Abnormal Operating Conditions Research Articles

Resiliency Assessment of Power Systems Using Deep Reinforcement Learning.

Evaluating the resiliency of power systems against abnormal operational conditions is crucial for adapting effective actions in planning and operation. This paper introduces the level-of-resilience (LoR) measure to assess power system resiliency in terms of the minimum number of faults needed to produce a system outage (blackout) under sequential topology attacks. Four deep reinforcement learning (DRL)-based agents: deep Q-network (DQN), double DQN, the REINFORCE (Monte-Carlo policy gradient), and REINFORCE with baseline are used to determine the LoR. In this paper, three case studies based on IEEE 6-bus test system are investigated. The results demonstrate that the double DQN network agent achieved the highest success rate, and it was the fastest among the other agents. Thus, it can be an efficient agent for resiliency evaluation.

Multi-kernel correntropy based extended Kalman filtering for state-of-charge estimation

As a powerful tool for real-time battery management, the extended Kalman filter (EKF) can achieve an online estimation for state of charge (SOC). The EKF, however, may yield biased estimates since the measured system suffers from the abnormal operation conditions, i.e., sensor faults, sensor bias and sensor noise. Thus, this paper proposes a robust extended Kalman filter based on maximum multi-kernel correntropy (MMKC-EKF) for SOC estimate when the system is subjected to complex non-Gaussian disturbances. To derive MMKC-EKF, a batch-mode regression is formulated by integrating the uncertainties of process and measurement, which is solved by using maximum multi-kernel correntropy (MMKC) criterion to suppress the influences of abnormal conditions. An effective optimization method is introduced to determine the free parameters of MMKC, and a fixed-point iteration method gives the state estimation. Then, the posterior error covariance matrix is updated with the help of total influence function, which contributes to the robustness improvement. In addition, a novel filtering scheme is presented for reducing computational complexity, which is beneficial for solving battery pack state estimation in practice. Extensive simulations are carried out for SOC estimate to validate the accuracy and robustness of the proposed MMKC-EKF in the Gaussian and non-Gaussian distributed process and measurement noises.

Analysis of active islanding detection techniques for grid-connected inverters systems

<span>An island is a section of the electrical grid that contains producing assets and loads that are separated from the main grid and powered by these generators, such as solar systems, with voltage and frequency maintained at nominal levels. It's worth noting that the concept of islanding is linked to time. When the inverter detects an isolated grid activity for a particular period of time, the inverter is compelled to decouple from the general grid, according to the criteria that dictate the working principle of a photovoltaic (PV) systemThis paper presents research and comparisons of the main islanding detection techniques for single-phase systems based on various structures, as well as a comparison of the improvement of the traditional islanding detection method using three different methods (active frequency drift (AFD), slip mode phase shift (SMS) and Sandia frequency shift (SFS)). Under normal and abnormal operating conditions, a comparison of these three examined improvements was made. Additionally, physical security information management (PSIM) software simulation results are generated to test the performance and effectiveness of the effective technique plan.</span>

INVESTİGATİON OF CARBON GAS İN OİL TRANSFORMERS

By their structure and design, transformers are devices that are reliable in operation and easy to maintain. In the operation of transformers, the main factors are timely inspection, performance of current and major repairs, as well as the necessary preventive tests. One of the important conditions for the correct use of this device is the correct introduction of the temperature regime and observance to the allowed overloading. Monitoring the condition of the oil is fundamental to the correct operation of the transformer. During the period of use, the oil ages and it loses its original properties. In addition to the deterioration of the properties of the oil due to aging, there is also a change in its properties under abnormal operating conditions of the transformer. So with local heating, build-up of insulation between the steel sheets of the magnetic circuit, poor contact in the tap changer or at the winding terminals under the transformer cover, and with turn short circuits in the windings, oil decomposes. Transformer oil has at least three functions in transformers. Task of transformer oil in oil-filled transformers cooling function, extinguishing the arc, and providing insulation is the main [1]. The appearance of defects caused by both electrical and thermal faults is accompanied by the formation of gases, and their further dissolution in transformer oil. In addition to normal operation, during the operation of the transformer, local thermal overheating (of an insulated conductor, core, etc.) and electrical discharges (partial, spark and arc discharges) can occur. Such defects lead to the destruction of insulation, physical wear and tear, and irreversible damage to the active part of the transformer. To determine the state of oil-filled electrical equipment, gases are used: hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO), carbon dioxide (CO2). In addition to the above gases, oxygen (O2) and nitrogen (N) are also present, and their concentration varies depending on the tightness of the transformer tank and gases such as propane, butane, butene and others can be released. The study of these gases for diagnostic purposes is not widespread [2]. In this regard, the method of analysis of gases dissolved in oil (DGR) has proven itself all over the world. The main advantage of this method is the timely diagnosis of the malfunction, identification of the nature and control of the defect, which leads to lower economic costs and time savings. Fault diagnostics can be performed using various methods, such as: the ratio of pairs of gas concentrations using the Duval triangle, Japanese ETRA, monogram method, etc. The most important tool for diagnosing the condition of a transformer is observing the evolution of gases by comparing different analyzes of the amount of dissolved gases and understanding this information. The Duval Triangle Method is based on the existing IEC 60599 Gas Ratio Method. Within this triangle there are six potential damage zones. Zones include partial discharges, electrical and thermal damage. The use of this triangle method is based on three main gases (CH2, C2H4 and C2H2). Firstly the concentration levels of these gases are calculated. Then lines are drawn along three sides of an equilateral triangle: on each side, the relative value of a certain gas is laid, which can vary from 0 to 100% clockwise. The sum of the values of the concentrations of these gases is taken as 100. When drawing the boundaries of the zones of defects, the lines are drawn parallel to the lagging side of the triangle. The considered method not only clearly shows the "location" of the defect, but also allows you to observe the trajectory of its development [3]. The oil analysis results reflect the overall condition of the transformer. For this analysis, the oils are sent to a laboratory for testing. The most important indicators are the rate of formation of the total amount of combustible gases and individual gases. Carrying out timely sampling makes it possible to track the dynamics of malfunctions and take appropriate decisions in time. To obtain more reliable results, you must follow the sampling rules, store the samples and transport the samples to the analysis site correctly. Some gases dissolved in transformer oil (ethylene and acetylene) may indicate overheating in the contacts of switching devices, heating of the shield attachment points and bushing pins, short-circuiting of the winding conductors and other defects. The main goal of this article is to solve the problem associated with the extraction of hydrocarbon gases from oil, and to correct the interpretation based on the obtained data [4]. Keywords: DGR, Duval triangle, transformer oil, gas discharges.

Algorithms for processing self-powered neutron detector signals important for determination of local parameters in each part of the VVER core

These investigation findings prove the possibility of a engineering solution for VVER core automatic protection during operation in both nominal and transient conditions within ICIS using local parameters (i.e. linear heat power of the most stressed fuel rod, departure from nucleate boiling ratio). Such engineering solution will be implemented by safety system software-hardware (PTK-Z) on the basis of signals coming from in-core neutron flux detectors, temperature sensors, primary coolant flow and coolant pressure transducers. The article presents the following: a list of in-core neutron flux detectors, a list of transducers of primary coolant monitoring for thermal engineering conditions (temperature, flow rate, pressure), whose signals are delivered to the terminals of instrumentation gauges (PTK-Z) for the purpose of actuating safety actions in accordance with local parameters. The paper shows arrangement of in-core neutron flux detector equipment, above view of self-powered neutron detector (SPND) location in safety channels, axial arrangement of SPND along the core, algorithms for processing SPND signals important for determination of local parameters in each part of the core in both normal and abnormal operation conditions.

Impact of alarm management and automation on abnormal operations: A human-in-the-loop simulation study

Process control consists of complex states of performance that require a significant amount of operator attention and skill to manage. Automation and smart alarms can enhance these processes. This study investigated the effects of alarm management and automation on process control operator workload (NASA-TLX) and performance via material lost through flaring outcomes. Eleven console qualified operators participated in a human-in-the-loop, high-fidelity simulation-based training exercise. Three levels of alarm management (no rationalization, rationalization, and state-based smart alarm) and two levels of automation (no automation and automation) conditions were implemented using six scenarios. A repeated measures two-way MANOVA indicated that both alarm management schema and automation significantly affected operator workload and performance. These results indicate that state-based smart alarm management and automation schemas may assist operators in reducing workload and material lost through flare release during abnormal operating conditions.

Field measurements on emission characteristics, chemical profiles, and emission factors of size-segregated PM from cement plants in China

Cement manufacturing is a major contributor to ambient particulate matter (PM) pollution in China, threatening urban and regional air quality improvement. Here, we tested the typical outlets (kiln tail, kiln head, and coal mill) in one shaft kiln and three rotary kilns to investigate the mass concentrations, size distributions, and chemical compositions of size-segregated PM. Results show that the concentrations of PM in most samples are lower than the strict local emission standards (10 mg/Nm3). We show that the characteristic chemical compositions in PM for most tested outlets are Ca and Ca2+, while for shaft kiln are K, S, K+, and SO42−, and organic carbon. Elemental carbon accounts for a relatively high proportion of PM emitted from coal mills. Meanwhile, unstable and abnormal operating conditions and variations on feed coal compositions will cause high levels of NH4+ and Cl− in PM from the kiln tail/head. Besides, the emission factors (EFs) of PM2.5, PM10, and PM after air pollution control devices for typical outlets of cement plants are calculated, which fall in ranges of 0.16–2.48, 1.49–18.46, and 3.32–35.35 g/(t of clinker), respectively. It suggests that mass emission characteristics, source profiles, and EFs of PM have changed notably as emission standards become more stringent. We believe the newly detailed size-segregated PM EFs and chemical profiles will help update and compile the refined emission inventory for current cement production in China.

POWER CABLE INSULATION PRODUCTION LINE CONTROL

The paper considers the problem of control the cable insulation continuous vulcanization line. The axisymmetric mathematical model of the power cable rubber insulation vulcanization technological process has been developed. The model describes the heat transfer process inside the vulcanization pipe and the vulcanization process kinetics inside the insulation. The mathematical model is based on the energy conservation equation and it is presented in the form of a differential equations system closed by boundary conditions The mathematical model makes it possible to estimate the influence of various technological and design parameters on the temperature distribution along the radius and length of the cable. Based on the temperature distribution curves, the cable insulation vulcanization degree is calculated The study of the raw rubber compositions vulcanization properties was carried out by rotary rheometer, where the samples of the material were subjected to an oscillating load at different temperatures. As a result of the experiment, the curves family of the torque dependence on the curing time at different temperatures was obtained. To describe the vulcanization process kinetics, a parametric expression is used based on the experimentally obtained torque dependences. The obtained temperature dependence of the parametric expression coefficients makes it possible to describe the vulcanization process taking into account the uneven temperature distribution along the cable radius and length. The developed model makes it possible to calculate the speed at which the vulcanization degree reaches a given value. Algorithms for calculating and correcting line speed and extruder flow rate are proposed on the basis of a mathematical model. It allows us to determine the speed and flow rate of the extruder for various: cable designs, insulation materials, abnormal operating conditions of the production line. The results can be used at enterprises engaged in the production of cable and wire products with cross linked insulation. In cases where it is required to quickly calculate a new technological mode, make changes to the cable design, change the insulation material, and also take into account possible pressure drops inside the vulcanization pipe and deviations in the cable properties during the production process.

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human-Robot Interaction.

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human–robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of was achieved without data quality indication. The overall performance of the sensor system could be further improved to by monitoring the data quality, adding an additional layer of safety for human–robot interaction.

A reliable intelligent diagnostic assistant for nuclear power plants using explainable artificial intelligence of GRU-AE, LightGBM and SHAP

When abnormal operating conditions occur in nuclear power plants, operators must identify the occurrence cause and implement the necessary mitigation measures. Accordingly, the operator must rapidly and accurately analyze the symptom requirements of more than 200 abnormal scenarios from the trends of many variables to perform diagnostic tasks and implement mitigation actions rapidly. However, the probability of human error increases owing to the characteristics of the diagnostic tasks performed by the operator. Researches regarding diagnostic tasks based on Artificial Intelligence (AI) have been conducted recently to reduce the likelihood of human errors; however, reliability issues due to the black box characteristics of AI have been pointed out. Hence, the application of eXplainable Artificial Intelligence (XAI), which can provide AI diagnostic evidence for operators, is considered. In conclusion, the XAI to solve the reliability problem of AI is included in the AI-based diagnostic algorithm. A reliable intelligent diagnostic assistant based on a merged diagnostic algorithm, in the form of an operator support system, is developed, and includes an interface to efficiently inform operators.

Multi-Length Scale X-Ray CT and Thermal Analysis for Battery Safety Assessment

Lithium-ion battery failure occurs across multiple length and time scales. This work presents the properties of thermal and mechanical failure in batteries of cylindrical, pouch and coin cell format by using a combination of Accelerating Rate Calorimetry (ARC) and X-ray Computed Tomography (CT) at various length scales; from whole cell (macro) to bulk electrode (micro) to particle (nano).Typical features of a thermal runaway (TR) sequence in a commercial 18650 cell, such as the onset temperature of TR, the order of and rate of heat generation from cell component breakdown, such as SEI layer deformation, are shown using a heat-wait-search protocol in an ARC. Alongside this, pre- and post-mortem X-ray CT images reveal the effects of thermal failure on the bulk electrode structure within the cell, such as deformations due to gas formation and release. Electrode material characteristics, such as porosity, particle size distribution, tortuosity, and particle cracking, are also quantified and compared before and after cell failure in the bulk electrode (micro–X-ray CT) and individual particles (nano-X-ray CT)[1].A novel X-ray CT scanning protocol is also demonstrated for characterising bespoke pouch cells (single and multi-layer) with sufficient resolution to reconcile electrode morphology, for example to evaluate the morphology changes during thermal abuse by nail penetration.Furthermore, this work investigates the sequence of reactions leading up to thermal runaway using a novel coin cell Differential Scanning Calorimeter (DSC). Heat flux signals are used to quantify the contribution of various exothermic and endothermic reactions to the overall heat generated by a cell as it undergoes failure. Some key features of early-stage TR reactions, such as the decomposition of the SEI layer are revealed for NMC111, NMC622 and NMC811. Some of the typical thermal characteristics of cells as they undergo ‘normal’ charge and discharge are also shown and compared to abnormal operating conditions such as, temperatures up to 60 °C, high C-rates, and cycle numbers.[1] D. Patel, J. B. Robinson, S. Ball, D. J. L. Brett, and P. R. Shearing, J. Electrochem. Soc., 167, 090511 (2020). Figure 1

MAKİNE ÖĞRENMESİ YAKLAŞIMLARI İLE İNDÜKSİYON MOTORLARI İÇİN AKILLI HATA TESPİTİ VE SINIFLANDIRMADA DENEYSEL BİR DEĞERLENDİRME

The concept of Industry 4.0 aims fully digital and autonomous production. For manufacturing systems to work properly, their maintenance must be done correctly. However, while unnecessary maintenance causes waste of money and time, skipping necessary maintenance can also cause unexpected down times in production. Predictive maintenance (PdM) aims to predict and diagnose faults at an early stage and also the time remaining for future failures of equipment which might provide significant cost savings compared to traditional maintenance approaches. Today's sensor and data collection technologies have become more accessible and reliable which paved the way for manufacturers to continuously monitor their equipment, collect and store large volume of data in their production systems. Using this data with machine learning (ML) algorithms and analyzing the fingerprints of equipment faults can help making more informed decision regarding maintenance in manufacturing which might help increasing production quality and capacity. In our study, induction motors (IM) which are widely used in factories for different purposes and their failure scenarios are targeted. Triaxial vibration data collected from two similar induction motors under different operating conditions are examined. Various features of vibration data are extracted, scaled and labeled with a status information of the operation state. The obtained dataset is analyzed with six different ML algorithms. Performances are examined and compared against each other. In this study, we present our promising experimental results and experimentally show that the abnormal operating conditions of IMs can be successfully detected utilizing ML algorithms for a PdM application.

Fault detection in water pumps based on sound analysis using a deep learning technique

Machine fault detection is designed to automatically detect faults or damage in machines. When a machine operates, it produces vibrations and sound signals that can be analyzed to provide information about the status of the machine. This study proposed a method to detect the faults in a machine based on sound analysis using a deep learning technique. The sound signals generated by the machine were obtained and analyzed under different operating conditions. These signals were first pre-processed to eliminate noise, and then the features were extracted as mel-spectrograms so that the convolutional neural network could automatically learn the appropriate features required for classification. Experiments were conducted on three different water pumps during suction from and discharge to the water tank under normal and abnormal operating conditions. The high accuracies in fault detections in both known and unknown machines indicated that the proposed model performed very well in the detection of machine faults.

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Automation Pyramid as Constructor for a Complete Digital Twin, Case Study: A Didactic Manufacturing System.

Nowadays, the concept of Industry 4.0 aims to improve factories’ competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.

Performance and Microbial Community Analysis of an Electrobiofilm Reactor Enhanced by Ferrous-EDTA.

The biological reduction of ferrous ethylenediaminetetraacetic acid (EDTA-FeII-NO and EDTA-FeIII) is an important process in the integrated electrobiofilm reduction method, and it has been regarded as a promising alternative method for removing NOx from industrial boiler flue gas. EDTA-FeII-NO and EDTA-FeIII are crucial substrates that should be biologically reduced at a high rate. However, they inhibit the reduction processes of one another when these two substrates are presented together, which might limit further promotion of the integrated method. In this study, an integrated electrobiofilm reduction system with high reduction rates of EDTA-FeII-NO and EDTA-FeIII was developed. The dynamic changes of microbial communities in the electrobiofilms were mainly investigated to analyze the changes during the reduction of these two substrates under different conditions. The results showed that compared to the conventional chemical absorption-biological reduction system, the reduction system exhibited better performance in terms of resistance to substrate shock loading and high microbial diversities. High-throughput sequencing analysis showed that Alicycliphilus, Enterobacteriaceae, and Raoultella were the dominant genera (>25% each) during the process of EDTA-FeII-NO reduction. Chryseobacterium had the ability to endure the shock loading of EDTA-FeIII, and the relative abundance of Chryseobacterium under abnormal operation conditions was up to 30.82%. Ochrobactrum was the main bacteria for reducing nitrate by electrons and the relative abundance still exhibited 16.11% under shock loading. Furthermore, higher microbial diversity and stable reactor operation were achieved when the concentrations of EDTA-FeII-NO and EDTA-FeIII approached the same value (9 mmol·L−1).

Signal-based diagnostic approach to enhance fuel cell durability

To face the dependency to fossil fuels and limit the carbon emissions, fuel cells are a very promising technology and appear to be a key candidate to face the increase of the energy demand and promote the energy transition and energy mix. To meet future needs for both automotive and stationary applications, fuel cells will require improved durability. In this paper, a new concept to enhance fuel cell performance and lifetime is provided, by introducing a disruptive and high-efficiency diagnostic approach based on signal processing. The results show that the failures on the fuel cell system can be detected and isolated efficiently thanks to fault indicators. The innovative concept proposed here, can guarantee improvements of the durability and of the reliability of the fuel cell system by detecting abnormal operating conditions rapidly and efficiently.

Optimal reconfiguration for vulnerable radial smart grids under uncertain operating conditions

Modern smart grid prospects necessitate handling abnormal operating conditions besides conventional demands for improving power systems capabilities. Uncertain load and generation, and line outages during contingency conditions of electric power systems should be properly and efficiently dealt with. Lately, lockdown situations because of COVID-19 pandemic have greatly influenced energy demands in many areas in the World. Vulnerable operation of power networks, especially in either isolated microgrids or large-scale smart grids can be significantly avoided through proposing optimal reconfigurable network. In this paper, employing the distribution network (DN) reconfiguration is deeply studied for achieving fault-tolerance and fast recovery to reliable configurable DN in smart grids. Since radiality is among crucial properties of DN topology, searching for feasible configuration of DN is NP-hard optimization problem. Therefore, the recent Manta Ray Foraging Optimization (MRFO) is considered for solving such DN optimization instance. Performance of MRFO is examined against two common optimizers: the Particle Swarm Optimization (PSO) and the Grey Wolf Optimization (GWO). Different operating conditions for both the IEEE 33-bus and IEEE 85-bus systems are analysed using these optimization techniques. The goal is to search for feasible reconfigured DN with the minimum power losses and the optimal enhanced voltage profile. Simulation results reveal that the proposed MRFO approach provides efficient and outstanding behaviour in various operation scenarios. The efficiency and the robustness of the proposed MRFO approach are verified, the power loss reduction ratio ranges between 21% and 41% in different studied scenarios and adequate voltage profile enhancement is achieved.

Process Safety for Sustainable Applications

Process safety techniques have been used in industry for decades to make processes and systems safer and to optimize them, and thus to improve sustainability. Their main aim is to prevent damage to people, equipment and the environment.In this overview, process safety and risk management techniques are shown that can be applied in the different life cycle phases of an application without much implementation effort. A broad and universal applicability in a wide range of business sectors is set as the main focus.In addition to the application of system improvement techniques, a number of additional considerations, such as maintenance and the consideration of abnormal operating conditions, are included in order to be able to comprehensively improve a system or application.

CALCULATION OF RADIOACTIVE SOURCE TERM RELEASE FROM FLEXBLUE NPP

One of the National Research Programs (PRN) in the energy sector of the Indonesian Ministry of Research and Technology for the period of 2020-2024 is small modular reactor (SMR) nuclear power plant (NPP) assessment. The France’s Flexblue is a PWR-based SMR submerged reactor with a power of 160 MWe. The Flexblue reactor module was built on the ocean site and easily provided the supply of reactor modules, in accordance with the conditions of Indonesia as an archipelagic country. Therefore, it is necessary to know the release of fission products (source term), which is necessary for the study of the radiation safety of a nuclear reactor. This paper aims to examine the source term in normal operating conditions and abnormal normal operating conditions, as well as postulated accidents. Based on the Flexblue reactor core parameter data, the calculation of the reactor core inventory uses the ORIGEN2 software is previously evaluated. The source term calculation uses a mechanistic approach and a graded approach. The normal source term is calculated assuming the presence of impurities on the fuel plate, due to fabrication limitations. Meanwhile, the abnormal source term is postulated in the LOCA event. The core reactor inventory and source term is divided into 8 radionuclide groups which are Noble gasses group (Xe, Kr); Halogen (I); Akali Metal (Cs, Rb); Tellurium Group (Te, Sb, Sc); Barium-Strontium Group (Ba, Sr); Noble Metals (Ru, Rh, Pd, Mo, Tc, Co); Lanthanides group (La, Zr, Nd, Eu, Nb, Pm, Pr, Sm, Y, Cm, Am) and Cerium Group (Ce, Pu , Np).