Reliability Assessment Techniques Research Articles

Assessing the Heavy Metal Contamination Prevailing in Groundwater at Rishipur Village, West Bengal, India

The recent surge in interest surrounding water contamination has prompted a study to evaluate the water quality of groundwater in Rishipur village, Malda District, West Bengal, India. The area's groundwater is crucial for drinking, residential, and irrigation purposes due to its proximity to agricultural fields, where most local residents live. The intended purpose of the study is to comprehensively appraise the groundwater quality of a dug well, specifically analysing the intensities of different heavy-metals existing in the water, resulting in valuable insights into its quality. The native residents rely on the usage of this groundwater for drinking and domestic purposes without any purification. However, potential contamination may be present due to nearby agricultural activities. Thus, the study will enable us to make recommendations for mitigating any identified contamination. Exemplary samples of water from a well were consistently collected near the agricultural region over the course of a year, from April 2021 to March 2022. The groundwater samples were analysed for the presence of heavy elements: iron; zinc; copper; manganese; nickel; chromium; cobalt and lead to evaluate the water excellence. To ascertain the overall water eminence status, the Heavy-Metal-Pollution-Index; (HPI), was calculated, which is considered an efficient and reliable technique for water eminence assessment. In this study, the value of HPI; 101.66 in January, indicates high heavy-metal pollution, although it remained within the desired range for the rest of the months. In the analysis of the descriptive statistical data, both substantial positive and negative relations were observed, which were illustrated in a correlation coefficient matrix. Furthermore, the interaction of heavy-metal variables was also explored by utilising the R-Square values derived from the multiple linear regression analysis, which allowed to gain insights into how these variables interplayed with one another. The investigation conclusively established that the eminence of water is deemed appropriate for consumption alongside other domiciliary purposes, with the exception of January.

Reliable technique for acromion density assessment on CT.

Acromial stress fracture (ASF) is an uncommon but acknowledged complication of reverse total shoulder arthroplasty (RTSA). There is no standardised method to directly measure the bone mineral density of the acromion to allow a detailed analysis of the potential mechanism of ASF. The aim is to establish a reliable and reproducible technique for measurement of acromial density on computer tomography (CT). A retrospective review on CT scans obtained for three groups of patients: those planned for RTSA (n = 26); age and gender-matched non-operative (n = 26); and young non-operative patients (n = 28) were performed. Standardised axial images of 1 mm thickness at 1 mm increments were created following horizontal straightening on the coronal and sagittal views. To assess inter-rater reliability, two senior CT radiographers performed density measurements using standard region of interest (ROI) tool on the CTs with the ROI placed on the mid (ROI 1) and posterior (ROI 2) acromion. ROIs were selected as the most common locations for acromion fracture post RTSA. Measurements were repeated at least 6 weeks apart. Intra-class coefficients (ICC) were used to determine intra- and inter-rater reliability. ICCs demonstrated good to high intra-rater and inter-rater reliability for both ROI 1 and ROI 2 across all three groups. The lower margin of 95% confidence intervals was more than 0 for all intra-class coefficients. This study demonstrates a reliable method of measuring acromion density on CT. This method can be used to assess bone mineral density in the clinical setting and in future studies investigating ASF following RTSA.

Power system reliability assessment technique and modeling approach based on quantum computing theory

Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing.

A model assisted probability of detection for guided wave SHM systems: comparative study on recent statistical developments

Despite the potential benefits, industrial implementation of guided wave monitoring systems remains limited today. One of the reason behind this is the lack of consolidated reliability assessment techniques. Recent works clearly illustrated that the classical statistical methodology adopted from nondestructive evaluation to estimate POD is not directly applicable to structural health monitoring (SHM): the hypothesis of independence does not hold for the data obtained from permanently attached sensors and growing defects. The experimental determination of POD for SHM systems requires extensive and costly studies, in addition to the numerous representative structures. Involving numerical studies can complement experimental trials, inform POD evaluation and reduce this cost drastically. We propose in this work a comparative study to demonstrate the application of Model Assisted Probability Of Detection approach (MAPOD) for elastic guided wave monitoring systems. We apply classical and recently developed statistical methods proposed in the literature, using simulated data obtained thanks to the CIVA SHM module. The MAPOD estimation is validated in comparison to experimental studies.

Utilizing phased array ultrasound technique to examine seal surface contact status in premium connections

Premium connections are commonly employed in wells subjected to high temperature and high pressure (HTHP) conditions. The key parameter in evaluating the sealing capability of premium connections is the contact condition between the sealing surface. This paper presents a method based on phased array ultrasound testing (PAUT) technique to examine the metal-to-metal contact states of the seal surface in premium conditions, and presents experimental work on contact status of the seal surface in premium tubing connections using PAUT, testing impacts of torque, seal surface defects and thread compounds dosage. The experimental findings demonstrate an inverse relationship between the observed ultrasonic reflection amplitude and the applied torque on the premium. Additionally, there is a robust exponential correlation manifested between the ultrasonic reflection amplitude and the torque exerted on the premium. The ultrasonic reflection amplitude at the defect of the seal surface increases. Under equivalent make-up torque conditions, the ultrasonic reflection amplitude on the sealing surface of premium connections escalates as the dosage of thread compounds diminishes, and the importance of precise control of the amount of thread compound used was confirmed. These data demonstrate that PAUT provides a reliable detection technique for performance assessment on premium connections, both in quality control and field inspection.

An updated software reliability model using the shanker model and failure data

AbstractSoftware developers' goal is to develop reliable and superior software. Due to the fact that software errors frequently generate large societal or financial losses, software reliability is essential. Software reliability growth models are a widely used technique for software reliability assessment. This study examines various nonhomogeneous Poisson process models with the newly developed software reliability distribution and evaluates the unknown model parameters based on frequentist and Bayesian methods of estimation. Finally, we conduct evaluations on real datasets using a variety of evaluation criteria to compare the results of previous software reliability growth models and show how the proposed model may be applied under both approaches in a practical setting. According to this study, the innovative model's mean square error, R2, , bias, predicted relative variation, Theil statistic, and mean error of prediction values show the lowest values under the Bayesian approach for data sets II to IV, and both approaches perform well for data set I. These implementation findings demonstrate the effectiveness of our specific approach based on our examination of failure data.

Reliability of AI Algorithms in Safety Applications

The integration of Artificial Intelligence (AI) algorithms into safety-critical applications has become increasingly prevalent across various domains, including autonomous vehicles, medical diagnosis, industrial automation, and aerospace systems. These applications rely heavily on AI to make decisions that directly affect human safety, economic stability, and operational efficiency. Given the critical nature of these tasks, it is essential to rigorously assess the reliability of AI algorithms to ensure they perform consistently and accurately under all conditions. Reliability, in this context, refers to the AI system's ability to function without failure over a specific period, under defined operational conditions. In safety-critical domains, even minor errors or inconsistencies in AI decision-making can lead to catastrophic outcomes, such as traffic accidents involving autonomous vehicles, incorrect medical diagnoses leading to improper treatments, or failures in industrial processes that may cause costly downtime or even human casualties. The increasing complexity and deployment of AI technologies in these domains highlight the urgent need for a comprehensive understanding and evaluation of AI reliability. This paper provides a detailed analysis of the design considerations and methodologies for enhancing the reliability of AI algorithms. The discussion begins by exploring the underlying principles of reliability in AI systems, focusing on both theoretical and practical perspectives. We examine key factors that influence reliability, including data quality, algorithmic robustness, model interpretability, and system integration. The paper then delves into various reliability assessment techniques, such as fault tolerance mechanisms, error detection and correction methods, redundancy, and validation processes. To provide a deeper understanding of reliability in AI, we introduce mathematical models and statistical evaluation techniques that quantify reliability metrics. For instance, reliability modeling using exponential distribution, Monte Carlo simulations for probabilistic reliability analysis, and error propagation studies using Jacobian matrices are presented. We also explore the use of machine learning-specific reliability metrics, such as the Area Under the Curve (AUC) in Receiver Operating Characteristic (ROC) analysis, which helps evaluate the performance of AI in critical decision-making contexts. Furthermore, this paper addresses the current challenges and limitations in ensuring AI reliability, including computational complexity, ethical considerations, and regulatory compliance issues. We highlight the difficulties in developing AI models that can maintain their reliability across diverse and unpredictable real-world scenarios. The potential for bias, lack of transparency in AI decision-making, and difficulties in explaining complex AI models also present significant hurdles that need to be addressed to enhance reliability. The findings and methodologies discussed in this paper aim to contribute to a deeper understanding of the complex landscape of AI reliability, providing a framework for researchers, practitioners, and policymakers to develop safer, more reliable AI systems that can be trusted to operate in environments where safety is paramount.

Terahertz pulsed imaging of low velocity impact damage in woven fiber composite laminates

Recently, terahertz pulse imaging has been used extensively to characterize internal defects in various electrically insulating materials. In this work, terahertz pulse imaging is used to identify the damage induced by low-velocity impact on woven glass-fiber reinforced polyamide laminates. Several impact energies are considered to study the damage initiation and propagation. The permanent indentation, known to be a relevant damage indicator, is extracted from the terahertz results and validated through comparison with profilometry. Further, the criticality of the damage, in terms of the number of composite plies in which the cracks propagate, can be clearly determined from terahertz imaging. These observations are validated through X-ray tomographic observations and analysis. Finally, a strong connection is established between the evolution of the permanent indentation and the appearance, and criticality, of the low-velocity impact-induced damage. These observations suggest that terahertz imaging is a reliable technique for the nondestructive assessment of impact damage in glass fiber composites.

Gammagrafía de perfusión miocárdica en la evaluación del riesgo cardiovascular en el trasplante hepático de adultos

IntroductionThe non-invasive diagnostic study of cardiovascular risk in patients who are going to undergo liver transplantation is not clear, especially in asymptomatic patients. Regarding myocardial perfusion scintigraphy (MPS), it has been thought that the impaired vasodilator reserve in these patients may reduce its performance. The objective is to assess the role of the MPS in the pre-surgical evaluation of patients who are going to undergo liver transplantation. Material and methodsRetrospective, descriptive and observational study was designed. All adult patients undergoing liver transplantation between 2017 and 2021 who had previous MPS were included. The findings of MPS were described and correlated with the findings of invasive angiography and with the appearance or not of peri- and post-transplant cardiovascular events. ResultsThere were a total of 188 transplanted patients (mean age: 57 years, SD: 12), 178 had previous myocardial perfusion, 82 (46%) patients had no cardiovascular risk factors, and 5 (2.8%) had a history of coronary disease.Of the MPS, 177 were with dipyridamole stress performed on average 10 months before transplantation. Only 17/178 (9.5%) studies were abnormal. The mean follow-up was 38 months (SD: 10). Of the patients with normal MPS, only 2 (1.2%) presented cardiovascular events, both with studies performed more than 2years before the procedure. There were no deaths of cardiovascular origin. ConclusionsMPS is a safe and reliable technique for cardiovascular assessment of patients who are candidates for liver transplantation, given the low rate of false negatives during follow-up.

Forest emissions reduction assessment from airborne LiDAR data using multiple machine learning approaches

Objective: This study aims to evaluate the accuracy of different modeling methods and tree structural parameters extracted from airborne LiDAR for estimating carbon emissions reduction and assess their reliability as Certified Emission Reduction (CER) assessment techniques.Methods: LiDAR data was collected from an afforestation project in Beijing, China. Various modeling methods, including statistical regression and machine learning algorithms, were used to estimate biomass and carbon emissions reduction. The models were evaluated under two schemes: tree-species-specific modeling scheme (Scheme 1) and all-sample modeling scheme (Scheme 2) using cross-validation and compared with ground-based estimations and pre-estimated emission reductions.Results: Totally, the biomass estimation models in scheme 1 showed better accuracy than scheme 2. In scheme 1, The Random Forest (RF) and Cubist models achieved the highest prediction accuracy (R2 = 0.89, RMSE = 22.87 kg, CV RMSE = 52.00 kg), followed by GDBT and Cubist, with SVR and GAM performing the weakest. In scheme 2, Cubist model had the highest accuracy (R2 = 0.75, RMSE = 33.95 kg, CV RMSE = 36.05 kg), followed by RF and GBDT, with SVR and GAM performing the weakest. LiDAR-based estimates of carbon emissions reduction were closer to ground-based estimations and higher than pre-estimated values.Conclusion: This study demonstrates that LiDAR-based models using tree structural parameters can accurately assess carbon emissions reduction. The models outperformed traditional methods in terms of cost and accuracy. Considering tree species in the modeling process improved the accuracy of the models. LiDAR technology has the potential to be a reliable assessment technique for carbon emissions reduction in forestry projects. The pre-trained models can be used for multiple predictions, reducing the cost of carbon sink surveys. Overall, LiDAR-based models provide a promising approach for assessing carbon emissions reduction and can contribute to mitigating climate change.

Myocardial perfusion scintigraphy in the assessment of cardiovascular risk in adult liver transplantation

IntroductionThe non-invasive diagnostic study of cardiovascular risk in patients who are going to undergo liver transplantation is not clear, especially in asymptomatic patients. Regarding myocardial perfusion scintigraphy (MPS), it has been thought that the impaired vasodilator reserve in these patients may reduce its performance. The objective is to assess the role of the MPS in the pre-surgical evaluation of patients who are going to undergo liver transplantation. Material and methodsRetrospective, descriptive and observational study was designed. All adult patients undergoing liver transplantation between 2017 and 2021 who had previous MPS were included. The findings of MPS were described and correlated with the findings of invasive angiography and with the appearance or not of peri- and post-transplant cardiovascular events. ResultsThere were a total of 188 transplanted patients (mean age: 57 years, SD: 12), 178 had previous myocardial perfusion, 82 (46%) patients had no cardiovascular risk factors, and 5 (2.8%) had a history of coronary disease.Of the MPS, 177 were with dipyridamole stress performed on average 10 months before transplantation. Only 17/178 (9.5%) studies were abnormal. The mean follow-up was 38 months (SD: 10). Of the patients with normal MPS, only 2 (1.2%) presented cardiovascular events, both with studies performed more than 2 years before the procedure. There were no deaths of cardiovascular origin. ConclusionsMPS is a safe and reliable technique for cardiovascular assessment of patients who are candidates for liver transplantation, given the low rate of false negatives during follow-up.

EFFECT OF EPISTEMIC UNCERTAINTY ON SEISMIC FRAGILITY OF BASE-ISOLATED RC-BUILDING FRAME USING INCREMENTAL DYNAMIC ANALYSIS

The quantification and impact of epistemic uncertainty in the RC frame building can analyze due to recent advancements in processing power and the availability of reliable seismic vulnerability assessment techniques. In the present study, a base-isolated 10-story RC building frame is designed and analyzed per the latest Indian standards, with lead rubber bearing as the base isolation system. The selected base-isolated RC frame building was analyzed using probabilistic and deterministic approaches. As the fundamental epistemic uncertainty parameter, the mechanical strength of concrete and steel is considered. The Monte-Carlo method is determined the unpredictability of the RC frames' structures. The incremental dynamic analysis is conducted per the FEMA P695 guidelines to evaluate the wide range of 32 far-field ground motions on the base-isolated RC frame structure. Maximum roof drift ratio responses are utilized to generate the seismic fragility curve by adopting the several threshold values of damage index, such as, slight, moderate, severe, and complete. The traditional linear regression method is implemented to derive the seismic fragility curves of various damage thresholds. The study results reveal that the deterministic strategy is more likely to predict damage by 10 to 25 percent less than the probabilistic approach. The probabilistic analysis approach shows that epistemic uncertainty significantly affects the prediction of the seismic damage probability

Comparison of different hydrological and stability assumptions for physically-based modeling of shallow landslides

Physically-based models are reliable techniques for landslide susceptibility assessment, therefore, several models have been proposed in the literature. In this study, two different hydrological models (FSLAM and TRIGRS) and two slope stability calculation methods (infinite slope and limit equilibrium method with spherical failure surface in SCOOPS3D) were compared. The rainfall-triggered shallow landslides that occurred in Kaptanpaşa, Rize (Turkey) in 2017 were selected as a case study. The main focus is investigating the impact of landslide morphology on the success of the slope stability models. For this purpose, landslides in the inventory were grouped according to their morphological characteristics, such as the landslide depth, length, width and their ratios, and their compatibility with model assumptions (e.g. the infinite slope assumption). Then, the model performances in different landslide types were evaluated separately. To calibrate the model parameters a new time-saving, semi-automated calibration strategy, based on a sensitivity analysis, is proposed. Concretely, stability parameters c′∅′Z were calibrated via a MATLAB code by considering the area under the curve (AUC) and extreme conditions: saturated and initial conditions. Overall, SCOOPS3D is deduced to have better performance than infinite slope analysis. However, the accuracy of SCOOPS3D is revealed to decrease for landslides with a high ratio of length/depth and width/depth. From the comparison of hydrological models, FSLAM yielded more plausible results than TRIGRS with the available data. It is demonstrated that the morphological features of the landslides significantly influence the performance of the physically-based stability models. Furthermore, it is highlighted that the resolution of the Digital Elevation Model in comparison to the width and length of the landslides in an inventory can be influential in the performance of the physical model. 10 m resolution of DEM is deemed to be suitable for the study area judging the predictive performance in small landslides.

Characterization of Photothermal Desorption-Compatible Diffusive Samplers for Volatile Organic Compounds.

Products and starting materials containing volatile organic compounds (VOCs) can easily be found in a variety of businesses, making them a common source of occupational exposure. To prevent negative impacts on employee health, field industrial hygienists must conduct regular sampling to ensure exposures remain below the regulatory limits set by governmental and professional associations. As such, the need for sensitive and reliable exposure assessment techniques becomes evident. Over the preceding decade, the industrial hygiene research group at the University of Alabama at Birmingham (UAB) has been working on the development of an emerging, preanalytical technique known as photothermal desorption (PTD) to improve upon the analytical sensitivity of currently employed methods. PTD's novel design uses pulses of high-energy light to desorb analytes from thermally conductive, carbonaceous sorbents, to be delivered to downstream analytical detectors. Since PTD's conception, the theoretical framework and advances in sorbent fabrication have been investigated; however, further work is needed to produce a field-ready sampling device for use with PTD. As such, objectives of the present work were to design a PTD-compatible diffusive sampler prototype and characterize the prototype's sampling efficiencies for toluene, n-hexane, trichloroethylene, and isopropyl alcohol. In pursuit of these objectives, the study empirically quantified the sampled masses of toluene, n-hexane, trichloroethylene, and isopropyl alcohol, at occupationally relevant air concentrations, to be 12.17 ± 0.06, 8.2 ± 0.1, 3.97 ± 0.06, and 8.0 ± 0.1 mg, respectively. Moreover, the analyte sampling efficiencies were found to be 2.2 ± 0.1, 1.7 ± 0.1, 1.2 ± 0.1, and 0.51 ± 0.05 (unitless) when comparing empirically (i.e., laboratory observed) sample mass values to theoretically predicted values. The sampling efficiencies and collected sample masses reported herein demonstrate the promising design of PTD-compatible diffusive samplers. When used in conjunction with the PTD method, the prototype samplers present strong evidence for improving analytical sensitivity in exposure assessments of VOCs in the workplace.

Reliability Improvement of a Hybrid Electric Vehicle Integrated Distribution System

The recent trend in hybrid electric vehicles (HEV) has increased the need for vehicle charging stations (VCS) in the distribution system. In this condition, the additional load in the system leads to an increase in power loss, reduction in voltage and reliability of the system. The drawbacks of introducing this additional load can be rectified by integrating distributed generation (DG) into the distribution system. In this paper, the ideal location for placing DG is identified through the voltage stability index. The power loss minimization objective function is formulated with all the required constraints to estimate the size of DG required for the distribution system. Moreover, loss of load probability is used as a reliability assessment technique, through which the system reliability is analyzed after assessing the impact of integrating VCS and DG. Simulations are carried out to compare the following cases: a system without VCS and DG, a system that has only VCS and a system that has both VCS and DG. The IEEE 12-bus and 33-bus test systems are considered. In the 12-bus system with both VCS and DG, the power loss is reduced by 56% when compared with the system with only VCS, while the net reliability is also improved. The reliability of the system is evaluated for a 24 h load variation. The proposed work provides an efficient tool to improve the reliability of the system with support from DG.

Role of fine needle aspiration cytology in the diagnosis of cutaneous and subcutaneous swellings

A wide range of inflammatory as well as neoplastic lesions can present as cutaneous and subcutaneous swellings all over the body. FNAC (Fine Needle Aspiration Cytology) is considered to be the first reliable technique for diagnosing them. To assess the cytological spectrum of lesions presenting as skin and subcutaneous swellings. It was an observational study conducted in the Department of Pathology, Govt. Medical College, Jammu. FNAC was performed on patients presenting with palpable skin and subcutaneous swellings. May-Grunwald-Giemsa (MGG) and Papanicolaou (PAP) stained smears were examined under light microscope. The cytological features were studied and diagnosis was made. The present study had 290 patients which included 169 (58%) males and 121 (42%) females. On FNA examination, 111 (38.3%) were non-neoplastic and 179 (61.7%) were neoplastic. Epidermal inclusion cyst was the most common non- neoplastic lesion. Among neoplastic lesions, 166 cases (57.2%) were benign and 13 cases (4.5%) were malignant. Lipoma was the most common benign neoplastic lesion and was also overall the commonest lesion in our study. Metastatic carcinoma cases were maximum among malignant lesions. Fine Needle Aspiration Cytology is a rapid, reliable technique for initial assessment of cutaneous and subcutaneous swellings and thereby guiding further management.

Quality Analysis of Canola and Mustard Oil Using Fluorescence Spectroscopy.

The potential of Fluorescence spectroscopy has been utilized for the quality analysis of canola and mustard oil along with the effect of heating on their molecular composition has been investigated. Laser diode at 405nm has been employed directly to oil surface to excite both oil type samples and their emission spectra has been recorded by an in-house developed Fluorosensor. The emission spectra of both oil types unveiled that they contain carotenoids, isomers of vitamin E and chlorophylls that exhibit their fluorescence at 525 and 675/720nm, and these can be used as markers for their quality assurance. Fluorescence spectroscopy is a fast, reliable and non-destructive analytical technique for the quality assessment of both oil types. Moreover, the effect of temperature on their molecular composition has been investigated by heating them at 110, 120, 130, 140, 150, 170, 180 and 200°C, each sample for 30min which was done because both oils are used for cooking and frying. On heating, the deterioration of carotenoids and isomers of vitamin E in both oil types occurred with an increase in the oxidised products. However, it was found that up to 150°C, both oil types can be used safely for cooking/frying purpose where they do not lose much of their valuable ingredients and up to 180°C for deep frying, both oils can be used with less deterioration and after that both deteriorated much due to rapid increase of the oxidized products. The portable Fluorosensor, therefore, proved as an excellent device for quality screening of edible oils based on carotenoids and vitamin E.

Imaging glucose metabolism to reveal tumor progression.

Purpose: To analyze and review the progress of glucose metabolism-based molecular imaging in detecting tumors to guide clinicians for new management strategies. Summary: When metabolic abnormalities occur, termed the Warburg effect, it simultaneously enables excessive cell proliferation and inhibits cell apoptosis. Molecular imaging technology combines molecular biology and cell probe technology to visualize, characterize, and quantify processes at cellular and subcellular levels in vivo. Modern instruments, including molecular biochemistry, data processing, nanotechnology, and image processing, use molecular probes to perform real-time, non-invasive imaging of molecular and cellular events in living organisms. Conclusion: Molecular imaging is a non-invasive method for live detection, dynamic observation, and quantitative assessment of tumor glucose metabolism. It enables in-depth examination of the connection between the tumor microenvironment and tumor growth, providing a reliable assessment technique for scientific and clinical research. This new technique will facilitate the translation of fundamental research into clinical practice.

A Multi-Level Fuzzy Evaluation Method for the Reliability of Integrated Energy Systems

With the increase in environmental pressure and rapid development of renewable energy technologies, an integrated energy system has been recognized as an effective approach to accommodate large-scale renewables and achieve environmental sustainability. While an integrated energy system significantly improves energy efficiency, the interaction between different energy systems may also bring multiple operational risks to its reliability, which necessitates an effective reliability assessment technique. In this paper, we proposed a multi-level fuzzy evaluation model based on combined empowerment for the reliability evaluation of an integrated energy system. The analytic hierarchy process method and entropy weight method were used to calculate the weight of each index in the evaluation model. Fuzzy evaluation matrix was constructed by the membership degree of a single factor, which was defined by the fuzzy comprehensive evaluation method. The multi-level fuzzy evaluation results were obtained based on single-level evaluation results. Finally, case studies were carried out based on a practical integrated energy system; we proposed 5 first-level indicators such as reliability and economy and 12 second-level indicators such as mean time of incapacity. The simulation results (85.15) showed the effectiveness and advantages of the proposed model.

Reliability assessment of cyber-physical power systems considering the impact of predicted cyber vulnerabilities

This paper presents a reliability assessment technique for cyber-physical power systems (CPPSs) that incorporates cybersecurity issues by considering non-normal random variables with non-linear dependencies. Our model uses bidirectional encoder representations from transformers (BERTs) to predict the severity of cyber vulnerabilities by analyzing their textual description. This practical model allows the engineer to manually enter text that describes threats to predict the probability of cyber vulnerability. We use a Bayesian attack graph to simulate the attack paths and a Markov model to demonstrate the consequences of cyber attacks on the CPPS. Furthermore, a hybrid approximate-analytical approach is employed to assess the overall reliability of the CPPS when considering the load and wind generation uncertainties, given their non-linear dependencies. The proposed method is implemented on the modified IEEE Reliability Test System. The results demonstrate that the reliability indices for a simultaneous cyber attack on corporate networks and wind farm control centers are more significant than those obtained for other attack scenarios. The proposed prediction method is highly accurate, and the predicted vulnerability scores align with those provided by the National Vulnerability Database. The results are compared with sequential Monte Carlo simulation, subset simulation, and cross-entropy-based importance sampling methods. The comparison shows that the proposed method outperforms other methods in terms of convergence speed and accuracy.