Early Warning Technology Research Articles

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

Development of a portable coal rock charge monitoring instrument and its application for rockburst control

Effective monitoring techniques and equipment are essential for the prevention and control of coal and rock dynamic disasters such as rockburst. Based on the fact that there is charge generation during deformation and rupture of coal rock body and the charge signals contain a large amount of information about the mechanical process of deformation and rupture of coal rock, the rockburst charge sensing monitoring technology has been formed. In order to improve the charge sensing technology for monitoring and early warning of rockburst disasters, this paper develops a new generation of portable coal rock charge monitoring instrument on the basis of the original instrument and carries out laboratory and underground field application. The primary advancement involves enhancing the external structure of the sensor and increasing the charge sensing area, which can more comprehensively capture the charge signals from the loaded rupture of the coal rock body. The overall structure of the data acquisition instrument has been improved, the monitoring channels have been increased, and the function of displaying the monitoring data curve has been added, so that the coal and rock body force status can be grasped in time. The results of the experimental study show that the abnormal charge signals can be monitored during the rupture process of rock samples under loading, and the monitored charge signals are in good agreement with the sudden change of stress in the rock samples and the formation of crack extension. There is a precursor charge signal before the stress mutation, and the larger the loading rate is, the earlier the precursor charge signal appears. The charge monitoring instrument can monitor the charge signal of the coal seam roadway under strong mining pressure. In the zone of elevated overburden pressure, the amount of induced charge is large, and anomalously high value charge signals can be monitored when a coal shot occurs. The change trend of the charge at different measuring points of strike and inclination has a good consistency with the distribution of overrunning support pressure and lateral support pressure, which can reflect the stress distribution and the degree of stress concentration of the coal body through the size and location of the charge, foster early warning and analysis of rockburst, and provide target guidance for the prevention and control of rockburst.

A Real-Time Inverted Velocity Model for Fault Detection in Deep-Buried Hard Rock Tunnels Based on a Microseismic Monitoring System

Microseismic monitoring is an effective and widely used technology for dynamic fault disaster early warning and prevention in deep-buried hard rock tunnels. However, the insufficient understanding of the distribution of native faults poses a major challenge to yielding precise early warnings of disasters using an MS (Microseismic Monitoring System). Velocity field inversion is a reliable means to reflect fault information, and there is an urgent need to establish a real-time velocity field inversion method during tunnel excavation. In this paper, a method based on an MS is proposed to achieve the inversion of the velocity field in the monitoring area using microseismic event and excavation blasting data. The velocity field inversion method integrates the reflected wave ray-tracing method based on PSO (Particle Swarm Optimization) theory and FWI (Full-Waveform Inversion) theory. The accuracy of the proposed velocity inversion method was verified by various classic numerical simulation cases. In numerical simulations, the robustness of our method is evident in its ability to identify anomalous structural surfaces and velocity discontinuities ahead of the tunnel face.

Research on Flood Disaster Monitoring and Early Warning Technology Based on UAV Remote Sensing and AI Algorithm

This paper, based on UAV far-flung sensing science and synthetic brain algorithm, studies the monitoring and early warning technological know-how of flood catastrophes. Firstly, the far-flung sensing monitoring science of flood catastrophes is summarized. Then, blended with geographic records machine (GIS) technology, the UAV far-flung sensing science software in flood catastrophe monitoring is analyzed. Finally, combining the micro-UAV technological know-how and AI technology, the key applied sciences of the micro-UAV flood catastrophe monitoring and early warning device primarily based on AI imaginative and prescient are studied, along with the lookup of the micro-UAV key gadget and the lookup of AI imaginative and prescient key technology. The key device lookup of micro-UAV is the definition of mindset. Mindset A mindset lookup on key applied sciences of AI, imaginative and prescient broadly speaking, expounds the precept of CNN and introduces Faster RCNN. It lays the groundwork for the later lookup on flood catastrophe monitoring and early warning technological know-how primarily based on UAV far-off sensing and AI algorithms.

Innovative landslide disaster monitoring: unmanned aerial vehicle-deployed GNSS technology

Real-time monitoring technologies of surface deformation represented by the global navigation satellite system (GNSS) are essential for landslides early warning. Monitoring methods such as GNSS require on-site manual installation. Therefore, it becomes nearly impossible to deploy surface monitoring equipment when landslides are located in high mountain valleys that are challenging for personnel to access, and/or in hazardous situations. We propose an intelligent real-time monitoring and early-warning technology that employs an unmanned aerial vehicle (UAV) as a carrier to deploy GNSS equipment. A system was initially designed with five components: an adaptive sampling GNSS receiver, an intelligent cooperative network transmission module, UAV-dropped GNSS equipment, special delivery UAV, and an intelligent monitoring and early warning cloud platform. This new technology was applied to a real landslide in Gansu Province, China, in 2020. The mean absolute error of 30 days in the east, north, and upward directions were 1.2, 1.3, and 2.9 mm/d, respectively, comparing the deformation velocity between the traditional monitoring station and the UAV-dropped monitoring station. This observation facilitated the successful prediction of a landslide hazard on 27 January 2021, allowing for a timely alert to be issued. Collectively, the results of this study established that UAV-deployed GNSS technology can accomplish unmanned deployment of GNSS equipment for landslide monitoring to ensure early warning issuance in inaccessible and/or high-risk areas.

Highly sensitive standardized toxicity biosensors for rapid water quality warning

Microbial electrochemical sensor (MES) using electroactive biofilm (EAB) as the sensing element represents a broad-spectrum technology for early warning of biotoxicity of water samples. However, its commercial application is impeded by limited sensitivity and repeatability. Here, we proposed a layered standardized EAB (SEAB) with enriched Geobacter anodireducens SD-1 in the inner layer and self-matched outer layer. The SEAB sensors showed a 2.3 times higher sensitivity than conventional EAB acclimated directly from wastewater (WEAB). A highly repeatable response sensitivity was concentrated at 0.011 ± 0.0006 A/m2/ppm in 4 replicated batches of SEAB sensors (R2 > 0.95), highlighting their potential for reliable toxicity monitoring in practical applications. In contrast, the sensing performance of all WEAB sensors was unpredictable. SEAB also exhibited a better tolerance towards low concentration of formaldehyde, with only a 4 % loss in viability. Our findings improved the sensitivity and reproducibility of standardized MES for toxicity early warning.

The Ojai California Earthquake of 20 August 2023: Earthquake Early Warning Performance and Alert Recipient Response in the Mw 5.1 Event

Abstract A magnitude 5.1 earthquake in California rarely generates more than momentary notice—a headline in local newspapers and a mention with footage on the evening news—then fades into obscurity for most people. But this earthquake, which occurred near the city of Ojai, is important for seismologists, social scientists, emergency managers, policymakers, and others who are engaged in implementing and improving earthquake early warning (EEW) technology and in assessing its value in public warnings. In this earthquake, ShakeAlert, the EEW system for the West Coast of the United States operated by the U.S. Geological Survey (USGS), was publicly activated and, for the first time, a substantial number of those who received alerts provided feedback on various aspects of the alerts they received. To capture data related to public attitudes and assessments regarding this and future alerts, a supplemental questionnaire was developed and associated with the “Did You Feel It?” (DYFI) earthquake reporting system, also operated by the USGS. The DYFI system received over 14,000 felt reports; 2490 of these were by people who received or expected to receive an alert before the onset of earthquake motion at their locations. This article analyzes the aggregate results of these EEW user reports, touching on the respondent’s situation upon receiving the alert, characteristics of the alert received, and, perhaps, most importantly, how the alert recipient responded if received before feeling earthquake motion. The new DYFI EEW supplemental questionnaire also inquired about respondent views of alert usefulness and preferences in future alerts. Our report provides a first glimpse of a range of behaviors, attitudes, and assessments by users of the recently implemented EEW system for the U.S. West Coast.

Research on Early Warning Technology for Highway Geological Disasters Induced by Precipitation Conditions in Changbai Mountains

Research on Early Warning Technology for Highway Geological Disasters Induced by Precipitation Conditions in Changbai Mountains

Early warning for thermal runaway in lithium-ion batteries during various charging rates: Insights from expansion force analysis

Thermal runaway introduces a significant challenge in the widespread application of lithium-ion batteries, necessitating advanced early-warning technologies to ensure safety, particularly during charging. Only monitoring the temperature and voltage limit the performance of diagnostic algorithms. The expansion behavior of batteries, which is linked to their operating status, offers a vital indicator of safety. In this study, the evolution of multidimensional signals during overcharging experiments at different current rates is comprehensively investigated. The result shows that the abnormal expansion force can be detected at temperatures as low as 35.4 °C, which achieves an early warning signal 11 min earlier than the onset of battery thermal runaway. The effect of charging rate on battery safety is comprehensively analyzed, showing that the time interval between the warning signal of the expansion force and temperature increases steadily from 151 s to 682 s as the charging rate decreases. However, the charging rate hardly affects the stage of charge boundary of venting, which is around ±118 %. These insights are crucial for understanding early warning mechanisms in overcharged batteries, offering valuable guidance for enhancing the safety of electric vehicles and energy storage systems.

Failure precursors recognition method for loading coal and rock using the fracture texture features of infrared thermal images

Early warning of the catastrophic failure of rocks is a challenging rock mechanics problem. This article proposed a failure precursor recognition method based on infrared thermal image texture features for coal and rock. Based on spatiotemporal background noise correction for infrared thermal images, a new thermal image parameter of loading coal and rock, Contrast Texture Feature Values (CTFV), is proposed to extract subtle changes in the thermal image caused by crack evolution based on the Gray Level Co-occurrence Matrix (GLCM). The cumulative Crack Texture Thermal Image (CTTI) is reconstructed using CTFV, which can accurately reflect the spatial evolution process of loading coal and rock cracks. The CTFV remains at 0 in the early stage of loading and gradually increases with stress increase at the unstable crack propagation stage, which can serve as a reference for precursor warning of coal and rock failure. For shale, sandstone, and limestone, the precursor of CTFV at grayscale levels of 7, 8, and 9, can be classified into high failure risk, medium failure risk, and low failure risk, respectively. For coal samples, the CTFV is only applicable for the critical warning of high failure risk when the grayscale level is 7. Then, an adaptive identification method for failure precursors based on the sliding window probability density estimation method is proposed. The research results can enhance the reliability of IR monitoring technology for rock failure and instability early warning and can provide support for the prevention and control of rock engineering and geological disasters.

Research on Early Warning Technology for High-Grade Highway Pavement Damage

Road surface damage on high-grade highways has a serious impact on driving safety. Conventional road surface damage detection mainly adopts manual detection technology. The detection efficiency is low, and the detection results are easily disturbed by human factors, which is not conducive to objective and accurate evaluation of road damage. To solve the problems of accurate collection, efficient identification, and correct positioning of high-grade road surface damage data, the research group has studied the early warning technology of road surface damage. The proposed early warning technology scheme enables drivers to take preventive driving action in a timely manner, avoids the occurrence of traffic accidents, and provides drivers with reasonable avoidance schemes to effectively prevent the occurrence of traffic accidents.

Early warning technology for common characteristic resistances of lithium-ion batteries with thermal runaway.

A technology for early warning (of over 25 minutes) of thermal runaway in lithium-ion batteries based on common characteristic resistances supported by thermodynamic calculations was derived. With great potential in practical application for avoiding property losses and human casualties, this technology was proven to be efficient and accurate.

Assessing network-based earthquake early warning systems in low-seismicity areas

Earthquake early warning (EEW) technology, designed to alert the public of earthquake risks after initial P-wave detection but before the onset of strong tremors, has developed rapidly. Methodologies from various fields are combined in EEW systems to estimate earthquake locations, magnitudes, and expected intensities based on the initial P-wave data. These systems operate automatically because prompt responses are required. However, as no common evaluation framework for EEW system verification exists, potentially divergent evaluations from reviewers or countries could ensue. Moreover, evaluating EEW systems is more complicated when the target area does not experience frequent earthquakes. We aimed to establish a guidance review process for low-seismicity areas to ensure reliable and stable integrated EEW system operation. We incorporated management aspects through actual system operator surveillance and designed an EEW assessment process based on feedback from our surveys. Using this approach, we created a comprehensive and well-informed evaluation process that considers the diverse perspectives of experts involved in EEWs. Our proposed assessment method allows for a uniform and consistent evaluation process, regardless of changes in the methods or technologies used by EEW systems. The method aims to guide EEW system assessments in low-seismicity areas.

Membrane flux response technology for early warning of initial surface scaling in membrane distillation

Membrane distillation (MD) is a promising green separation technology for the treatment of high salinity wastewater attributed to excellent salt rejection, which is significant in relevant environmental and chemical engineering application. However, membrane scaling is an inevitable issue during long-term membrane distillation process. Thus, we developed a membrane flux response technology with data processing method to in-situ monitor the membrane scaling according to the reduction of permeate flux. By real-time analyzing the derivative of deviation between theoretical and experimental fluxes, early warning of initial membrane surface scaling was observed at initial nucleation stage. Besides, such timely detection was also achieved under different temperatures and flow velocities of feed solution, even for the poor light transmittance aqueous solution (such as NiSO4 and CoSO4 solution), which exhibited wide application potential on diverse high salinity wastewater systems. In addition, the metastable zone width of salt solution within MD system was obtained for the efficient operation design. To sum up, membrane flux response technology provides an early warning at imminent membrane scaling, which will maintain the great separation effect for a fresh feed solution after a facile cleaning by pure water, indicating the extension of device lifetime.

Research and Practice of Risk Early Warning Technology for Lost Circulation with Drilling under the Conditions of Geological Engineering Information Fusion: The Example of the Yuanba Area

In the Yuanba area, many lost circulation accidents occur due to the complex geological structure, broken strata, and developed fractures, which seriously affect construction safety and drilling costs. Therefore, the Yuanba district is the focus of this paper. Firstly, a three-dimensional geomechanical model is established through multi-source information fusion to identify the main geological risk factors of lost circulation, according to the data on drilling and well logging in the operation area. Then a risk warning model and evaluation methods for loss circulation, based on geological engineering data fusion, are established, through which the characteristic parameters of various loss of circulation risks can be calculated in real time, parameters along the well trajectory can be extracted and compared, and thus, the risk of lost circulation is warned and analyzed in real time. The early warning of lost circulation risk can be achieved during drilling operations, which can improve the drilling efficiency and shorten the drilling cycle as a result.

Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning

Coalbed methane (CBM) disasters are a major safety problem in coal mining. CBM concentration prediction and early-warning technology play a vital role in the prevention and control of CBM disasters. Traditional prediction methods have some shortcomings such as unreasonable data analysis, inability to predict in real time, long prediction time, and overfitting. This study uses a large amount of coal mine CBM data and develops a fast and high-precision CBM concentration prediction method based on deep learning theory, which can be used for CBM disaster early-warning. The proposed method has the following advantages: (1) it combines three exponential smoothing methods, the autoregressive model, wavelet domain denoising method, and principal component analysis. This method optimizes data with outliers, missing values, and noise. (2) Particle swarm optimization and genetic algorithm are used to optimize the network parameters of the gated recurrent unit. The application and verification of the optimized model show that the running time and accuracy are significantly improved. (3) By combining the optimized prediction model with Spark streaming, an early-warning system is developed, which can complete the efficient early-warning of CBM concentration within 8 s. The proposed method provides decision-making support for mine safety and CBM disaster prevention and control.

Earthquake Event Recognition on Smartphones Based on Neural Network Models.

Using sensors embedded in smartphones to study earthquake early warning (EEW) technology can effectively reduce the high construction and maintenance costs of traditional EEW systems. However, due to the impact of human activities, it is very difficult to accurately detect seismic events recorded on mobile phones. In this paper, to improve the detection accuracy of earthquakes on mobile phones, we investigated the suitability of different types of neural network models in seismic event detection. Firstly, we collected three-component acceleration records corresponding to human activities in various scenarios such as walking, running, and cycling through our self-developed mobile application. Combined with traditional strong-motion seismic event records fusing typical mobile phone accelerometer self-noise, all records were used for establishing the training and testing dataset. Finally, two types of neural network models, fully connected and convolutional neural networks, were trained, validated, and tested. The results showed that the accuracy rates of the neural network models were all over 98%, and the precision rate for seismic events and the recall rate for non-earthquake events could both reach 99%, indicating that the introduction of neural networks into the earthquake recognition on smartphones can significantly enhance the accuracy of seismic event recognition. Therefore, we can exceedingly reduce the amount of data transmitted to the processing server, further lowering the load on the server processor and effectively increasing the lead time at each target site for an EEW system.

A Detection Method for Individual Infected Pine Trees with Pine Wilt Disease Based on Deep Learning

Pine wilt disease (PWD) can cause destructive death in many species of pine trees within a short period. The recognition of infected pine trees in unmanned aerial vehicle (UAV) forest images is a key technology for automatic monitoring and early warning of pests. This paper collected UAV visible and multispectral images of Korean pines (Pinus koraiensis) and Chinese pines (P. tabulaeformis) infected by PWD and divided the PWD infection into early, middle, and late stages. With the open-source annotation tool, LabelImg, we labeled the category of infected pine trees at each stage. After coordinate-correction preprocessing of the ground truth, the Korean pine and Chinese pine datasets were established. As a means of detecting infected pine trees of PWD and determining different infection stages, a multi-band image-fusion infected pine tree detector (MFTD) based on deep learning was proposed. Firstly, the Halfway Fusion mode was adopted to fuse the network based on four YOLOv5 variants. Simultaneously, the Backbone network was initially designed as a dual branching network that includes visible and multispectral subnets. Moreover, the features of visible and multispectral images were extracted. To fully utilize the features of visible and multispectral images, a multi-band feature fusion transformer (MFFT) with a multi-head attention mechanism and a feed-forward network was constructed to enhance the information correlation between visible and multispectral feature maps. Finally, following the MFFT module, the two feature maps were fused and input into Neck and Head to predict the categories and positions of infected pine trees. The best-performing MFTD model achieved the highest detection accuracy with mean average precision values (mAP@50) of 88.5% and 86.8% on Korean pine and Chinese pine datasets, respectively, which improved by 8.6% and 10.8% compared to the original YOLOv5 models trained only with visible images. In addition, the average precision values (AP@50) are 87.2%, 93.5%, and 84.8% for early, middle, and late stages on the KP dataset and 81.2%, 92.9%, and 86.2% on the CP dataset. Furthermore, the largest improvement is observed in the early stage with 14.3% and 11.6%, respectively. The results show that MFTD can accurately detect the infected pine trees, especially those at the early stage, and improve the early warning ability of PWD.

Flood Risk Forecasting and Early Warning Technology for Medium and Small Rivers in the Yellow River Basin Induced by Heavy Rain

The Yellow River Basin is one of the important sand-producing and sediment-transporting areas in China, and one of the three most important sand-producing areas in the world. The amount of sand and dust days in the “Three Norths” (Dongbei, Xibei, and Huabei) area has increased, and regional sand and dust storms have occurred frequently. There are generally more serious hidden danger points of debris flow geological disasters in small and medium-sized river basins. The technical achievements of flood risk forecasting and early warning for medium and small rivers in the Yellow River Basin based on rainstorm-induced floods are important technical supports for flood forecasting and early warning for medium and small rivers. Based on this, a case study was carried out on the problems such as the weak forecasting and early warning ability of flood disasters induced by heavy rain and the low accuracy of flood disaster loss assessment in the flood disasters of medium and small rivers, for the reference of relevant personnel.

Voiceprint Feature Extraction of Large Power Transformers in Uninterrupted Diagnosis and Early Warning Technology

Condition-based maintenance is to select the most suitable maintenance method according to the real-time operating state of the converter. Transformer condition evaluation and fault diagnosis is an important basis for condition maintenance, and it is also the key to ensure the normal implementation of condition maintenance. The main purpose of this paper is to study the application of voiceprint feature extraction in large-scale power transformers in uninterruptible power failure diagnosis and early warning technology. In this paper, a comprehensive and in-depth study of the state quantity of transformer uninterrupted detection is carried out. Experiments show that when CR<0.10, the matrix is satisfactory and consistent, and the weight distribution is reasonable.