Metaheuristic Based Solution for the Non‐Linear Controller of the Multiterminal High‐Voltage Direct Current Networks

Multi-terminal High-Voltage Direct Current (MTDC) networks are emerging as the promising technology in modern transmission systems for efficient and bulky renewable energy sources transfer over long distances. Nonetheless, the computational burden introduced for modeling MTDC networks with detailed models is heavy due to the inherent switching events of the Voltage Source Converters (VSCs). Transient system-level studies with less simulation time step-size can be achieved with Average-Value Models (AVMs). This paper delivers step-by-step modeling of the MTDC network with AVM -VSC in a systematic approach for steady-state and dynamic operation studies using Matlab/Simulink platform. The interaction between the MTDC network hierarchical control layers with the AVM -VSC model is covered and developed for a 4-terminal radial MTDC network. The DC voltage droop control is applied for the receiving end converters for the purpose of transmission losses optimization and to achieve power-sharing according to the converters ratings.

DC line faults raise significant concerns in multiterminal high voltage direct current (MTDC) networks. The fault current ascends rapidly with a sharp peak, but isolating the entire faulty part is a non-sustainable solution for this matter. Therefore, rapid detection and classification of faults with high accuracy is indispensable. In this paper, a protection scheme is presented to protect MTDC networks based on voltage source converter (VSC) for interconnection of renewable energy sources, which incorporates main protection and post fault analysis. The main protection uses the Successive Data Window Technique (SDWT) as a fault detector, later on verifying it with the harmonics of the system voltage so as to prevent maloperation of the breakers, while the post fault analysis makes use of the voltage drop to classify the nature of fault, in addition to providing a backup in case the prior fails. The simulations results validate the protection scheme, which precisely identifies the fault and classifies its nature effectively. The performance of the proposed scheme is evaluated in PSCAD for MTDC networks. Matlab also verifies the results making this scheme a promising one.

Multi-terminal high voltage direct current (HVDC) networks with long-distance interconnections across the region has extensive demands all over the world, particularly for integrating large-scale renewable generations in China. Autonomous converter control based on voltage-droop strategy, widely accepted in DC Microgrid, is however flawed in multi-terminal HVDC networks with long-distance interconnections. That is because DC line impedance makes converters not be able to accurately reach the active power reference value given by the secondary control. This paper proposes an improved autonomous converter control method to reduce the steady-state error of active power in multiterminal HVDC networks. It first illustrates the real operating condition of each converter with traditional autonomous converter control in a multi-terminal HVDC network, and further investigates the relationship between the error of DC current and the steady-error of active power. Then the effects of DC line impedance and the slope of voltage-droop control on the steady-state error of active power flow is analyzed. Based on the analysis, an improved autonomous converter control method is presented to ensure each converter accurately tracking the reference of DC current point calculated from the active power reference and ideal DC voltage. Comparisons between the traditional and presented methods are studied, and eventually simulation tests in PSCAD verify the availability of the proposed methed.

This paper investigates the probabilistic small-signal rotor angle/voltage stability of an AC/DC power system to which offshore wind generation is connected through a multi-terminal HVDC (MTDC) network. The paper applies an analytical method based on multi-point linearization to improve the accuracy of assessment of system probabilistic stability. It proposes to examine the probabilistic small-signal voltage stability of the AC/DC power system as the voltage stability is always a concern of HVDC network. The participation factor is then employed to further analyze this phenomenon and give effective remedial measures. In the paper, an example of 16-machine AC/DC power system to which three offshore wind farms is connected through aN MTDC network is presented. The proposed method in the paper is compared with the conventional Gram-Charlier expansion-based method and the nonlinear Monte Carlo simulation.

On small signal stability of an AC/DC power system with a hybrid MTDC network

With the growing maturity of power electronic devices, the applications of the voltage source converter (VSC) technique on the high-voltage direct current (HVDC) transmissions have attracted great attention. Due to the inherent control characteristics of the VSC, the realization of multi-terminal HVDC (MTDC) grids becomes less complicated. However, one significant issue that restrains the development of VSC MTDC grids is the proper management against DC faults. This paper proposes a DC fault management scheme, delayed-auto-re-configuration (DARC), for VSC MTDC systems. This scheme does not require additional component to be added to the existing system. With appropriate protection and control, the MTDC system can be well protected and after the isolation of the faulted line section, the unfaulted terminals will be controlled and recovered to form a new MTDC configuration. The effectiveness of the proposed scheme is validated by simulations using the real-time digital simulator (RTDS).

The objective of this paper is to enhance the control performance of voltage source converter (VSC) stations within multi-terminal high voltage direct current (M-HVdc) grids under dynamic conditions. This paper presents the application of particle swarm optimization (PSO) to tune the control parameters of VSC stations. VSCs are non-linear components of the M-HVdc grids. Conventional techniques use approximated linear models to tune the control parameters which do not produce optimal results. Thus, PSO is employed to optimally tune the control parameters of VSC stations. The algorithm of the proposed objective function applies simultaneous optimization of inner and outer control layers. Dynamic simulations of a four-terminal HVdc system are developed in PSCAD/EMTDC to extol the merits of the presented optimization technique. Results show the steady state and dynamic control performances, both for classically and PSO-based tuned parameters, during load demand change from ac grids, wind power change, and eventual permanent VSC disconnection.

The Voltage Source Converter (VSC)-based MultiTerminal High Voltage DC (MT-HVDC) system is now considered the most effective and reliable method for integrating Offshore Wind Farms (OWF) with onshore grids. This paper focuses on the methods by which wind-generated power can be shared among individual VSCs during contingencies without violating system-wide voltage constraints. A four terminal VSCHVDC system is modeled in PSCAD/EMTDC that connects OWFs with onshore grids via VSCs which share the available power based on the voltage droop. The power sharing and AC/DC voltage control responsibility is distributed among the converters. In case of a converter loss, sharing the desired power while maintaining AC/DC voltage stability is a challenge that this paper aims to address. A droop control strategy is presented to minimize voltage fluctuations in the absence of the converter station controlling the DC voltage. Simulation results are presented for various operating case scenarios.

A unified power-flow algorithm of AC power systems integrated with multi-terminal high-voltage direct current (MTDC) grids incorporating the interline DC power-flow controller (IDCPFC) is presented in this paper. The MTDC grids are based on voltage-sourced converter (VSC) technology. Pulse-width modulation control is employed for the VSCs. As against most of the research works published in this area, in this algorithm, the VSC modulation indices are considered as unknowns. The IDCPFC considered for the power-flow management of the DC grid is a generalized one, with an arbitrary number of DC voltage sources. In the proposed model converter losses are also included. All case studies are carried out in MTDC grids embedded in the IEEE-300 bus test system. For all the case studies, the power-flow algorithms were implemented with MATLAB. In all occurrences, a mismatch error tolerance of $$10^{-10}$$ p.u. was selected. Results obtained with case studies on AC–MTDC systems validate the proposed work.

The voltage source converter (VSC) based multiterminal high voltage direct current (MTDC) transmission system is an interesting technical option to integrate offshore wind farms with the onshore grid due to its unique performance characteristics and reduced power loss via extruded DC cables. In order to enhance the reliability and stability of the MTDC system, an adaptive neuro fuzzy inference system (ANFIS) based coordinated control design has been addressed in this paper. A four terminal VSC-MTDC system which consists of an offshore wind farm and oil platform is implemented in MATLAB/ SimPowerSystems software. The proposed model is tested under different fault scenarios along with the converter outage and simulation results show that the novel coordinated control design has great dynamic stabilities and also the VSC-MTDC system can supply AC voltage of good quality to offshore loads during the disturbances.

The system studied in this article is a multi-terminal HVDC (MTDC) network composed by four voltage-source converter terminals. It is intended to be a model of the future “super-grid” in the North Sea, where Norway, Germany and the UK are interconnected together with offshore wind farms on the east coast of England. The main challenges related with multi-terminal HVDC grids are market and regulatory issues, control and coordination of the grid, and protection and circuit-breakers. This article focuses on the technical challenges, especially those in relation with DC voltage control and power flow balancing. Several studies have proposed different methods for controlling the DC voltage in MTDC grids. This article uses a DC voltage droop control scheme. The system is analysed by means of simulations and experimentally in a down-scaled laboratory model with ratings in the range of 60 kVA. In order to evaluate the robustness of the droop-control strategy, several large disturbances are emulated: abrupt variation in wind production, sudden change in droop line set-points and loss of two converter terminals during full wind production. Finally, the similarities and differences between the experimental set-up and a real system are discussed.

This paper explains a hybrid control approach to model a controller for voltage source converters (VSCs) in offshore wind farm (OSWF) applications, where the OSWFs are integrated to AC grid through the multi-terminal high voltage direct current (MT-HVDC) transmission system. Proper DC link voltage provides the effective power dispatch. So, a constant DC link voltage should be maintained to achieve the effective power dispatch between OSWFs and AC grid. A new control approach is a compound of proportional-integral and sliding mode control to regulate the DC link voltage on the grid side VSC. Also, the proposed control approach can control AC voltage and reactive power on wind farm and grid side VSCs respectively. Time- domain simulations executed in MATLAB/Simulink software are used to verify the proposed and conventional control approaches. Transient stability analysis is carried out for a case study. The three-terminal VSC-HVDC system has taken for study, where two OSWFs are fed to AC grid.

Long-distance offshore wind power transmission systems utilize multi-terminal high voltage direct current (MT-HVDC) connections based on voltage source converters (VSCs). In addition to having the potential to work around restrictions, the VSC-based MT-HVDC transmission system has significant technical and economic merits over the HVAC transmission system. Offshore wind farms (OWFs) will inevitably grow because of their outstanding resistance to climate change and ability to provide sustainable energy without producing hazardous waste. Due to stronger and more persistent sea winds, the OWF often has a higher generation capacity with less negative climate effects. The majority of modern installations are distant from the shore and produce more power than the early OWF sites, which are situated close to the shore. This paradigm shift has compelled industry and professional researchers to examine transmission choices more closely, specifically HVAC and HVDC transmission. This article conducts a thorough analysis of grid connection technologies for massive OWF integration. In comparison to earlier assessments, a more detailed discussion of HVDC and HVAC topologies, including HVDC based on VSCs and line-commutated converters (LCCs), and all DC transmission systems, is offered. Finally, a selection criterion for HVDC transmission is advised, and its use is argued to be growing.

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

The artificial bee colony (ABC) algorithm is an important branch of evolutionary algorithms and has been shown to be competitive with other algorithms for solving optimisation problems. However, there are always some insufficiencies in ABC algorithm such as lower convergence speed and easily get trapped in the local optima. To increase depth search capabilities of onlooker bees and ensure scout bees do not discard the current optimal solution, we proposed a modified ABC algorithm (denoted as ORABC) based on the optimisation strategy and retained strategy of the best individual in this paper. Compared with ABC algorithm, ABC* (added retained strategy to ABC) and ORABC algorithm, experiments are conducted on a set of numerical benchmark functions. The numerical simulation results demonstrate that ORABC algorithm improves the convergence characteristics of ABC algorithm and provides very remarkable performance in solving complex numerical optimisation problems compared to original algorithm.