Artificial Intelligence-based Control Techniques for HVDC Systems

The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PD

Intelligent STATCOM Voltage Regulation using Fuzzy Logic Control

Reactive power compensation is a very important and challenging task in electrical power systems today. Future trends foreseen in power systems such as high interconnectivity and the integration of renewable energy resources produce even more issues related to power system control and stability. Flexible AC transmission systems are vastly used in power systems in order to mitigate several performance aspects found in typical power systems. One shunt connected device in particular, STATCOM, is very powerful and commonly used in voltage regulation at the power transmission level. STATCOM uses voltage sourced converters to inject or absorb reactive power from the power grid as commanded to stabilize the transmission line voltage at the point of connection. The control of STATCOM has relied historically on using traditional PI controllers, however, since the dynamic response of STATCOM highly affects its ability to perform its task, improving the capabilities of STATCOM using more advanced control approaches has become vital for both manufacturers and power systems operators. Fuzzy logic control, as one area of artificial intelligence techniques, has been emerging in recent years as a complement to the conventional methods in various areas of power systems control. The most significant advantage of fuzzy controller as an intelligent controller is that it doesn’t require mathematical modelling. It is robust and nonlinear in its nature, and expert’s knowledge can be utilized in generating control rules. The main contribution is to use fuzzy logic control theory to design a pure fuzzy logic control and another fuzzy adaptive PI control strategies for STATCOM that are superior in performance to traditional PI control approach. This will increase STATCOM’s ability to seamlessly perform their task in voltage regulation. This work investigates the performance of classical PI controlled STATCOM then compares it with fuzzy logic based STATCOM and fuzzy adaptive PI controlled STATCOM. Simulations done using MATLAB on a three generator test system show that adaptive fuzzy PI control technique is faster in responding to voltage variations and better in tracking the reactive current reference. Results also show that a direct control using fuzzy logic provides even faster voltage regulation and acts almost as a perfect tracker for reference reactive current

Modelling, Simulation and Fuzzy Self-Tuning Control of D-STATCOM in a Single Machine Infinite Bus Power System

© 2019 Bentham Science Publishers. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.2174/2352096511666180314141205In recent years, demand for electricity has increased considerably, while the expansion of generation and transmission has been very slow due to limited investment in resources and environmental restrictions. Methods: As a result, the power system becomes vulnerable to disturbances and instability. FACTS (Flexible AC Transmission Systems) technology has now been accepted as a potential solution to this problem. This paper deals with the modelling, simulation and fuzzy self-tuning control of a D-STATCOM to enhance the stability and improve the critical fault clearing time(CCT) in a single machine infinite bus (SMIB).A detailed modelling of the D-STATCOM and comprehensive derivation of the fuzzy logic self-tuning control is presented. Results: The dynamic performance of the power system with the proposed control scheme is validated through in a simulation study carried out under Matlab/Simulink and SimPowerSystems toolbox. Conclusion: The results demonstrate a significant enhancement of the power system stability under the simulated fault conditions considered.Peer reviewe

VAr Compensation Based Stability Enhancement Of Wind Turbine Using STATCOM

Maintenance of power system stability becomes vital during disturbances like faults, contingency etc. This work deals with a novel priority oriented optimal reactive power compensation of Doubly-Fed Induction Generator (DFIG) based wind turbine using Static Synchronous Compensator (STATCOM). A multi-objective problem will be formulated to maintain voltage within its tolerance levels using Voltage Severity Index (VSI) and to mitigate low frequency oscillations by using Transient Power Severity Index (TPSI) during post-fault conditions. An optimal solution to this proposed problem will be obtained using Fuzzy Logic. In order to justify the proposed methodology it is simulated and tested using 2 MW DFIG with MATLAB- Simulink.nbs

Fuzzy Logic Controller for grid connected Wind Energy Conversion System

The use of renewable power sources, like wind power, has been increased recently due to climatic changes caused by fossil fuels and fast depletion of fossil fuels. This has lead to the tremendous increase in the interconnection of wind turbines to power system grid. This interconnection on a large number in to grid causes problems such as power quality, maintaining system voltage, reactive power compensation, control of grid frequency and aspects of power system grid stability. In this proposed scheme, a fuzzy logic based controller is employed for a STATCOM to improve the power quality. The proposed control scheme supplies the required reactive power to the system and thus relieves the source, leading to Unity Power Factor (UPF) at the source and also it injects currents to reduce total harmonic distortion (THD) to satisfy IEC standard. For extracting the reference currents, an instantaneous reactive power theory based control algorithm is employed. To determine the effectiveness of the proposed fuzzy logic controller, a comparative analysis is also performed with a PI controller and the results have been presented

Fully Evolvable Optimal Neurofuzzy Controller Using Adaptive Critic Designs

A near-optimal neurofuzzy external controller is designed in this paper for a static compensator (STATCOM) in a multimachine power system. The controller provides an auxiliary reference signal for the STATCOM in such a way that it improves the damping of the rotor speed deviations of its neighboring generators. A zero-order Takagi-Sugeno fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming (HDP) based approach is used to further train the controller and enable it to provide nonlinear near-optimal control at different operating conditions of the power system. Based on the connectionist systems theory, the parameters of the neurofuzzy controller, including the membership functions, undergo training. Simulation results are provided that compare the performance of the neurofuzzy controller with and without updating the fuzzy set parameters. Simulation results indicate that updating the membership functions can noticeably improve the performance of the controller and reduce the size of the STATCOM, which leads to lower capital investment

Robust fuzzy-sliding mode based UPFC controller for transient stability analysis in autonomous wind-diesel-PV hybrid system

This study presents a comparative study of transient stability and reactive power compensation issues in an autonomous wind–diesel-photovoltaic based hybrid system (HS) using robust fuzzy-sliding mode based unified power flow controller (UPFC). A linearised small-signal model of the different elements of the HS is considered for the transient stability analysis in the HS under varying loading conditions. An IEEE type 1 excitation system is considered for the synchronous generator in the HS, with negligible saturation characteristic, for detailed voltage stability analysis. It is noted from the simulation results that the performance of UPFC is superior to static VAR compensator and static synchronous compensator in improving the voltage profile of the HS. Further, fuzzy and fuzzy-sliding mode based UPFC controller is designed in order to improve the transient performance. Simulation results reflect the robustness of the proposed fuzzy-sliding mode controller for better reactive power management to improve the voltage stability in comparison with the conventional PI and fuzzy-PI controllers. In addition to this, system stability analysis is performed based on eigenvalue, bode and popov for supporting the robustness of the proposed controller

Advances and Trends in Mathematical Modelling, Control and Identification of Vibrating Systems

This book introduces novel results on mathematical modelling, parameter identification, and automatic control for a wide range of applications of mechanical, electric, and mechatronic systems, where undesirable oscillations or vibrations are manifested. The six chapters of the book written by experts from international scientific community cover a wide range of interesting research topics related to: algebraic identification of rotordynamic parameters in rotor-bearing system using finite element models; model predictive control for active automotive suspension systems by means of hydraulic actuators; model-free data-driven-based control for a Voltage Source Converter-based Static Synchronous Compensator to improve the dynamic power grid performance under transient scenarios; an exact elasto-dynamics theory for bending vibrations for a class of flexible structures; motion profile tracking control and vibrating disturbance suppression for quadrotor aerial vehicles using artificial neural networks and particle swarm optimization; and multiple adaptive controllers based on B-Spline artificial neural networks for regulation and attenuation of low frequency oscillations for large-scale power systems. The book is addressed for both academic and industrial researchers and practitioners, as well as for postgraduate and undergraduate engineering students and other experts in a wide variety of disciplines seeking to know more about the advances and trends in mathematical modelling, control and identification of engineering systems in which undesirable oscillations or vibrations could be presented during their operation