On machine learning-based techniques for future sustainable and resilient energy systems

Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified

Combining Empirical and Physics-Based Models for Solar Wind Prediction

Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to enhance solar wind prediction. This innovative method aims to combine the strengths of both modeling approaches to achieve more accurate and efficient solar wind predictions. In this work, we show the variability of the proposed physics-informed loss across multiple deep learning models. We also study the effect of training the models on different solar cycles on the model\u27s performance. This work represents the first effort to predict solar wind by integrating deep learning approaches with physics constraints and analyzing the results across three solar cycles. Our findings demonstrate the superiority of our physics-constrained model over other unconstrained deep learning predictive models

Validation and Verification of Future Integrated Safety-Critical Systems Operating under Off-Nominal Conditions

Loss of control remains one of the largest contributors to aircraft fatal accidents worldwide. Aircraft loss-of-control accidents are highly complex in that they can result from numerous causal and contributing factors acting alone or (more often) in combination. Hence, there is no single intervention strategy to prevent these accidents and reducing them will require a holistic integrated intervention capability. Future onboard integrated system technologies developed for preventing loss of vehicle control accidents must be able to assure safe operation under the associated off-nominal conditions. The transition of these technologies into the commercial fleet will require their extensive validation and verification (V and V) and ultimate certification. The V and V of complex integrated systems poses major nontrivial technical challenges particularly for safety-critical operation under highly off-nominal conditions associated with aircraft loss-of-control events. This paper summarizes the V and V problem and presents a proposed process that could be applied to complex integrated safety-critical systems developed for preventing aircraft loss-of-control accidents. A summary of recent research accomplishments in this effort is also provided

Health Condition Assessment of Multi-Chip IGBT Module with Magnetic Flux Density

To achieve efficient conversion and flexible control of electronic energy, insulated gate bipolar transistor (IGBT) power modules as the dominant power semiconductor devices are increasingly applied in many areas such as electric drives, hybrid electric vehicles, railways, and renewable energy systems. It is known that IGBTs are the most vulnerable components in power converter systems. To achieve high power density and high current capability, several IGBT chips are connected in parallel as a multi-chip IGBT module, which makes the power modules less reliable due to a more complex structure. The lowered reliability of IGBT modules will not only cause safety problems but also increase operation costs due to the failure of IGBT modules. Therefore, the reliability of IGBTs is important for the overall system, especially in high power applications. To improve the reliability of IGBT modules, this thesis proposes a new health state assessment model with a more sensitive precursor parameter for multi-chip IGBT module that allows for condition-based maintenance and replacement prior to complete failure. Accurate health condition monitoring depends on the knowledge of failure mechanism and the selection of highly sensitive failure precursor. IGBT modules normally wear out and fail due to thermal cycling and operating environment. To enhance the understanding of the failure mechanism and the external characteristic performance of multi-chip IGBT modules, an electro-thermal finite element model (FEM) of a multi-chip IGBT module used in wind turbine converter systems was established with considerations for temperature dependence of material property, the thermal coupling effect between components, and the heat transfer process. The electro-thermal FEM accurately performed temperature distribution and the distribution electrical characteristic parameters during chip solder degradation. This study found an increased junction temperature, large change of temperature distribution, and more serious imbalanced current sharing during a single chip solder aging, thereby accelerating the aging of the whole IGBT module. According to the change of thermal and electrical parameters with chip solder fatigue, the sensitivity of fatigue sensitive parameters (FSPs) was analyzed. The collector current of the aging chip showed the highest sensitivity with the chip solder degradation compared with the junction temperature, case temperature, and collector-emitter voltage. However, the current distribution of internal components remains inaccessible through direct measurements or visual inspection due to the package. As the relationship between the current and magnetic field has been studied and gradually applied in sensor technologies, magnetic flux density was proposed instead of collector current as a new precursor for health condition monitoring. Magnetic flux density distribution was extracted by an electro-thermal-magnetic FEM of the multi-chip IGBT module based on electromagnetic theory. Simulation results showed that magnetic flux density had even higher sensitivity than collector current with chip solder degradation. In addition, the magnetic flux density was only related with the current and was not influenced by temperature, which suggested good selectivity. Therefore, the magnetic flux density was selected as the precursor due to its better sensitivity, selectivity, and generality. Finally, a health state assessment model based on backpropagation neural network (BPNN) was established according to the selected precursor. To localize and evaluate chip solder degradation, the health state of the IGBT module was determined by the magnetic flux density for each chip and the corresponding operating conduction current. BPNN featured good self-learning, self-adapting, robustness and generalization ability to deal with the nonlinear relationship between the four inputs and health state. Experimental results showed that the proposed model was accurate and effective. The health status of the IGBT modules was effectively recognized with an overall recognition rate of 99.8%. Therefore, the health state assessment model built in this thesis can accurately evaluate current health state of the IGBT module and support condition-based maintenance of the IGBT module

A review on Day-Ahead Solar Energy Prediction

Accurate day-ahead prediction of solar energy plays a vital role in the planning of supply and demand in a power grid system. The previous study shows predictions based on weather forecasts composed of numerical text data. They can reflect temporal factors therefore the data versus the result might not always give the most accurate and precise results. That is why incorporating different methods and techniques which enhance accuracy is an important topic. An in-depth review of current deep learning-based forecasting models for renewable energy is provided in this paper

Enhancing Grid Reliability With Phasor Measurement Units

Over the last decades, great efforts and investments have been made to increase the integration level of renewable energy resources in power grids. The New York State has set the goal to achieve 70% renewable generations by 2030, and realize carbon neutrality by 2040 eventually. However, the increased level of uncertainty brought about by renewables makes it more challenging to maintain stable and robust power grid operation. In addition to renewable energy resources, the ever-increasing number of electric vehicles and active loads have further increased the uncertainties in power systems. All these factors challenge the way the power grids are operated, and thus ask for new solutions to maintain stable and reliable grids. To meet the emerging requirements, advanced metering infrastructures are being integrated into power grids that transform traditional grids into \u27\u27 smart grids . One example is the widely deployed phasor measurement units (PMUs), which enable generating time-synchronized measurements with high sampling frequency, and pave a new path to realize real-time monitoring and control in power grids. However,the massive data generated by PMUs raises the questions of how to efficiently utilize the obtained measurements to understand and control the present system. Additionally, to meet the communication requirements between the advanced meters, the connectivity of the cyber layer has become more sophisticated, and thus is exposed to more cyber-attacks than before. Therefore, to enhance the grid reliability with PMUs, robust and efficient grid monitoring and control methods are required. This dissertation focuses on three important aspects of improving grid reliability with PMUs: (1) power system event detection; (2) impact assessment regarding both steady-state and transient stability; and (3) impact mitigation. In this dissertation, a comprehensive introduction of PMUs in the wide-area monitoring system, and comparisons with the existing supervisory control and data acquisition (SCADA) systems are presented first. Next, a data-driven event detection method is developed for efficient event detection with PMU measurements. A text mining approach is utilized to extract event oscillation patterns and determine event types. To ensure the integrity of the received data, the developed detection method is further designed to identify the fake events, and thus is robust against cyber-threat. Once a real event is detected, it is critical to promptly understand the consequences of the event in both steady and dynamic states. Sometimes, a single system event, e.g., a transmission line fault, may cause subsequent failures that lead to a cascading failure in the grid. In the worst case, these failures can result in large-scale blackouts. To assess the risk of an event in steady state, a probabilistic cascading failure model is developed. With the real-time phasor measurements, the failure probability of each system component at a specific operating condition can be predicted. In terms of the dynamic state, a failure of a system component may cause generators to lose synchronism, which will damage the power plant and lead to a blackout. To predict the transient stability after an event, a predictive online transient stability assessment (TSA) tool is developed in this dissertation. With only one sample of the PMU voltage measurements, the status of the transient stability can be predicted within cycles. In addition to the impact detection and assessment, it is also critical to identify proper mitigations to alleviate the failures. In this dissertation, a data-driven model predictive control strategy is developed. As a parameter-based system model is vulnerable to topology errors, a data-driven model is developed to mimic the grid behavior. Rather than utilizing the system parameters to construct the grid model, the data-driven model only leverages the received phasor measurements to determine proper corrective actions. Furthermore, to be robust against cyber-attacks, a check-point protocol, where past stored trustworthy data can be used to amend the attacked data, is utilized. The overall objective of this dissertation is to efficiently utilize advanced PMUs to detect, assess, and mitigate system failure, and help improve grid reliability

Integrated Socio-environmental Vulnerability Assessment of Coastal Hazards Using Data-driven and Multi-criteria Analysis Approaches

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals’ biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers\u27 opinions

Development of horizontal axis hydrokinetic turbine using experimental and numerical approaches

“Hydrokinetic energy conversion systems (HECSs) are emerging as viable solutions for harnessing the kinetic energy in river streams and tidal currents due to their low operating head and flexible mobility. This study is focused on the experimental and numerical aspects of developing an axial HECS for applications with low head ranges and limited operational space. In Part I, blade element momentum (BEM) and neural network (NN) models were developed and coupled to overcome the BEM’s inherent convergence issues which hinder the blade design process. The NNs were also used as a multivariate interpolation tool to estimate the blade hydrodynamic characteristics required by the BEM model. The BEM-NN model was able to operate without convergence problems and provide accurate results even at high tip speed ratios. In Part II, an experimental setup was developed and tested in a water tunnel. The effects of flow velocity, pitch angle, number of blades, number of rotors, and duct reducer were investigated. The performance was improved as rotors were added to the system. However, as rotors added, their contribution was less. Significant performance improvement was observed after incorporating a duct reducer. In Part III, a computational fluid dynamics (CFD) simulation was conducted to derive the optimum design criteria for the multi-turbine system. Solidity, blockage, and their interactive effects were studied. The system configuration was altered, then its performance and flow characteristics were investigated. The experimental setup was upgraded to allow for blockage correction. Particle image velocimetry was used to investigate the wake velocity profiles and validate the CFD model. The flow characteristics and their effects on the turbines performance were analyzed”--Abstract, page iv