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In the islanded microgrid, it is necessary to distribute the harmonic powers correctly among generators and mitigate the voltage harmonics at the point of common coupling (PCC) for effective microgrid operation. These control objectives are hard to satisfy at the same time with only one virtual impedance component. To simultaneously improve the microgrid voltage quality and share the distortion powers, a consensus‐based distributed power‐sharing scheme is proposed along with PCC harmonics mitigation. In the proposed control scheme, the controllable parts of output impedance at the fundamental and harmonic frequencies are separately tuned by simple integral controllers without depending on feeder impedance parameters. Compared to conventional control schemes, the proposed method is less complicated while ensuring a high voltage quality despite sudden changes in load power. A small‐signal state‐space model of the microgrid is analysed to validate the stability of the proposed control scheme, and its effectiveness is verified by experiments with a scaled‐down microgrid system.

Due to line impedance mismatch among renewable energy sources (RESs), it is hard to realize accurate power sharing in the DC microgrid system. To solve this issue, a distributed power sharing strategy for adjusting the RES output voltage is developed by adding shifted output voltage into each local controller. Thanks to the shifted voltage, the influence of voltage drop caused by the droop controller is effectively mitigated, so that the DC bus voltage is constantly balanced regardless of the load changes. The proposed method is realized with a centralized approach, and all the required control variable to determine the reference voltage is transmitted through low-bandwidth communication. The controller design and system stability are analyzed in detail with a simplified microgrid model. Small-scale DC microgrid is simulated to verify the effectiveness of the centralized shifted voltage control method.

In a practical islanded microgrid system, the mismatched line impedance causes serious problems, such as inaccurate power sharing and circulating current. In order to overcome these issues, this article proposes a coordinated virtual impedance control strategy for distributed generation (DGs) units. In the proposed control strategy, both virtual resistance and virtual inductance are simultaneously tuned to compensate the mismatched line impedance among DGs, and the microgrid system stability is enhanced by increasing damping for the whole system. Also, the virtual impedance is adaptively regulated in order to guarantee a good power-sharing performance even if the communication is interrupted or the load changes suddenly. The controller design process and theoretical analysis of the whole system are presented in detail based on small-signal analysis. The feasibility and effectiveness of the proposed control strategy were validated by simulations and experiments with a 1.3-kVA prototype microgrid composed of three DGs.

A microgrid (MG) is an effective way to integrate various distributed generators (DGs) into a power distribution system. The synchronization of DG voltage with MG voltage is indispensable to prevent inrush currents before connection to a MG system and for working in parallel with other DGs in the MG. In the past, synchronization in terms of both phase and magnitude was realized using additional voltage sensors to measure the MG voltage. However, this increased the system cost. In this paper, a cost-effective synchronization strategy is proposed to allow a DG to connect with an islanded MG system without any additional voltage sensors. The feasibility and effectiveness of the proposed strategy were validated by experiment with a scaled-down islanded microgrid.

This paper presents an enhanced compensation strategy for distributed generation (DG) systems to simultaneously achieve accurate power sharing and harmonics compensation at the point of common coupling (PCC). In the proposed control strategy, the output impedance is modified at the fundamental and harmonic frequencies to compensate for the line impedance mismatches among DG units, which results in accurate power sharing. In addition, load harmonic currents are effectively adjusted by the DG units to effectively compensate the PCC voltage harmonics. Additionally, the DG equivalent impedance is regulated adaptively to ensure accurate harmonic power sharing even in the presence of sudden load changes. Furthermore, a distributed communication network is adopted instead of a central controller to increase the reliability and stability of the microgrid system. The proposed control strategy is applied to a prototype microgrid system, and its effectiveness and reliability are experimentally validated.

Inaccurate reactive power sharing and voltage distortion at the point of common coupling (PCC) always exist in a parallel inverter system due to feeder impedance mismatches and nonlinear load disturbances. An enhanced inverter control scheme through an output impedance adjustment is proposed to address these issues. Inverter output impedance is regulated considering fundamental and harmonic frequencies, and accurate power sharing and improved PCC voltage quality are realized at the steady state. In addition, the compensation scheme is easily applied in multiple parallel inverters without any prior knowledge of the feeder impedance. Experimental results with three parallel inverters validate the feasibility of the proposed control scheme.

Line impedance mismatch and unregulated harmonic currents cause serious problems for an islanded microgrid, such as inaccurate reactive power sharing and voltage distortion at the point of common coupling (PCC). To overcome these issues, a coordinated virtual resistance and capacitance are introduced together with its control scheme for parallel distributed generation (DG) in an islanded microgrid. The proposed coordinated virtual impedance control scheme directly modifies the DG output impedance at the fundamental frequency to realise accurate current sharing among DG units. In addition, a variable harmonic impedance control loop is proposed for each DG to absorb the non-linear load harmonic current. Thanks to the proposed control scheme, the PCC harmonic voltage distortion is successfully compensated and power-sharing among DG units is accurately achieved. The stability of the microgrid system was analysed in detail to verify the feasibility of the coordinated virtual impedance control scheme. Experiments on a laboratory prototype microgrid are performed to validate the performance of the proposed control scheme.

This paper presents an adaptive virtual capacitance control method for distributed generation (DGs) in an islanded microgrid to mitigate the voltage harmonics at the point of common coupling (PCC). To achieve the control target, the proposed method emulates the inverter output capacitance at the dominant harmonic frequency to compensate for the mismatched voltage drop on the line impedance. In addition, the virtual capacitance at the fundamental frequency is regulated to improve the reactive power sharing performance of the inverter units. The proposed capacitance control method is developed based on simple integral controllers without prior knowledge of the feeder impedances and local nonlinear load currents. By continuously adjusting the inverter output capacitance, the microgrid power sharing performance and voltage quality are ensured despite any load changes. The effectiveness and superiority of the proposed strategy are theoretically analyzed and verified.

This paper proposes virtual shunt filter to achieve the harmonic compensation at the point of common coupling (PCC) and virtual damping resistance to damp microgrid resonance. The proposed control objective is achieved by modifying the virtual impedances at both fundamental and harmonic frequencies to compensate the mismatched impedance and voltage distortion at the PCC. By actively inserting the virtual damping resistance at high frequency, the virtual damping impedance damps the microgrid resonance and rejects the disturbances, which increases the stability and reliability of the microgrid. Based on the system closed-loop model, the behavior of the inverter output impedance is analyzed in detail with the proposed control method. The effectiveness and feasibility of the virtual shunt filter and virtual damping resistance are verified by theoretical analysis and experimentally results.

This paper proposes an enhanced virtual impedance control strategy to achieve the accurate harmonic power sharing as well as PCC harmonic compensation at the same time without degrading the reactive power sharing accuracy. The proposed control strategy is achieved by modifying the virtual impedances at fundamental and harmonic frequencies simultaneously, and the mismatched impedance between distribution generators (DGs)is effectively compensated with the improved fundamental and harmonic power sharing performance. Based on inverter modelling, the comparative frequency domain of the inverter output impedance and controller is analyzed. And also, the central controller is removed by means of the distributed communication network, which reduces the system complexity and increases the stability and reliability of the whole microgrid. The reliability and effectiveness of the proposed control strategy are experimentally validated together with theoretical analysis.

In this paper, an intelligent control scheme based on fuzzy proportional-integral-derivative controller (FPIDC) is proposed for PCC voltage harmonic compensation in islanded microgrid. The proposed FPIDC method is composed of a closed-loop control of the virtual impedance at harmonic frequency to absorb the harmonic current from the nonlinear load, control harmonic sharing between distributed generators. As a result, the PCC voltage quality is improved with the total harmonic distortion (THD) significantly reduced. With the feedback of the PCC voltage and well-designed fuzzy controller, the uncertainty and unstable value of proportional-integral-derivative (PID) parameters are removed by adaptive tuning. Therefore, the PCC voltage quality is improved smoothly, and the system becomes more stable even load condition is changed. Compared with the traditional PID controller, the dynamic response and stability of the microgrid system are improved with the proposed FPIDC. The comparison and analysis of the proposed control with the conventional control are carried out to verify the superiority of the proposed method.

In the islanded micro-grid, parallel distributed generators (DGs) are normally controlled with the aid of the droop control scheme. However, the droop control method is still concerned to improve the accurate of reactive power sharing and variation of frequency and voltage at the point of common coupling (PCC). This paper proposes a control scheme to solve the limitation of microgrid in islanded operation such as reactive power sharing accuracy and PCC voltage and frequency drop by using a close-loop of the virtual impedance to compensate the feeder mismatch between DGs. Therefore, the power sharing performance is improved, and the circulating current between DGs is suppressed. In order to achieve the accurate reactive power sharing and PCC voltage restoration, a secondary control is implemented in both the microgrid central controller (MGCC) and local controller by using the low bandwidth communications. The effectiveness of the proposed control method is analyzed through the simulation.

In this paper, an intelligent control scheme based on fuzzy proportional-integral-derivative controller (FPIDC) is proposed for distributed generations in islanded microgrid. The proposed FPIDC method is composed of a close-loop of the virtual impedance to compensate the feeder mismatch between distributed generators (DGs). Together with feedback of the inaccurate reactive power, the uncertainty of proportional-integral-derivative (PID) parameters is removed by adaptive tuning. Therefore, the power sharing performance is improved and the circulating current between DGs is mitigated. The dynamic response of the microgrid system with the proposed FPIDC is much better than that of the conventional PID controller. The comparison and analysis of the proposed control with conventional control are carried out to evaluate the superiority of the proposed method.

DC-DC converter is widely used in industrial applications with the aid of proportional-integral-derivative (PID) controller. The PID controller is commonly used because of its simple, efficiency and small steady state error. However, DC-DC converter is mainly nonlinear system due to its inherent switching operation, which makes the PID parameters very difficult to be found. In this paper, an intelligent PID controller based on a hybrid combination with the fuzzy logic controller (FLC) is proposed to control DC-DC converter. The proposed fuzzy based PID controller (FPIDC) is a close-loop control of the nonlinear PID parameters by measuring output voltage of the DC-DC converter, and it is designed effectively to reduce computation burden. By tuning the PID parameters continuously according to the load condition, the performance of the DC-DC converter is improved much better. The comparison between the proposed and conventional PID control method are investigated under various operating conditions. Simulation results and model analysis are carried in MATLAB/SIMULINK to prove the effectiveness of the proposed FPIDC method.

Graphs are of growing importance in modeling complex structures such as chemical compounds, proteins, images, and program dependence. Given a query graph Q , the subgraph isomorphism problem is to find a set of graphs containing Q from a graph database, which is NP-complete. Recently, there have been a lot of research efforts to solve the subgraph isomorphism problem for a large graph database by utilizing graph indexes. By using a graph index as a filter, we prune graphs that are not real answers at an inexpensive cost. Then, we need to use expensive subgraph isomorphism tests to verify filtered candidates only. This way, the number of disk I/Os and subgraph isomorphism tests can be significantly minimized. The current practice for experiments in graph indexing techniques is that the author of a newly proposed technique does not implement existing indexes on his own code base, but instead uses the original authors' binary executables and reports only the wall clock time. However, we observe this practice may result in several problems. In order to address these problems, we have made significant efforts in implementing all representative indexing methods on a common framework called iGraph. Unlike existing implementations which either use (full or partial) in-memory representations or rely on OS file system cache without guaranteeing real disk I/Os, we have implemented these indexes on top of a storage engine that guarantees real disk I/Os. Through extensive experiments using many synthetic and real datasets, we also provide new empirical findings in the performance of the full disk-based implementations of these methods.

We build on our earlier finding that more than 95% of the triples in actual RDF triple graphs have a remarkably tabular structure, whose schema does not necessarily follow from explicit metadata such as ontologies, but for which an RDF store can automatically derive by looking at the data using so-called “emergent schema” detection techniques. In this paper we investigate how computers and in particular RDF stores can take advantage from this emergent schema to more compactly store RDF data and more efficiently optimize and execute SPARQL queries. To this end, we contribute techniques for efficient emergent schema aware RDF storage and new query operator algorithms for emergent schema aware scans and joins. In all, these techniques allow RDF schema processors fully catch up with relational database techniques in terms of rich physical database design options and efficiency, without requiring a rigid upfront schema structure definition.

We motivate and describe techniques that allow to detect an "emergent" relational schema from RDF data. We show that on a wide variety of datasets, the found structure explains well over 90% of the RDF triples. Further, we also describe technical solutions to the semantic challenge to give short names that humans find logical to these emergent tables, columns and relationships between tables. Our techniques can be exploited in many ways, e.g., to improve the efficiency of SPARQL systems, or to use existing SQL-based applications on top of any RDF dataset using a RDBMS.

One of the prime goals of the LOD2 project is improving the performance and scalability of RDF storage solutions so that the increasing amount of Linked Open Data (LOD) can be efficiently managed. Virtuoso has been chosen as the basic RDF store for the LOD2 project, and during the project it has been significantly improved by incorporating advanced relational database techniques from MonetDB and Vectorwise, turning it into a compressed column store with vectored execution. This has reduced the performance gap (“RDF tax”) between Virtuoso’s SQL and SPARQL query performance in a way that still respects the “schema-last” nature of RDF. However, by lacking schema information, RDF database systems such as Virtuoso still cannot use advanced relational storage optimizations such as table partitioning or clustered indexes and have to execute SPARQL queries with many self-joins to a triple table, which leads to more join effort than needed in SQL systems. In this chapter, we first discuss the new column store techniques applied to Virtuoso, the enhancements in its cluster parallel version, and show its performance using the popular BSBM benchmark at the unsurpassed scale of 150 billion triples. We finally describe ongoing work in deriving an “emergent” relational schema from RDF data, which can help to close the performance gap between relational-based and RDF-based storage solutions.

The semantic web uses RDF as its data model, providing ultimate flexibility for users to represent and evolve data without need of a schema. Yet, this flexibility poses challenges in implementing efficient RDF stores, leading from plans with very many self-joins to a triple table, difficulties to optimize these, and a lack of data locality since without a notion of multi-attribute data structure, clustered indexing opportunities are lost. Apart from performance issues, users of huge RDF graphs often have problems formulating queries as they lack any system-supported notion of the structure in the data. In this research, we exploit the observation that real RDF data, while not as regularly structured as relational data, still has the great majority of triples conforming to regular patterns. We conjecture that a system that would recognize this structure automatically would both allow RDF stores to become more efficient and also easier to use. Concretely, we propose to derive self-organizing RDF that stores data in PSO format in such a way that the regular parts of the data physically correspond to relational columnar storage; and propose RDFscan/RDFjoin algorithms that compute star-patterns over these without wasting effort in self-joins. These regular parts, i.e. tables, are identified on ingestion by a schema discovery algorithm -- as such users will gain an SQL view of the regular part of the RDF data. This research aims to produce a state-of-the-art SPARQL frontend for MonetDB as a by-product, and we already present some preliminary results on this platform.

Benchmarking graph-oriented database workloads and graph-oriented database systems is increasingly becoming relevant in analytical Big Data tasks, such as social network analysis. In graph data, structure is not mainly found inside the nodes, but especially in the way nodes happen to be connected, i.e. structural correlations. Because such structural correlations determine join fan-outs experienced by graph analysis algorithms and graph query executors, they are an essential, yet typically neglected, ingredient of synthetic graph generators. To address this, we present S3G2: a Scalable Structure-correlated Social Graph Generator. This graph generator creates a synthetic social graph, containing non-uniform value distributions and structural correlations, which is intended as test data for scalable graph analysis algorithms and graph database systems. We generalize the problem by decomposing correlated graph generation in multiple passes that each focus on one so-called correlation dimension; each of which can be mapped to a MapReduce task. We show that S3G2 can generate social graphs that (i) share well-known graph connectivity characteristics typically found in real social graphs (ii) contain certain plausible structural correlations that influence the performance of graph analysis algorithms and queries, and (iii) can be quickly generated at huge sizes on common cluster hardware.

Linked Stream Data, i.e., the RDF data model extended for representing stream data generated from sensors social network applications, is gaining popularity. This has motivated considerable work on developing corresponding data models associated with processing engines. However, current implemented engines have not been thoroughly evaluated to assess their capabilities. For reasonable systematic evaluations, in this work we propose a novel, customizable evaluation framework and a corresponding methodology for realistic data generation, system testing, and result analysis. Based on this evaluation environment, extensive experiments have been conducted in order to compare the state-of-the-art LSD engines wrt. qualitative and quantitative properties, taking into account the underlying principles of stream processing. Consequently, we provide a detailed analysis of the experimental outcomes that reveal useful findings for improving current and future engines.

Graphs provide a powerful way to model complex structures such as chemical compounds, proteins, images, and program dependence. The previous practice for experiments in graph indexing techniques is that the author of a newly proposed technique does not implement existing indexes on his own code base, but instead uses the original authors' binary executables and reports only the wall clock time. However, we observed that this practice may result in several problems [6]. In order to address these problems, we have implemented all representative graph indexing techniques on a common framework called iGraph [6]. In this demonstration we showcase iGraph and its visual tools using several real datasets and their workloads. For selected queries of the workloads, we show several unique features including visual performance analysis.

Low density parity check (LDPC) codes are error-correcting codes that offer huge advantages in terms of coding gain, throughput and power dissipation. Error correction algorithms are often implemented in hardware for fast processing to meet the real-time needs of communication systems. However hardware implementation of LDPC decoders using traditional hardware description language (HDL) based approach is a complex and time consuming task. This paper presents an efficient high level approach to designing LDPC decoders using a collection of high level modelling tools. The proposed new methodology supports programmable logic design starting from high level modelling all the way up to FPGA implementation. The methodology has been used to design and implement representative LDPC decoders. A comprehensive testing strategy has been developed to test the designed decoders at various levels. The simulation and implementation results presented in this paper prove the validity and productivity of the new high level design approach. Refereed/Peer-reviewed

Background As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users’ requests for extracting information from the RDF data as well as the lack of users’ knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. Results In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Conclusions Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.