Joaquim Meléndez

Extensive Events Database Development using ATP and Matlab to Fault Location in Power Distribution Systems

Opportune fault location in power distribution systems is an important aspect related to power quality, and especially to maintain good continuity indexes. Fault location methods which use more information than RMS values of voltage and current, are the commonly known as knowledge based methods - KBM. Those require of a complete fault database to adequately perform the training and validation stages, and as a consequence successfully perform the fault location task. In this paper, the modeling of a power distribution system and its protective relaying to obtain an extensive fault database using the capabilities of ATP and Matlab is described. The obtained database can be used to perform different types of system analysis and in this specific case to solve the problem of fault location in power distribution systems. As a result a methodology to perform automatic simulations and a data base with 930 fault situations in a 25 kV test system was obtained

QUALITATIVE REPRESENTATION OF PROCESS TRENDS FOR SITUATION ASSESSMENT BASED ON CASES

Abstract Situation assessment in complex systems is often achieved by expert operators taking into account evolution of signals and comparing it with previous experiences. The criteria used by operators to compare actual situations with previous ones are not easily explainable and in fact they are part of the cognitive procedure. This paper proposes to use qualitative representations of signal trends as experienced cases. The work is centred in two main aspects. First, episodes based representation of signal trends proposed in the CHEM project is used as a description of cases. Then, a similarity criterion among signal representations is defined by a Dynamic Time Warping approach. The usefulness of the approach is shown in an illustrative example by representing and comparing signal dynamics with the goal of Situation Assessment.

Predicting aerodynamic instabilities in a blast furnace

This paper discusses the analysis of differential pressure signals in a blast furnace stack by using principal component analysis (PCA) and qualitative trend analysis (QTA) based on episodes. These methods can work jointly or separately and are applied using two toolboxes developed within the European CHEM project. The objective in this paper is to predict aerodynamic instability in a blast furnace with sufficient warning to enable the blast volume to be reduced in order to minimise that instability. Both methods based on signals and the expert knowledge provide an efficient approach to slip prediction. ^(C)xxx 2004. All rights reserved.

Expert supervision based on cases

The paper focuses on taking advantage of large amounts of data that are systematically stored in plants (by means of SCADA systems), but not exploited enough in order to achieve supervisory goals (fault detection, diagnosis and reconfiguration). The methodology of case base reasoning (CBR) is proposed to perform supervisory tasks in industrial processes by re-using the stored data. The goal is to take advantage of experiences, registered in a suitable structure as cam, avoiding the tedious task of knowledge acquisition and representation needed by other reasoning techniques as expert systems. An outlook of CBR terminology and basic concepts are presented. The adaptation of CBR in performing expert supervisory tasks, taking into account the particularities and difficulties derived from dynamic systems, is discussed. A special interest is focused in proposing a general case definition suitable for supervisory tasks. Finally, this structure and the whole methodology is tested in a application example for monitoring a real drier chamber.

Feature characterization of power quality events according to their underlying causes

This paper addresses the problem of extracting effective features for the analysis of underlying causes of power quality (PQ) disturbances. For each underlying cause, we define and extract a set of features based on analysis of voltage/current waveforms or the combination of them. The proposed feature sets are then used for building a rule-based classification framework for automatic identification of the underlying causes stored in PQ databases. These rules are based on the extracted features. Using the proposed features and rules, the proposed classifier has yielded a correct classification rate of 95.8% for a total of 96 disturbance sequences, demonstrating a high accuracy distinguishing between the different underlying causes in PQ events.

Extending a Fault Dictionary Towards a Case Based Reasoning System for Linear Electronic Analog Circuits Diagnosis

There are plenty of methods proposed for analog electronic circuit diagnosis, but the most popular ones are the fault dictionary techniques. Admitting more cases in a fault dictionary can be seen as a natural development towards a CBR system. The proposal of this paper is to extend the fault dictionary towards a Case Based Reasoning system. The case base memory, retrieval, reuse, revise and retain tasks are described. Special attention to the learning process is taken. An application example on a biquadratic filter is shown. The faults considered are parametric, permanent, independent and simple, although the methodology could be extrapolated for catastrophic and multiple fault diagnosis. Also, the method is focused and tested only on passive faulty components. Nevertheless, it can be extended to cover active devices as well.

A hybrid method for sag source location in power network

The work presented in this paper belongs to the power quality knowledge area and deals with the voltage sags in power transmission and distribution systems. Propagating throughout the power network, voltage sags can cause plenty of problems for domestic and industrial loads that can financially cost a lot. To impose penalties to responsible party and to improve monitoring and mitigation strategies, sags must be located in the power network. With such a worthwhile objective, this paper comes up with a new method for associating a sag waveform with its origin in transmission and distribution networks. It solves this problem through developing hybrid methods which hire multiway principal component analysis (MPCA) as a dimension reduction tool. MPCA reexpresses sag waveforms in a new subspace just in a few scores. We train some well-known classifiers with these scores and exploit them for classification of future sags. The capabilities of the proposed method for dimension reduction and classification are examined using the real data gathered from three substations in Catalonia, Spain. The obtained classification rates certify the goodness and powerfulness of the developed hybrid methods as brand-new tools for sag classification.

Opportunities and challenges for smart power restoration and reconfiguration smart decisions with smart grids

Smart grids bring new opportunities but also new challenges for designing and implementing restoration and reconfiguration algorithms. The efficiency of power restoration and network reconfiguration can be improved with the deployment of ICT technologies in smart grids. Protecting, monitoring, controlling, and metering the distribution power network will benefit from high interconnectivity and interoperability among multiple devices and an efficient exploitation of available information. Moreover, artificial intelligence and data mining techniques can also have a positive contribution in that sense. Nevertheless, new restoration techniques should also take into account power quality and network stability. They should also take full advantage of the possibility to change the topology, the increase of available data, and the communications and processing capability. More efforts should be devoted to designing powerful AI techniques. Our traditional vision of power distribution networks has also to be changed. Distribution network, power restoration, network reconfiguration, smartgrid, smart grid, intelligent grid, distributed generation, CBR, heuristic, metaheuristic, IED, power meter, islanding, power quality

Service workflow monitoring through complex event processing

This paper presents an approach for service monitoring through workflow modeling and complex event processing. Workflows allow the representation of services process interactions while complex event processing (CEP) is a concept for event driven architectures which offers an alternative solution for monitoring and supervision. In this paper we propose a methodology to combine both technologies where CEP is used to monitor workflows and to predict possible delays. A case study on medical equipment maintenance business is shown.

Embedding objects into Matlab/Simulink for process supervision

This paper introduces how artificial intelligence technologies can be integrated into a known computer aided control system design (CACSD) framework, Matlab/Simulink, using an object oriented approach. The aim is to build a framework to aid supervisory systems analysis, design and implementation. The idea is to take advantage of an existing CACSD framework, Matlab/Simulink, so that engineers can proceed: first to design a control system, and then to design a straightforward supervisory system of the control system in the same framework. Thus, expert systems and qualitative reasoning tools are incorporated into this popular CACSD framework to develop a computer aided supervisory system design (CASSD) framework. Object-variables an introduced into Matlab/Simulink for sharing information between tools.