Emergency Shutdown Research Articles

Damage Detection and Localization at the Jacket Support of an Offshore Wind Turbine Using Transformer Models

Early detection of damage in the support structure (submerged part) of an offshore wind turbine is crucial as it can help to prevent emergency shutdowns and extend the lifespan of the turbine. To this end, a promising proof-of-concept is stated, based on a transformer network, for the detection and localization of damage at the jacket-type support of an offshore wind turbine. To the best of the authors’ knowledge, this is the first time transformer-based models have been used for offshore wind turbine structural health monitoring. The proposed strategy employs a transformer-based framework for learning multivariate time series representation. The framework is based on the transformer architecture, which is a neural network architecture that has been shown to be highly effective for natural language processing tasks. A down-scaled laboratory model of an offshore wind turbine that simulates the different regions of operation of the wind turbine is employed to develop and validate the proposed methodology. The vibration signals collected from 8 accelerometers are used to analyze the dynamic behavior of the structure. The results obtained show a significant improvement compared to other approaches previously proposed in the literature. In particular, the stated methodology achieves an accuracy of 99.96% with an average training time of only 6.13 minutes due to the high parallelizability of the transformer network. In fact, as it is computationally highly efficient, it has the potential to be a useful tool for implementation in real-time monitoring systems.

Accelerated Limitation of Frequency Reduction and Increase in Frequency in Small Power Systems

The article is devoted to the issues of preventing shutdowns of generating equipment, in particular, gas piston units (GPU) in low-power power systems (LPPS) during active power gains/resets. A review of management and decision-making methods for preventing emergency shutdowns of power units at a power plant with its subsequent repayment, large frequency deviations in low-power power systems is carried out. The simulation of the automatic speed controller of the LPPS station is performed and its effect on frequency control in case of an imbalance of active power is considered. The requirements are substantiated and the algorithm of a special queue (express) of automatic frequency unloading (SQ AFU) is proposed.

Predictive models of the dependability of electric power transmission (using the example of AO Tyvaenergo)

Aim. The paper analysed the damage rate of the electric power networks of AO Tyvaenergosbyt between 2014 and 2022 and preventively evaluates emergency shutdowns in the company’s networks in 2023. The author provides a brief historical reference regarding the company’s foundation, a description of the structure of its power supply network. The damage rate of elements, as well as main electrical equipment of the company’s power supply network were examined. The quantity of electrical power undersupplied to customers as the result of emergency shutdowns was analysed. The number of failures was classified depending on the amount of undersupplied electrical power. The paper studied the applicability of certain methods of prediction of the number of emergency shutdowns for the obtained interpolated series of preceding failures. The most appropriate method was established that was used for preventively evaluating the damage rate of the examined power networks for each month of 2023.Methods. The paper uses methods of mathematical analysis, general scientific methods of research, properties and capabilities of the MATLAB graphic interface and Excel tables. Statistical and cybernetical methods of prediction were considered for the purpose of predictive model development. The probability of failures within the examined power networks was evaluated. Equations were obtained for autoregression failure models that enable predictive evaluation of failure by months.The Results of the study may prove to be useful to the experts of Tyvaenergosbyt, as well as other power grid companies that are developing advanced emergency prevention activities aimed at improving the reliability of power supply.Conclusion. The approach to predictive evaluation of emergency shutdowns proposed in the paper showed that the most efficient method of predicting failures in a company’s power networks is the method of autoregression models using interpolation of data on preceding failures. The probability of prediction fulfilment is 95%.

Optimization of safety instrumented system performance and maintenance costs in Algerian oil and gas facilities

Gas processing industry is associated with high risks which have the potential to cause catastrophic accidents. Safety Instrumented Systems (SISs) are considered the most effective safety barriers in this sector aiming to prevent undesired events and mitigate their consequences. However, several factors can affect their performance including maintenance strategy in place. In this paper, a Stochastic Petri Net (SPN) model is proposed for evaluating maintenance strategies related to SIS. The model is applied to analyze the performance of an Emergency Shutdown System (ESD) in a Flared Gases Recovery Unit located at south Algerian field. Furthermore, the paper investigates the financial impact of proof tests, including direct costs (such as manpower, equipment, and transportation) and indirect costs (such as production losses and gas flaring tax). These costs can be effectively managed and reduced by optimizing proof test intervals and scheduling tests during planned plant shutdowns. The results demonstrate that the proposed Stochastic Petri Net (SPN) model successfully analyzes the impact of imperfect full and partial proof tests on PFD average values of Safety Instrumented Functions (SIFs). In addition, it has been shown through reliability analysis that the proposed model is able to minimize spare parts expenses leading to significant cost savings while maintaining the required safety integrity levels (SILs), about 60% benefit achieved within two years compared to the actual procurement process over same period.

Emergency Shutdown Valve damage classification by machine learning using synthetic data

Emergency Shutdown Valves (EDSVs) are used in industrial applications to interrupt internal fluid flow in pipelines during hazardous events. During their operation, these valves can suffer cumulative damage on their reinforced polytetrafluoroethylene (RPTFE) seats that can put their operation and effectiveness at risk. One of the options for detecting the occurrence of damage is to analyse data acquired from fluid pressure and torque sensors during the closing and opening cycles of the valve. The resulting operational signatures are commonly evaluated through so-called Transition Points (TPs) that are manually marked in a subjective manner by a trained operator. In addition to being slow and laborious, this approach discards most of the acquired information. Alternatives to this method would be the use of Damage Indexes (DIs) that are extracted from the signatures, or even the evaluation of the complete pressure or torque signature. These methods, when associated with machine learning (ML) algorithms, could use the acquired information more efficiently and reliably, and would have the potential to completely automate the monitoring process. In this work, these three processing options were tested and compared using real and synthetic data that were generated with the Monte Carlo (MC) method. The results indicate that the evaluation of the complete signature with a Gradient Boosting Classifier (GBC) algorithm would be the most effective strategy, which correctly detects the damage in 99% of the cases considered.

Research on Neutronics Safety Parameters of the AP1000 Nuclear Reactor under Different Conditions

Changes in temperature during reactor operation may cause changes in physical parameters, leading to core overheating and accidents. It is essential to analyze and assess the safety parameters of the core under different operating conditions. This paper investigates the effects of fuel temperature, moderator density, boron concentration, and control rod state on AP1000 safety parameters. The study uses RMC and NJOY to calculate the changes in reactivity factor, effective delayed neutron fraction, and neutron generation time of the AP1000 reactor under different operating conditions. The changes in reactivity coefficients, neutron fluxes, and relative power densities of AP1000 reactors are analyzed for normal and accidental operating conditions. The results indicated that the reactivity coefficient remained negative under accident conditions, which ensured the safe operation of the reactor. The delayed neutron fraction, neutron flux, and power density distributions are affected by fuel temperature, moderator density, and control rod position. The control rod worth was sufficient for the emergency shutdown of the reactor under accidental conditions. It is demonstrated that the operation of the AP1000 reactor under study conditions is safe and controllable.

Prediction of LOCA’s break size and location based on random forest and Multi Tasking Deep Neural Network

This work presents an approach for predicting break size and location in nuclear power plants (NPP) loss of coolant accidents (LOCA) with or without emergency shutdown of a nuclear reactor (SCRAM). To accomplish that, a multi-tasking deep neural network (MTDNN) model is proposed. The training and validation data sets were generated on a PWR simulator at the Human-Systems Interface Laboratory (LABIHS) at Instituto de Engenharia Nuclear (IEN), Brazil. For each simulation, the time behaviors of more than a hundred state variables were recorded and a random forest (RF) algorithm was applied to select the most relevant variables. Using the set of 15 variables selected, the MTDNN model was trained and applied to a test set, in which accuracy of 97.26 % for location and relative error of about 3.77 were obtained for break sizes between 0 and 1200 cm2.

ON THE ISSUE OF ENSURING THE MICROCLIMATE CONDITIONS AND OPERATIONAL SAFETY OF HEAT SUPPLY SYSTEMS DURING EMERGENCY HEAT SUPPLY SHUTDOWNS

The issue of ensuring the microclimate conditions of the premises in the buildings and structures during cold season are of exceptional importance, as it is the basis for ensuring the safety of human life and health. Emergencies and significant violations of the thermal regime in premises and buildings worsen microclimate conditions for an indefinite period of time, until its parameters reach extreme values dangerous for human life. These issues are particularly relevant in the context of the growing number of emergency situations in heat supply systems in Ukraine, both due to the high level of wear and tear of their infrastructure in general, and wartime emergencies. Therefore, conducting research to ensure the safety of heat supply systems operation during emergency shutdowns by determining the regularities of temperature changes in rooms, reaching the critical temperature and operating time of heat supply systems, taking into acount the spatial location of rooms in buildings and their design features, is an urgent task. The purpose of the article. Study of the risk of reaching critical modes of heat supply systems operation in emergency situations, taking into acount the structural features of buildings and ensuring the necessary microclimate conditions. Conclusion. On the basis of the conducted research, the regularities of the structural features of buildings and structures in emergency situations in heat supply systems to ensure microclimate conditions in the premises have been determined. The limit conditions for achieving critical indoor microclimate conditions due to accidents in heat supply systems are determined, which allows predicting the limit conditions for eliminating the consequences of emergency situations and optimizing the activities of repair and rescue services, which is especially important in connection with the failure of outdated heat supply systems and combat operations on the territory of the country.

Dynamic response of a dual-opposed free-piston Stirling generator

As a type of distributed power generation device, free-piston Stirling generator (FPSG) is characterised by compactness, high efficiency, and long life. This work devotes to exploring the dynamic response of a low-vibration dual-opposed FPSG by employing the newly-developed time-domain acoustic-electrical analogy (TDAEA) method. Transient evolutions of self-sustained oscillations are first exhibited, which contains the start-up and steady stages. An exploration on steady-state performance is then conducted, which shows the maximum thermal-to-electric efficiency of 46.6% with an electric power of 3.92 kW, implying its enormous potential in distributed power generation for low-vibration applications due to its dual-opposed configuration. Dynamic response with jump in external load and gradual variation in heating temperature are subsequently investigated in detail. The results indicate that an acute drop in electric resistance leads to an oscillation attenuation, particularly, a short circuit results in an emergency shutdown. With the heating temperature declining gradually, the oscillation attenuates and even stops. Based on these results, some general operating regulation principles for FPSGs are further concluded to avoid the cylinder striking as well as frequent start and stop. This study gives a deeper understanding on the dynamic characteristics of FPSGs, as well as broadens the usage of the TDAEA method.

Problems of safe operation of Zaporizka NPP in the conditions of war in Ukraine

Among the specified types of terrorist threats, the most dangerous today is the nuclear threat, which has moved from the realm of the probable to a real military-terrorist attack on the Zaporizhzhia NPP and its capture by the occupiers. Therefore, the issue of determining the level of nuclear and environmental safety of Ukraine's nuclear power plants in extreme war conditions, using the example of the situation that developed at the Zaporizhzhia Nuclear Power Plant (ZNPP), the largest in Europe, is extremely urgent, precisely because of the station's location in the war zone.During the period of the full-scale war, there have already been dozens of warheads hitting the objects of the critical infrastructure of the ZNPP, the external and emergency power supply of the ZNPP (transformer equipment, power supply systems to consumers, the premises of diesel generators, the industrial site of the station, and others). There have been corresponding emergency shutdowns of the power units of the ZNPP due to the blackout of the station and due to the forced disconnection of consumers as a result of the destruction of various objects in the power grid systems. These events cause great alarm and concern of the world public regarding the possibility of new global nuclear-ecological disasters due to nuclear-radiation accidents at the ZNPP. Many years of global experience in the operation of nuclear power reactor facilities (NPRF) and their safety regulation, as well as lessons from the largest nuclear and radiation accidents at NPPs, have revealed the insufficient validity of traditional probability indicators and safety criteria for NPPs, including the Zaporizhzhya plant. Probabilistic approaches to assessing the objective level of safety of the Zaporizhzhya NPP in extreme conditions of war are insufficiently substantiated, taking into account, among other things, the need to predict the possibility of the occurrence of unlikely emergency events and their combinations, which will have catastrophic consequences.Therefore, conducting an objective assessment of the level of nuclear and radiation safety at the ZNPP due to the station's location in the war zone, as well as substantiating practical recommendations for preventing nuclear and environmental disasters at the NPPs of Ukraine, is an extremely urgent issue for the entire nuclear energy industry of Ukraine.

A machine-learning method to accurately recognize the leakage pressure-drop signals in trunk natural gas pipelines

A block valve installed along a trunk natural gas pipeline can automatically shut down when it detects a pressure-drop rate that is over a threshold value for a specified duration, indicating that leakage accidents have occurred. However, current methods have difficulties on identifying the differences among the pressure-drop rate signals caused by small-hole leakage, compressor startup, and block valve emergency shut-down conditions. This usually leads to improperly shutting down the block valve. Based on an optimized support vector machine, a machine-learning method is proposed to recognize the pressure-drop rate signals through the eigenvalue of the pressure-drop curve instead of only using the pressure-drop signal and its duration. Firstly, the singular decomposition method and the support vector machine method are applied to initially classify the pressure-drop rate signals of three operating conditions: the gas leakage, compressor startup, and block valve emergency shut-down conditions. Secondly, a teaching-learning-based optimization method is applied to optimize the penalty factor and kernel function parameter in the support vector machine model. Particularly, the tent chaotic maps and adaptive inertia weight methods are applied to improve the teaching-learning-based optimization method to balance its local and global search performance. Thirdly, an improved teaching-learning-based support vector machine method is established. Finally, 960 sets of simulated pressure-drop signals taken from three trunk natural gas pipelines and pressure-drop rate signals collected from an actual pipeline are applied to verify the accuracy of the proposed model. The results show that the improved teaching-learning-based support vector machine model achieved high classification and recognition accuracy. Specifically, it achieved accuracy rates of 99.4%, 100%, 98.3%, and 100% for the pressure-drop rate signals generated by pipelines A, B, C, and Cangzhou branch in the presence of pipeline leakage (the leak hole diameter is from 25 to 125 mm). Additionally, it demonstrated an average recognition accuracy of 97.42% for the pressure-drop rate signals generated by pipelines under other operating conditions. Through cooperating with Supervisory Control And Data Acquisition system, this method provides a more relevant approach to determine the shutdown conditions of a block valve while preventing mistaken actions.

Analysis of the Causes of the Emergency Shutdown of Natural Gas-Fired Water Peak Boilers at the Large Municipal Combined Heat and Power Plant

The paper presents a cause-and-effect analysis of the failure of a 130 MWt gas-fired water boiler. The fault was a rupture of the helically finned tubes in the first rows of the second-stage water heater (ECO2). The high frequency of failures forced the boiler user to investigate their causes. The rapid drop in water flow in the ECO2 and the tightly finned pipes suggested that the permissible operating temperature of the steel used was exceeded. The only possible way to assess the working conditions was through a CFD simulation of the operation of the ECO2. Validated with the data acquisition system, the results show that the main reason for the failure was the overheating of the first rows of finned water heater pipes, regardless of the boiler load. The high heat flux value, exceeding 500,000 W/m2, and the increased flue gas temperature in front of the ECO2, almost reaching 900 °C, affected the appearance of the boiling film, limiting the cooling of the tube wall. Heat radiation and eddies behind the tubes significantly impacted the non-uniform temperature distribution, resulting in high pipe wall stress. By analyzing the service life of the first row of pipes based on the Larson–Miller parameter, it was concluded that the pipes would fail after only a few tens of hours.

Prioritisation of operations during emergency shutdown of a crude distillation unit by interval-valued intuitionistic fuzzy analytic hierarchy process

A very reliable control system for offering a safety layer in emergency scenarios is an emergency shutdown system, or ESD system. It aids in preventing crises from having disastrous effects on the economy, the environment or business operations. In any plant, emergency shutdown systems reduce the risk of harm to the working population, the environment or equipment damage by guarding against leaks, hydrocarbon escapes, fire outbreaks and explosions. The ESD system halts process activity in an emergency, ensures that the hazard is isolated and does not worsen. It is essential that there is a clearly defined emergency procedure in place when a facility needs to be shut down in an emergency. When preparing an emergency procedure for use in an emergency, it is crucial to prioritise the operations. The factory uses an Interval-valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIFAHP) to prioritise activities in emergency situations. It is possible to design emergency shutdown systems using the aforementioned order of operations. It is possible to properly set up safety measures using the multi-criteria decision-making approach (MCDM). The Analytical Hierarchy Process (AHP) compares variables without taking measurement scales or units into consideration, which is a distinct benefit. The AHP’s handling of ambiguity, uncertainty and imprecise data is improved by the IVIFAHP.

Nonlinear coupling vibration analyses of large-scale wind turbine system

With the development of wind energy, the unit capacity of wind turbine is becoming larger and the height of the support tower is getting higher. Limited by the transport conditions, the diameter of the tubular steel tower cannot further increase with the increase of the tower height, which results in the lower natural frequencies of the tower. The higher and more slender wind turbine towers are more sensitive to the external excitation and many tower collapses have occurred in recent years because of the interplay with the longer rotor blades. A 2 MW wind turbine system with a 120 m full steel tubular tower is taken as an example in this study to research the nonlinear coupling vibration mechanism under the normal operation case and the emergency shut-down case. An integrated model of large-scale wind turbine system including the tower, blades, hub, and nacelle is established by the open-source software FAST to study its nonlinear interaction vibration mechanism. Under the normal operation load case, the acceleration response of the steel wind turbine tower is greatly affected by the rotational frequency of the rotor. The maximum accelerations in the fore-aft and side-side directions both appear around the middle-upper height of the tower. Under the emergency shut-down load case, the maximum fore-aft acceleration response appears at the top of the tower, but the maximum side-side response still appears around the middle-upper height of the tower. The first-order vibration mode plays a dominant role in the top fore-aft acceleration and the second-order vibration mode has great contribution to the side-side acceleration under the emergency shut-down load case. The numerical results of this study can provide insight into the vibration control mechanism of the full steel tower.

Quantitative risk assessment of China's first liquid hydrogen refueling station.

Hydrogen refueling stations (HRSs) are among the most important infrastructures for fuel cell vehicles. However, the safety issue of HRSs has become a key constraint to the wide application and development of hydrogen energy. This article presents a quantitative risk assessment of the first liquid HRS (LHRS) in China and conducts a comprehensive assessment in terms of both individual (IR) and societal risks (SRs). The results showed that both the IRs and SRs related to the LHRS exceeded the risk acceptance criteria. The rupture of the flexible hose of the dispenser and the leak from the compressor are the main contributors to these risks. On the other hand, implementing appropriate mitigation measures on the level of the LHRS dispenser and compressor, including the addition of breakaway couplings in the flexible hose of the dispenser, the installation of hydrogen detection sensors, the arrangement of automatic and manual emergency shutdown buttons, and the elevation of the compressor, is capable of reducing the risk of the LHRS to be within the risk acceptance criteria.

An adaptable end-to-end maintenance performance diagnostic framework

PurposeThis paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.Design/methodology/approachThe case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.FindingsTo improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.Originality/valueMethodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

Noise resistant steam generator water level reconstruction for nuclear power plant based on deep residual shrinkage network

Nuclear power plant (NPP) steam generator (SG) water level has strong nonlinear pattern and lagging characteristic under control system and manual adjustment. SG water level signal accidental missing and frequent abnormity may lead to incorrect operational intervention or even emergency shutdown. Deep learning SG water level signal reconstruction method trained with historical sensor data can provide supportive information for operators’ decision-making to decrease the financial cost of maintenance and improve the safety of NPP assets. However, the sensor data transferred by electronic devices can be mixed up with noise in transients or complex conditions. This study proposed a noise resistant SG water level signal reconstruction method based on deep residual shrinkage network (DRSN). The dataset in this study is collected from a power-down process collected from a NPP in China. Results from the experiments under different levels of noise are conducted to illustrate the efficacy and robustness of the proposed approaches compared with other deep learning signal reconstruction methods.

Reduction of emissions and improvement of dynamic responses of a supercritical clean coal generation unit via neural network inverse control strategy

Coal power plants have been a major source of undesirable emissions. Despite the technological advancements in renewable energies, coal units are still in-service in many developed and developing countries due to their reliability, adequacy, and flexibility for power delivery. There are some promising technologies for cleaner operation during power production from coal, including supercritical boiler (SC) design and carbon capture and storage (CCS), however, the challenging in innovating effective methods is still open to expand the boundary of knowledge in this speciality. This paper introduces a novel and simple method for reducing CO2 emissions and improving the dynamic responses of a 600 MW SC coal power plant by Artificial Neural Network (ANN) technique. A wide-range data-driven feedforward ANN model has been identified and verified for the various operations recorded as closed-loop data-sets, which covers all situations of startup, once-through mode, and even emergency shutdown of the unit. The closed-loop SC plant model has been augmented with an inverse multivariable coordinate NN controller, developed by analogous learning algorithm to improve the plant automation. With precisely selected setpoints, as operational rules, of temperature, pressure, and earliest possible power demand signals, the automated SC plant has been capable to operate with lower coal consumption - and thus lower emissions – than the existing operation strategy during startup, normal operation, and emergency shutdown modes. The improvement in dynamic responses have been quantified through simulations with comparison with existing performance, which have resulted in an overall average reduction of 2.143 Kg/s in coal consumption.

Fragility and vulnerability development of offshore wind turbines under aero-hydro loadings

Wind technology is an effective solution for the carbon neutral over the world. Offshore wind turbines (OWTs) have therefore seen unprecedented development over the decades. Fragility and vulnerability of OWTs are vital in structural performance evaluation and risk assessment of infrastructural systems under natural hazards. Previous research mainly focused on the fragility of OWTs with monopile substructure in the normal operating or parked conditions. In the present study, a realistic situation with respect to the emergency shutdown mode is considered for the OWTs with monopile, tripod, and jacket substructures. A procedure to establish fragility and vulnerability models is developed. Numerical models using a combination of FE software are implemented and validated for dynamic analyses and pushover analyses. Then, the fragility and vulnerability models of the main structural subsystems including rotor, nacelle, tower, and substructure are separately established according to the developed procedure, so as to the fragility and vulnerability models of the whole wind turbine. Besides, impact of operational condition and characteristics of substructure on fragility and vulnerability is comprehensively compared and discussed.

OPTIMIZING THE ELECTRONIC CONTROL OF SUCTION VALVES FOR GAS COMPRESSION UNITS

In certain compression stations where twin-screw compressors assemblies are installed, certain operational problems have been observed when starting the assembly again after it was shut down. The downtime can vary from several minutes to a few days, depending on the shutdown causes. The gases accumulated in the compressor suction pipe can cause the inlet gas pressure to be above the typical pressure values ranging from atmospheric pressure (1 bar) to 1.5 bar. The compressor is automatically shut down via the sequences implemented in the automation control system if the pressure at the inlet of the compression unit exceeds 1.5 bar, this value being the maximum suction pressure that the compressor unit is designed to withstand. The solution introduces a potentiometer for providing feedback on the suction valve opening angle and thus optimizing the valve control to prevent the occurrence of higher pressures which may lead to failed start-ups or emergency shutdowns.