Emergency Control Measures Research Articles

SE-CNN based emergency control coordination strategy against voltage instability in multi-infeed hybrid AC/DC systems

For receiving-end power systems with multi-infeed HVDCs and large-scale renewable energy generation, the challenges of weak reactive power support and limited anti-interference capacity pose a threat to the power system voltage stability. To enhance the voltage stability under large disturbances, this paper proposes a data-driven method for coordinated emergency control strategy against voltage instability in multi-infeed hybrid AC/DC systems based on the squeeze and excitation (SE) module and convolutional neural network (CNN). Firstly, the SE module and CNN are integrated to intuitively capture correlations among various feature channels and extract key features related to voltage stability levels. Secondly, a voltage stability evaluation method is proposed based on the SE-CNN model, utilizing a dual channel structure to unveil the quantitative relationship between key response characteristics and voltage stability margin. Finally, regarding the control measures containing load shedding and DC modulation, the emergency control sensitivity index is proposed to assess the stability margin improvement effect of various control objects, and the optimal emergency control strategy is formulated to coordinate multiple voltage stability emergency control measures. Case studies were conducted on a typical China local region receiving-end power grid test system with voltage instability problems, and the simulation results verified the effectiveness of the proposed method. © 2017 Elsevier Inc. All rights reserved.

Emergency control measures against long term voltage instability

Emergency control measures against long term voltage instability

Assessing the impact of emergency measures in varied population density areas during a large dengue outbreak

BackgroundThe patterns of dengue are affected by many factors, including population density and climate factors. Densely populated areas could play a role in dengue transmission due to increased human-mosquito contacts, the presence of more diverse and suitable vector habitats and breeding sites, and changes in land use. In addition to population densities, climatic factors such as temperature, relative humidity, and precipitation have been demonstrated to predict dengue patterns. To control dengue, emergency measures should focus on vector management. Most approaches to assessing emergency responses to dengue risks involve applying simulation models or describing emergency activities and the results of implementing those responses. Research using real-world data with analytical methods to evaluate emergency responses to dengue has been limited. This study investigated emergency control measures associated with dengue risks in areas with high and low population densities, considering their different control capacities. MethodologyData from the 2015 dengue outbreak in Kaohsiung City, Taiwan, were utilized. The government database provided information on confirmed dengue cases, emergency control measures, and climatic data. The study employed a distributed lag non-linear model (DLNM) to assess the effect of emergency control measures and their time lags on dengue risk. Principal findingsThe findings revealed that in areas with high population density, the absence of emergency measures significantly elevated the risks of dengue. However, implementing emergency measures, especially a higher number, was associated with lower risks. In contrast, in areas with low population density, the risks of dengue were only significantly elevated at the 1st week lag if no emergency control measures were implemented. When emergency activities were carried out, the risks of dengue significantly decreased only for the 1st week lag. ConclusionsOur findings reveal distinct exposure-lag-response patterns in the associations between emergency control measures and dengue in areas with high and low population density. In regions with a high population density, implementing emergency activities during a significant dengue outbreak is crucial for reducing the risk. Conversely, in areas of low population density, the necessity of applying emergency activities may be less pronounced. The implications of this study on dengue management could provide valuable insights for health authorities dealing with limited resources.

A comprehensive localization and assessment method for SSO in power systems with wind power

Subsynchronous Oscillation (SSO) is a common dynamic stability issue in power systems that integrate wind power. This paper proposes a comprehensive method for assessing the localization of SSO in wind power integrated systems. The localization method applies the transient energy flow approach to identify SSO sources in systems with wind power. Additionally, a relevant SSO index system is established where the value of each index is calculated. By using the Fuzzy Inference System (FIS) to judge the index, the SSO disturbance source contribution rating of each area is obtained. Simulation cases demonstrate that the localization method of the transient energy flow approach can effectively identify the SSO disturbance source. Moreover, the SSO disturbance source contribution rating method based on FIS can determine the SSO contribution degree of each area, thus identifying the dominant oscillation disturbance sources. The proposed method can provide valuable insights for formulating emergency control measures.

Multi-DC commutation safety and system stability coordination control evaluation method

Due to the influence of multiple DC commutation failure after AC failure and stable interaction of system frequency, voltage and power Angle, the operation control of power grid is greatly difficult. The control scenario of multi-feed DC commutation failure or system instability is constructed, the comprehensive control performance cost ratio index of emergency control measures to the safety of multi-turn DC commutation and system stability is proposed, and the search method of coordinated control strategy based on global optimal is developed to avoid the risk of chain failure caused by negative control effects. The evaluation of control search path and control target after joint optimization is realized, which is conducive to the construction of system-level defense method and improves the adaptability of strategy.

The Impact of COVID-19 Prevention Policy on Stock Market Return of China

In the wake of the COVID-19 outbreak, China implemented emergency epidemic prevention and control measures, which played a positive role in economic and social development as well as the containment of the epidemic. Based on stock trading data from February 2020 to December 2022, this paper examines the impact of these measures on the stock returns of A-share listed companies. The findings of the study indicate that social distancing and health measures generally increase stock market returns, despite the varying magnitude of their effects. In addition, the analysis of two aspects, namely the listed sectors and the industry response analysis, further corroborates the main conclusion that social distancing policies have a more pronounced effect on the stock market. The study helps investors understand how the stock market responds to government announcements and enables policymakers to further explore the impact of policy responses during major public health events and their influence on economic and social welfare.

Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake

The issue of sudden water pollution resulting from accidents is a challenging environmental problem to address. The frequency of transport accidents involving hazardous materials over tributary bridges is steadily rising due to rapid industrialization and urbanization processes. This trend poses a significant threat to both the water’s ecological environment and human well-being. To effectively mitigate the risks associated with water pollution caused by accidents during the transportation of dangerous goods, this research focused on Baiyangdian Lake, the largest freshwater lake in North China. Thid study employed the expert judgment fuzzy language method and Bayesian network model as analytical tools to assess and analyze the potential risks associated with sudden water pollution accidents caused by the transportation of hazardous materials on bridges spanning tributaries. Through an examination of the various risk factors involved, the research identified four primary indicators and ten secondary indicators. Additionally, an oil leakage accident scenario was simulated, and recommendations for risk prevention and control measures were provided. The findings of the study indicated that: (1) The likelihood of risk associated with driver factors, vehicle emergency factors, fuel tank emergency factors, road factors, and lighting factors is elevated. (2) The probability of a dangerous goods transportation accident occurring on the Baiyangdian cross-tributary bridge is substantial, thereby presenting a potential hazard to both the water environment and human health. (3) Vehicle emergency factors, vehicle wear factors, and weather factors exert a significant influence on the incidence of accidents. (4) The highest likelihood of accidents is associated with a combination of factors, including driver fatigue, vehicle and fuel tank deterioration, and adverse weather conditions. (5) In instances where the vehicle and fuel tank are well-maintained, the probability of accidents is greatest on the cross tributary bridge, particularly when the driver is fatigued, weather conditions are unfavorable, and there is a lack of street lighting during nighttime. Implementing emergency prevention and control measures proved to be an effective approach in mitigating the risk of sudden water pollution accidents. This study offers valuable insights into risk mitigation and management strategies for emergent water pollution incidents, and the framework presented herein can be readily applied to other rivers worldwide confronting comparable risk challenges.

CNN‐LSTM based power grid voltage stability emergency control coordination strategy

AbstractThe occurrence of cascading failure and small probability contingency in hybrid AC/DC power grid aggravates the mismatch risk of traditional emergency control with offline‐predetermination–online‐practice (ODOP) mode. To ensure the voltage stability of power grid under large disturbances, this paper proposes a voltage stability emergency control coordination strategy based on convolutional neural network (CNN) and long short‐term memory (LSTM) network. First, the mismatch mechanism of traditional emergency control is revealed under the variation of operating conditions. Second, the start criterion of complementary emergency control with ODOP mode is proposed, and the CNN‐LSTM network is established to quantitatively evaluate the voltage stability margin. Finally, the emergency control sensitivity index is proposed to predict the stability margin enhancement under alternative emergency control measures, and the optimal ODOP‐based emergency control strategy is determined to coordinate multiple voltage stability emergency control measures. Case studies are performed in the actual Northwest China local region hybrid AC/DC power grid with voltage instability problems, and the simulation results verify the effectiveness of the proposed method.

Visual analysis of the prevention and control measures of COVID-19 in Chinese ports.

In 2022, COVID-19 solutions in China have entered a normal stage, and the solutions imported from ports have been transformed from emergency prevention and control measures to investigative long-term prevention and control measures. Therefore, it is necessary to study solutions for COVID-19 at border ports. In this study, 170 research papers related to the prevention and control measures of COVID-19 at ports from 2020 to September 2022 were retrieved from Wanfang database, HowNet database, Wip database, and WoS core collection. Citespace 6.1.R2 software was used to research institutions visualize and analyze researchers and keywords to explore their research hotspots and trends. After analysis, the overall volume of documents issued in the past 3years was stable. The major contributors are scientific research teams such as the Chinese Academy of Inspection and Quarantine Sciences (Han Hui et al.) and Beijing Customs (Sun Xiaodong et al.), with less cross-agency cooperation. The top five high-frequency keywords with cumulative frequency are as follows: COVID-19 (29 times), epidemic prevention and control (29 times), ports (28 times), health quarantine (16 times), and risk assessment (16 times). The research hotspots in the field of prevention and control measures for COVID-19 at ports are constantly changing with the progress of epidemic prevention and control. Cooperation between research institutions needs to be strengthened urgently. The research hotspots are the imported epidemic prevention and control, risk assessment, port health quarantine, and the normalized epidemic prevention and control mechanism, which is the trend of research and needs further exploration in the future.

Impacts of COVID-19 Lockdown on Air Quality in Shenzhen in Spring 2022

The short-term reduction of air pollutant emissions is an important emergency control measure for avoiding air pollution exceedances in Chinese cities. However, the impacts of short-term emission reductions on the air qualities in southern Chinese cities in spring has not been fully explored. We analyzed the changes in air quality in Shenzhen, Guangdong before, during, and after a city-wide lockdown associated with COVID-19 control during March 14 to 20, 2022. Stable weather conditions prevailed before and during the lockdown, such that local air pollution was strongly affected by local emissions. In-situ measurements and WRF-GC simulations over the Pearl River Delta (PRD) both showed that, due to reductions in traffic emissions during the lockdown, the concentrations of nitrogen dioxide (NO2), respirable particulate matter (PM10), and fine particulate matters (PM2.5) in Shenzhen decreased by (-26±9.5)%, (-28±6.4)%, and (-20±8.2)%, respectively. However, surface ozone (O3) concentration did not change significantly[(-1.0±6.5)%]. TROPOMI satellite observations of formaldehyde and nitrogen dioxide column concentrations indicated that the ozone photochemistry in the PRD in spring 2022 was mainly controlled by the volatile organic compound (VOCs) concentrations and was not sensitive to the reduction in nitrogen oxide (NOx) concentrations. Reduction in NOx may even have increased O3, because the titration of O3 by NOx was weakened. Due to the small spatial-temporal extent of emission reductions, the air quality effects caused by this short-term urban-scale lockdown were weaker than the air quality effects across China during the widespread COVID-19 lockdown in 2020. Future air quality management in South China cities should consider the impacts of NOx emission reduction on ozone and focus on the co-reduction scenarios of NOx and VOCs.

Efficient online detection system of power disturbances based on Deep-Learning approach

The economic and social effects from electric disruptions have been increasing. They can cause current and voltage interferences and result in disasters. Therefore, online assessment of transit stability is essential for the control of power systems, which can prevent transmission of power disturbances and enable operators to decide on emergency control measures. This paper proposes a power-disturbance-detection system to analyze online voltage signals and prevent transmission of disturbance signals. The system consists of four stages. The first stage is data augmentation, which adds noisy signals and more generated signals with multiple events with different magnitudes of disturbance time and locations. The second stage is data division, which partitions the generated dataset into trained and evaluated datasets. The third stage constructs convolutional neural network (CNN) architecture and trains it. The fourth stage develops the constructed CNN model into an electronic circuit to analyze the online voltage signal and prevent transmission of the disturbance signal. To investigate the suggested stages of the detection system, several experiments are conducted. The experimental results demonstrate that the proposed data augmentation stage provides a beneficial influence, and the proposed CNN model is architecturally efficient. It is not affected by the noise, period of modifications, or displacements of the disturbance signals in the real-time detection process. In addition, it obtained a clear improvement rate in the weighted accuracy metrics over the state-of-the-art algorithms.

Synergistic Effects of Graphene Oxide and Pesticides on Fall Armyworm, Spodoptera frugiperda.

Fall armyworm Spodoptera frugiperda, a native insect pest in tropical and subtropical America, has rapidly spread to most parts of China and become a major pest of corn and other crops since invading in early January 2019. As an emergency and important control measure, chemical control of S. frugiperda has the advantages of quick effect and low cost. However, long-term and large-scale use of pesticides might pollute the environment and increase pest resistance. By improving the control effect and reducing the dosage of chemical pesticides, graphene oxide (GO) is used synergistically with insecticides to increase control efficacy to achieve low-cost and sustainable management of insect pests as a new type of synergist. In this study, graphene oxide was compounded with insecticides to form nanocomposites. To clarify pest physiological responses, the laboratory toxicity of graphene oxide-insecticide nanocomposites was measured on the larvae of S. frugiperda. The results demonstrated that GO could enhance the activity of four selected pesticides: chlorantraniliprole (Chl), beta cypermethrin (Bet), methoxyhydrazide (Met) and spinetoram (Spi). Compared with pesticides alone, the toxicity of Chl-GO, Bet-GO, Met-GO and Spi-GO mixtures to the third instar larvae of S. frugiperda increased by 1.56, 1.54, 2.53 and 1.74 times, respectively. The easy preparation and higher bioactivity of GO-pesticide nanocomposites indicated their promising application potential in pest control.

A Coordination Optimization Method for Load Shedding Considering Distribution Network Reconfiguration

Load shedding control is an emergency control measure to maintain the frequency stability of the power system. Most of the existing load shedding methods use the extensive form of directly cutting off the outlet of the substation, featuring low control accuracy and high control cost. A network reconfiguration technique can adjust the topology of the distribution network and offers more optimization space for load shedding control. Therefore, this paper proposes a reconfiguration–load shedding coordination optimization scheme to reduce the power loss caused by load shedding control. In the proposed method, a load shedding mathematical optimization model based on distribution network reconfiguration is first established. The tie switches and segment switches in the distribution network are used to perform the reconfiguration of the distribution network, and the load switches are adopted to realize the load shedding. To improve the solving efficiency of the model, a solving strategy that combined a minimum spanning tree algorithm with an improved genetic algorithm is trailed to address the nonlinear and nonconvex terms. The application of the proposed method and model are finally verified via the IEEE 33 bus system, and the advantages in reducing the loss cost and the number of outage users are accordingly proven.

Clinical Characteristics and Risk Factors for Pulmonary Infection in Emergency ICU Patients.

Background Pulmonary infection in the emergency ICUs increases patient morbidity, hospital stay, treatment costs, and the risk of related adverse events. Methods This study included 695 patients admitted to our emergency ICU between December 2019 and March 2021. Medical records of emergency ICU patients were reviewed to collect their clinical data, including antibiotic use, history of tracheostomy, history of mechanical ventilation, presence or absence of underlying disease, history of smoking, alcohol consumption, age, gender, and history of shock. Bacterial cultures were performed. The incidence, main clinical features, main pathogens, and risk factors of pulmonary infection in emergency ICU were analyzed. Results In this study, 69 of the 695 emergency ICU patients (9.93%) developed pulmonary infection. The main clinical features of patients with pulmonary infection included cough and expectoration (97.10%), shortness of breath and chest tightness (95.65%), leukocyte elevation (69.57%), confusion (31.88%), drowsiness (28.99%), persistent fever (27.54%), and nausea and vomiting (10.14%). The main pathogenic bacteria in those with pulmonary infection included Klebsiella pneumoniae (62.32%), Pseudomonas aeruginosa (49.28%), Streptococcus pneumoniae (21.74%), Staphylococcus aureus (39.13%), Candida albicans (7.25%), Pneumococcus pneumoniae (15.95%), Pseudomonas aeruginosa (24.64%), and lung diplococcus inflammatory (13.04%). Univariate analysis showed that there were no significant differences in the occurrence of pulmonary infection with regard to sex, smoking, and alcohol consumption, but there were significant differences with regard to age, basic disease, invasive surgery, and shock. Logistic regression analysis confirmed that age ≥ 80 years, invasive surgery, shock, and basic diseases ≥ 2 were important risk factors for pulmonary infection in emergency ICU patients. Conclusion Considering the clinical features and risk factors for pulmonary infection in the emergency ICU, preventive and control measures are required to minimize its occurrence and ensure good outcomes.

Real-time power system generator tripping control based on deep reinforcement learning

In case of faults or severe disturbances, the power system will enter an emergency operation state. After the system instability is detected, oscillation and blackout will occur in the system if effective control measures are not taken in time. Generator tripping control (GTC) is the most effective emergency control measure. In view of the mismatch between the traditional GTC algorithm and the transient stability assessment method based on machine learning, a new real-time GTC method is needed. In this paper, a three-part control framework is designed for the GTC problem. The control agent is endowed with decision-making ability by interacting with the simulation environment in the offline pre-learning part. Then the trained agent is transplanted to the online application which can help system operators make decisions. Meanwhile, the agent is updated with real data to be better adapted to the actual system in the online learning part. A deep reinforcement learning algorithm, deep deterministic policy gradient (DDPG) is employed to train the control agent in this framework. A modified DDPG algorithm and the corresponding reward function are designed for the GTC problem. Convolution neural network (CNN) is added to the DDPG network, by which the training time of the agent is shortened and the generalization ability of the algorithm is improved. Trained with simulation data and real system experience, the control agent can determine control strategies timely according to the system operating conditions. Simulation results on the IEEE-39 bus system and the realistic regional power system of Eastern China show the effectiveness, generalizability, and timeliness of the decision algorithm.

Identification of sources with abnormal radon exhalation rates based on radon concentrations in underground environments

When there is poor ventilation or an irregular radon exhalation rate in an underground environment, it is necessary to judge whether the radon concentration is abnormal. To protect personal safety and health from radon gas, it is necessary to track the location of an abnormal radon source and measure its release rate to formulate emergency control and eradication measures. However, in an underground environment, it is impossible to fully monitor the radon concentration at every location, and as a result, blind spots are present, making it difficult to obtain timely early warnings in the event of an abnormal radon exhalation rate. Based on the distribution of radon concentration in an underground environment, this research establishes a theoretical mathematical model of an underground ventilation network containing radon. We combined particle swarm optimization with the long short-term memory (PSO-LSTM) method, which uses part of a time series signal of monitored radon concentrations to track the location of an abnormal radon source and determine an abnormal radon exhalation rate. Performing experiments of theoretical examples and actual underground ventilation environment examples, we prove the necessity of optimizing the monitoring position of the angle-connected ventilation network. The results show that the PSO-LSTM model based on radon concentration monitoring can process time series signals. Its accuracy and decision coefficient greater that is than 0.9 indicate the reliability of the model and method.

Optimization Method for Multiple Measures to Mitigate Line Overloads in Power Systems

Line overload is one of the important causal factors of cascading failures and blackouts in power systems. An optimization method for protection and control measures to mitigate line overloads is proposed in this study. The method consists of two main parts, i.e., the modeling process and the solving process. In the modeling process, an optimization model including overload protection and emergency control measures is developed using PFT (Power Flow Tracing). In the solving process, a multi-stage optimization method using IBSO (Improved Brain Storm Optimization algorithm) is proposed to obtain the final result. The aim of this study is to form a coordinated protection and control strategy that reduces the power on the overloaded line within the safety limits and minimizes the load loss of the power system. The simulation results show the effectiveness of the proposed method.

A Gateway to Rapid Prediction of Water Quality: A Case Study in China’s South-to-North Water Diversion Project

Water quality assurance is the primary factor for the successful operation of water diversion projects across river basins. The rapid prediction of water pollution is the basis for timely and effective emergency control and disposal measures. In China, since the open channels intersect with numerous waterways and traffic arteries, water transfer projects are prone to sudden water pollution accidents. In this paper, the rapid prediction method was developed for sudden water pollution accidents that possibly occurred in the East Route of the South-to-North Water Diversion Project (ERP) in Shandong Province. With the empirical formula of the pollution transfer law, a rapid prediction model of water quality (WQRP) was established based on the simulation of the typical accidents in the main channel. Finally, four typical accidents were selected as application examples, and the prediction results were compared with the results from a computer numerical simulation to demonstrate the validity of the model. The results showed that the prediction results by the WQRP model meet the accuracy requirements. This method is of great significance for providing water transport security in the extreme conditions of long-distance water transfer projects.

Impact of the the COVID-19 pandemic on a queen conch (Aliger gigas) fishery in The Bahamas.

The onset of the coronavirus (COVID-19) pandemic in early 2020 led to a dramatic rise in unemployment and fears about food-security throughout the Caribbean region. Subsistence fisheries were one of the few activities permitted during emergency lockdown in The Bahamas, leading many to turn to the sea for food. Detailed monitoring of a small-scale subsistence fishery for queen conch was undertaken during the implementation of coronavirus emergency control measures over a period of twelve weeks. Weekly landings data showed a surge in fishing during the first three weeks where landings were 3.4 times higher than subsequent weeks. Overall 90% of the catch was below the minimum legal-size threshold and individual yield declined by 22% during the lockdown period. This study highlights the role of small-scale fisheries as a ‘natural insurance’ against socio-economic shocks and a source of resilience for small island communities at times of crisis. It also underscores the risks to food security and long-term sustainability of fishery stocks posed by overexploitation of natural resources.

Implications for emergency response to the severe odor incident occurred in source water: Potential odorants and control strategy.

Sudden odor incidents occurring in the source water have been a severe problem for water suppliers. In order to apply emergency control measures effectively, it is necessary to identify the target compounds responsible for odor incidents rapidly. The present work identified the odorants and explored emergency disposal mechanisms for sudden and severe odors in the QT River's drinking water source (HZ city, China). Medicinal, chemical, septic, and musty odors with strong intensities were detected in the source water. The effect of conventional treatments of drinking water treatment plant (DWTP) on odors' removal was limited, which was evident by the presence of medicinal, chemical, and musty odors with moderate intensities in the effluent of DWTP. Total seventeen odorants were identified successfully in the source water of QT River and the effluent of DWTP. The measured OAVs and reconstituting the identified odorants explained 87, 87, 89, and 94% of medicinal, chemical, septic, and musty odors, respectively, in the source water of the QT River and 90, 87, and 88% of medicinal, chemical, and musty odors in the effluent. Styrene, phenol, 2-chlorophenol, 2-tert-butylphenol, and 2-methylphenol were associated with the medicinal odor, while propyl sulfide, diethyl disulfide, propyl disulfide, and indole were related to the septic odor. Geosmin and 2-methylisoborneol (2-MIB) were responsible for the musty odor, and cyclohexanone, 1,4-dichlorobenzene, and nitrobenzene were involved with the chemical odor. Based on the characteristics of identified odorants, powdered activated carbon (PAC) was applied to control the odors in the QT River. The results indicated that the medicinal, chemical, septic, and musty odors could be removed entirely after adding 15 mg/L PAC, which effectively controlled emergency odor problems. Overall, the study would offer a scientific basis and operational reference for emergency odor management and control in DWTP with similar complicated odor incidents.