) concentration near the ground in Wuhan City was significantly reduced (40%) during the emission control period. The aerosol concentration decreased rapidly with an increase in height, and the maximum loading height was consistent with the atmospheric boundary layer height. Conversely, due to factors such as the reduction of the precursor nitrogen dioxide (NO 2) and rising temperatures, the near-ground O 3 concentration increased by 59%. Despite the significant reduction in emission sources, lidar observations still captured distinct aerosol transport layers on March 7 and March 10, 2020. These transport layers gradually descended, affecting the ground-level aerosol concentration. Additionally, one typical process of O 3 external transport was observed in the vertical direction. Our study provides a theoretical basis for a deeper understanding of the spatiotemporal distribution characteristics of pollutants during special control scenarios, aiding policymakers in formulating effective air pollution control strategies. • Pollutant 3D evolution first analyzed using mobile vehicle-lidar during Wuhan's emergency control period. • The aerosol concentration decreased, and the concentration decreased with increasing altitude. • Despite the reduction in emissions, the ozone concentration increased. • External transport of pollutants was observed during the control period. [ABSTRACT FROM AUTHOR]

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid. • Multifaceted challenges for large-scale power system emergency control such as scalability, adaptiveness and security require convergence: merging ideas and approaches from a wide range of areas. • A convergence framework is proposed for integrating physics, machine learning, advanced computing, and control to realize intelligent grid emergency control. • Open-source high performance platform GridPACK-Gym for developing and testing DRL-based power system dynamic control algorithms. • The proposed solution has been tested on a large-scale Texas-size power system with more than 3000 buses and 56 000 scenarios. • Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. [ABSTRACT FROM AUTHOR]

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems.

The combined usage of phasor measurement units and machine learning algorithms provides the opportunity for developing response-based wide-area system integrity protection scheme against transient instability in power systems. However, only the transient stability status is usually predicted in the literature, which is not enough for real-time decision-making for response-based emergency control. In this paper, an integrated approach is proposed. The GRU-based predictor is firstly proposed for post-disturbance transient stability prediction. On this basis, a multi-task learning framework is proposed for the identification of unstable machines and also the estimation of generation shedding. Case study on the IEEE 39-bus system demonstrates that, apart from the basic task of transient stability prediction, the proposed GRU-based multi-task predictor can predict the grouping of unstable machines correctly. Moreover, based on the estimated amount of generation shedding, the generated remedial control actions can retain the synchronism of the power system.

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods. [ABSTRACT FROM AUTHOR]

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre-defined data. The purpose of this study is to develop an adaptable algorithm of turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator Torque—Load angle. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre-defined parameters of a power system model are required. [ABSTRACT FROM AUTHOR]

Cyanobacteria can accumulate as a heavy biomass on the leeward side of large eutrophic lakes, posing a potential threat to public health. The mitigating capacity of three flocculants and their potential impacts on the major environmental features of water and sediments was evaluated. Results indicate that polyaluminum chloride (PAC) and ferric chloride (FeCl3) are efficient flocculants that can rapidly mitigate cyanobacterial blooms with chlorophyll-a concentrations higher than 1 500 µg/L within 15 min. In comparison, cationic starch with chitosan could only treat cyanobacterial blooms in chlorophyll-a concentrations of less than 200 µg/L. The addition of FeCl3 caused a decline in the pH value, while dissolved oxygen in the water column dropped to 2 mg/L during cationic starch with chitosan treatment for a high cyanobacterial biomass group. Thus, a combination of flocculants and oxygenators should be considered when treating high-concentration cyanobacterial blooms for emergency purposes. Additionally, the cell lysis of cyanobacteria caused by cationic starch with chitosan can result in an increase in total dissolved phosphorus and total dissolved nitrogen. Furthermore, the high accumulation of nutrients in sediments after the settling of cyanobacteria can cause high internal phosphorus pollution. The increase in the total organic carbon of the sediments can threaten lake restoration achieved by planting submerged macrophytes. [ABSTRACT FROM AUTHOR]

The widespread application of phasor measurement units has improved grid operational reliability. However, this has increased the risk of cyber threats such as false data injection attack that mislead time-critical measurements, which may lead to incorrect operator actions. While a single incorrect operator action might not result in a cascading failure, a series of actions impacting critical lines and transformers, combined with pre-existing faults or scheduled maintenance, might lead to widespread outages. To prevent cascading failures, controlled islanding strategies are traditionally implemented. However, islanding is effective only when the received data are trustworthy. This paper investigates two multi-objective controlled islanding strategies to accommodate data uncertainties under scenarios of lack of or partial knowledge of false data injection attacks. When attack information is not available, the optimization problem maximizes island observability using a minimum number of phasor measurement units for a more accurate state estimation. When partial attack information is available, vulnerable phasor measurement units are isolated to a smaller island to minimize the impacts of attacks. Additional objectives ensure steady-state and transient-state stability of the islands. Simulations are performed on 200-bus, 500-bus, and 2000-bus systems. [ABSTRACT FROM AUTHOR]

The use of electrical energy storage systems opens up new opportunities for improving the quality of electrical operation management and improves the performance of electrical power systems. Today, the new types of storage devices have been increasingly used for energy storage, recently. Among them are electrochemical energy storage, compressed-air storage, superconducting storage and supercapacitors, pumped-storage power plants. A brief review of main storage system types with various development and implementation levels is performed. The main energy storage systems characteristics are presented. They are comparing different energy storage technologies for power industry application. The energy storage possibilities have been determined for steadystate and transient operation in integrated power system of Ukraine. The energy storage are increasingly being used in the practice of power system operation and man agement. This is increasing the power supply reliability of consumers. [ABSTRACT FROM AUTHOR]

This paper proposes a design methodology for a six-wheeled rover that adapt to different stairs and maintain its stability based on the robot's parameters, the kinematics constraints, the maximum height, and the minimum length of the step required to climb up and down. We also propose an emergency controller to prevent falls during the climb up or down using the contact angle measurement between wheel–ground by a laser scanner sensor on each side of the robot. Thus, the geometry terrain information and the wheel contact loss detection with the ground can be obtained. This loss of contact with the ground is a determining factor in an emergency case where the robot's stability is at risk. Therefore, the controllers kick in to regain the wheel contact with the step, preventing the robot from falling. Simulations and experimental results when the robot climbs up and down stairs demonstrate the ability to react to possible falls. [ABSTRACT FROM AUTHOR]

The increasing complexity of power systems makes existing deep reinforcement learning-based emergency voltage control methods face challenges in learning speed and data utilization efficiency. Meanwhile, the accumulated data containing expert experience and domain knowledge has not been fully utilized to improve the performance of the deep reinforcement learning methods. To address the above issues, a novel hybrid emergency voltage control method that combines expert experience and machine intelligence is proposed in this paper. Specifically, the expert experience in the off-line demonstration is extracted through a behavioral cloning model and the deep reinforcement learning method is applied to discover and learn new knowledge autonomously. A special supervised expert loss is designed to utilize the pre-trained behavioral cloning model to assist the self-learning process. The demonstration is dynamically updated during the training process such that the behavioral cloning model and the deep reinforcement learning model can facilitate each other continuously. Experiments are conducted on the open-source RLGC platform to validate the performance and the experimental results show that the proposed method can effectively improve the learning speed and the applicability of the model to different test situations. [ABSTRACT FROM AUTHOR]

The operation of transmission systems with large share of wind power is specially challenging under storm conditions. Under the stormy wind speed conditions, wind turbine protection system is designed to shut down the turbine to avoid excessive mechanical load. The sudden loss of wind power from large offshore plants is difficult to forecast accurately, which results in a large amount of power imbalance. The severity of such a wind power imbalance towards frequency stability needs to be studied for the future power systems. In addition, the overhead transmission lines can also be affected during storms, thereby increasing their probability of failure in the operation of power system under the islanded conditions. This paper investigates how the stormy weather can threaten the frequency stability of future Danish power system with large share of wind power and how to avoid the frequency instability through proper control and defence strategies such as high-voltage direct current (HVDC) control and load shedding. Sensitivity studies are performed for ramp rates of HVDC control, load shedding strategies, inertia of the system with different volumes of disturbances to understand their impact on frequency stability.

The real-time transient stability assessment (TSA) and emergency control are effective measures to suppress accident expansion, prevent system instability, and avoid large-scale power outages in the event of power system failure. However, real-time assessment is extremely demanding on computing speed, and the traditional method is not competent. In this paper, an improved deep belief network (DBN) is proposed for the fast assessment of transient stability, which considers the structural characteristics of power system in the construction of loss function. Deep learning has been effective in many fields, but usually is considered as a black-box model. From the perspective of machine learning interpretation, this paper proposes a local linear interpreter (LLI) model, and tries to give a reasonable interpretation of the relationship between the system features and the assessment result, and illustrates the conversion process from the input feature space to the high-dimension representation space. The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China. The result demonstrates that the proposed method has rapidity, high accuracy and good interpretability in transient stability assessment.

In this study, we investigate two kinds of control modes of battery energy storage systems (BESSs) to improve the short-term frequency stability after a sudden loss of generation. One is a current control mode that directly controls current output, and the other is a voltage control mode that controls the frequency of the voltage output. The methods are verified by numerical simulation in a multi-machine power system model considering transmission network. Numerical examples show that the voltage control mode is more effective than the current control mode for suppressing frequency drop and the rate of change of frequency (RoCoF) on condition that RoCoF data is unavailable for the control of the BESSs. It is also shown that the use of RoCoF data makes the current control mode effective for the alleviation of frequency drop and RoCoF as well as the voltage control mode. [ABSTRACT FROM AUTHOR]

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre-defined data. The purpose of this study is to develop an adaptable algorithm of turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator Torque—Load angle. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre-defined parameters of a power system model are required.

Non-negligible amounts of Distributed Generating Units (DGUs) are already connected to power systems, predominantly to medium- and low-voltage networks. The interaction between distribution and transmission systems is gaining the attention of system operators. Given the high controlability of DGUs, the active distribution networks can potentially support the transmission system during emergency situations. Previously proposed voltage emergency controls involving distribution network assets focused on preserving the transmission system integrity, even if it implies affecting the distribution system operation (intrusive schemes). More attention needs to be devoted to supporting transmission voltages, while preserving the distribution network operation (non-intrusive schemes). This paper introduces a new adaptive and non-intrusive voltage emergency control scheme based on the synchronization of DGUs and the load tap changer of the corresponding distribution transformer. The conclusions are derived from time-domain simulations using an extended version of the IEEE Nordic test system for voltage stability assessment. [ABSTRACT FROM AUTHOR]

Maritime management is a crucial concern for movable bridge safety. Irregular management of water vehicles near movable bridges may lead to collision among ships and bridge infrastructures, causing massive losses of life and property. The paper presents a theoretical framework and simulation of an intelligent water vehicle management system for movable bridges corresponding to vehicle traffic responses. The water regime around the bridge is considered in virtually separated domains to estimate the desired safety actions based on the position of the approaching ships. An emergency clash avoidance control system is represented to prevent ship-infrastructure collision and ensure transportation safety. In addition, a simulation platform is developed specifically adaptable for movable bridge maritime and dynamic traffic management. The proposed theory is experimented using the simulation platform for different ship speeds and bridge-vehicle traffic volumes. Based on analyzing the velocity profile of approaching ships at different incidents, the bridge is found incapable of evacuating vehicles and unable to open promptly in case of speeding ships and high traffic density of vehicles on the bridge. Computational results show that the emergency control system is effective in reducing ship speed and prevent certain collisions. Lastly, the transportation policy for the newly proposed maritime management system is validated by real-world implementation in movable bridges across the world. [ABSTRACT FROM AUTHOR]

In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme. [ABSTRACT FROM AUTHOR]

In the event of accidents in power systems associated with a reduction in frequency in order to prevent the complete repayment of the energy district and accelerate the elimination of the accident, frequency division automatics (FDA) are used. Existing technical solutions for the implementation of the FDA do not always provide a balance of the generated and consumed power under the action of the FDA, since the current circuit-mode situation is not taken into account. The aim of this work is to develop effective algorithms for the functioning of adaptive FDA, which allocates power units of a power plant to a balanced load of an isolated energy district, taking into account the current circuit-mode situation. To achieve this goal, we use the method of structural and parametric synthesis of emergency control algorithms, which consists in creating a composite algorithmic model of adaptive FDA. The result of this work is the developed algorithmic model of an adaptive FDA of a power plant, consisting of input and processing algorithms for input analog signals of a FDA, algorithms for input and processing of input discrete signals of a FDA, algorithms for generating start-up organs of a FDA, algorithms for allocating a power plant to a balanced load of a power district. The proposed algorithm for the functioning of adaptive FDA is universal and allows you to automatically allocate power units of a power plant to a balanced load of an isolated energy district, regardless of the type of accident and its cause, the configuration of the electrical network and the values of electricity generation and consumption, as well as the current circuit-mode situation.

The purpose of the article is to study the possibility and feasibility of participation of nuclear power plants (NPPs) with VVER in emergency frequency control in power systems with a high proportion of nuclear power units and, at the same time, of reducing the power consumption for the own needs of the main circulation pumps during modes with power below nominal. To solve these problems, it was proposed to increase the achievable speeds of power gain (load increase) due to the installation of frequency controlled drives of the MCP. Large system frequency variations (caused by large imbalances between generation and demand) may jeopardize electrical equipment, in terms of maintaining stable and reliable operating conditions. For NPPs, the task of preventing or localizing accidents is even more important than for TPPs, since in case of major system accidents, it is possible to completely stop external power supply of the NPPs own needs. Thus, besides the requirements for the primary control of the frequency of NPPs with VVER, today we need more stringent requirements for their emergency acceleration and mobility. The operation of NPPs with long-term non-recoverable active power shortage causes a decrease in the speed of the main circulation pumps of NPPs with VVER and a decrease in the coolant flow rate. It is shown that the installation of variable frequency drives of the MCPs at NPP with VVER is appropriate not only to save energy consumption for their drive in partial modes, but also to increase the power of NPP above the nominal (without reducing the reserve before the heat exchange crisis in the reactor core) for the elimination of system accidents, and thus to improve the safety of the NPPs included in the power system.