Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system

<p>In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.</p>

Replica-mean-field limits of fragmentation-interaction-aggregation processes

Network dynamics with point-process-based interactions are of paramount modeling interest. Unfortunately, most relevant dynamics involve complex graphs of interactions for which an exact computational treatment is impossible. To circumvent this difficulty, the replica-mean-field approach focuses on randomly interacting replicas of the networks of interest. In the limit of an infinite number of replicas, these networks become analytically tractable under the so-called ‘Poisson hypothesis’. However, in most applications this hypothesis is only conjectured. In this paper we establish the Poisson hypothesis for a general class of discrete-time, point-process-based dynamics that we propose to call fragmentation-interaction-aggregation processes, and which are introduced here. These processes feature a network of nodes, each endowed with a state governing their random activation. Each activation triggers the fragmentation of the activated node state and the transmission of interaction signals to downstream nodes. In turn, the signals received by nodes are aggregated to their state. Our main contribution is a proof of the Poisson hypothesis for the replica-mean-field version of any network in this class. The proof is obtained by establishing the propagation of asymptotic independence for state variables in the limit of an infinite number of replicas. Discrete-time Galves–Löcherbach neural networks are used as a basic instance and illustration of our analysis.

State Estimation in Electric Power Systems Leveraging Graph Neural Networks

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

State Estimation in Electric Power Systems Leveraging Graph Neural Networks

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies.

Equivalent Electronic Circuit of a System of Oscillators Connected With Periodically Variable Stiffness

In this paper, we have shown the electronic circuit equivalence of a mechanical system consists of two oscillators coupled with each other. The mechanical design has the effects of the magnetic, resistance forces and the spring constant of the system is periodically varying. We have shown that the system&rsquo;s state variables, such as the displacements and the velocities, under the effects of different forces, lead to some nonlinear behaviors, like a transition from the fixed point attractor to the chaotic attractor through the periodic and quasi-periodic attractors. We have constructed the equivalent electronic circuit of this mechanical system and have verified the numerically obtained behaviors using the electronic circuit.

Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neurons models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control. Our methods are tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors. We illustrate our approach both in a canonical chaotic model and in a phenomenological neuron model, showing that many unknown parameters can be uncovered reliably and accurately from short and noisy observed time traces. Our method holds promise for estimation in larger-scale systems, given ongoing improvements in calcium reporters and genetically-encoded voltage indicators.

Design and Analysis of a Voltage-Mode Non-Linear Control of a Non-Minimum-Phase Positive Output Elementary Luo Converter

The positive output elementary Luo (POEL) converter is a fourth-order DC–DC converter having highly non-linear dynamic characteristics. In this paper, a new dynamic output voltage feedback controller is proposed to achieve output voltage regulation of the POEL converter. In contrast to the state-of-the-art current-mode controllers for the high-order boost converters, the proposed control strategy uses only the output voltage state variable for feedback purposes. This eliminates the need for the inductor current sensor to reduce the cost and complexity of implementation. The controller design is accompanied by a strong theoretical foundation and detailed stability analyses to obtain some insight into the controlled system. The performance of the proposed controller is then compared with a multi-loop hysteresis-based sliding-mode controller (SMC) to achieve the output voltage-regulation of the same POEL converter. The schemes are compared concerning ease of implementation, in particular, the number of state variables and current sensors required for implementation and the closed-loop dynamic performance. Experimental results illustrating the features of both controllers in the presence of input reference and load changes are presented.

Updated IQ and Well-being Scores for the 50 U.S. States

At the level of the 50 U.S. states, an interconnected nexus of well-being variables exists. These variables have been shown to strongly correlate with estimates of state IQ in interesting ways. But the state IQ estimates (McDaniel 2006) are now more than 16 years old, and the state well-being estimates (Pesta et al., 2010) are over 12 years old. Updated state IQ and well-being estimates are therefore needed. I thus first created new state IQ estimates by analyzing scores from both the Program for the International Assessment of Adult Competency (for adults), and the National Assessment of Educational Progress (for fourth and eighth grade children) exams. I also created new global well-being scores by analyzing state variables from the following four well-being subdomains: crime, income, health, and education. When validating the nexus, several interesting correlations existed among the variables. For example, state IQ most strongly predicted FICO credit scores, alcohol consumption (directly), income inequality, and state temperature. Interestingly, state IQ derived here also correlated .58 with state IQ estimates from over 100 years ago. Global well-being likewise correlated with many old and new variables in the nexus, including a correlation of .80 with IQ. In sum, at the level of the U.S. state, a nexus of important, strongly correlated variables exists. These variables comprise well-being, and state IQ is a central node in this network.