Deterministic Hybrid Models Research Articles

Artificial Intelligence, Machine Learning and Neural Networks for Tomography in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies

Until now, the neural network identification methodology for the branch number identification (NNIM-BNI) has identified the number of branches for a given overhead low-voltage broadband over powerlines (OV LV BPL) topology channel attenuation behavior [1]. In this extension paper, NNIM-BNI is extended so that the lengths of the distribution lines and branch lines for a given OV LV BPL topology channel attenuation behavior can be approximated; say, the tomography of the OV LV BPL topology. NNIM exploits the Deterministic Hybrid Model (DHM) and the OV LV BPL topology database of Topology Identification Methodology (TIM). By following the same methodology of the original paper, the results of the neural network identification methodology for the distribution line and branch line length approximation (NNIM-LLA) are compared against the ones of the newly proposed TIM-based methodology, denoted as TIM-LLA.

A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.

Information Technology, Artificial Intelligence and Machine Learning in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Branch Number Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies

Broadband over Power Lines (BPL) networks that are deployed across the smart grid can benefit from the usage of machine learning, as smarter grid diagnostics are collected and analyzed. In this paper, the neural network identification methodology of Overhead Low-Voltage (OV LV) BPL networks that aims at identifying the number of branches for a given OV LV BPL topology channel attenuation behavior is proposed, which is simply denoted as NNIM-BNI. In order to identify the branch number of an OV LV BPL topology through its channel attenuation behavior, NNIM-BNI exploits the Deterministic Hybrid Model (DHM), which has been extensively tested in OV LV BPL networks for their channel attenuation determination, and the OV LV BPL topology database of Topology Identification Methodology (TIM). The results of NNIM-BNI towards the branch number identification of OV LV BPL topologies are compared against the ones of a newly proposed TIM-based methodology, denoted as TIM-BNI.

Business Analytics and IT in Smart Grid – Part 1: The Impact of Measurement Differences on the iSHM Class Map Footprints of Overhead Low-Voltage Broadband over Power Lines Topologies

Due to the smart grid (SG) operation, the power utilities are dealing with a cataclysm of big data that demands advanced information technology (IT) infrastructure and business analytics while one cause of this growth is the nature of the power grid operation that demands real-time measurements. In [1], [2], the theoretical framework and the numerical results for the interoperability of Deterministic Hybrid Model (DHM), initial Statistical Hybrid Model (iSHM), the definition procedure and the class maps have been presented for the overhead low-voltage broadband over power lines (OV LV BPL) networks as well as the iSHM footprints. On the basis of the five real indicative OV LV BPL topologies of [1], [2], the impact of measurement differences that follow either continuous uniform distributions (CUDs) or normal distributions (NDs) of different intensities is first highlighted on iSHM footprints. Citation: Lazaropoulos, A. G. (2020). Business Analytics and IT in Smart Grid – Part 1: The Impact of Measurement Differences on the iSHM Class Map Footprints of Overhead Low-Voltage Broadband over Power Lines Topologies. Trends in Renewable Energy, 6, 146-176. DOI: 10.17737/tre.2020.6.2.00117

A Framework for Dynamic Stability Analysis of Power Systems With Volatile Wind Power

We propose a framework employing stochastic differential equations to facilitate the long-term stability analysis of power grids with intermittent wind power generations. This framework takes into account the discrete dynamics which play a critical role in the long-term stability analysis, incorporates the model of wind speed with different probability distributions, and also develops an approximation methodology (by a deterministic hybrid model) for the stochastic hybrid model to reduce the computational burden brought about by the uncertainty of wind power. The theoretical and numerical studies show that a deterministic hybrid model can provide an accurate trajectory approximation and stability assessments for the stochastic hybrid model under mild conditions. In addition, we discuss the critical cases that the deterministic hybrid model fails and discover that these cases are caused by a violation of the proposed sufficient conditions. Such discussion complements the proposed framework and methodology and also reaffirms the importance of the stochastic hybrid model when the system operates close to its stability limit.

Semi Deterministic Hybrid Model for Path Loss Prediction Improvement

Mobile radio path loss modeling is an important aspect of network planning which ensures an improved quality of service to the subscribers. In this paper, the predictions made by COST 231 Walfisch-Ikegami propagation model to estimate the path loss of the mobile signals are improved by proposing a hybrid model. The modified model combines the deterministic aspects of ray tracing along with statistical processing of the empirical measurements. It includes a loss term computed for multiple reflections by using the method of images. The ten ray urban street canyon model is extended to compute reflections from multiple paths. The deterministic approach is merged with the statistical variations in the original Walfisch-Ikegami model. It consists of modeling the multi screen diffraction loss, roof to street loss and street orientation function by considering the height of the buildings, the separation and the road orientation angle as random variables with Gaussian distribution. The proposed hybrid model is validated by measurements of GSM 900MHz signals collected in Hyderabad city of Southern India. The performance of the proposed model is evaluated in terms of Prediction Error, Root Mean Square Error and other metrics. The results justify the improvement in the path loss prediction of the proposed semi deterministic hybrid model. This could be useful in link budget analysis and deployment of future cellular networks for the specified urban region and similar scenarios.

Convergence of trajectories and optimal buffer sizing for AIMD congestion control

We study the interaction between the AIMD (Additive Increase Multiplicative Decrease) multi-socket congestion control and a bottleneck router with Drop Tail buffer. We consider the problem in the framework of deterministic hybrid models. First, we show that trajectories always converge to limiting cycles. We characterize the cycles. Necessary and sufficient conditions for the absence of multiple jumps in the same cycle are obtained. Then, we propose an analytical framework for the optimal choice of the router buffer size. We formulate this problem as a multi-criteria optimization problem, in which the Lagrange function corresponds to a linear combination of the average goodput and the average delay in the queue. Our analytical results are confirmed by simulations performed with MATLAB Simulink.

Convergence of trajectories and optimal buffer sizing for MIMD congestion control

We study the interaction between the MIMD (Multiplicative Increase Multiplicative Decrease) congestion control and a bottleneck router with Drop Tail buffer. We consider the problem in the framework of deterministic hybrid models. We study conditions under which the system trajectories converge to limiting cycles with a single jump. Following that, we consider the problem of the optimal buffer sizing in the framework of multi-criteria optimization in which the Lagrange function corresponds to a linear combination of the average throughput and the average delay in the queue. As case studies, we consider the Slow Start phase of TCP New Reno and Scalable TCP for high speed networks.

Composite lot sizing with quantity and freight discounts

The EOQ model is aggregated into an extended composite model by including shipping costs, inspection costs, in-transit holding costs, backorder costs, finite replenishment, quality rejects, quantity discounts, and freight rate discounts in its formulation. The embellished model can be disaggregated into numerous different deterministic hybrid models by using simplified decomposition rules that apply to a variety of specific operational conditions. Optimum lot sizing formulations are derived for all non-discount situations as well as discount situations without stockouts. For these situations, efficient algorithms are used to obtain optimum lot sizes. For incremental discount situations with stockouts, efficient algorithms along with numerical analysis are required to converge upon optimum lot sizes. The simple EOQ is shown to be a special disaggregated case of its composite parent. A foundation is provided for a decision support system to deal with the variegated mix of operational conditions.

A composite EOQ model for situational decomposition

The simple EOQ model is embellished into a composite EOQ model by including shipping costs, inspection costs, in-transit holding costs, backorder costs, finite replenishment, and quality rejects in its formulation. The composite model can be disaggregated into numerous different deterministic hybrid models by using simple decomposition rules that apply to a variety of different operational situations. Additionally, composite EOQ extensions for quantity discounts and transitional price changes are devised which can be easily disaggregated with the same simple decomposition rules.