Enrico De Santis

Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification

Detecting faults in electrical power grids is of paramount importance, both from the electricity operator and consumer point of view. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all components belonging to the whole infrastructure (e.g., cables and related insulation, transformers, and breakers). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid are collected, such as meteorological information. Designing an efficient recognition model to discriminate faults in real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of dissimilarity measures learning and one-class classification techniques. We provide here an in-depth study related to the available data and to the models based on the proposed one-class classification approach. Furthermore, we perform a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based decision rule.

Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition

In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.

Genetic optimization of a fuzzy control system for energy flow management in micro-grids

In this paper we present an interesting application of Computational Intelligence techniques for the power demand side and flow management optimization in a microgrid. In particular, we used a Fuzzy Logic Controller (FLC) for Time-of use Cost Management program in the microgrid. FLC can either sell and buy energy from outside the microgrid making use of an aggregate of energy storage capacity realized with lithium ion batteries. According to the hybrid Fuzzy-GA paradigm, the Fuzzy Logic Controller that operates decision making on energy flows is optimized by a Genetic Algorithm. The experimental results show that the proposed control system can manage effectively the energy trade with the main grid on the basis of real time prices.

Optimization of a microgrid energy management system based on a Fuzzy Logic Controller

This paper presents a novel power flow optimization strategy for a Grid Connected microgrid (MG) equipped with a Battery Energy Storage System (BESS), namely a Li-Ion battery pack. A BESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. In the proposed system the MG is composed by an aggregation of distributed power generators and loads and a BESS is adopted to manage the power over-production/over-demand in real time, in order to maximize the prosumer profit looking at the current energy prices and the BESS State of Charge (SOC). The Energy Management System (EMS) is based on a Fuzzy Logic Controller (FLC) with a suitable rule inference system designed by an Expert Operator (EO). The control strategy is tested with different power profiles and BESS capacities in order to verify its effectiveness and limits. Furthermore, the FLC has been optimized by a Genetic Algorithm to increase the total profit exploiting the BESS as energy buffer. The optimization results have been compared to the initial FLC designed by the EO, taking into account both the profit and the deterioration of the BESS measured through a suitable battery stress index.

Multi objective optimization of a fuzzy logic controller for energy management in microgrids

This paper presents a novel power flow optimization strategy in Micro Grids (MGs) connected to the main grid. When the MG includes stochastic energy sources, such as photovoltaic and micro eolic-generators, it is very useful to rely on Energy Storage Systems (ESSs) to buffer energy. In fact, an ESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. The Energy Management System is based on a Fuzzy Logic Controller (FLC) optimized by a Multi-Objective Genetic Algorithm in order to maximize both the total profit in energy trading with the main grid and the State of Health (SOH) of the ESS. The FLC manages the neat aggregate energy deficit and surplus inside the MG, analyzing in real time the state of the MG (aggregated energy demand and production, State of Charge of the ESS, energy sale and purchase prices). The FLC is tested on a MG composed by a photovoltaic solar generator, a domestic user and a Li-ion battery. A multi-objective genetic algorithm is in charge to find the set of solutions on the Pareto front. The results are compared with the same FLC optimized by a mono-objective Genetic Algorithm (GA) minimizing in a first case only the total profit and in the second case a convex linear combination of the total profit and a measure of the battery stress.

Fault recognition in smart grids by a one-class classification approach

Due to the intrinsic complexity of real-world power distribution lines, which are highly non-linear and time-varying systems, modeling and predicting a general fault instance is a very challenging task. Power outages can be experienced as a consequence of a multitude of causes, such as damage of some physical components or grid overloads. Smart grids are equipped with sensors that enable continuous monitoring of the grid status, hence allowing the realization of control systems related to different optimization tasks, which can be effectively faced by Computational Intelligence techniques. This paper deals with the problem of faults modeling and recognition in a real-world smart grid, located in the city of Rome, Italy. It is proposed a suitable classication system able to recognize faults on medium voltage feeders. Due to the nature of the available data, the one-class classication framework is adopted. Experiments are presented and discussed considering a three-year period of measurements of fault events gathered by ACEA Distribuzione S.p.A., the company that manages the smart grid system under analysis. Results demonstrate the effectiveness and validity of our approach.

Evolutionary Optimization of a One-Class Classification System for Faults Recognition in Smart Grids

The Computational Intelligence paradigm has proven to be a useful approach when facing problems related to Smart Grids (SG). The modern SG systems are equipped with Smart Sensors scattered in the real-world power distribution lines that are able to take a fine-grained picture of the actual power grid state gathering a huge amount of heterogeneous data. Modeling and predicting general faults instances by means of processing structured patterns of faults data coming from Smart Sensors is a very challenging task. This paper deals with the problem of faults modeling and recognition on MV feeders in the real-world Smart Grid system that feeds the city of Rome, Italy. The faults recognition problem is faced by means of a One-Class classifier based on a modified k-means algorithm trained through an evolutive approach. Due to the nature of the specific data-driven problem at hand, a custom weighted dissimilarity measure designed to cope with mixed data type like numerical data, Time Series and categorical data is adopted. For the latter a Semantic Distance (SD) is proposed, capable to grasp semantical information from clustered data. A genetic algorithm is in charge to optimize system’s performance. Tests were performed on data gathered over three years by ACEA Distribuzione S.p.A., the company that manages the power grid of Rome.

A learning intelligent system for fault detection in Smart Grid by a One-Class Classification approach

The analysis and recognition of fault status in the Smart Grid field is a challenging problem. Computational Intelligence techniques have already been shown to be a successful framework to face complex problems related to a Smart Grid. The availability of huge amounts of data coming from smart sensors allows the system to take a fine grained picture of the power grid status. This data can be processed in order to offer an instrument in aiding humans operators to better understand the power grid status and to take decisions on grid operations. This paper addresses the problem of fault recognitions in a real-world power grid (i. e. the power grid that feds the city of Rome, Italy) with the One-Class Classification paradigm by a combined approach of dissimilarity measure learning by means of an evolution strategy and clustering techniques for modeling the decision regions between fault status and the standard functioning of the power system. In this paper we present an in-depth study of the performance of two clustering algorithms in building up the model of faults, as the core procedure of the proposed recognition system.

A Dissimilarity Learning Approach by Evolutionary Computation for Faults Recognition in Smart Grids

In a modern power grid known also as a Smart Grid (SG) its of paramount importance detecting a fault status both from the electricity operator and consumer feedback. The modern SG systems are equipped with Smart Sensors scattered within the real-world power distribution lines that are able to take a fine-grain picture of the actual power grid status gathering a huge amount of heterogeneous data. The Computational Intelligence paradigm has proven to be a useful approach in pattern recognition and analysis in facing problems related to SG. The present work deals with the challenging task of synthesizing a recognition model that learns from heterogeneous information that relates to environmental and physical grid variables collected by the Smart Sensors on MV feeders in the real-world SG that supplies the entire city of Rome, Italy. The recognition of faults is addressed by a combined approach of a multiple weighted Dissimilarity Measure, designed to cope with mixed data types like numerical data, Time Series and categorical data, and a One-Class Classification technique. For Categorical data the Semantic Distance (SD) is proposed, capable of grasping semantical information from clustered data. The faults model is obtained by a clustering algorithm (k-means) with a suitable initialization procedure capable to estimate the number of clusters k. A suited evolutionary algorithm has been designed to learn from the optimal weights of the Dissimilarity Measure defining a suitable performance measure computed by means of a cross-validation approach. In the present work a crisp classification rule on unseen test patterns is studied together with a soft decision mechanism based on a fuzzy membership function. Moreover a favorable discrimination performance between faults and standard working condition of the (One-Class) classifier will be presented comparing the SD with the well-known Simple Matching (SM) Distance for categorical data.