Fault analysis in TCSC-compensated lines using wavelets and a PNN

This paper describes an algorithm to detect, localize and classify fault events in overhead transmission lines compensated with a thyristor-controlled series capacitor (TCSC). During a fault event, a complex pattern of traveling wave reflections and refractions is generated at the point of fault inception. The proposed algorithm uses the discrete wavelet transform combined with a probabilistic neural network to analyze all this information and determine whether a fault condition exists in the line, the fault type and also the fault distance. In order to assess the algorithm performance, several studies were carried out under varied conditions. The obtained results demonstrate that the algorithm accuracy for calculating the fault distance is smaller than 1% of the total line length, and a 100% efficiency for determining the fault type. The algorithm is also immune to harmonic interaction due to low-frequency harmonics generated by the TCSC. A comparative advantage over previous algorithms for TCSC-compensated transmission lines is the fact that the proposed algorithm not only identifies the faulted line section but also localizes accurately the distance to the fault, using only measurements at one end of the line.

     

ESTIMATION OF SOLID WASTE BIOMETHANIZATION: DEVELOPMENT OF NOVEL METHODOLOGY AND ANFIS MODELS

This article presents the application of a technique of artificial intelligence (AI) that explores the possibility of using a model to estimate the biomethanization of municipal solid waste (MSW). The model uses data from an experiment in which MSW is anaerobically digested under three different moisture regimes by leachate recycling. A method utilizing a neurofuzzy inference system is used because AI systems have a high capacity for empiric learning.

Considering the importance of finding an effective selection of the most valuable variables for the model, this methodology includes the following techniques: Exhaustive Search (or brute-force search); Stepwise, a step-by-step regression method; and the use of Expert Knowledge. With the use of the fuzzy logic toolbox (MATLAB®), nine models were generated. However, when a case study is used to detail the method, the proposed methodology can also be used with any other system with a set of input and output data.     

Step-up Transformerless Seven-level DC-AC Hybrid Topology for Interconnection of Renewable-based DC Sources to Microgrids

This article introduces a new step-up transformerless multi-level DC-AC hybrid topology for interconnecting renewable DC sources to loads or microgrids. This enabling technology incorporates the best characteristics of three modified basic topologies—a DC-DC multi-level boost converter, a DC-DC multi-level buck converter, and an H-bridge—to obtain a seven-level step-up DC-AC hybrid structure using only one DC input and nine power switches for a single-phase output with field-programmable gate array based control. The advantages of the step-up seven-level structure compared to other proposals are higher efficiency, a reduced number of power switches, and high power density associated with transformerless characteristic. Furthermore, in contrast to conventional topologies, the proposed design does not require voltage/current monitoring of the capacitors or a capacitor-balancing control scheme, and only one DC source input is used. Consequently, a high-performance configuration is obtained. The laboratory results demonstrate the validity of the design and the performance of the prototype.     

Analysis of dynamic Cournot learning models for generation companies based on conjectural variations and forward expectation

Electricity spot markets generally operate on an hourly basis; under this condition GENCOs can closely observe their competitors’ market behavior. For this purposes, a detailed dynamic model is one of the tools used by GENCOs to understand the behavioral variations of competitors over time. The required abilities to rapidly adjust one's own decision-making create a need for new learning procedures and models. Conjectural variations (CV) have been proposed as a learning approach. In this paper a model based on forward expectations (FE) is proposed as a learning approach, and through illustrative examples it is shown that the market equilibria found by the CV model are also obtained by the FE model.