Sensitivity-based Approach Research Articles

Sensitivity-Based Electromagnetic Performance Calculation Model for Radome-Covered Array Antennas

Antenna design and optimization must ensure robust electrical performance, making its analysis a crucial step in all antenna design processes. Traditionally, this analysis involves setting up various cases after establishing the calculation model, comparing the performance of each case, and summarizing the impact of relevant factors to guide design and optimization. However, this method is time-consuming and inefficient. This paper proposes a sensitivity-based approach for analyzing antenna electrical performance, using a radome-covered array antenna as an example. First, we derive the formulas for calculating the antenna’s electrical performance and its sensitivity to the current amplitude, array element position, and radome thickness. We then design comparative experiments to analyze the antenna’s performance using the sensitivity-based method and the traditional case enumeration method. Comparing the conclusions of both methods, we find that they yield the same results regarding antenna performance. The proposed sensitivity-based method offers a quantitative evaluation of various influencing factors and provides a more scientific and systematic approach to analyzing antenna electrical performance.

Hydrologic Sensitivity of a Critical Turkish Watershed to Inform Water Resource Management in an Altered Climate

This study introduces a novel sensitivity analysis approach to assess the resilience and susceptibility of hydrologic systems to the stresses of climate change, moving away from conventional top-down methodologies. By exploring the hydrological sensitivity of the upper Kızılırmak River basin using the Variable Infiltration Capacity (VIC) hydrologic model, we employed a sensitivity-based approach as an alternative to the traditional Global Climate Model (GCM)-based methods, providing more insightful information for water managers. Considering the consistent projections of increasing temperature over this region in GCMs, the hydrologic system was perturbed to examine gradients of a more challenging climate characterized by warming and drying conditions. The sensitivity of streamflow, snow water equivalent, and evapotranspiration to temperature (T) and precipitation (P) variations under each perturbation or “reference” climate was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. As the reference climates become drier, streamflow sensitivity to P increases, indicating that meteorological drought impacts on water availability could be exacerbated. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: (1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, (2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and (3) streamflow sensitivity to hydroclimatic variability will increase, meaning that there will be more extreme impacts to water availability. Water managers will need to plan for a larger set of extreme conditions.

Distributed coordinated control of mixed expressway and urban roads: a sensitivity-based approach

The inherent interdependency between mixed expressway and urban road networks calls for an effective coordinated traffic control approach. This study introduces a sensitivity-based distributed method to coordinate ramp metering and traffic signal control. Firstly, the quantitative relationships between network nodes are established through a developed traffic flow model for mixed networks. The network-level sensitivity information is analyzed to assess the influence of controllers on the traffic state. Additionally, a dynamic subproblems decomposition algorithm is proposed, considering the network’s spatial structure and dynamic traffic characteristics. By employing community detection techniques, tightly connected controllers are clustered to enable efficient and adaptive distributed control. The optimization problem is then decomposed into several subproblems, each formulated as a bi-level programming problem within a model predictive control framework. Numerical experiments demonstrate that the proposed strategy enhances the traffic efficiency of mixed networks with a 14.5% reduction in total travel time during peak hours, which shows better performance compared with the other three existing control strategies.

Nonlinear seismic analysis of city gate architectural heritages using a sensitive-based model updating method

Due to the unknown accumulation of internal damage and severe degradation of structural mechanisms, updating numerical model related to current damage using Operational Modal Analysis and performing nonlinear seismic analysis pose significant challenges. In this paper, a nonlinear seismic analysis method is proposed involving a three-stage finite element model updating process. This method not only updates the global parameters and local parameters of substructure, but also updates the FEM to true current damage state based on elastic-plastic constitutive model considering current damage. First, on-site vibration and material performance tests were conducted to obtain modal data and material properties of the structure. Second, each component in the model was updated to detect damage, employing a sensitivity-based approach in Stage 1. Then, each element within the severely damaged components obtained from Stage 1 was updated for damage detection, again using a sensitivity-based approach in Stage 2. Finally, the current damage state was updated based on elastoplastic constitutive model considering the current damage and a seismic analysis was performed in Stage 3. The results indicate that, in Stage 1, updating the global parameters of the model aligns the calculated data with the measured data. In Stage 2, updating the local parameters to accurately identify the location and severity of damage within the heavily affected components is demonstrated by a simple example. These results can establish a scientific foundation for the nonlinear seismic analysis of city gate architectural heritages.

Dynamic Management of Flexibility in Distribution Networks through Sensitivity Coefficients

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system.

A Sensitivity-Based Approach to Self-Triggered Nonlinear Model Predictive Control

A Sensitivity-Based Approach to Self-Triggered Nonlinear Model Predictive Control

A sensitivity-based approach to optimal sensor selection for complex processes

Sensor selection is critical for state estimation, control and monitoring of nonlinear processes. However, it is impractical to evaluate the performance of all the combinations of the potentially available sensors using exhaustive search unless the sensor set is small. In this paper, we present a sensitivity-based approach for determining the minimum number of sensors and their optimal locations for state estimation. The observability is measured using the local sensitivity matrix of the output measurements to initial states. Based on this matrix, we determine the minimum number of sensors required to achieve full column rank. The optimal sensor placement is thought to be the sensor set that provides the maximum degree of observability among all sets that meet the full-rank requirement. The computational complexity of sensor selection is significantly reduced by successive orthogonalization of the columns of the sensitivity matrix. To validate the effectiveness of the proposed method, it is applied to two processes: four continuous stirred-tank reactors and a wastewater treatment plant. In both cases, the proposed approach can obtain the optimal sensor subset.

From “Is It Unconfounded?” to “How Much Confounding Would It Take?”: Applying the Sensitivity-Based Approach to Assess Causes of Support for Peace in Colombia

Previous articleNext article No AccessFrom "Is it unconfounded?" to "How much confounding would it take?": Applying the sensitivity-based approach to assess causes of support for peace in ColombiaChad Hazlett and Francesca ParenteChad Hazlett Search for more articles by this author and Francesca Parente Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Journal of Politics Just Accepted Sponsored by the Southern Political Science Association Article DOIhttps://doi.org/10.1086/723995 Views: 16Total views on this site HistoryAccepted December 12, 2022 © 2023 Southern Political Science Association. All Rights reserved.PDF download Crossref reports no articles citing this article.

Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design

In this work, we propose a sensitivity-based approach to construct reduced-order state estimators based on recurrent neural networks (RNN). It is assumed that a mechanistic model is available but is too computationally complex for estimator design and that only some target outputs are of interest and should be estimated. A reduced-order estimator that can estimate the target outputs is sufficient to address such a problem. We introduce an approach based on sensitivity analysis to determine how to select the appropriate inputs and outputs for data collection and data-driven model development to estimate the desired outputs accurately. Specifically, we consider the long short-term memory (LSTM) neural network, a type of RNN, as the tool to train the data-driven model. Based on it, an extended Kalman filter, a state estimator, is designed to estimate the target outputs. Simulations are carried out to illustrate the effectiveness and applicability of the proposed approach.

Input-output selection for LSTM-based reduced-order state estimator design

In this paper, we present a sensitivity-based approach to construct reduced-order state estimators based on recurrent neural networks (RNNs). Our approach assumes that a mechanistic model is available but too computationally complex for estimator design and that only a subset of the states needs to be estimated. To address this problem, a reduced-order estimator that can estimate the target outputs is sufficient. We propose an approach based on sensitivity analysis to determine the appropriate inputs and outputs for data collection and data-driven model training so that the target outputs can be estimated accurately. In particular, we consider RNN networks as the tool to train the data-driven model and extended Kalman filter (EKF) as the estimator. Simulations are carried out to illustrate the effectiveness of the proposed approach.

Short-term load forecasting for multiple buildings: A length sensitivity-based approach

With the rapid development of large-scale building energy monitoring platforms, it is of great significance to develop precise forecasting methods for buildings on a large scale to achieve better energy system design, system operation, energy management, and renewable energy integration in the grid. Traditionally, using all available historical data to train a data-driven model has been widely employed to ensure prediction performance because more historical information can be learned. However, this strategy may introduce more noise, especially for short-term load forecasting. Thus, this study proposes a novel approach for selectively utilizing building historical data to determine the amount of data that should be used to train the data-driven model. First, the CV(RMSE) curve of each building reflecting the relationship between training data length and forecasting accuracy is obtained using LightGBM. Second, clustering techniques such as k-means are used to identify buildings that are sensitive to the training data length based on CV(RMSE) curves. Finally, the optimal training data length for day-ahead forecasting is estimated for each building. The case study shows that approximately 20% of buildings in the Building Data Genome are labeled as length-sensitive buildings, and adopting appropriate training data lengths can reduce the prediction error of these buildings by up to 15%.

A Three-Phase Sensitivity-Based approach for smooth Line-Switching in islanded networks

This paper deals with a new smooth line-switching method that facilitates the network reconfiguration of islanded networks. Its distinct features include the ability to handle network asymmetries and to minimize the line current during the switching action. This is attained by developing a three-phase sensitivity-based method to determine the operating set-points of distributed generators (DGs) that minimize the current of the candidate line participating in the switching action. These set-points correspond to the positive-sequence powers as well as to the negative- and zero-sequence currents of DGs. Furthermore, the network constraints such as voltage limits and power limits of DGs are always satisfied. Simulations are performed in a balanced 33-bus islanded network as well as in the unbalanced IEEE 8500-node network to evaluate the performance of the proposed method.

Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis

Systematic uncertainty analysis can be used to quantitatively evaluate variation in model outputs and identify the critical sources of uncertainty to improve the reliability and stability of a system. To analyze the effects of uncertainties in transit networks that may be caused by probabilistic travel demand, congestion, or vehicle frequencies, this study develops a sensitivity-based uncertainty analysis approach as a post-analysis tool for equilibrium transit systems. The congestion effect is considered in the waiting time and in-vehicle travel time of a passengers’ route-choice model. The hyperpath concept is used to manage passengers’ riding strategies due to the common-line problem at transit stops. A hyperpath-based gradient projection (GP) solution algorithm is developed for the solution of the variational inequality formulation of the transit equilibrium assignment problem (TEAP). A restricted sensitivity analysis approach originally developed for road networks is re-developed for the TEAP in transit networks. An analytical sensitivity-based approach is derived to conduct uncertainty analysis for the TEAP, which enables the simultaneous propagation of uncertainties from different input sources to the model outputs. Numerical examples are provided for the following purposes. (1) To demonstrate three applications of the sensitivity analysis of the TEAP, namely the perturbed solution estimation problem, the critical parameter identification problem, and the paradox analysis problem. (2) To illustrate the use of uncertainty analysis of the TEAP, such as estimating the variance and confidence level of model outputs with respect to various model inputs/parameters as random variables, and ranking the importance of arcs using sensitivity-based uncertainty analysis. (3) To demonstrate the applicability of the proposed approach to real transit networks. The findings demonstrate not only the importance of the analytical sensitivity analysis development for the TEAP, but also for the practical applications of sensitivity and uncertainty analyses.

Global Simultaneous Optimization of Oil, Hysteretic and Inertial Dampers Using Real-Valued Genetic Algorithm and Local Search

A method for global simultaneous optimization of oil, hysteretic and inertial dampers is proposed for building structures using a real-valued genetic algorithm and local search. Oil dampers has the property that they can reduce both displacement and acceleration without significant change of natural frequencies and hysteretic dampers possess the characteristic that they can absorb energy efficiently and reduce displacement effectively in compensation for the increase of acceleration. On the other hand, inertial dampers can change (prolong) the natural periods with negative stiffness and reduce the effective input and the maximum acceleration in compensation for the increase of deformation. By using the proposed simultaneous optimization method, structural designers can select the best choice of these three dampers from the viewpoints of cost and performance indices (displacement, acceleration). For attaining the global optimal solution which cannot be attained by the conventional sensitivity-based approach, a method including a real-valued genetic algorithm and local search is devised. In the first stage, a real-valued genetic algorithm is used for searching an approximate global optimal solution. Then a local search procedure is activated for enhancing the optimal character of the solutions by reducing the total quantity of three types of dampers. It is demonstrated that a better design from the viewpoint of global optimality can be obtained by the proposed method and the preference of damper selection strongly depends on the design target (displacement, acceleration). Finally, a multi-objective optimization for the minimum deformation and acceleration is investigated.

Nodal Sensitivity-Based Smart Inverter Control for Voltage Regulation in Distribution Feeder

Overvoltage is one of the issues in distribution grids with high penetration of photovoltaics (PVs). Centralized or droop-based methods of active power curtailment (APC) and/or reactive power control of PVs are viable solutions to prevent overvoltage. This article proposes two distributed methods to control PV inverters, which are based on nodal sensitivities. Then, the performance of the proposed methods is compared with two commonly used control methods, i.e., a distributed method that follows IEEE-1547 but uses arbitrarily chosen droops and a centralized optimal power flow (OPF) based method. Performance is evaluated using a 730-node feeder with up to 100% penetration of inverters. Based on the case studies, the key findings are: first, local droop setting as per IEEE-1547, whether the droops are arbitrarily chosen or systematically calculated using sensitivities, can eliminate overvoltage if reactive power control and APC are coordinated, second, the proposed sensitivity-based approach yields the best voltage performance index computed based on voltage profile compared to the maximum allowed upper bound, and third, OPF-based method is desirable if communication infrastructure exists and minimum energy curtailment is sought.

Using sensitivity analysis to identify factors promoting higher versus lower infection prevalence in multi-host communities

Relationships between host species richness and levels of disease in a focal host are likely to be context-dependent, depending on the characteristics of which particular host species are present in a community. I use a multi-host epidemiological model with environmental transmission to explore how the characteristics of the host species (e.g., competence and competitive ability), host density, and the pathogen transmission mechanism affect the proportion of infected individuals (i.e., infection prevalence) in a focal host. My sensitivity-based approach identifies the indirect pathways through which specific ecological and epidemiological processes affect focal host infection prevalence. This in turn yields predictions about the context-dependent rules governing whether increased host species richness increases (amplifies) or decreases (dilutes) infection prevalence in a focal host. For example, in many cases, amplification and dilution are predicted to occur when added host species are sources or sinks of infectious propagules, respectively. However, if the added host species have strong and asymmetric competitive effects on resident host species, then amplification and dilution are predicted to occur when the added host species have stronger competitive effects on resident host species that are sources or sinks of infectious propagules, respectively. My results also predict that greater dilution and less amplification is more likely to occur under frequency-dependent direct transmission than density-dependent direct transmission when (i) the added hosts have lower competence than resident host species and (ii) interspecific competition between the added host species and resident host species is lower; the opposite conditions promote greater amplification and less dilution under frequency-dependent direct transmission. This work helps identify and explain the mechanisms shaping the context-dependent relationships between host species richness and disease in multi-host communities.

Robustly Coordinated Distributed Voltage Control Through Residential Demand Response Under Multiple Uncertainties

This article proposes a multilevel coordinated voltage control (CVC) strategy for facilitating distributed residential demand response for network voltage optimization. The proposed CVC scheme coordinates multiple home energy management systems (HEMS) connected on a low-voltage radial distribution network through a sensitivity-based approach to minimize voltage deviations in multiple timescales owing to high photovoltaic (PV) penetrations and load variability. In order to mitigate the effects of uncertain PV output and load during actual system operation, a day-ahead two-stage robust optimization (TSRO) model has been developed, which optimizes network operation in the longer timescale, for worst-case realization of uncertainties in shorter timescales. This is sequentially followed by fully distributed hour-ahead CVC which is decomposed into network optimization and energy management subproblems through alternating direction method of multipliers. In order to counteract the effects of real-time uncertainties, local adjustments are performed by redispatching HEMS at individual households to locally adapt to unforeseen variations, thereby minimizing real-time voltage deviations. Extensive simulations are performed on the modified Dutch LV network. Simulation results demonstrate effectiveness of the proposed multilevel CVC approach to robustly handle uncertainties while ensuring reliable operation and maximum user-comfort.

Climate change impacts on peak river flows: Combining national-scale hydrological modelling and probabilistic projections

Potential future increases in flooding due to climate change need to be taken into consideration when designing flood defences or planning new infrastructure or housing developments. Existing guidance on climate change allowances in Great Britain was based on research that developed a sensitivity-based approach to estimating the impacts of climate change on flood peaks, which was applied with catchment-based hydrological models. Here, the sensitivity-based approach is applied with a national-scale grid-based hydrological model, producing modelled flood response surfaces for every river cell on a 1 km grid. This provides a nationally consistent assessment of the sensitivity of flood peaks across Britain to climatic changes. The flood response surfaces are then combined with the most recent climate change projections, UK Climate Projections 2018 (UKCP18), to provide location-specific information on the potential range of impacts on floods across the country, for three flood return periods, three future time-slices and four emissions scenarios. An accompanying web-tool provides a convenient way to explore the large amount of data produced. Consideration is now being given to how to use the latest work to update guidance on climate change and flood peaks, including a workshop held to gather stakeholder views.

A Sensitivity-Based Approach for Reliability Analysis of Randomly Excited Structures With Interval Axial Stiffness

Abstract Reliability assessment of linear discretized structures with interval parameters subjected to stationary Gaussian multicorrelated random excitation is addressed. The interval reliability function for the extreme value stress process is evaluated under the Poisson assumption of independent up-crossing of a critical threshold. Within the interval framework, the range of stress-related quantities may be significantly overestimated as a consequence of the so-called dependency phenomenon, which arises due to the inability of the classical interval analysis to treat multiple occurrences of the same interval variables as dependent ones. To limit undesirable conservatism in the context of interval reliability analysis, a novel sensitivity-based procedure relying on a combination of the interval rational series expansion and the improved interval analysis via extra unitary interval is proposed. This procedure allows us to detect suitable combinations of the endpoints of the uncertain parameters which yield accurate estimates of the lower bound and upper bound of the interval reliability function for the extreme value stress process. Furthermore, sensitivity analysis enables to identify the most influential parameters on structural reliability. A numerical application is presented to demonstrate the accuracy and efficiency of the proposed method as well as its usefulness in view of decision-making in engineering practice.

Effective sensitivity-based method for N-1 contingency analysis of VSC-based MTDC power grids considering power generation droop speed controls

This paper introduces a sensitivity-based approach for the efficient calculation of the post-disturbance, steady-state conditions in VSC-based MTDC power grids subject to generation outages and VSC disconnections. This approach is useful for the N-1 contingency analysis, a crucial tool in power system control centres. In order to maintain a high degree of fidelity, the approach considers state-of-the-art VSC control strategies: converters with DC voltage regulation, power-injection control, DC voltage/power droop control, and converters imposing frequency regulation on passive grids. The speed-governing response of power plants is also considered, enabling accurate calculations of AC/DC power flows and AC system frequencies. Indeed, the derived sensitivity factors permit the primary frequency response of the AC systems and DC grid to be efficiently assessed, something that would require several hours of simulation by standard electromagnetic transient (EMT) simulators. Evidently, this novel approach allows a much faster calculation of the AC/DC network’s post-disturbance conditions than is possible with dynamic simulation tools. A formulation with such modelling versatility and practicality does not currently exist elsewhere.The usefulness of this novel formulation has been confirmed using an MTDC system comprising six VSC-connected AC grids and a 13-bus DC grid. The disconnections of VSC units and power plants have also been evaluated. The study shows that the post-disturbance, steady-state conditions computed by the sensitivity-based approach agree well with those obtained by full dynamic simulations, because the relative errors between the two fundamentally different methods are smaller than 0.1% for the AC system frequencies and smaller than 4% for the AC and DC transmission line power flows.