An Investigation of the Wiener Approach for Nonlinear System Identification Benchmarks

We evaluate the effectiveness of the Wiener model structure in modeling of the given benchmark problems. Two different approaches are proposed for parameter estimation. The results are compared for three problems, i.e. Silver Box, Wiener-Hammerstein, and Wiener-Hammerstein with noise. The aim is to evaluate the capability of the algorithms on the other benchmark problems in future works as well

A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes

Monitoring and fault detection of industrial processes is an important area of research in data science, helping effective management of the plant by the remote operator. In this article, a data-driven statistical model of a process is estimated using the principal component analysis (PCA) method and the associated probability density function. The aim is to use the model to monitor and detect the incurred faults in the industrial plant. The experimental data are collected by finding the suitable subsystems of a Recycle Gas in Ethylene Oxide production process, and a subset of nine variables are extracted for further statistical analysis of the system. The performance of the developed model for monitoring purpose is evaluated by using faulty and close to faulty inputs as the new test data

Adaptive Optimal Control of Faulty Nonlinear DC Microgrids with Constant Power Loads: Dual-Extended Kalman Filter Approach

This article investigates the problem of estimating actuator fault and states and controlling the bus voltage in direct current microgrids (DC MGs) with linear and nonlinear constant power loads (CPLs). It is considered that the DC MG states are not fully measurable and the utilized sensors are not ideal and noisy. Additionally, the actuator fault occurs and it is modeled as an additive term in the power system dynamics. These issues, including nonlinearities, un-measurable states, noisy measures, and actuator fault indispensably degrade the operation of the DC MG. To solve this issue, initially, a dual-extended Kalman filter (dual-EKF) is suggested for the fault and state estimation. It decomposes the process of estimating the state and actuator fault to reduce the online computational burden. For the control purpose, a linear parameter varying (LPV) model predictive control (MPC) is suggested to regulate the current and voltage of the DC MG. It benefits the nonlinear system modeling of LPV representation and constrained-based design procedure of the MPC to result in an accurate and low online computational burden dealing with system constraints. By deploying the overall robust adaptive dual-EKF estimation-based LPV-MPC, there is no need to have any prior knowledge of all system states and actuator faults in prior. The theoretical analysis and controller design are validated by numerical simulations on a typical islanded DC MG and comparisons are done with state-of-the-art estimation and control strategies.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Extended Kalman filter-based approach for Nodal pricing in active distribution networks

This article presents an analytical approach based on Extended Kalman Filter (EKF) for nodal pricing in distribution networks containing private distributed generation (DG). An appropriate nodal pricing policy can direct active distribution network (ADN) to optimal operation mode with minimum loss. However, there are several crucial challenges in nodal pricing model such as: equitable loss allocation between DGs, obtain minimum merchandising surplus (MS), and equitable distribution of remuneration between DGs, which is difficult to achieve these goals simultaneously. However, in the proposed method, the issue was embedded in the form of the EKF updates. The measurement update reduces the MS, and in the time update, DG's nodal prices as state variables are modified based on their contribution to the loss reduction. Therefore, all aspects of the problem are considered and modeled simultaneously, which will prepare a realistic state estimation tool for distribution companies in the next step of operation. The proposed method also has the ability to determine the nodal prices for distribution network buses in a wide range of power supply point prices (PSP), which other methods have been failed, especially at very low or high PSP prices. Eventually, using the new method will move system towards to the minimum possible losses with the equitable condition. The application of the proposed nodal pricing method is illustrated on 17-bus radial distribution test systems, and the results are compared with other methods.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations