scholarly journals Equivalent Impedance Calculation Method for Control Stability Assessment in HVDC Grids

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6899
Author(s):  
Fisnik Loku ◽  
Patrick Düllmann ◽  
Christina Brantl ◽  
Antonello Monti
Keyword(s):  
Calculation Method ◽  
Detailed Knowledge ◽  
Configuration Change ◽  
Box Models ◽  
Impedance Characteristics ◽  
Equivalent Impedance ◽  
Control Stability ◽  
Impedance Calculation ◽  
Operating Points ◽  
Black Box Models

A major challenge in the development of multi-vendor HVDC networks are converter control interactions. While recent publications have reported interoperability issues such as persistent oscillations for first multi-vendor HVDC setups with AC-side coupling, multi-terminal HVDC networks are expected to face similar challenges. To investigate DC-side control interactions and mitigate possible interoperability issues, several methods based on the converters’ and DC network’s impedances have been proposed in literature. For DC network’s impedance modelling, most methods require detailed knowledge of all converters’ design and controls. However, in multi-vendor HVDC networks, converter control parameters are not expected to be shared due to proprietary reasons. Therefore, to facilitate impedance-based stability analyses in multi-vendor MTDC networks, methods that do not require the disclosure of the existing converter controls are needed. Here, detailed impedance measurements can be applied; however, they are time-consuming and require new measurement for a single configuration change. This paper proposes an equivalent impedance calculation method suitable for multi-vendor DC networks, which for available black-box models or converter impedance characteristics can be modularly applied for various network configurations, including different control settings and operating points, while significantly reducing the required time for obtaining an equivalent DC network impedance.

scholarly journals Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6749
Author(s):  
Reda El Bechari ◽  
Stéphane Brisset ◽  
Stéphane Clénet ◽  
Frédéric Guyomarch ◽  
Jean Claude Mipo
Keyword(s):  
Branch And Bound ◽  
Prediction Error ◽  
Global Solutions ◽  
Black Box ◽  
High Fidelity ◽  
Electromagnetic Devices ◽  
Box Models ◽  
Element Simulation ◽  
The Cost ◽  
Black Box Models

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

Mathematical Modeling of CSTR Bioreactor Control for Production of Recombinant Protein

2014 ◽  
Vol 894 ◽  
pp. 311-315
Author(s):  
Xiao Yi Jia ◽  
Yu Tian Lin ◽  
Hui Bin Lin ◽  
Ling Gao ◽  
Jian Qun Lin ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Recombinant Protein ◽  
Fermentation Process ◽  
Protein Production ◽  
Continuous Stirred Tank Reactor ◽  
High Productivity ◽  
Box Models ◽  
Microbial Cultivation ◽  
Continuous Stirred Tank ◽  
Black Box Models

Fermentation process using recombinant strain for production of recombinant protein is widely used in commercialization of the biotechnologies. The continuous stirred tank reactor (CSTR) is a typical microbial cultivation method, has the major advantage of high productivity. Mathematical modeling and simulation is useful for analysis and optimization of the CSTR fermentation process. Most of the mathematical models developed for CSTR are black box models without information of the intracellular dynamics and regulations. In this research, a mathematical model is built based on gene regulation for recombinant protein production using CSTR, and simulation is made using this model.

An overview of deep learning techniques

2018 ◽  
Vol 66 (9) ◽  
pp. 690-703 ◽  
Author(s):  
Michael Vogt
Keyword(s):  
Deep Learning ◽  
Single Layer ◽  
Visual Image ◽  
Image Understanding ◽  
Number Of Layers ◽  
Box Models ◽  
Learning Techniques ◽  
Engineering Work ◽  
Computational Resources ◽  
Black Box Models

Abstract Deep learning is the paradigm that profoundly changed the artificial intelligence landscape within only a few years. Although accompanied by a variety of algorithmic achievements, this technology is disruptive mainly from the application perspective: It considerably pushes the border of tasks that can be automated, changes the way products are developed, and is available to virtually everyone. Subject of deep learning are artificial neural networks with a large number of layers. Compared to earlier approaches with ideally a single layer, this allows using massive computational resources to train black-box models directly on raw data with a minimum of engineering work. Most successful applications are found in visual image understanding, but also in audio and text modeling.

Creating Dynamic Pretrade Models: Beyond the Black Box

2018 ◽  
Keyword(s):  
Functional Form ◽  
Information Leakage ◽  
Black Box ◽  
Sensitivity Analyses ◽  
Stock Selection ◽  
Market Participants ◽  
Box Models ◽  
Portfolio Managers ◽  
Black Box Models ◽  
Shed Light

We provide a framework for investment managers to create dynamic pretrade models. The approach helps market participants shed light on vendor black-box models that often do not provide any transparency into the model’s functional form or working mechanics. In addition, this allows portfolio managers to create consensus estimates based on their own expectations, such as forecasted liquidity and volatility, and to incorporate firm proprietary alpha estimates into the solution. These techniques allow managers to reduce overdependency on any one black-box model, incorporate costs into the stock selection and portfolio optimization phase of the investment cycle, and perform “what-if” and sensitivity analyses without the risk of information leakage to any outside party or vendor.

scholarly journals The Role of Textualisation and Argumentation in Understanding the Machine Learning Process

2017 ◽  
Author(s):  
Kacper Sokol ◽  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Spatial Perception ◽  
Black Box ◽  
High Dimensional ◽  
Box Models ◽  
Machine Learning Applications ◽  
Black Box Models ◽  
Machine Learning Models

Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation -- a natural language narrative of selected phenomena -- can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.

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