AC-DC Network Research Articles

An AC Z-bus-based distribution factors for contingency analysis of AC–DC networks

The AC–DC networks are susceptible to deliberate and unforeseen contingencies, much like any other traditional power network. Due to the complex form of the unified impedance matrix derived for the provided network, the classic Z-bus-based contingency analysis approach cannot comprehend the AC–DC network. The distribution factors thus obtained from a unified complex Z-bus lead to an indication of the flow of complex current for DC lines. Therefore, an alternative method to discover post-contingency consequences is looked for. A streamlined approach for visualizing an equivalent AC network for the given AC–DC network has been proposed in this work. The AC-equivalent network takes the original AC–DC network and recreates it using two parallel AC networks to get the requisite distribution factors. The ultimate current/power distributions of the original AC–DC network can be determined by applying the superposition rule to the parallel networks, by the linear characteristics of the suggested technique. With this, the standard AC-contingency analysis method to complete the steady-state contingency analysis for the AC–DC network can be utilized. The proposed approach has been validated by the encouraging outcomes achieved for AC–DC networks with 10, 13, and 66 buses.

Analysis of ultra-low frequency oscillation mechanism of hydraulic turbine governor under different control modes

Abstract As the southwest power grid with a high proportion of hydropower was transformed into UHV AC-DC network structure, some ultra-low frequency oscillations have appeared in the power grid. In this paper, according to the characteristics of the hydropower unit governor, a small disturbance model was established, and the influence of various parameters of the hydropower unit governor on the system stability in primary frequency modulation was discussed by using the eigenvalue analysis method, and the power mode and the opening mode were compared. The results showed that the parameter adjustable range of the opening mode was larger than that of the power mode. This paper lays a foundation for further research on suppression methods of ultra-low frequency oscillations.

Dual Converters Coupled to Thermal Grid for Simultaneous Control on Regulation and Compensation in a Hybrid AC-DC Network

In conventional, radial, AC distribution system, the unbalances in load voltages & source currents at distributor junction (DJ) may affect the quality of power. Mostly, a single converter is employed to address any one unbalance at an instant and the power transfer during critical conditions is not met out effectively. To overcome this, an effective, coordinated operation of dual converters (C1&C2) in a hybrid AC-DC network coupled to thermal grid (Electro-thermal network) for meeting the heating demand of a community, is enunciated in this paper. The DC micro grid (DCM) is energized by renewable for serving the community’s heating and public electrical demands. Irrespective of source voltage & load conditions, the C1&C2 aim at voltage regulation & load compensation at DJ, respectively. Also, the control exhibits around 3% THD which is well within the standard levels. During any source outage/less power production, DCM supplies power to loads through inversion. An uninterrupted, quality power to loads at DJ by the dual converters, irrespective of source voltage & load variations along with a conservative approach through the thermal grid, claims the prime merit of the setup. Various unbalance cases including voltage sag & swell, no source & excess DCM power cases are examined through a supervisory control algorithm for analyzing the effectiveness of the control & system flexibility. The effective performance of the above is verified in real time using Hardware-in-Loop approach through DS1104 (dSPACE Controller) and OP4510 (Opal-Real Time) Controllers.

An algorithm for contingency analysis of the AC-DC networks using modified Z-bus technique

The steady state Contingency Analysis (CA) utilizes non-iterative distribution-factors (DFs) based linear estimation method of current-redistribution, to deliver approximate solutions in a shorter timeframe, which is favored over recurrent usage of iterative power flow-based non-linear techniques. However, the established principles of conventional DF-based CA cannot be directly implemented on burgeoning AC-DC networks. The DFs obtained from single unified Z-bus constructed for the entire AC-DC network leads to erroneous estimation of current redistribution post any contingency event. The reason being unified Z-bus built overlooked the characteristics of AC and DC lines. As a paradigm change toward a co-existing AC-DC network emerges, an adequate framework for understanding contingencies within such a network becomes critical. We present a quick non-iterative DF-based approach for the CA of any AC-DC network in this research by merging the use of separate AC and DC impedance matrices while accounting for the intertwined implications of the AC and DC networks. The interlinking converters (ILCs) are simulated using a reactive current bypassing approach. The proposed CA method is capable of handling contingencies occurring within ILCs, AC and the DC networks. The ETAP simulations and the Newton-Raphson (NR) technique for the AC-DC network have been utilized to corroborate the results.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This "associative cluster-dependent chain" (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous "Thunderstruck" song intro and then flexibly play it in a "bossa nova" rhythm without further training.

An optimal planning model for AC-DC distribution systems considering the converter lifetime

In this paper, a novel two-stage stochastic planning model is proposed based on graph theory for hybrid AC-DC distribution networks. In the proposed model, all possible AC-DC arrangements of the network are investigated to determine the optimal configuration. The Monte Carlo simulation method is applied to account for uncertainties in the output power of distributed generators (DGs) and load demands. In the first stage of the optimization, the planning problem is modeled as a mixed-integer programming (MIP) problem. Moreover, the structure of the AC-DC network is determined using a genetic algorithm (GA) to minimize the system investment costs. The second stage of the optimization is a non-linear programming (NLP) problem. In this step, the optimal power flow (OPF) model is created, and the lifetime of the converters for the determined AC-DC structure is maximized. Moreover, PLECS is used to mathematically model the converter lifetime based on its thermal cycles and fit the converter efficiency curve according to its input current function. Finally, the proposed method is applied for designing a 13-bus system, including various distributed generation sources and AC-DC loads. The evaluation of the results in several scenarios indicates that the proposed model is efficient in improving the converter lifetime and reducing the system costs.

Frequency-Domain Stability Criteria for Converter-Based Power Systems

Frequency-domain stability criteria are developed for converter-based power systems with any number of converters. The converters may be grid following or grid forming, and the grid can be an ac or hybrid ac-dc network. The converters and the network are all modeled in a common (stationary) reference frame based on small-signal sequence immittances. Coupling over frequency and ac-dc coupling are considered in the most general form. For each type of systems, a frequency-domain model is developed first by nodal analysis and then reformulated to fit the form of a feedback loop with open-loop stability guaranteed by practical conditions. System stability is then determined by a Nyquist-like criterion without the need to check open-loop right-half-plane poles. Further simplification of the system models is made possible by a) generalized <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-port equivalent circuits that include both types of coupling, b) equivalence of stability at internal and external nodes, and c) relationship between dynamics at the perturbation and the coupling frequency. Reduction of a system model into separate single-input-single-output models is presented for several special systems along with their practical applications. Quantitative and experimental results are presented to verify the developed theories.

A Newton‐Raphson ‐based unified load flow of grid‐connected and islanded AC–DC microgrids

The existing Newton-Raphson (NR)-based load flow for AC–DC microgrids (MG) implements a decoupled approach where the AC and DC sub-systems are modeled using separate variable sets. Therefore, such types of approach are not genuinely unified in nature. Here, in this article, we have attempted to formulate an NR-based load flow for AC–DC MG based on single set of variables. The approach has been developed by transforming the whole AC–DC system in terms of AC variables alone, which involves the conversion of DC lines and buses into their respective AC equivalent. At the same time, the proposed transformation concept eliminates the problem related to application of NR-based load flow with DC lines having their R/X ratio as infinity. Thus, a single Jacobian is sufficient enough to obtain the load flow solution. The proposed method applies to the grid-connected mode and also to the islanded mode of operation by considering the absence of slack bus. The proposed technique has been verified against existing results obtained using other methods and proves itself competent and accurate. The method further has been formulated for an unbalanced three-phase AC–DC network.

A Novel Adaptive Control Approach Based on Available Headroom of the VSC-HVDC for Enhancement of the AC Voltage Stability

Increasing offshore wind farms are rapidly installed and planned. However, this will pose a bottle neck challenge for long-distance transmission as well as inherent variation of their generating power outputs to the existing AC grid. VSC-HVDC links could be an effective and flexible method for this issue. With the growing use of voltage source converter high-voltage direct current (VSC-HVDC) technology, the hybrid VSC-HVDC and AC system will be a next-generation transmission network. This paper analyzes the contribution of the multi VSC-HVDC system on the AC voltage stability of the hybrid system. A key contribution of this research is proposing a novel adaptive control approach of the VSC-HVDC as a so-called dynamic reactive power booster to enhance the voltage stability of the AC system. The core idea is that the novel control system is automatically providing a reactive current based on dynamic frequency of the AC system to maximal AC voltage support. Based on the analysis, an adaptive control method applied to the multi VSC-HVDC system is proposed to realize maximum capacity of VSC for reactive power according to the change of the system frequency during severe faults of the AC grid. A representative hybrid AC-DC network based on Germany is developed. Detailed modeling of the hybrid AC-DC network and its proposed control is derived in PSCAD software. PSCAD simulation results and analysis verify the effective performance of this novel adaptive control of VSC-HVDC for voltage support. Thanks to this control scheme, the hybrid AC-DC network can avoid circumstances that lead to voltage instability.

Hierarchy and collaborative networks: a study on the colour of culture

Abstract Some theorists argue that collaborative networks are self-regulated structures, and their coordination strategy must be essentially horizontal and shaded by a “hierarchy.” In contrast, others assume that structuring of coordination processes and the establishment of roles is considered to be necessary to achieve the desired results that hierarchise relationships between different actors. Based on this apparent counterpoint, this article is dedicated to understanding the coordination structure of The Colour of Culture (A Cor da Cultura - ACDC) network. Specifically, we are guided by these questions: Can ACDC be characterised as a self-regulated network or as a hierarchical arrangement? What are the functions/dysfunctions of the network’s coordination elements on the ACDC network? The operationalisation of the research relied on the use of primary and secondary data: coded, categorised, and classified using the thematic content analysis technique. The results of the case study indicate how the coordination instruments adopted led some to view the ACDC network as a hierarchical arrangement. However, there are indications that it was the adoption of these instruments that facilitated the achievement of these results. That is, the case demonstrates the possibility of coexistence between a network and hierarchical coordination mechanisms.

Small-signal modelling of AC/MTDC hybrid power systems using Multi-Layer Component Connection Method

In order to simplify the stability analysis of an AC/MTDC (multi-terminal direct current) power system, this paper presents a Multi-Layer Component Connection Method (MLCCM)-based small signal model for AC/MTDC hybrid power systems. Based on ML-CCM, the system is partitioned as small individual system or components, including generator units, voltage source converter (VSC) units, time delay units, AC network and DC network. The modelling procedure can be 3 steps. First, the individual components are independently modelled. Second, several small individual components are assembled together to build the AC system model based on component interconnection relationship. Third, all AC power systems and DC network model are assembled together to build the whole hybrid power based on the interconnection relationship. There are three features for the MLCCM: (1) these component models can be built individually; (2) their interconnection relationship is a linear algebra matrix; (3) subsystem model can be verified or debugged individually. Due to the three features, the whole hybrid power can be built easily and it is convenient for finding modelling fault and debugging. An AC/DC hybrid system model in MATLAB/Simulink is also built to validate the effectiveness of the MLCCM-based small signal model.

Variable Time-Stepping Parallel Electromagnetic Transient Simulation of Hybrid AC–DC Grids

The complicated hybrid ac-dc network architecture creates new challenge for electromagnetic transient (EMT) simulation of large-scale systems in terms of accuracy and efficiency. By utilizing the variable time-stepping (VTS) method and graphics processing unit (GPU) parallelism, this article proposes a four-level dynamic parallelism architecture for variable time-stepping EMT simulation of hybrid ac-dc grids. By applying the proposed hierarchical system decomposition and VTS scheme, multiple time-step areas (TSAs) that contain subsystems with the same time-step size and adaptation criteria can be computed in different GPU blocks in parallel. Taking advantage of the dynamic parallelism feature of GPUs, a four-level dynamic parallelism is proposed to fully exploit the possibility of parallelizing the VTS simulation, though which the subsystems within each TSA and the detailed equipment models within each subsystem can run also in parallel via elaborate configurations. The transient waveforms and execution time speed-ups indicate that the proposed method can extremely accelerate the simulation process while guaranteeing reasonable accuracy compared to the fixed time-step based simulation.

The OPF Formulation and Solution for Energy Router Incorporated AC&amp;DC Network

Background: The energy router based on power electronic technology is the core equipment of energy internet. Energy router can control the voltage of the port and regulate the power injected into the network flexibly. It is of great significance to study how the capability of the energy router can be used to improve characteristics of the power system. Methods: Based on the analysis of energy router, we construct a unified AC-DC power flow model for energy router considering its different control modes. Then, we establish an optimal power flow formulation for the energy router incorporated AC-DC network and propose the multi-objective function to optimize the comprehensive performance of the network. Several cases are observed and analyzed on a modified IEEE-57 system. Results: The result verifies that the optimal power flow formulation can optimize the control values of ports of the energy router and improve the operating state of the power system. The optimal target presented in this paper can improve the characteristics of the grid better than a single optimal target. Conclusion: Energy router will play a vital role in the energy internet based on the power system. The characters of the power system can be improved and the operating costs can be reduced by utilizing the functionality of energy router in regulating power flow.

Subjective–objective methodology on selection of power quality compensation technology in AC–DC network with electrified railways

Electrified railways contain DC devices and cause power quality (PQ) issues such as harmonics, unbalance, and low-power factor, which should be solved by the most effective compensation technology to reduce the cost. In addition, it is common that more than one issue is caused at a supply bus, with either some issue affecting more than the others or all issues equally affecting, depending on different buses. Different PQ compensation technologies are used in the past work for the improvement of PQ at the network bus side. However, few of them have provided theoretical justification on which technologies should be selected for different network buses with different issues. This study investigates a subjective–objective methodology to provide suggestions on which compensation technologies should be selected for network buses, why and how. The importance of different PQ issues will be obtained and shown as overall weighting factors. The overall weighting factors are derived from the subjective weighting factor, obtained from existing industry standards, and the objective weighting factor, obtained from the entropy of information. The methodology will be applied to a real 124-bus AC–DC network with electrified railways. Based on the overall weighting factor obtained, the most effective compensation technology can be determined.

A reliability-based stochastic planning framework for AC-DC hybrid smart distribution systems

This paper introduces a stochastic multi-objective optimization model for the planning of AC-DC hybrid smart distribution systems (DSs). The proposed model determines the optimal AC-DC network configuration that achieves two main objectives: (1) minimizing system costs, and (2) maximizing system reliability. The second objective is achieved through the minimization of the expected energy not supplied. Network buses and lines can become either AC or DC in order to achieve the planning objectives. The model features a Monte-Carlo simulation (MCS) for addressing stochastic variations related to load demands and renewable distributed generators (DGs). The hybrid planning problem has been solved by means of a multi-objective genetic algorithm, and the trade-off between the DS costs and reliability has been evaluated using the Pareto optimality concept. The proposed model has been tested using a case study involving a hybrid DS that included a variety of types of loads and DGs. Solving the same case study using a traditional AC planning technique provided verification of the benefits offered by the proposed model, whose efficacy was confirmed through a comparison of the AC and hybrid Pareto fronts.

A new unified approach for the state estimation and bad data analysis of electric power transmission systems with multi-terminal VSC-based HVDC networks

The aim of this paper is the proposal of a static state estimation approach suitable for electric power transmission systems containing fully controlled multi-terminal high voltage direct current networks. An integrated electric circuit model of the voltage source converter station, including its on-load tap-changer (OLTC) transformer, is used to determine the state variables associated with the AC–DC interface. These controllable variables are added to the state vector, together with the nodal voltages associated with the AC and DC networks, as well as the duty cycle of the DC–DC converters, for a unified SE solution in a single frame of reference. The SE problem is formulated based on Hachtel’s augmented matrix method in order to directly consider the operational constraints associated with the AC–DC network. In addition, a new technique for performing multiple bad data analysis is proposed in which the original level of measurement redundancy is maintained unaltered during the estimation process. The applicability of the proposed SE approach and bad data analysis is demonstrated by estimating the operating state of two integrated AC–DC multi-terminal transmission networks. The numerical results show that the proposal retains good convergence properties in terms of the number of iterations required to achieve a reliable state estimation as well as robustness to deal with bad data.

A Planning Approach for the Network Configuration of AC-DC Hybrid Distribution Systems

This paper proposes a novel stochastic planning model for AC-DC hybrid distribution systems (DSs). Taking into account the possibility of each line/bus being AC or DC, the model finds the optimal AC-DC hybrid configuration of buses and lines in the DS. It incorporates consideration of the stochastic behavior of load demands and renewable-based distributed generators (DGs). The stochastic variations are addressed using a Monte-Carlo simulation technique. The objective of the planning model is the minimization of DS installation and operation costs. The optimal planning solution is obtained by dividing the hybrid planning problem into two nested optimization problems: 1) the main problem is formulated using a genetic algorithm (GA) to search for the optimal AC-DC configuration and 2) the subproblem is used for determining the optimal power flow solution for each configuration generated by the GA. The proposed model has been employed for finding the optimal configuration for a suggested case study that included photovoltaic panels, wind-based DG, and electric vehicle charging stations. The same case study was also solved using a traditional AC planning technique in order to evaluate the effectiveness of the proposed model and the associated cost-savings. The results demonstrate the advantages offered by the proposed model. The proposed framework represents an effective technique that can be used by DS operators to identify the optimal AC-DC network configuration of future DSs.

Nonlinear control of voltage source converters in AC–DC power system

This paper presents the design of a robust nonlinear controller for a parallel AC–DC power system using a Lyapunov function-based sliding mode control (LYPSMC) strategy. The inputs for the proposed control scheme are the DC voltage and reactive power errors at the converter station and the active and reactive power errors at the inverter station of the voltage-source converter-based high voltage direct current transmission (VSC-HVDC) link. The stability and robust tracking of the system parameters are ensured by applying the Lyapunov direct method. Also the gains of the sliding mode control (SMC) are made adaptive using the stability conditions of the Lyapunov function. The proposed control strategy offers invariant stability to a class of systems having modeling uncertainties due to parameter changes and exogenous inputs. Comprehensive computer simulations are carried out to verify the proposed control scheme under several system disturbances like changes in short-circuit ratio, converter parametric changes, and faults on the converter and inverter buses for single generating system connected to the power grid in a single machine infinite-bus AC–DC network and also for a 3-machine two-area power system. Furthermore, a second order super twisting sliding mode control scheme has been presented in this paper that provides a higher degree of nonlinearity than the LYPSMC and damps faster the converter and inverter voltage and power oscillations.

Quantum Dots: A Quantum Jump for Molecular Imaging?

<h3>Abstract</h3> Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This “associative cluster-dependent chain” (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous “Thunderstruck” song intro and then flexibly play it in a “bossa nova” rhythm without further training.

Prediction of voltage collapse in an integrated AC-DC network using the singular-value decomposition concept

Abstract A power system under heavy loading conditions may face the problem of voltage instability followed by voltage collapse. The minimum singular value of the AC power flow Jacobian has been popularly used as a static voltage stability index showing the distance between the steady-state operating point and the point of voltage collapse. In this paper, the concept of minimum singular value and condition number of the load flow Jacobian has been extended to study the voltage instability of a combined AC-DC system. The proposed method has been applied to determine the voltage collapse point in four sample AC-DC systems, three of them derived from the IEEE 14-bus system and the fourth a practical 156-bus Indian system. The model includes a switching scheme to check the converter control parameter limit violations. The effect of load changes at a single bus as well as at all the buses simultaneously on voltage collapse has also been studied.