Tomas McKelvey

Space-time adaptive processing aims to provide an accurate estimate of the distribution of the clutter interference and the noise. Such an estimate is commonly calculated from secondary observations associated with the most recent coherent processing interval. In this paper we consider airborne bistatic radar systems and use a parametric model of the clutter distribution that depends on a few parameters that describe the radar scenario. With this formulation, an estimate of the scenario parameters from the secondary data yields the clutter distribution through the parametric model. The scenario parameters represent the position and velocities of the platforms comprising the airborne bistatic configuration. Moreover, the framework of radar scenarios enables information from previously considered coherent processing intervals to contribute to a clutter distribution estimate. Consequently, as the scenario parameters denote motion characteristics of a platform, the scenario parameters can be tracked over time by assuming a motion model. Thus, the estimator of this paper uses a combination of the likelihood density of the most recent set of radar observations, together with a motion model which enables the propagation of the scenario parameters dynamics through time. Hence,the density of the likelihood and the prior density from the propagation through time of previous scenario parameter estimates is combined to calculate a maximum a-posteriori estimate of the scenario parameters. In numerical simulations, the maximum a-posteriori estimate is compared with the corresponding scenario parameter estimate considering only a maximum likelihood estimate. The numerical simulations show clearly that the maximum a-posteriori estimate has lower variance and RMSE compared to the maximum likelihood estimate.

This paper presents an online data-driven method to estimate the inertia constant of synchronous generators (SGs) and the virtual inertia of converter-based resources (CBRs), which enables time-dependent inertia tracking in the normal operation of a power system. The proposed method is based on continuous monitoring of frequency response functions (FRFs) of SGs and CBRs, which are identified by ambient wide-area measurements of phasor measurement units (PMUs). To identify FRFs, a novel non-parametric approach, namely the local rational model (LRM), is used which does not require correct model order selection. LRM approach has a low computational burden and requires a short window of data, both of which are essential for estimating time-dependent inertia using ambient data. The applicability of the proposed method is evaluated in the IEEE 39-bus system and an actual system. The results demonstrate the accuracy, robustness to noise, and effectiveness of the proposed method in estimating the time-dependent inertia of power systems.

A regression model with more parameters than data points in the training data is overparametrized and has the capability to interpolate the training data. Based on the classical bias-variance tradeoff expressions, it is commonly assumed that models which interpolate noisy training data are poor to generalize. In some cases, this is not true. The best models obtained are overparametrized and the testing error exhibits the double descent behavior as the model order increases. In this contribution, we provide some analysis to explain the double descent phenomenon, first reported in the machine learning literature. We focus on interpolating models derived from the minimum norm solution to the classical least-squares problem and also briefly discuss model fitting using ridge regression. We derive a result based on the behavior of the smallest singular value of the regression matrix that explains the peak location and the double descent shape of the testing error as a function of model order.

Compensation for nonlinear distortion caused by power amplifiers being driven into compression is a field that has been extensively explored by a number of authors. More complex models describing the coupled nonlinear distortion that can occur in an active antenna array also exist. To identify these models most authors assume that the output of each power amplifier can be measured directly. For a system with many antennas over-the-air identification might be favorable. The problem of identifying a system describing the joint effects of coupled nonlinear distortion over-the-air has been solved by other authors assuming model or setup simplifications. To tackle the problem with a more accurate model, an algorithm for identifying parameters of a Volterra series model describing coupled non-linear distortion in a digital radar is explored and illustrated with simulated data.

In this research work, deep machine learning-based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics, and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low-pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low-pass Butterworth filter is used together with a linear finite impulse response (FIR)-based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40-ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM, and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the abovementioned methods. Finally, a Simulink and GPU-based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics.

A partial discharge (PD) is a small, localized breakdown which often appears in high-voltage insulation systems. PDs can be initiated within material defects such as voids or cracks when exposed to sufficiently high electric field levels. In this research work, we study and propose a machine learning (ML) approach to detect and classify PDs originating from multilevel pulsewidth modulation (PWM) waveforms as utilized in power electronic devices, such as inverters and variable frequency drives. Due to the increased use of power conversion units, new types of higher magnitude PDs have been observed to increase insulation degradation. By creating a cavity placed at different locations within the test object and exposing it to PWM voltage waveforms with two different rise times, a total of 345 660 PD events are recorded and organized into ten different classes. The maximum PD amplitude, duration, the time distance between consecutive PDs, and the area under the PD are used as features for PD classification. A unique way of concatenating a sequence of the extracted features to capture the temporal dependence of consecutive PDs is also presented. It is observed that when creating a sequence of information from consecutive observed PDs, a significant increase in classification accuracy can be obtained. Trained classifiers based on ensemble bagged decision trees (DTs) and long short-term memory (LSTM) architecture resulted in 95.3% and 98.5% average classification accuracy on test data respectively.

Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in environments of clutter and jammers. Effective STAP implementations are dependent on an accurate estimate of the space-time covariance matrix, which characterizes noise and interference in the radar signal. Inside-looking monostatic radar systems, the estimate based on secondary radar observations is rather straight forward as all the samples in secondary data can be argued to be from a single distribution, and the sample covariance can be used as an estimate of the space-time covariance matrix. However, in many other radar configurations, the vital underlying STAP training assumption that secondary data are identically distributed is violated, which implies that detection performance can be significantly degraded. This article develops a new method that can be used when secondary data do not share a common distribution due to geometry-induced range dependencies. This phenomenon is of particular concern in bistatic radar systems. We propose a model-based approach, where the distribution of noise and clutter for each range bin is parameterized by a set of scenario parameters. Using secondary data, the scenario parameters are estimated by maximizing the likelihood function. Based on the estimated scenario parameters, the STAP covariance estimate is formed for the cell under test. The presented method is compared with other state-of-the-art methods for bistatic radar STAP via numerical simulations. The simulations indicate that the presented method, with a proper initialization, yields an estimate of the STAP covariance matrix that significantly increases the signal-to-interference-plus-noise ratio compared to the other investigated methods.

We present a three-way catalyst (TWC) cold-start model, calibrate the model based on experimental data from multiple operating points, and use the model to generate a Pareto-optimalcold-start controller suitable for implementation in standard engine control unit hardware. The TWC model is an extension of a previously presented physics-based model that predicts carbon monoxide, hydrocarbon, and nitrogen oxides tailpipe emissions. The model axially and radially resolves the temperatures in the monolith using very few state variables, thus allowing for use with control-policy based optimal control methods. In this paper, we extend the model to allow for variable axial discretization lengths, include the heat of reaction from hydrogen gas generated from the combustion engine, and reformulate the model parameters to be expressed in conventional units. We experimentally measured the temperature and emission evolution for cold-starts with ten different engine load points, which was subsequently used to tune the model parameters (e.g. chemical reaction rates, specific heats, and thermal resistances). The simulated cumulative tailpipe emission modeling error was found to be typically − 20% to + 80% of the measured emissions. We have constructed and simulated the performance of a Pareto-optimal controller using this model that balances fuel efficiency and the cumulative emissions of each individual species. A benchmark of the optimal controller with a conventional cold-start strategy shows the potential for reducing the cold-start emissions.

We present a numerical method for generating the state-feedback control policy associated with general undiscounted, constant-setpoint, infinite-horizon, nonlinear optimal control problems with continuous state variables. The method is based on approximate dynamic programming, and is closely related to approximate policy iteration. Existing methods typically terminate based on the convergence of the control policy and either require a discounted problem formulation or demand the cost function to lie in a specific subclass of functions. The presented method extends on existing termination criteria by requiring both the control policy and the resulting system state to converge, allowing for use with undiscounted cost functions that are bounded and continuous. This paper defines the numerical method, derives the relevant underlying mathematical properties, and validates the numerical method with representative examples. A MATLAB implementation with the shown examples is freely available.