nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Modeling of Electric Springs

The use of “Electric Springs” is a novel way of distributed voltage control while simultaneously achieving effective demand-side management through modulation of noncritical loads in response to the fluctuations in intermittent renewable energy sources (e.g., wind). The proof-of-concept has been successfully demonstrated on a simple 10-kVA test system hardware. However, to show the effectiveness of such electric springs when installed in large numbers across the power system, there is a need to develop simple and yet accurate simulation models for these electric springs which can be incorporated in large-scale power system simulation studies. This paper describes the dynamic simulation approach for electric springs which is appropriate for voltage and frequency control studies at the power system level. The proposed model is validated by comparing the simulation results against the experimental results. Close similarity between the simulation and experimental results gave us the confidence to use this electric spring model for investigating the effectiveness of their collective operation when distributed in large number across a power system. Effectiveness of an electric spring under unity and non-unity load power factors and different proportions of critical and noncritical loads is also demonstrated.
A New H-IRKA Approach for Model Reduction with Explicit Modal Preservation: Application on Grids with Renewable Penetration

Multi-input multi-output (MIMO) model reduction is essential for applying many modern control design methods in power systems. The challenges of MIMO model order reduction of the modern power grid increases with the inclusion of renewables such as inverter-interfaced wind farms, which introduces complexity in such power system models. The difficulty in reducing such models using gramian-based and modal truncation approaches is demonstrated, whereas a relatively new Interpolatory approach of the Iterative Rational Krylov Algorithm (IRKA) shows promising results. Next, a heuristic-based IRKA is proposed to improve the accuracy of model order reduction of modern grids with explicit preservation of the “critical modes” of the system. Tests on a 16-machine New England–New York system with two doubly-fed induction generator-based wind farms and a larger Brazilian system model demonstrate the effectiveness of the proposed approach.
Graph-cut optimization of the ratio of functions and its application to image segmentation

Optimizing the ratio of two functions of binary variables is a common task in many image analysis applications. In general, such a ratio is not amenable to graph-cut based optimization. In this paper, we show that if the numerator and the denominator of a ratio are individually graph- representable functions, then their ratio can be optimized via graph-cut based technique. As an example of such a ratio function we choose Yezzi et al.'s energy function (A. Yezzi et al., 1999), minimization of which produces a binary labeling of an image. Through examples, we illustrate the advantage of working with graph-cut-based optimization for the aforementioned ratio in finding a global solution as opposed to the local solutions found by level set methods proposed in (A. Yezzi et al., 1999).
Texture based background subtraction

Background subtraction is an effective technique for motion detection. A traditional background subtraction algorithm assumes a moving object (or objects) with respect to a static background, and segments the moving object(s) by classifying pixels into foreground and background with trained statistical models. Because classical background subtraction algorithms work with intensity images, they cannot handle situations in which all pixels are moving. To address this deficiency, we present a novel background subtraction algorithm in this paper that is capable of detecting objects of interest while all pixels are in motion. The key idea behind our algorithm is to work with feature images, rather than the raw intensity images, in which foreground and background exhibit sufficiently different statistics. We in particular use texture as the feature, extracted with circular Gabor filters at five different bands, to study the problem of detecting large objects (rocks) moving amid small fragments, in the application of detecting large frozen ore lumps traveling into a crusher. We will provide experimental results on real image sequences to illustrate the superior performance of our algorithm, compared with the classical intensity-based algorithm.
Cell Tracking in Video Microscopy Using Bipartite Graph Matching

Automated visual tracking of cells from video microscopy has many important biomedical applications. In this paper, we model the problem of cell tracking over pairs of video microscopy image frames as a minimum weight matching problem in bipartite graphs. The bipartite matching essentially establishes one-to-one correspondences between the cells in different frames. A key advantage of using bipartite matching is the inherent scalability, which arises from its polynomial time-complexity. We propose two different tracking methods based on bipartite graph matching and properties of Gaussian distributions. In both the methods, i) the centers of the cells appearing in two frames are treated as vertices of a bipartite graph and ii) the weight matrix contains information about distance between the cells (in two frames) and cell velocity. In the first method, we identify fast-moving cells based on distance and filter them out using Gaussian distributions before the matching is applied. In the second method, we remove false matches using Gaussian distributions after the bipartite graph matching is employed. Experimental results indicate that both the methods are promising while the second method has higher accuracy.
Real-Time Identification of Dynamic Events in Power Systems Using PMU Data, and Potential Applications—Models, Promises, and Challenges

This paper explores the task of real-time identification of dynamic events leading to a layer of situational awareness that can become a reality due to increased penetration of phasor measurement units in transmission systems. Two underlying models for this task—data driven and physics based—are explored with examples. Challenges, advantages, and drawbacks of each model are discussed based on the availability of data, attributes of such data, and processing options. Potential applications of the task to improve security of power system protection and anomaly detection in the case of a cyberattack are conceptualized. Some known issues in data communications are discussed vis-a-vis the requirements imposed by the proposed task.
Damping and Relative Mode-Shape Estimation in Near Real-Time Through Phasor Approach

A technique for estimating damping and electromechanical mode-shape in near real-time as oscillations develop under transient condition is presented. At each sampling instant, measured signals are expressed as phasors using corrected values of modal frequencies. Damping is obtained from the exponential variation of estimated phasor magnitude using a moving window least squares (LS) algorithm. The relative mode-shape is computed directly from the magnitude and phase angle of the phasors. Random variations in loads are considered to examine possible impact on phasor estimation, especially the frequency correction loop. Accuracy and speed of convergence is validated by comparing the time variation of estimated dampings and relative mode-shapes against the actual values obtained from the linearized models under respective operating conditions. Besides the well-known four-machine, two-area test system, a 16-machine, five-area system is considered for illustration of the concept. Monte Carlo simulations are used to capture the statistical variability in estimation as a result of persistent disturbances (e.g., random fluctuations in loads) leading to different signal-to-noise ratios (SNRs). Results from a commercial real-time simulator illustrate the practical feasibility of the proposed approach.
Interaction between conventional and adaptive phasor power oscillation damping controllers

Adaptive phasor power oscillation damping controllers (APPODs) produce superior large signal performance with very little reliance on accurate system models. However, recursive phasor estimation, online frequency correction and adaptive phase-shift are involved which makes modeling such APPODs difficult in stability studies. Possible interaction with other controllers (e.g. lead-lag compensators) is thus unforeseeable restricting their acceptability in practice. In this paper potential interaction between an APPOD and a conventional POD (CPOD) is investigated. The study is primarily simulation based as mathematical analysis of such interaction is not straightforward due to highly complex non-linear nature of APPODs. Simulation results presented here show no adverse interaction which needs to be substantiated for large-scale systems.
A case study on challenges for robust wide-area phasor POD

This paper illustrates the challenges towards achieving a robust phasor POD (power oscillation damping), a concept introduced and developed by ABB. A Phasor POD extracts the oscillatory part of the measured signal in the form of a phasor and applies a suitable phase shift to derive the damping control action. For a thyristor controlled series capacitor (TCSC), a phase shift of 90 degrees with respect to local power flow is recommended. However, the present case study on a simple test system with a TCSC reveals that a 90 degree phase shift on the local power does not necessarily yield the desired results. In fact, the phase shift required for satisfactory performance differs from one operating condition to another. Use of a wide-area signal (wide-area phasor POD) is shown to improve the damping functionality compared to local power flow provided an appropriate phase shift — which might not be intuitive — is given. Moreover, extraction of individual phasors out of a multi-modal signal is a challenge for practical systems exhibiting complex multi-modal behavior.
A supervisory approach towards cyber-secure generator protection

Modern microprocessor-based relays can be vulnerable to attacks from sophisticated cyber-adversaries. In the case of synchronous generators, specifically, out of step (OOS) protection, we show that a “hacked” relay can be made to respond exactly contrary to design, which can be disastrous to system operation. We propose a computational alternative - using the concept of Dynamic State Estimation (DSE) and inputs from Phasor Measurement Units to address this problem. Since DSE does not rely on the “settings” used in traditional relays, it can be used to independently supervise the operation of the traditional relay. The generator's dynamic states are continuously tracked and monitored using the DSE module during steady state conditions and the dynamics following a fault. We show that this approach can detect OOS conditions consistent with traditional schemes, thus supervising conventional protection and help detection of anomalous behavior from a compromised relay. Dynamic simulation results are presented on the IEEE 10 machine, 39-bus system to illustrate the proposed approach.
Modeling and simulation of dynamic communication latency and data aggregation for wide-area applications

This paper introduces a framework to model two factors: (a) dynamic communication latency and (b) the phasor data concentrator (PDC) for wide area monitoring and control (WAMC) applications. A transport delay model is introduced to simulate continuous variations in latencies and a time synchronization algorithm is represented to mimic the effect of PDCs. The models allow the analysis of latency build-up and data aggregation that occurs at the PDC. The proposed models are used to assess the impact of these factors for a WAMC application, namely power oscillation monitoring. Numerical simulations are conducted on the IEEE 39 bus, 10 machine system assummng a PMU at each of the generator buses and across 4 tielines in the system. Modal extraction from the dynamic response using Prony analysis shows that latency has a signrncant impact on the estimates.
Optimum kernel function design from scale space features for object detection

Scale-space representation of an image is a significant way to generate features for classification. However, for a specific classification task, the entire scale-space may not be useful; only a part of it is typically effective. Toward this end, we design a data dependent classification kernel function, which is a weighted mixture of kernels defined on individual scales. In order to choose the optimum weights in the mixture kernel function (MKF), we propose an optimization criterion that leads to the minimization of Raleigh quotient in the positive orthant. This optimization is in general a difficult, non-convex, quadratically constrained quadratic programming. Utilizing a property of ratio of functions, we reduce the aforementioned optimization into a novel binary search, which is essentially a series of quadratic programming. As an application we choose a significant detection problem in oil sands mining called large lump detection from videos. Employing support vector classifier with our MKF yields encouraging results on these difficult-to-process images and compares favorably against the kernel alignment method  as well as Fisher criterion adopted in .
Power oscillation damping control using wide-area signals: A case study on Nordic equivalent system

Power oscillation damping (POD) control employing wide-area signals is illustrated in an equivalent system model representing key characteristics of Nordic power system. Phasor measurement units (PMUs) in Norway and Finland are used to obtain feedback signals for supplementary control of a large SVC unit located in the south-east of Norway. Comparison has been made between two control design approaches- (i) robust linear time invariant model based POD (MBPOD) - dependant on accurate system model and (ii) indirect adaptive POD (IAPOD) - which is fixed structure but time-varying and relies only on measurements. An optimization problem is formulated to design the controller parameters for MBPOD while the IAPOD is based on online Kalman filter estimation and adaptive pole-shifting control. Performances of both MBPOD and IAPOD are found to be quite similar even though IAPOD requires very little prior information about the system. A number of simulations are carried out under different tie-line outages to verify the performance.
A concave cost formulation for parametric curve fitting: Detection of leukocytes from intravital microscopy images

We formulate parametric curve fitting as a concave cost minimization problem. Our formulation is general encompassing any parametric curve where parameters can be free or constrained. The proposed concave cost opens the future possibility of applying several available concave programming algorithms in curve fitting. In this paper, we propose a fast local minimization of the concave cost and utilize it to a specific application- automated detection of ellipse-shaped leukocytes (white blood cells) from microscopy images. We illustrate that our solution can cope well with outliers compared to other competitive methods of ellipse fitting and leukocyte detection.
System stability improvement through optimal control allocation in voltage source converter-based high-voltage direct current links

Control of both active and reactive power in voltage source converter (VSC)-based high-voltage direct current (HVDC) links could be very effective for system stability improvement. The challenge, however, is to properly allocate the overall control duty among the available control variables in order to minimise the total control effort and hence allow use of less expensive converters (actuators). Here relative gain array and residue analysis are used to identify the most appropriate control loops avoiding possible interactions. Optimal allocation of the secondary control duty between the two ends of the VSC HVDC link is demonstrated. Active and reactive power modulation at the rectifier end, in a certain proportion, turns out to be most effective. Two scenarios, with normal and heavy loading conditions, are considered to justify the generality of the conclusions. Subspace-based multi-input–multi-output system identification is used to estimate and validate linearised state-space models through pseudo random binary sequence probing. Linear analysis is substantiated with non-linear simulations in DIgSILENT PowerFactory with detailed representation of HVDC links.
Robust Damping of iNTER-AREA oSCILLATIONS in AC-MTDC grids using H∞ Mixed-sensitivity approach

This paper demonstrates robust damping of interarea oscillations in an AC-Multiterminal DC (MTDC) grid following major contingencies like the outage of a converter pole and line outage. Damping controller design based on H∞ mixed-sensitivity formulation in the Linear Matrix Inequality (LMI) framework has been performed. Emphasis has been placed on achieving robustness in the design by incorporating proper weighing filters for disturbance rejection and control energy minimization. The proposed controller uses feedback signals from remote locations and modulates the real power reference of two converter poles in a converter station of the asymmetric bipolar MTDC grid, which is integrated with AC systems. A 6-machine 3-area system and a 16-machine, 5-area system were used as the AC test systems. Robustness in damping performance in case of converter outage and AC line outage was successfully achieved by the designed controllers in both the test systems.
A robust convergence index filter for breast cancer cell segmentation

COnvergence INdex (COIN) filter, a successful tool for cell localization, evaluates the degree of convergence of the gradient vectors within the neighborhood (region of support) toward a pixel of interest. All previous efforts were to increase the adaptability of the region of support to make the COIN filter robust and accurate. However, improving the quality of the image gradient map was ignored, which results in poor performance of the members of the COIN family in noisy settings. We propose a new Robust Convergence Index (RCI) filter that tailors the COIN filter in a noisy environment by (a) spreading the gradient vectors within non-homogeneous object regions by convolving an Aggregated Edge Probability Map (AEPM) with an edge preserving gradient vector kernel, and (b) increasing the convergence of the gradient vectors through the integration of the sine and cosine distribution as well as the magnitude of the gradient vectors. AEPM is computed through the consensus of the responses of a number of edge detectors over a wide range of scales, which lessens the effects of clutter by enforcing higher weights to the actual edges, and a non-parametric Kernel Density Estimation (KDE) is used to compute the edge probability map. Experimental results demonstrate that it obtains state-of-the-art performance.
Spectrogram based features selection using multiple kernel learning for speech/music discrimination

This paper presents a multiple kernel learning (MKL) approach to speech/music discrimination (SMD). The time-frequency representation (spectrogram) implemented by short-time Fourier transform (STFT) of audio segment is decomposed by wavelet packet transform into different subband levels. The subbands, which contain rich texture information, are used as features for this discrimination problem. MKL technique is used to select the optimal subbands to discriminate the audio signals. The proposed MKL based algorithm is applied for SMD of a standard dataset. The experimental results show that the proposed technique yields noticeable improvements in classification accuracy and tolerance toward different noise types compared to the existing methods.
Ratio-Based Selective Inertial and Primary Frequency Support Through MTDC Grids With Offshore Wind Farms

Provision of inertial and primary frequency support in a controlled manner through multiterminal direct current (MTDC) grid connecting asynchronous AC areas and offshore wind farms (OWFs) is explored. Under nominal condition, only voltage droop is considered to allow the AC systems operate asynchronously as they are meant to. Following an AC-side disturbance, the corresponding converter transmits basic information via a distress signal through the DC lines or existing communication channels used for common DC voltage-droop control. Upon receiving the distress signal, a new decentralized inertial-droop-based controller and the traditional decentralized frequency-droop controller are activated in preselected participating converters with values that are predetermined based on our proposed design procedure. The procedure ensures a new ratio-based performance while providing frequency support. The proposed scheme is based on an <inline-formula><tex-math notation="LaTeX">$N{\text{th}}$</tex-math></inline-formula>-order model of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>-asynchronous-area MTDC system. Furthermore, to extract frequency support from an OWF connected to an AC-MTDC system, its power reference is modified such that the whole of the AC-MTDC system with OWF is emulated as an <inline-formula><tex-math notation="LaTeX">$N$</tex-math> </inline-formula>-asynchronous-area MTDC system. The proposed strategies are first implemented in the <inline-formula> <tex-math notation="LaTeX">$N{\text{th}}$</tex-math></inline-formula>-order model for theoretical validation and later, in full-order models of study systems (with and without OWF) for a more rigorous verification.
Selective Power Routing in MTDC Grids for Inertial and Primary Frequency Support

The possibilities of selectively providing inertial and primary frequency support as an emergency support service to asynchronous AC areas through multiterminal direct current (MTDC) grid with minimal-communication are explored. Under nominal condition, only voltage droop is considered to allow the AC systems operate asynchronously as they are meant to. When an AC system needs support, the corresponding converter transmits basic information via a distress signal through the DC lines or existing communication channels used for common DC voltage-droop control. Following AC-side disturbances, the frequency droop is activated with values that are predetermined based on our proposed design procedure, which ensures primary frequency support through selective converter participation. Model predictive control, which aims to achieve the best possible frequency dynamics with a single-point actuation is also implemented at the converter station connected to the affected area to provide inertial support. Following a converter outage, MTDC power references are modified to ensure minimal impact on the AC-side frequencies. Simulation studies showing the effectiveness of the proposed strategies are presented.
An aggregation scheme for offshore wind farms with VSC-based HVDC collection system

This paper proposes a new aggregation scheme for large-scale offshore wind farms. The aggregation scheme is based on direct-drive, variable-speed, wind-power units which are interfaced with a High-Voltage DC (HVDC) collector substation. Each wind-power unit employs a diode-bridge rectifier cascaded with a Sinusoidal Pulse-Width Modulated (SPWM), Voltage-Sourced Converter (VSC). The use of the diode-rectifier is expected to bring about higher efficiency, lower converter cost, and higher reliability. The offshore HVDC collector employs a three-level Neutral-Point Clamped (NPC) converter which adopts a single-pulse switching strategy. Therefore, a high voltage level is permitted while the switching losses are minimized. Moreover, the pulse width of the HVDC AC voltage is chosen such that a twelve-pulse waveform is approximated, thereby, avoiding phase-shifting transformers in the interest of lower weight and footprint. The feasibility and effectiveness of the proposed aggregation scheme and its controls are demonstrated by simulation studies in the PSCAD/EMTDC software environment.