Abnormal Operating Conditions Research Articles

Long‐Term Estimation of SoH Using Cascaded LSTM‐RNN for Lithium Batteries Subjected to Aging and Accelerated Degradation

ABSTRACTAccurate estimation of state of health (SoH) of the battery over long‐term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short‐term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet‐based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM‐RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM‐RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery.

Overview and Advancement of Power System Topology Addressing Pre- and Post-Event Strategies under Abnormal Operating Conditions

Overview and Advancement of Power System Topology Addressing Pre- and Post-Event Strategies under Abnormal Operating Conditions

Research on comprehensive quality consistency evaluation strategy for TCM granules: A case study with sugar-free Yangwei Granules produced by fluid-bed granulation

ObjectiveTo develop a quality consistency evaluation strategy for traditional Chinese medicine (TCM) granules using sugar free Yangwei Granules as a model drug, and demonstrate the effectiveness of the developed method. MethodsThe strategy integrates several methods including, HPLC fingerprint and physical fingerprint methods analyze the similarity in chemical and physical properties of the TCM granule samples. Near-infrared (NIR) spectroscopy with principal components cluster analysis method is used to monitor normal operating conditions (NOC) samples accurately and to identify different types of abnormal operating conditions (AOC) samples, particularly those that deviate from the normal range. ResultsThe combined use of HPLC fingerprint and physical fingerprint provides insights into the chemical and physical properties of the samples. NIR spectroscopy, combined with principal components cluster analysis, achieves high accuracy in monitoring NOC samples and identifying AOC samples without misjudgment. The approach proves useful as a complementary method in cases where HPLC fingerprint and physical fingerprint alone lack sufficient resolution. ConclusionThis study establishes the feasibility and utility of the integrated approach for assessing the quality consistency of TCM granules. The strategy shows a high degree of generalization and holds significant importance for enhancing the quality control processes of TCM granules.

Power Quality Enhancement in High-Voltage Transmission Systems Using STATCOM by Type 1 and Type 2 Fuzzy Logic Controllers

The continuing growth of load demand leads to increased power system complexity and interconnections. During faults and abnormal operating conditions, conventional power systems change the load flow in transmission lines, which may cause voltage drops or swells, and other power quality issues. Voltage regulation is connected to reactive power compensation in system busbars, and fixed capacitor banks are usually used for this purpose, but they are not flexible and dynamic enough to meet the power quality requirements. With power electronics development, flexible alternative current transmission systems (FACTS) became a popular solution for power quality improvement and power transmission capability increment in power systems, and FACTS can be categorized into series and shunt systems. In this study, STATCOM was used to improve the voltage profile in part of Iraq’s high-voltage transmission system during abnormal operating conditions, such as sudden load addition and removal, which causes voltage dip and voltage swell. Type 1 fuzzy logic controller and type 2 fuzzy logic controller were used as the outer controller in the STATCOM; both controllers were able to regulate the voltage magnitude by reducing the voltage dip from 1.32 to 0.4%, voltage swell from 1.02 to 0.5%, and voltage THD at the connecting point did not exceed 3%, indicating that SATACOM with FLC improves the voltage profile under different operating conditions.

Uncertainty-Aware and Explainable Human Error Detection in the Operation of Nuclear Power Plants

The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs. In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked. Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.

Parameters estimation and sensitivity analysis of lithium-ion battery model uncertainty based on osprey optimization algorithm

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions.

Experimental Study of the Behaviour of Ring Main Unit-Type Panels in the Event of Internal Arcing in Different Compartments

If an R.M.U. (Ring Main Unit) cubicle is installed, operated and maintained in accordance with current standards and the manufacturer’s instructions, the likelihood of internal arcing is reduced, but should not be completely ignored. This article outlines the experimental situations in which arcing can occur and the catastrophic effects seen if R.M.U. is not properly designed for this type of damage. This type of failure can be caused by a fault, abnormal operating conditions or a malfunction and represents a danger during its installation and to persons present. The situations in which this type of failure can occur are presented and experimentally analysed: bi-phase internal arcing in the connections compartment, three-phase internal arcing in the switching compartment, internal arcing in the fuse compartment and single-phase arcing between a phase and the switchgear enclosure.

Optimization of Abnormal Hydraulic Fracturing Conditions of Unconventional Natural Gas Reservoirs Based on a Surrogate Model

Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty.

Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network

The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

An integrated assessment method of real-time source term for high temperature gas-cooled reactor

To address high temperature gas-cooled reactor (HTR) nuclear power plant (NPP) safety analysis and emergency response, this paper proposes an integrated source term assessment (ISTA) method, guided by the principles of reality, rapidity and rationality. The fundamental concept of ISTA is to establish a connection between the theoretical model of source term (ST) calculation and actual conditions of HTR NPPs. According to the ISTA method, STs can be evaluated by mapping key parameters through monitoring variables during any abnormal operational conditions or accident conditions, resulting in a more realistic representation. Furthermore, the output of STs in various typical spaces within an HTR NPP further expands the application scope beyond the development of nuclear emergency protection strategies. In addition, a corresponding tool (ISTAT) for engineering application is developed and the case is given to verify the feasibility. This research highlights the technological advancement affecting radiological release scenarios and avoiding unnecessary conservatism, which also can give reference for other advanced reactor NPPs.

Entropy-Based Methods for Motor Fault Detection: A Review.

In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years.

Anti-Pandemic stringency measures and performance of tourism and hospitality firms worldwide: what makes a firm resilient to a crisis?

ABSTRACT This study delves into the impact of COVID-19 pandemic-induced stringency measures on firm performance within the global tourism and hospitality sector, exploring the factors that shield firms from such measures. The results of a sample drawn from 41 countries reveal a negative association between stringency measures and firm performance. Notably, firms in countries with a stringency score at the 75th percentile are found to exhibit a 263% lower return on assets compared to their counterparts in countries with a stringency score at the 25th percentile. Moreover, larger and established firms, as well as those with high (low) sustainable growth (financial constraints) demonstrate a lower susceptibility to the adverse effects of stringency measures. These results underscore the critical need to customize government support schemes, ensuring they align with the vulnerability levels of businesses during abnormal operating conditions. Such customization will promote fairness and equity among businesses, recognizing their distinct challenges.

Power control strategies for modular-gravity energy storage plant

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research.

Transient simulation and analysis of a supercritical CO2 heat removal system under different abnormal operation conditions

The supercritical carbon dioxide (sCO2) heat removal system, which is based on multiple closed Brayton cycles with sCO2 as the working fluid, is an innovative, self-propelling and modular heat removal system for existing and future nuclear power plants. Previous studies analysed its design, layout, control and operation. In addition, this study considers different sudden failures during the accident progress, e.g. failure of single sCO2 cycles, control systems and valves. These abnormal conditions were investigated with the thermal-hydraulic system code ATHLET for a generic Konvoi pressurized water reactor. In most cases, the failure of a single sCO2 cycle can be compensated. On the one hand, failure of the fans of the gas cooler leads to a pressure increase which may be mitigated by an inventory control system or cycle shutdown. On the other hand, unintended fan speed-up can cause compressor surge without adequate countermeasures. Furthermore, the system can operate under the cyclic blow-off from the steam generator safety valves when the relief valves are not available. Finally, the unintended closure of the valve which controls the steam flow through the compact heat exchanger triggers a fast cycle shutdown but a subsequent restart might be possible.

Dual fault warning method for coal mill based on Autoformer WaveBound

The coal mill is one of the important auxiliary equipment of thermal power units. Power plant performance and reliability are greatly influenced by the coal mill. To avoid abnormal operating conditions of coal mills in time and effectively, a dual fault warning method for coal mill is proposed. Three typical faults of coal mill plugging, coal breakage and deflagration are warned by this method. Firstly, the maximum information coefficient method and double complex wavelet packet transform are employed to approximate the historical data of the coal mill. Secondly, WaveBound regularization is incorporated to improve the generalization capabilities of Autoformer. Normal state data is used in the construction of unsupervised learning models. Then, combined with a multi-step rolling prediction method, vital monitoring parameters are supervised by setting the corresponding thresholds. Furthermore, an abnormal detection method based on Wasserstein distance is developed for coal mills overall state monitoring. A dual alarm method considering univariate abnormal detection and multivariate coupling thresholds is formed. Finally, the proposed method is validated with real data from a medium-speed coal mill. The results illustrate that early warning is effectively carried out using the method.

A Novel Multi-Loop PID Controller for Photovoltaic-Grid Interface DC Energy Utilization Farm

This paper proposes a novel control system for control of a hybrid photovoltaic PV farm utilization with alternative power source for DC type loads. A controller consisting of two different controllers is mainly used to regulate the DC-DC converter and also to control power flow from alternative power source to reduce a weighted total sum of all loop errors, to mainly track a given speed reference trajectory depicting the demand for discharge or flow and also to ensure power quality, reliability and stability. The proposed control function is digitally simulated using the MATLAB/Simulink/SimPower System software environment. The dynamic performance of the hybrid system is examined for the control system validation under normal and abnormal operating conditions.

A Novel Multi-loop Fuzzy Logic Dynamic Controller for Wind/Photovoltaic-Grid Interface DC Energy Utilization Farm

This paper proposes a novel control system for control of a hybrid wind/photovoltaic (PV) farm utilization with alternative power source for DC type loads. A controller consisting of six different loop controllers is mainly used to regulate the DC-DC converter to reduce a weighted total sum of all loop errors, to mainly track a given speed reference trajectory depicting the demand for discharge or flow and also to ensure power quality, reliability and stability. The proposed control function is digitally simulated using the MATLAB/Simulink/ SimPower System software environment. The dynamic performance of the hybrid system is examined for the control system validation under normal and abnormal operating conditions.

Grounding System Modelling and Its Impact on Computational Refunding Analysis for Equipment Damages

This paper is aimed at presenting the computational results of the influence of the grounding systems representation in the refunding request analysis of equipment damages due to distribution system disturbances. The assessments include representation of grounding systems with linear and nonlinear parameters to produce a better representation of the overall electrical system at abnormal operating conditions. The model is included in a existing computational programme to deal with the subject of establishing a relationship between system occurrences, dielectric and thermal impacts on equipments and their withstand capabilities. This paper proposed grounding representation is based on a non-linear approach combining method already published in the literature. The methodology is then implemented in a computational tool facility to deal with the mentioned subject and the new software version is then used to evaluate practical situations. Comparisons between results derived from a typical distribution disturbance using simple ground modelling and the new proposition are carried out so as to highlight the importance of a more realistic modelling for the overall electrical complex.

Attention-based early warning framework for abnormal operating conditions in fluid catalytic cracking units

Fluid Catalytic Cracking Unit (FCCU) is a critical processing technology in the oil refining industry, playing a vital role in energy efficiency and environmental protection. However, FCCU often encounters various abnormal operating conditions, leading to safety hazards, downtime, and reduced production efficiency. Early warning of these abnormal conditions is crucial but challenging due to high noise, strong hysteresis, and class imbalance problems. To tackle these challenges, a novel and universal attention-based framework called AEW-AOC (Attention-based Early Warning for Abnormal Operating Conditions) is specifically designed for FCCU applications. The proposed AEW-AOC framework incorporates three key components: (1) a Self-Correlation Denoiser (SCD) module is proposed to exploit spatiotemporal data correlation to effectively reduce noise; (2) a Convolutional Long Short-Term Memory (Conv-LSTM) module is employed to address the issue of strong hysteresis by capturing temporal variation features of process parameters; (3) an Anomaly Pattern Attention (APA) module is proposed to enhance the distinguishability of abnormal operating conditions based on clustering results from historical abnormal instances. Extensive experiments demonstrate the effectiveness and superiority of the proposed AEW-AOC framework, particularly in practical applications. Specifically, the AEW-AOC framework obtains an impressive fβ score of 91.00% on LIC201, 90.45% on LIC202, and 90.64% on LIC801. The proposed AEW-AOC framework shows great potential in enhancing safety, reducing downtime, optimizing efficiency, promoting sustainability, and expanding its applicability beyond FCCU. Its proactive and versatile nature makes it a valuable tool for improving industrial processes and driving advancements in the field of abnormal operating condition detection and prevention.