scholarly journals Power Grid Dynamic Performance Enhancement via STATCOM Data-Driven Control

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2361
Author(s):  
David Rivera ◽  
Daniel Guillen ◽  
Jonathan C. Mayo-Maldonado ◽  
Jesus E. Valdez-Resendiz ◽  
Gerardo Escobar
Keyword(s):  
Reactive Power ◽  
Performance Enhancement ◽  
Dynamic Performance ◽  
Data Driven ◽  
Operating Point ◽  
Voltage Profile ◽  
Static Synchronous Compensator ◽  
Model Free ◽  
Free Data ◽  
Data Driven Approach

This work proposes a data-driven approach to controlling the alternating current (AC) voltage via a static synchronous compensator (STATCOM). This device offers a fast dynamic response injecting reactive power to compensate the voltage profile, not only during load variations but also depending on the operating point established by the grid. The proposed control scheme is designed to improve the dynamic grid performance according to the defined operating point into the grid. The mathematical fundamentals of the proposed control strategy are described according to a (model-free) data-driven-based controller. The robustness of the proposed scheme is proven with several tests carried out using Matlab/Simulink software. The analysis is performed with the well-known test power system of two areas, demonstrating that the proposed controller can enhance the dynamic performance under transient scenarios. As the main strength of the present work with respect to the current state-of-the-art, we highlight the fact that no prior knowledge of the system is required for the controller implementation, that is, a model or a system representation. The synthesis of the controller is obtained in a pure numerical way from data, while it can simultaneously ensure stability in a rigorous way, by satisfying Lyapunov conditions.

scholarly journals Flyback Cascaded Multilevel Inverter Based SHE-PWM Control for STATCOM Applications

2017 ◽  
Vol 8 (1) ◽  
pp. 100 ◽  
Author(s):  
Kah Haw Law ◽  
Wendy Pei Qin Ng ◽  
Wei Kitt Wong
Keyword(s):  
Closed Loop ◽  
Pulse Width Modulation ◽  
Reactive Power ◽  
Performance Enhancement ◽  
Static Synchronous Compensator ◽  
Loop Control ◽  
Flyback Converter ◽  
Harmonic Elimination ◽  
Multi Level ◽  
Cascaded Multilevel Inverter

This paper presents the closed loop DC-DC flyback converter with multi-level cascaded H-bridge inverter (MCHI) for transformer-less static synchronous compensator (STATCOM) system. The STATCOM system is proposed to be controlled using decoupled dq vector control incorporating the new reactive current reference algorithm and multi-level selective harmonic elimination pulse width modulation (MSHEPWM) technique. This ensures transient performance enhancement as well as simpler control and modulation technique implementation for dynamic systems. As the proposed MSHEPWM solely depends on adjustable DC-link voltage levels, flyback converter is designed for that purpose to suit wider applications. In current work, a single phase five-levels CHI based STATCOM system incorporated with the aforementioned converters are presented to compensate the reactive power (VAR) at the point of common connection (PCC). The dynamic as well as the transient performances of the developed STATCOM control system and the proposed voltage closed loop control of each converter are investigated to meet different VAR demands at balanced loading conditions. Simulation studies are performed to verify the effectiveness and theoretical analysis of the approaches presented.

Enhancement of Voltage Profile By Using Static Synchronous Compensator (Statcom) in Power System Networks

2016 ◽  
Vol 8 (02) ◽  
pp. 109-120 ◽  
Author(s):  
Ziaur Rahman ◽  
Amit Tiwari
Keyword(s):  
Power System ◽  
Test Data ◽  
Reactive Power ◽  
Power Transmission ◽  
Standard Test ◽  
Transmission System ◽  
Load Flow ◽  
Voltage Profile ◽  
Static Synchronous Compensator ◽  
Bus System

Voltage profile is one of the concerned issues in power system network studies. The voltage profile decay can be experienced by the system when system is subjected to load increment or disturbances. Unscheduled increment of load variation in a power transmission system has driven the system to be stressful, leading to potential cascading trip on the entire system. and capacitor placement. In this paper we introduced the Static Synchronous Compensator (STATCOM), a shunt connected Flexible AC Transmission System (FACTS) device which is capable to regulate the voltage profile by generating or observing the reactive power. Our objective has been tested with different size and different location of STATCOM on IEEE-4 Bus System and IEEE-9 Bus System by using the Newton-Raphson load flow method in MATLAB environment. In this work, firstly we have analysed IEEE-4 bus system and IEEE-9 bus system under the standard test data and after that analysed IEEE- 4 bus system and IEEE-9 bus system with STATCOM under the standard test data. After that, we have compared all the load flow results and observed the effect of STATCOM on voltage profile The different sizes of STATCOM used in the test systems are 20,40,60,80and 100MVAr.

scholarly journals Model-free data-driven computational mechanics enhanced by tensor voting

2021 ◽  
Vol 373 ◽  
pp. 113499
Author(s):  
Robert Eggersmann ◽  
Laurent Stainier ◽  
Michael Ortiz ◽  
Stefanie Reese
Keyword(s):  
Computational Mechanics ◽  
Data Driven ◽  
Tensor Voting ◽  
Model Free ◽  
Free Data

scholarly journals Unraveling Hidden Interactions in Complex Systems with Deep Learning

2020 ◽  
Author(s):  
Seungwoong Ha ◽  
Hawoong Jeong
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Training Data ◽  
Data Driven ◽  
Velocity Correlation ◽  
General Process ◽  
Vicsek Model ◽  
Model Free ◽  
Free Data ◽  
Insight Into

Abstract Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein--Uhlenbeck particles (non-Markovian) in which, notably, AgentNet's visualized attention values coincided with the true variable-wise interaction strengths and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.

Dynamic performance improvement of wind farms equipped with three SCIG generators using STATCOM

2014 ◽  
Vol 25 (4) ◽  
pp. 128-135 ◽  
Author(s):  
Othman Hasnaoui ◽  
Mehdi Allagui
Keyword(s):  
Power Flow ◽  
Wind Farm ◽  
Transient Stability ◽  
Reactive Power ◽  
Dynamic Performance ◽  
Wind Farms ◽  
System Stability ◽  
Static Synchronous Compensator ◽  
Power Flow Control ◽  
Ac Transmission

The main causes of wind farms disconnection from the grid is the three-phase grid faults at the point common coupling (PCC) e.g. the voltage dip. The use of a Static Synchronous Compensator (STATCOM) which is from the family of Flexible AC Transmission System (FACTS) devices can be used effectively in a wind park based on FSIG to provide transient voltage and to improve wind system stability. Due to the asynchronous operation nature, system instability of wind farms based on FSIG (Fixed Speed Induction Generator) is largely caused by the reactive power absorption by FSIG because due to the large rotor slip during grid fault. STATCOM contributes to control the grid voltage at PCC and maintain wind farm connection to the grid during some severe conditions of grid faults and used for power flow control and for damping power system oscillations. The evaluation of this control strategy using (STATCOM) is investigated in terms of regulation reactive power and transient stability of the wind farm during grid disturbances.

scholarly journals Model-free data-driven methods in mechanics: material data identification and solvers

2019 ◽  
Vol 64 (2) ◽  
pp. 381-393 ◽  
Author(s):  
Laurent Stainier ◽  
Adrien Leygue ◽  
Michael Ortiz
Keyword(s):  
Data Driven ◽  
Material Data ◽  
Model Free ◽  
Free Data

scholarly journals Unraveling hidden interactions in complex systems with deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seungwoong Ha ◽  
Hawoong Jeong
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Training Data ◽  
Data Driven ◽  
Velocity Correlation ◽  
General Process ◽  
Vicsek Model ◽  
Model Free ◽  
Free Data ◽  
Insight Into

AbstractRich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by scientists with human ingenuity. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein–Uhlenbeck particles (non-Markovian) in which, notably, AgentNet’s visualized attention values coincided with the true variable-wise interaction strengths and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.

scholarly journals Data-Driven Model-Free Control of Torque-Applying System for a Mechanically Closed-Loop Test Rig Using Neural Networks

2020 ◽  
Vol 66 (5) ◽  
pp. 337-347
Author(s):  
Aida Parvaresh ◽  
Mohsen Mardani
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
High Performance ◽  
Data Driven ◽  
Test Rig ◽  
Operational Conditions ◽  
Model Free ◽  
Actuation System ◽  
Data Driven Approach ◽  
Learning Factors

This paper presents a data-driven approach that utilizes the gathered experimental data to model and control a test rig constructed for the high-powered gearboxes. For simulating a wide variety of operational conditions, the test rig should be capable of providing different speeds and torques; this is possible using a torque-applying system. For this purpose, Electro-Hydraulic Actuators (EHAs) are used. Since applying accurate torque is a crucial demand as it affects the performance evaluation of the gearboxes, precise modelling of the actuation system along with a high-performance controller are required. In order to eliminate the need to solve complex nonlinear equations of EHA that originate from friction, varying properties of flow and similar, a data-driven system based on neural networks is used for modelling. In this manner, the model of the system, which captures the whole dynamic of the system, can be obtained without any simplifying assumptions. The model is validated with experimental data, and the learning factors are set to zero to reduce the high computational costs. After that, another network of neurons is used as a controller. The performance of the proposed controller under normal conditions and in the presence of disturbances are investigated. The results show a good tracking of this controller for various reference inputs in different conditions with acceptable characteristics. Additionally, the obtained results are compared with a conventional proportional-integral-derivative (PID) controller results, and the superior features of the proposed scheme is concluded.

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