| dc.description.abstract | In recent years, there has been an increased development and deployment of renewable energy resources to meet the
ever-increasing electric power demand and to limit the use of fossil fuels. This has spurred offshore wind farms due to
vast offshore wind energy potential. Large scale windfarms integration challenges such as need for long distance subsea
power transmission and managing the variability of wind power variation on in the power grid. These challenges can be
properly met by the use of Multi-Terminal Voltage Source Converter High Voltage DC transmission (MTDC) grid.
MTDC system consists of three or more HVDC converter station connected to a common DC transmission network.
Several control schemes were designed and analysed to use DC voltage as feedback signal and paid less intention by
researchers. The DC voltage measured signal is used to vary DC reference voltage in AC side outer loop control and
develops coordination between AC and DC sides. Thus, any uncertainty/variation in DC voltage feedback signal directly
reflects on the performance of ith terminal in terms of reduction in power and voltage sharing capability. The DC terminal
voltage is connected to DC bipolar transmission line and as results; it is enhanced probability of uncertainty. Hence,
power sharing and voltage sharing capability can be modified using DC voltage feedback signal.
As in the literature survey, it is found that most of the researchers have been taken only to improve power sharing
capability without consideration of terminal DC voltage variations against AC grid load demand change, outage of
terminals and integration of wind farms etc. It is not possible to get simultaneous minimum change in both terminal
power and DC voltage without consideration of intelligent control strategy. Thus, this thesis is focused to reduce
following research gaps as:
The fixed droop control method is not capable to maintain optimal power share among terminals in
presence of abnormal conditions.
Also, it is required to power transfer from a remote generation over a large distance is regulated with
enhanced power share and minimum oscillations against sudden change in load.
Further, the power sharing capability and DC voltage regulation is also distorted in presence of
transmission line resistance.
During outage of a terminal in VSC-MTDC, the power share is effectively distributed.
The unpredictive natures of renewable energy are affected on MTDC grids stability in terms of over
loading, oscillations in power and voltage.
However, in addition of above gaps, the contribution of the research work can be put into three areas, namely control
strategies used in VSC-MTDC system, analysis of ANN based droop control for optimal power distribution in VSC MTDC, impact analysis of transmission resistance, outage of terminals, wind power penetration impact and analysis of
optimal power sharing in a meshed MTDC Grid via a terminal based Current Flow Controller in the following thesis
Chapters.
Chapter 1 deals with the introduction to VSC-MTDC system, need of MTDC terminals, types of various MTDC
topologies, and discussed with their advantages and disadvantages, introduction to different control schemes used for
MTDC control and VSC-MMC schemes also described.
Chapter 2 discusses the detailed Literature survey in terms of scalability of controller design approach for VSC grid.
The various control methods like voltage droop control, control based on network topology, voltage margin control,
master slave control, PI control, optimal control, robust control, genetic control, coordinated control, adaptive droop
control, Artificial Intelligence based droop control and control strategy when MTDC integrated with renewable energy
sources. The advantages and drawbacks of each method have been discussed.
Chapter 3 proposes a neural network based intelligent adaptive droop control strategy on a five terminals MTDC system
to achieve optimal power share with minimum DC voltage deviations according to terminals rating and its topology. A
three-layer Artificial Neural Network (ANN) error back propagation algorithm-based learning rule is merged with a
Linear Quadratic Regulator (LQR). The ANN learning rule is used to estimate droop constants adaptively and LQR is
utilized to minimize transient in power flow at minimum DC voltage fluctuation. The robustness of the proposed control
strategy is illustrated with respect to transmission line admittance matrix uncertainty, step and random change of
reference power at constant power terminals. Also, a comparative scenario is demonstrated in terms of peak with existing
two controllers. The contribution in the area of ANN based intelligent adaptive droop control comprises results of step | en_US |
| dc.description.sponsorship | reference power variation at terminal T3 and T5, Sensitivity analysis with respect to reference power terminal change
and parameter uncertainty and their comparative demonstration, comparative analysis in presence of wind farm
integration, injection at terminal T5 and terminal outage in presence of wind farm power has been analysed.
Chapter 4 proposes a Linear Quadratic Regulator (LQR) based Current Flow Controller (CFC) to regulate the line power
flow within a permissible limit at VSC-MMC terminals. The proposed control scheme is applied on four terminals
meshed MTDC grids in order to get optimal current sharing with best concert in terms of not only over loading but also
enhances closed loop system concert, minimizes settling time, over/under shoots and oscillations. The proposed
controller improves the DC grid reliability by restraining the line currents under their thermal ratings.
Chapter 5 deals with general thesis conclusions and suggestions for future work. ANN based proposed droop control
scheme is used to enhance MTDC closed loop system characteristics in respect of optimal power share and small
redistribution of terminal DC voltages with negligible oscillations even in presence of AC grid load demand changes,
transmission line admittance matrix uncertainty, outage of terminals and integration of wind farms etc. This proposed
schemes also allowed to share the terminal power optimally and DC voltages changes at smallest level without
consideration of voltage margin at a specified terminal. Optimal power sharing in Meshed MTDC grid can be achieved
by using CFC. The control strategy is used to minimize the current flow rate over a transmission line in the meshed
MTDC system. This control method has optimal current sharing with better performance in terms of over loading. In
future, the VSC-MTDC system dynamics will be analysed through MATLAB simulation and designed an intelligent
based adaptive droop control method to improve transient response in presence of wind farms.
Keywords: Mutli-Terminal HVDC, Artificial Neural Network (ANN), VSC-MTDC, Current Flow Controller | en_US |
