Intelligent controlled shunt active power filter for voltage and current harmonic compensation in microgrid system

An integrated microgrid with a novel shunt active power filter (APF) using Elman neural network (ENN) is proposed in this study. The microgrid consists of a storage system, a photovoltaic (PV) system, the shunt APF, a linear load, and a nonlinear load. Moreover, the master/slave control algorithm is adopted in the microgrid. The storage system, which is considered as the master unit, is adopted to control the active and reactive power outputs (P/Q control) in grid-connected mode and the voltage and frequency of the microgrid (V/f control) in islanded mode. Furthermore, the PV system is considered as the slave unit to implement P/Q control in both grid-connected and islanded modes. In addition, the proposed shunt APF possesses dual functions of voltage and current harmonic compensation for microgrid under voltage harmonic propagation and nonlinear load to reduce the voltage and current total harmonic distortions (THD) effectively. Additionally, an ENN controller is adopted in the proposed shunt APF to improve the transient and steady-state responses of DC-link voltage during the switching between the grid-connected mode and islanded mode. Finally, some simulation results are provided to verify the feasibility and the effectiveness of the integrated microgrid with the intelligent controlled shunt APF.     

An improved decomposition scheme for assessing the reliability of embedded systems by using dynamic fault trees

The theories of fault trees have been used for many years because they can easily provide a concise representation of failure behavior of general non-repairable fault tolerant systems. But the defect of traditional fault trees is lack of accuracy when modeling dynamic failure behavior of certain systems with fault-recovery process. A solution to this problem is called behavioral decomposition. A system will be divided into several dynamic or static modules, and each module can be further analyzed using binary decision diagram (BDD) or Markov chains separately. In this paper, we will show a very useful decomposition scheme that independent subtrees of a dynamic module are detected and solved hierarchically. Experimental results show that the proposed method could result in significant saving of computation time without losing unacceptable accuracy. Besides, we also present an analyzing software toolkit: DyFA (dynamic fault-trees analyzer) which implements the proposed methodology.     

Intelligent-controlled doubly fed induction generator system using PFNN

An intelligent-controlled doubly fed induction generator (DFIG) system using probabilistic fuzzy neural network (PFNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous, and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, an intelligent PFNN controller is proposed for both the rotor and grid side converters to improve the transient and steady-state responses of the DFIG system at different operating conditions. The network structure, online learning algorithm, and convergence analyses of the PFNN are introduced in detail. Finally, the feasibility of the proposed control scheme is verified using some experimental results.     

Particle swarm optimization application on a micro grid for energy savings

Microsystem Technologies - This paper presents the effectiveness of the particle swarm optimization (PSO) to minimize the total active power loss of an island scale micro grid distribution network....     

OBDD-based evaluation of reliability and importance measures for multistate systems subject to imperfect fault coverage

Algorithms for evaluating the reliability of a complex system such as a multistate fault-tolerant computer system have become more important. They are designed to obtain the complete results quickly and accurately even when there exist a number of dependencies such as shared loads (reconfiguration), degradation, and common-cause failures. This paper presents an efficient method based on ordered binary decision diagram (OBDD) for evaluating the multistate system reliability and the Griffith's importance measures which can be regarded as the importance of a system-component state of a multistate system subject to imperfect fault-coverage with various performance requirements. This method combined with the conditional probability methods can handle the dependencies among the combinatorial performance requirements of system modules and find solutions for multistate imperfect coverage model. The main advantage of the method is that its time complexity is equivalent to that of the methods for perfect coverage model and it is very helpful for the optimal design of a multistate fault-tolerant system.     

Optimal cutting design and analysis for fault-tolerant scheme of redundant memories

Fault-tolerant design for memory production is beginning to play an important role in increasing the yield rate of manufacturing. To improve the reliability of memory manufacturing, there are many methods that have been proposed. One of the most used technologies is replacing the faulty cells with spare memory interleaved in the memory. Nowadays, laser-cutting technology improves the yield of memories because of the enhancement of the use of spare lines. However, the issue of choosing a cutting location significantly affects the utilisation of spare lines. A bad cutting location can even render it useless. To use spare lines more efficiently, this article proposes two algorithms. The first one is designed to seek out a good cutting location. It corrects some defects of previous algorithms and provides a better approach to finding cutting candidates. In addition, because most heuristic solution-finding algorithms do not work properly under the condition of cutting memory, the second algorithm, called modification of most-repair is proposed to help make the decision as to whether or not a solution exists for the faulty pattern. We can find an optimal solution by combing these two algorithms. The experimental results show that our proposed algorithms increase the reparable percentage of a 1024-by-1024 memory from 55 to 100% and also improve both the reliability of memory manufacturing and the flexibility of spare lines.