Historical Operation Data Research Articles

An Association Rule Mining-Based Method for Revealing the Impact of Operational Sequence on Nuclear Power Plants Operating

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators.

THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation.
 Keywords: Predictive Maintenance, System, ECOPFM, Technology.

Ultra-short-term operation situation prediction method of active distribution network based on convolutional neural network long short term memory

The large-scale and high-permeability access of distributed generations (DGs) is making the distribution networks develop into active distribution networks (ADNs). The increasing complexity of the structure of ADNs also increases the vulnerabilities for cyberattacks. To accurately predict the operation situation of ADNs no matter whether cyberattacks occurred, this paper proposes an ultra-short-term operation situation prediction method of ADNs based on CNN-LSTM. Firstly, an ADNs situation prediction index system is established, which can comprehensively evaluate the future operation situation of ADNs from different perspectives. Secondly, a multi-feature data reconstruction method based on the time sliding window algorithm is proposed, which is utilized for constructing 2-dimensional matrix and as input datasets for multiple independent CNN-LSTM models to predict each situation prediction index. Then, there are 8 independent CNN-LSTM models shared same input matrix to predict 8 ADNs situation prediction index value without interfering with each other. Among them, CNN is used to extract the spatial correlation feature vectors of ADNs historical operation data and meteorological factor data in ultra-short term, which are reconstructed in time series and used as the input data of LSTM. LSTM is used to extract long-term historical temporal relationship to obtain ADNs operation situation prediction index value. Finally, we utilize the fuzzy analytic hierarchy process to calculate the future operation situation of ultra-short-term ADNs. Taking a real ADNs with 33-node as an example, we compared our proposed methodology to other existing excellent machine learning-based models, the result demonstrates that our proposed prediction method exhibits outstanding performance in time consumption and prediction accuracy.

Time Series Feature Selection Method Based on Mutual Information

Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.

A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction

With the continuous global increase in installed wind power capacity and subsequent surge in power generation, the contradiction between the safe operation of the grid and the efficient consumption of new energy after large-scale grid connection has become increasingly prominent. This paper presents a hybrid model prediction method to further improve the stability and accuracy of wind power prediction. Firstly, variational modal decomposition (VMD) optimized by the coati optimization algorithm (COA) is employed to decompose original wind power, mitigating the non-stationary characteristics of the power sequence. Subsequently, permutation entropy (PE) is used to recombine the decomposed components, and the combined feature selection method is achieved by integrating the Spearman correlation coefficient (SCC) and the autocorrelation function (ACF). Then, the multivariate combined model is constructed, and the improved multi-objective coati optimization algorithm (IMOCOA) determines the weight coefficients of each model to enhance the performance of the hybrid model. Finally, research and analysis are conducted from multiple scenarios and time scales using historical operating data from a wind farm in Xinjiang. The experimental results show that the proposed prediction model effectively improves the accuracy and stability of the wind power prediction compared with other popular prediction models.

Determining the effects of U/Pu ratio on subsolidus phase transitions in U-Pu-Zr metallic fuel alloys

Ternary alloys consisting primarily of uranium, plutonium, and zirconium (U-Pu-Zr) are among the leading candidate fuel systems considered for fast spectrum nuclear reactors. Despite historical operation data from the testing of U-Pu-Zr rods in the Experimental Breeder Reactor-II, considerable uncertainty about the evolution of phases and microstructure across the ternary composition space exists. Due to sluggish kinetics and other difficulties in handling metal actinide specimens, quantitative measurements of phase-transitions in U-Pu-Zr alloys remain sparse in scientific literature, with most investigators reporting either phase-transition temperatures or phase identification data, but not both from the same specimens. The purpose of this paper is to critically compare experimental and calculated phase transition data and correlate with the microstructure and phase characterization data of as-cast and annealed U-Pu-Zr alloys. Phase transition peaks were measured using differential scanning calorimetry in the subsolidus regions (723−948 K) of three ternary U-Pu-Zr alloys with the same zirconium concentration but various U/Pu ratios. Overlapping peaks were deconvoluted using a Frazier-Suzuki peak fitting algorithm, and the critical peak temperatures and enthalpies were calculated. In general, increasing concentrations of Pu were associated with enhanced thermal stability of the body-centered cubic γ phase upon both heating and cooling. Experimental phase transition temperatures in this study tended to agree well with the predictions of the established ternary phase diagrams and other reported phase transition temperatures in literature. Additionally, the TAF-ID thermodynamic database was used to calculate a U-Pu-40 at.% Zr pseudobinary diagram as well as ternary diagrams from 773 to 973 K. The equilibrium phase transition temperatures tended to be considerably lower than measured peak temperatures upon both heating and cooling. Recommendations for improving the quality of data in future U-Pu-Zr characterization studies are also discussed.

Extracting operation rule of cascade reservoirs using a novel framework considering hydrometeorological spatiotemporal information based on artificial intelligence models

The scientific operation of cascade reservoirs is of great significance for completely utilizing water resources and maximizing their comprehensive benefits. The extraction of reservoir operation rules from massive historical operational data still remains a challenge. Therefore, a novel framework for extracting the operation rules of cascade reservoirs was proposed in this study based on artificial intelligence models by considering hydrometeorological spatiotemporal information. First, the observed precipitation was obtained by merging multi-source precipitation products through a convolutional long-short-term memory (ConvLSTM) network. Second, a novel extraction model by considering hydrometeorological spatiotemporal information was established, comprising a process of capturing the spatiotemporal information of interval precipitation using ConvLSTM and a feature aggregation process of aggregating precipitation and reservoir historical operation information using an LSTM network. Finally, three different extraction strategies (recursive, direct, and multi-input multi-output (MIMO) strategies) for cascade reservoir operation rules were developed and compared, and the influence of future multi-step forecast information was analyzed. Considering the example of Yalong River, the results are as follows: (1) the proposed extraction model has excellent fitting performance with a Nash-Sutcliffe model efficiency coefficient (NSE) of more than 0.95 and mean relative error (MRE) of less than 10 %, especially in reservoirs with large regulation capacity; (2) according to the regulation capacity of the reservoirs and reliability of forecast precipitation, appropriately adding multi-step forecast information can improve the performance of the extraction models; (3) each reservoir's NSE when using the three strategies is above 0.9, indicating that they can all be used to extract the operation rules of the cascade reservoir. Among them, the direct strategy is optimal.

Week-level early warning strategy for thermal runaway risk based on real-scenario operating data of electric vehicles

Effective detecting thermal runaway risk in batteries are crucial for the rapid development and widespread adoption of electric vehicles. In this study, a strategy based on signal analysis is developed to realize the early warning of battery thermal runaway risk at the weekly level, without being limited by battery material systems. Firstly, a longitudinal outlier average method is developed to quantify the potential risk of thermal runaway in batteries and compared with a preset threshold to identify cells with performance anomalies. Secondly, an alarm assessment mechanism is developed, which integrates ongoing and historical operating data of suspicious cells across multiple decision layers. By employing an improved information entropy weighting method, this mechanism provides a comprehensive assessment of battery pack consistency, addressing issues related to false alarms and sporadic alerts. Finally, the effectiveness of this strategy is validated through actual vehicles involved in thermal runaway.

Airport Surface Arrival and Departure Scheduling Using Extended First-Come, First-Served Scheduler

This paper demonstrates the effectiveness of the Extended First-Come, First-Served (EFCFS) scheduler for integrated arrival and departure scheduling by comparing the scheduling results with the recorded operational data at Incheon International Airport (ICN), Republic of Korea. The EFCFS scheduler can handle multiple capacity- or flow-rate-related constraints along the path of each flight, which is represented by a node–link graph structure, and can solve large-scale problems with low computational cost. However, few studies have attempted a systematic verification of the EFCFS scheduler by comparing the scheduling results with historical operational data. In this paper, flights are scheduled between gates and runways on the airport surface with detailed constraints such as runway wake turbulence separation minima and conflict-free taxiing. The scheduler is tested using historical flight data from 15 August 2022 at ICN. The input schedule is generated based on the flight plan data extracted from the Flight Operation Information System (FOIS) and airport surface detection equipment data, and the results are compared with the times extracted from the FOIS data. The scheduling results for 500 aircraft show that the average takeoff delay is reduced by about 19 min, while the average landing delay is increased by less than one minute when the gate occupancy constraint is not considered. The results also confirm that the EFCFS effectively utilizes the available time slots to reduce delays by switching the original departure or arrival orders for a small number of flights.

Deep Learning Based Effective Technique for Smart Grid Contingency Analysis Using RNN with LSTM

Smart grids play a crucial role in modernizing power systems, yet their susceptibility to disruptions necessitates effective contingency analysis for reliable operation. Traditional algorithms lack prediction accuracy for mitigation. To address this, a proposed method employs LSTM networks within a DL RNN framework, utilizing a logical collection of pertinent data—historical smart grid operation data, sensor measurements, weather conditions, and contingency events. This data undergoes preprocessing to train an LSTM-based DL RNN model capable of capturing temporal dependencies and complex dynamics within the smart grid system. The trained model yields a substantial improvement in prediction accuracy, increasing from 88% to 93%, while decreasing false positive and false negative rates from 3.2% to 1.25% with optimal time detection. Leveraging deep learning and recurrent neural networks, this approach offers accurate predictions, proactive decision support, and efficient restoration strategies, ultimately elevating the resilience and reliability of smart grid systems. The solution’s dataset encompasses a comprehensive array of relevant information. The neural network architecture integrates DL RNN with LSTM networks, showcasing its efficacy in enhancing prediction accuracy for smart grid contingency analysis. Evaluation metrics such as RMSE and MAPE providing a reliable foundation for proactive decision-making and fortifying the smart grid’s resilience against various contingencies.

Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics

Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detection and diagnostic algorithms, along with energy optimization techniques. This study presents labelled and long-term ULT-freezer performance dataset, the first of its kind, derived from 53 ULT freezers featuring two different control strategies. The dataset comprises high-resolution historical operation data spanning up to 10 years. More than 10 attributes are recorded from the freezing chamber and critical locations in the refrigeration systems. The dataset is labelled with regular events, such as door openings, as well as fault events obtained from 46 service reports. A scalable data pipeline, consisting of extraction, transformation, and loading processes, is developed to convert the raw data into a format ready for analysis. The dataset can be utilized to support the development of data-driven models and algorithms that advance the intelligent digital operation of ULT freezers.

Control Optimization of Air Supply System Based on Asymptotic Method Identification

In order to optimize the control quality of the air supply system aiming at its multi-disturbance, based on the historical operation data of the boiler air supply control system of a 300 MW unit, this paper adopts a two-stage identification method based on asymptotic theory and establishes an accurate model and simulation system of the air supply system. Based on the model established by asymptotic identification, we design a constrained Model Predictive Control with Kalman Filter state estimation and an Active Disturbance Rejection Control based on extended state disturbance observation two optimization schemes. The simulation based on field data shows that both schemes can be considered to possess comparatively higher practicability in the optimization of the air supply control system.

Fault detection and diagnosis of energy system based on deep learning image recognition model under the condition of imbalanced samples

Faults in energy systems impact the reliability of the energy supply and cause energy waste. Data-driven fault detection and diagnosis (FDD) methods can detect and diagnose system faults by mining historical operational data. However, the quantitative imbalance between fault and normal samples degrades the performance of the FDD method. Therefore, this study proposes a novel deep learning-based FDD method under the condition of imbalanced samples that converts the time-series signals into an image signal to extract the timing and coupling features, and then applies the improved conditional variational autoencoder-generative adversarial network (CVAE-GAN) to generate fault samples for balancing the training sample set. Subsequently, according to the framework of image recognition, a two-dimensional convolutional neural network was adopted to identify the image samples and achieve FDD. The experimental results showed that, under the condition of imbalanced samples, the proposed method could increase diagnosis accuracy by an average of 5.71% compared with other common data-driven methods. After the samples were augmented with the improved CVAE-GAN, the accuracy improved by an average of 3.79%. Consequently, the feasibility and superiority of the proposed method were demonstrated.

Why generative AI can make creative destruction more creative but less destructive

AbstractThe application of machine learning (ML) to operational data is becoming increasingly important with the rapid development of artificial intelligence (AI). We propose a model where incumbents have an initial advantage in ML technology and access to (historical) operational data. We show that the increased application of ML for operational data raises entrepreneurial barriers that make the creative destruction process less destructive (less business stealing) if entrepreneurs have only limited access to the incumbent’s data. However, this situation induces entrepreneurs to take on more risk and to be more creative. Policies making data generally available may therefore be suboptimal. A complementary policy is one that supports entrepreneurs’ access to ML, such as open source initiatives, since doing so would stimulate creative entrepreneurship.

Gaussian Noise Data Augmentation-Based Delay Prediction for High-Speed Railways

Accurately predicting delays for high-speed railways (HSRs) is a challenging yet significant task. The historical operation data of the HSRs, implicating delay derivation rules under the dispatchers’ rescheduling strategies, have sparsity characteristics, resulting in heterogeneous prediction performances under different scenarios. This article proposes a Gaussian noise data augmentation-based delay prediction method to cope with the sparsity. Specifically, the Gaussian noise is added to the original data based on the train operation data characteristics. Then, the delay data rather than the full-state dataset are selected as the training data for different designed machine learning prediction models. Numerous studies based on real HSR operational data from the Beijing Railway Bureau show that the proposed method could significantly improve the prediction accuracy under different scenarios with different machine learning models, verifying the effectiveness of the performance improvement. The relevant results could be significantly helpful for real-time train rescheduling and passenger management, thus improving the emergency response capabilities of HSRs.

A new model predictive control approach integrating physical and data-driven modelling for improved energy performance of district heating substations

District heating (DH) substations play a crucial role in ensuring the efficient and effective distribution of thermal energy necessary to provide space heating for buildings. However, optimizing their operation for energy savings while still ensuring indoor comfort poses significant challenges due to the complex dynamics of building demand and the inertia of building envelopes. To address these challenges, this study introduces a novel model predictive control (MPC) approach that combines a reduced-order physical model with a machine learning-based data-driven model to jointly optimize the operation parameters of a DH substation. In this approach, a reduced-order physical model is first used to capture essential operational principles and energy behaviors of the DH substations and generate candidate solutions for the control of the DH substations. Then, a data-driven model is constructed by integrating a Long Short-Term Memory model and a Back-propagation Neural Network, leveraging historical operational data of the DH substation concerned. The data-driven model is further formulated into a data-driven MPC framework to identify optimal control solutions from all candidates provided by the physical model. To evaluate the proposed approach, a data-driven surrogate model is developed using real operational data. Comparative analysis against the original fuzzy rule-based control strategy and a pure data-driven strategy demonstrates a substantial reduction in heat consumption of 4.77% and 19.47%, respectively. Moreover, compared with using a reduced-order physical model alone, this approach achieves additional benefits in reducing the energy consumption of the DH substation and minimizing indoor temperature fluctuations within the end-users.

Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station

Reasonable condition adjustment of the auxiliary machinery can optimize the operation performance of the Combined Cycle Gas Turbine (CCGT) power station. In this paper an optimization method for the operation control of the auxiliary equipment based on deep learning is proposed and applied to an F-class gas turbine generation unit. A database of the optimal operating conditions is established to achieve the best economic benefit based on the analysis of two years of historical operation data. The control optimization models are built using the machine learning algorithms including the Multi-Layer Perceptron (MLP), Supporting Vector Machine (SVM), Gaussian process regression and linear regression methods and their accuracy has been compared. The input parameters of these models consist of the load rate, the heating output and the ambient temperature, with the output parameters including the electrical currents for the mechanical draft cooling tower, the high pressure feed pump, the medium pressure feed pump and the condensate pump. The MLP model is designed with different network hidden layer structures and provides the highest calculation accuracy with the least average error of 1.82 %. Different training data sizes are compared and the optimal control trajectory is analyzed. The result can provide useful references for the optimization of auxiliary equipment operations.

Data-Driven Modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin

Confronted with the climate crisis, the world is making tremendous efforts in energy transition, such as expanding renewable energy that does not emit carbon. The importance of virtual power plant (VPP) operation technology has emerged to secure grid flexibility in response to the expanding renewable energy implemented due to these efforts. Accordingly, VPPs, which include photovoltaics, wind turbines, heating, ventilation, and air conditioning (HVAC), load, and EV, have been constructed. HVAC, one of the component resources, is a system that controls and regulates temperature, humidity, and airflow. Since it responds sensitively to the building’s heat capacity and changes in the external environment, it requires continuous and stable control. In this paper, we used data-based modeling to implement the HVAC required for the optimal operation of VPP. Since accurately creating an equation-based HVAC model was difficult considering building information modeling and external environment variables, we used historical HVAC operation data to perform data-based modeling. The model was implemented using nonlinear regression and machine learning, such as a support vector machine and artificial neural network. Then, the data-based HVAC and the actual HVAC operation results were comparatively analyzed based on a case study, and the model’s goodness-of-fit was evaluated based on performance metrics. Model performance indicators confirmed that the ANN-based HVAC model was most similar to the actual HVAC system.

Fault Diagnosis of Medium Voltage Circuit Breakers Based on Vibration Signal Envelope Analysis.

In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward a fault diagnosis approach based on vibration signal envelope analysis, including offline fault feature training and online fault diagnosis. During offline fault feature training, the envelope of the vibration signal is extracted from the historic operation data of the MVCB, and then the typical fault feature vector M is built by using the wavelet packet-energy spectrum. In the online fault diagnosis process, the fault feature vector T is built based on the extracted envelope of the real-time vibration signal, and the MVCB states are assessed by using the distance between the feature vectors T and M. The proposed method only needs to handle the envelope of the vibration signal, which dramatically reduces the signal bandwidth, and then the cost of the processing hardware and software could be cut down.

Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection

Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection.