An Introduction To Management Science Quantitative Approaches To Decision Making

https://hal.archives-ouvertes.fr/hal-03100614/document

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Identification of black-box transfer function models is considered. It is assumed that the transfer function models possess a certain shift-property, which is satisfied for example by all polynomial-type models. Expressions for the variances of the transfer function estimates are derived, that are asymptotic both in the number of observed data and in the model orders. The result is that the joint covariance matrix of the transfer functions from input to output and from driving white noise source to the additive output disturbance, respectively, is proportional to the inverse of the joint spectrum matrix for the input and driving noise multiplied by the spectrum of the additive output noise. The factor of proportionality is the ratio of model order to number of data. This result is independent of the particular model structure used. The result is applied to evaluate the performance degradation due to variance for a number of typical model uses. Some consequences for input design are also drawn.

Asymptotic variance expressions for identified black-box transfer function models

The ongoing electrification of mobility comes with the challenge of charging electric vehicles (EVs) sufficiently while charging infrastructure capacities are limited. Smart charging algorithms produce charge plans for individual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet. In practice, EV charging processes follow nonlinear charge profiles such as constant-current, constant-voltage (CCCV). Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power consumption. Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available. In this work we propose a data-driven approach for integrating a machine learning model to predict arbitrary charge profiles into a smart charging algorithm. We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models. Each charging process includes the time series of charging power. After preprocessing, the dataset contains 10.595 charging processes leading to 1.2 million data points in total. We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error (MAE) of 126W and a relative MAE of 0.06. Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21% more energy charged compared to smart charging without considering charge profiles. Furthermore, an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions. However, charging features are required including the number of phases used for charging.

Data-driven smart charging for heterogeneous electric vehicle fleets

The electrical power system has experienced several changes during the last decade, raised by continuously increasing load demand, rapid depletion in fossil fuels, and newly electrical deregulation policy. In the past, numerous review literatures has been published in the field of Load Frequency Control (LFC), which deals with different and recent control strategies for the successful operation of the power system. Moreover, due to changes in lifestyle, increasing load demand, expansion of industrialization, and environmental issues, Renewable Energy Sources (RES) integration becomes the obvious adaptive choice. Since generation from RES is stochastic in nature and depends upon the weather condition and other aspects at every instant of time. Therefore, high penetration may reflect specific issues regarding voltage instability, frequency stabilization, and reliability obstruction. Hence this issue has been covered effectively in this review work. Furthermore, the work precisely summarized and briefly explained the different scenarios of Energy Storage (ES), micro-grid and Flexible AC Transmission System (FACTS) to explore the possible solutions and future aspects. The merit and demerit of different controllers are also investigated with the help of a comparison's tables and also some other analytical comparisons. The overall study examines an in-depth review of recent technical core aspects for LFC based on classical and modern power system which involves the non-linear model, controller design parameters, soft-computing application, attributes of Load forecasting, as well as integration of RES in a deregulated environment. This effective, comprehensive literature survey is very helpful for researchers to bridge the gap between recent development, implementation, challenges and future trends of RES in LFC. 

A literature survey on load frequency control considering renewable energy integration in power system: Recent trends and future prospects

Decomposition based multiobjective evolutionary algorithm for PV/Wind/Diesel Hybrid Microgrid System design considering load uncertainty

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

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Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university’s PEV infrastructure The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

This paper describes a case study carried out to quantify the benefits achieved from the application of model predictive control (MPC) to regulate the pH in a brine dechlorination process. A mechanistic model of the process was developed and used to design a PI controller with feedforward compensation. The model was also used to quantify the potential benefits of commissioning an MPC controller on the process. Following the analysis with the mechanistic model, the designed MPC scheme was applied to the actual process. Through real-time results, it is shown that the deployment of this controller led to substantial improvements in disturbance rejection capabilities when compared to the existing PI controller with lead–lag feedforward compensator. The improved disturbance rejection led to a reduction in pH variation which resulted in reduced operational costs, more reliable production, and improvement in the efficiency of the dechlorination process.

Yusuf Abubakar Sha’aban

Papers

Decomposition based multiobjective evolutionary algorithm for PV/Wind/Diesel Hybrid Microgrid System design considering load uncertainty

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

Control Improvement Using MPC: A Case Study of pH Control for Brine Dechlorination

PID versus MPC Performance for SISO Dead-time Dominant Processes