Iñigo Monedero

Classification of Electrical Disturbances in Real Time Using Neural Networks

Power-quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. This paper presents a novel system based on neural networks for the classification of electrical disturbances in real time. In addition, an electrical pattern generator has been developed in order to generate common disturbances which can be found in the electrical grid. The classifier obtained excellent results (for both test patterns and field tests) thanks in part to the use of this generator as a training tool for the neural networks. The neural system is integrated on a software tool for a PC with hardware connected for signal acquisition. The tool makes it possible to monitor the acquired signal and the disturbances detected by the system.

MIDAS: detection of non-technical losses in electrical consumption using neural networks and statistical techniques

Datamining has become increasingly common in both the public and private sectors. A non-technical loss is defined as any consumed energy or service which is not billed because of measurement equipment failure or ill-intentioned and fraudulent manipulation of said equipment. The detection of non-technical losses (which includes fraud detection) is a field where datamining has been applied successfully in recent times. However, the research in electrical companies is still limited, making it quite a new research topic. This paper describes a prototype for the detection of non-technical losses by means of two datamining techniques: neural networks and statistical studies. The methodologies developed were applied to two customer sets in Seville (Spain): a little town in the south (pop: 47,000) and hostelry sector. The results obtained were promising since new non-technical losses (verified by means of in-situ inspections) were detected through both methodologies with a high success rate.

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

Abstract An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energy-intensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes.

Integrated expert system applied to the analysis of non-technical losses in power utilities

The detection of non-technical losses (NTLs), in most papers, commonly deals with the utilization of the registered consumption for each customer; besides, some researchers used the economic activity, the active/reactive ratio and the contract power. Currently, utility company databases store enormous amounts of information on both installations and customers: consumption, technical information on the measure equipment, documentation, inspections results, commentaries of inspectors, etc. In this paper, an integrated expert system (IES) for the analysis and classification of all the available useful information of the customer is presented. Customer classification identifies the presence of an NTL and the problem type. This IES include several modules: text mining module for analysis of inspector commentaries and extraction of additional information on the customer, data mining module to draw up the rules that determine the customer estimate consumption, and the Rule Based Expert System module to analyze each customer using the results of the text and data mining modules. This IES is used with real data extracted from Endesa company databases. Endesa is the most important power distribution company in Spain, and one of the most significant companies of Europe. This IES is used in the test phase by human experts in the Endesa company. In this phase, the IES is used as a Decision Support System (DSS), as it contains another module which provides a report with additional information about the customer and a summarized result that the inspectors can use to reach a decision.

Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

Automatic system to detect energy efficiency anomalies in smart buildings.Definition and testing of energy efficiency indicators to quantify energy savings.Knowledge extraction from data and HVAC experts through Data Mining techniques.In this study a full set of anomalous EE consumption patterns are detected.During test period more than 10% of day presented a kind of EE anomaly. The rapidly growing world energy use already has concerns over the exhaustion of energy resources and heavy environmental impacts. As a result of these concerns, a trend of green and smart cities has been increasing. To respond to this increasing trend of smart cities with buildings every time more complex, in this paper we have proposed a new method to solve energy inefficiencies detection problem in smart buildings. This solution is based on a rule-based system developed through data mining techniques and applying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is also proposed to detect anomalies. The data mining system is developed through the knowledge extracted by a full set of building sensors. So, the results of this process provide a set of rules that are used as a part of a decision support system for the optimisation of energy consumption and the detection of anomalies in smart buildings.

Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies

This paper proposes a comprehensive framework to detect non-technical losses (NTLs) and recover electrical energy (lost by abnormalities or fraud) by means of a data mining analysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and predictive data mining. The authors insist on the importance of the knowledge of the particular characteristics of the Power Company customer: the main features available in databases are described. The paper presents two innovative statistical estimators to attach importance to variability and trend analysis of electric consumption and offers a predictive model, based on the Generalized Rule Induction (GRI) model. This predictive analysis discovers association rules in the data and it is supplemented by a binary Quest tree classification method. The quality of this framework is illustrated by a case study considering a real database, supplied by Endesa Company.

Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant

Highlights? We developed a decision system to optimize the energy efficiency of a petrochemical plant. ? The decision system has been developed through a data mining process. ? The decision system is based on an algorithm and a kernel of neural networks. ? We have tested the system and obtained a save of 7%. ? The system has been integrated in a pilot software. The energy efficiency of industrial plants is an important issue in any type of business but particularly in the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring better product quality, and generally increasing profits. This article describes a decision system developed for optimizing the energy efficiency of a petrochemical plant. The system has been developed after a data mining process of the parameters registered in the past. The designed system carries out an optimization process of the energy efficiency of the plant based on a combined algorithm that uses the following for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in the past and, on the other hand, a module of two neural networks to obtain new interpolated operation points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to select the parameters most important in the plant and to study the behavior of the energy efficiency index. This study also helped ensure an optimal training of the neural networks. The robustness of the system as well as its satisfactory results in the testing process (an average rise in the energy efficiency of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to implement and to integrate the decision system as a pilot software in an SCADA.

Random Generation of Arbitrary Waveforms for Emulating Three-Phase Systems

This paper describes an apparatus for generating a signal representative of steady-state and transient disturbances in three-phase waveforms of an ac electrical system as described in IEEE Std 1159-09. It can be configured as a synthesizer of randomly distorted signals for different applications: for testing the effects of disturbed grid on equipment and to generate patterns of electrical disturbances for the training of artificial neural networks, which are used for measuring power quality tasks. For the first purpose, voltage and current amplifiers are added in the output stage, which allows the generation of disturbed signals at grid level.

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customers measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers.

A framework for development of integrated intelligent knowledge for management of telecommunication networks

Highlights? We present a new approach for distributed intelligent management networks. ? An intelligent framework and a language for formalizing knowledge management descriptions. ? Distributed intelligent system is designed through the normalization of knowledge management. ? Intelligent knowledge is integrated into the conceptual repository of management information. ? We outline the development of an intelligent system based on our proposed standard. The management of modern telecommunication networks is a complex and intensive task that requires the assimilation of vast amounts of information and knowledge management. The significance of management networks is growing, and more advanced techniques are needed to support these activities. It is necessary to develop new models that offer more possibilities. In this study, we present a new approach for distributed intelligent management networks. The goal of our study is the assignment and dispersed control of proper network resources, pertaining to hardware as well as software, to help operators manage their networks more effectively and also to promote reliability in network services. We propose a new paradigm where intelligent knowledge management is integrated into the conceptual repository of management information. This article presents a technique for the design and implementation of a distributed intelligent system that is designed through the normalization of knowledge management. Our study focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with an existing Open Systems Interconnection (OSI) management model. Further, this work outlines the development of an intelligent system named ScanEXP based on our proposed standard and describes the most important facets, advantages, and drawbacks that were found after prototyping our proposal.