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A new protection algorithm for DC line faults in multi-terminal high voltage DC (MTDC) systems is proposed in this study. A four-terminal MTDC model is used to investigate fault behaviour and detection using the simulation program PSCAD/EMTDC. The simulation results are post-processed using Matlab. The fault clearing must be done very rapidly, to limit the effect of the fault on neighbouring DC lines because of the rapid increase in DC current. However, before clearing the line, the fault location must be detected as soon as possible. A rapid fault location detection algorithm is therefore needed, preferably without communication. The protection algorithm proposed in this study uses wavelet analysis to detect the fault location based on local measurements. The protection algorithm consists of three independent fault criteria, of which two use wavelet analysis. The third criterion is based on a detection method in the time domain. The latter is an additional detection method independent of wavelet analysis.

Summary With the occurrence of the oscillation current (OC) in the direct current (DC) traction network of rail transit, malfunctions appear in the relay protection system frequently. For the purpose of improving the reliability of the protection system, more effective feature extraction methods are expected to be taken into consideration. Thus, in this paper, a novel approach to feature extraction is proposed to make a distinction between the short‐circuit fault current (FC) and the OC, combined ensemble empirical mode decomposition (EEMD) and sample entropy (SampEn). Firstly, on the basis of EEMD method, the feeder current signal is disassembled, and a range of intrinsic mode functions can be derived. Then the SampEn value of each intrinsic mode functions component is calculated. Finally, all the SampEn values are summed up to serve as the feature vector that involves information on the operation state of DC traction network. In accordance with the simulation results of typical feeder current signals, it is proved that the proposed method is capable of distinguishing the FC and OC effectively. The measured data calculation results show that the proposed method can control the misjudgment rate below 5%. Therefore, the proposed method can provide a good reference for identifying the operation state of the DC traction network.

This research deals with the efficiency comparison between AC and DC distribution networks that can provide electricity to rural and urban areas from the point of view of grid energy losses and greenhouse gas emissions impact. Configurations for medium- and low-voltage networks are analyzed via optimal power flow analysis by adding voltage regulation and devices capabilities sources in the mathematical formulation. Renewable energy resources such as wind and photovoltaic are considered using typical daily generation curves. Batteries are formulated with a linear representation taking into account operative bounds suggested by manufacturers. Numerical results in two electrical networks with 0.24 kV and 12.66 kV (with radial and meshed configurations) are performed with constant power loads at all the nodes. These simulations confirm that power distribution with DC technology is more efficient regarding energy losses, voltage profiles and greenhouse emissions than its AC counterpart. All the numerical results are tested in the General Algebraic Modeling System widely known as GAMS.

This paper addresses the problem of the optimal location and sizing of photovoltaic (PV) sources in direct current (DC) electrical networks considering time-varying load and renewable generation curves. To represent this problem, a mixed-integer nonlinear programming (MINLP) model is developed. The main idea of including PV sources in the DC grid is minimizing the total greenhouse emissions produced by diesel generators in isolated areas. An artificial neural network is employed for short-term forecasting to deal with uncertainties in the PV power generation. The general algebraic modeling system (GAMS) package is employed to solve the MINLP model by using the CONOPT solver that works with mixed and integer variables. Numerical results demonstrate important reductions of harmful gas emissions to the atmosphere when PV sources are optimally integrated (size and location) to the DC grid.

A new active impedance estimation based protection strategy which is suitable for utilisation in a DC zonal marine power distribution system is presented. This method uses two triangular current ‘spikes’ injections for system impedance estimation and protection when faults are detected. By comparing the estimated impedance with the pre-calibrated value, the fault location can be predicted and fault can be isolated without requiring communication between two injection units. Using co-ordinated double injections and line current measurement (directional fault detection), faults in the system with same impedance and different fault positions can be distinguished, located and isolated. The proposed method is validated using experimental test results derived from a 30 kW, 400 V, twin bus DC marine power system demonstrator. The experimental tests were applied to both faults during normal operation and faults that occur during system restoration.

This paper deals with the optimal reconfiguration problem of DC distribution networks by proposing a new mixed-integer nonlinear programming (MINLP) formulation. This MINLP model focuses on minimising the power losses in the distribution lines by reformulating the classical power balance equations through a branch-to-node incidence matrix. The general algebraic modelling system (GAMS) is chosen as a solution tool, showing in tutorial form the implementation of the proposed MINLP model in a 6-nodes test feeder with 10 candidate lines. The validation of the MINLP formulation is performed in two classical 10-nodes DC test feeders. These are typically used for power flow and optimal power flow analyses. Numerical results demonstrate that power losses are reduced by about 16% when the optimal reconfiguration plan is found. The numerical validations are made in the GAMS software licensed by Universidad Tecnológica de Bolívar.

This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software.

The authors consider a power system composed of several non-synchronous AC areas connected by a multi-terminal high-voltage direct current (HVDC) grid. In this context, the authors propose a distributed control scheme that modifies the power injections from the different AC areas into the DC grid, so as to make the system collectively react to load imbalances. This collective reaction allows each individual AC area to downscale its primary reserves. The scheme is inspired by algorithms for the consensus problem extensively studied by the control theory community. It modifies the power injections based on frequency deviations of the AC areas so as to make them stay close to each other. A stability analysis of the closed-loop system is reported as well as simulation results on a benchmark power system with five AC areas. These results show that with proper tuning, the control scheme makes the frequency deviations converge rapidly to a common value following a load imbalance in an area.

Integration of dynamic grid support is required for distributed power systems that are interconnected with medium voltage grids. This study proposes a comprehensive control solution to enhance fault ride through (FRT) capability for utility-scale photovoltaic (PV) power plants. Based on positive and negative sequence control schemes and PV characteristics, the approach alleviates dc-bus double-line-frequency ripples, reduces voltage stress on inverter power switches and DC-link capacitors, and minimises undesirable low-order voltage and current harmonics that are presented on the ac side. The study proposes a new feature to achieve superior FRT performance by using the overload capability of grid-tied inverters. A weak electric grid is used for the test case including a wind turbine induction generator, diesel engine driven synchronous generators and various loads. A comprehensive simulation verified the capability of the proposed control schemes for mitigating the voltage dip, enhancing the voltage response and further improving the stability of interconnected distributed generation in reaction to severe unbalanced voltage conditions because of asymmetrical grid faults.

This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.