A Data-driven Method for Estimating Parameter Uncertainty in PMU-based Power Plant Model Validation

2020 ◽  
Author(s):  
Jacob Eisenbarth ◽  
Josh Wold
Keyword(s):  
Power Plant ◽  
Model Validation ◽  
Parameter Uncertainty ◽  
Data Driven ◽  
Plant Model

Wind Power Plant Model Validation Using Synchrophasor Measurements at the Point of Interconnection

2015 ◽  
Vol 6 (3) ◽  
pp. 984-992 ◽  
Author(s):  
Yingchen Zhang ◽  
Eduard Muljadi ◽  
Dmitry Kosterev ◽  
Mohit Singh
Keyword(s):  
Power Plant ◽  
Wind Power ◽  
Model Validation ◽  
Wind Power Plant ◽  
Plant Model

Complementarity of Data-Driven and Simulation Modeling Based on the Power Plant Model of the Offshore Vessel

2017 ◽  
Author(s):  
Anna Swider ◽  
Stian Skjong ◽  
Eilif Pedersen
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Simulation Models ◽  
System Optimization ◽  
Data Driven ◽  
Plant Model ◽  
Potential Synergy ◽  
Board Monitoring ◽  
System Properties ◽  
Data Driven Modeling

Nowadays, there are more ships equipped with on-board monitoring systems and vessels with many sensors are becoming a standard. Available measurements can be employed for system optimization which play an important role in the vessel power plant configuration. The improvements in the power system design can be based on theoretical (modeling based on physical and empirical laws — for simulation purposes, for example [1]) or data-driven modeling (machine learning, statistical approach [2]). The data-driven models can be supportive confirming theoretical assumptions or simplifications from simulations. They are also helpful to understand the real systems, including vessel dynamic behavior and interactions. Therefore, the combination of simulation and data-driven modeling will be beneficial by identifying relationships that help explain unidentified variations. This approach is recommended when aiming for a more reliable tool for design optimization and to overcome the limitation of the simulation models that all system properties and dynamic effects must be known beforehand. The scope in this work is to present a potential synergy between the simulation and the machine learning approach. A data-driven method can be complementary to a model based on physical and empirical laws. This is shown in the example of power plant model connected with a thruster and vessel model to simulate the typical transit scenario and the data-driven model. The paper proposes a simultaneous analysis of the theoretical and machine learning models to predict the vessel power/speed and study the complex systems’ interactions in more detail, which are essential while exploring the system behavior.

Study of HVDC System and Subsynchronous Oscillation

2015 ◽  
Vol 1092-1093 ◽  
pp. 356-361
Author(s):  
Peng Fei Zhang ◽  
Lian Guang Liu
Keyword(s):  
Power Plant ◽  
Time Domain ◽  
Simulation Method ◽  
Time Domain Simulation ◽  
Stable Convergence ◽  
Turbine Generator ◽  
Plant Model ◽  
Subsynchronous Oscillation ◽  
Hvdc System ◽  
Simulation Results

With the application and development of Power Electronics, HVDC is applied more widely China. However, HVDC system has the possibilities to cause subsynchronous torsional vibration interaction with turbine generator shaft mechanical system. This paper simply introduces the mechanism, analytical methods and suppression measures of subsynchronous oscillation. Then it establishes a power plant model in islanding model using PSCAD, and analyzes the effects of the number and output of generators to SSO, and verifies the effect of SEDC and SSDC using time-domain simulation method. Simulation results show that the more number and output of generators is detrimental to the stable convergence of subsynchronous oscillation, and SEDC、SSDC can restrain unstable SSO, avoid divergence of SSO, ensure the generators and system operate safely and stably

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

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