Session Program


  • 10 July 2017
  • 02:00PM - 04:00PM
  • Room: Sveva
  • Chairs: Faa-Jeng Lin and Marco Mussetta
Abstract - The intensive use of electricity in life and modern society implies increasing demand and the need for increasingly high reliability. Smart Grids (SG) are the next technological breakthrough to be achieved for the generation, transmission and distribution of energy. Historically, it has been sought to automate each of these systems to perform main and ancillary services. Robust forecasting methodologies are essential for planning and operation of SG. However, SG data presents the characteristics of long memory time series, which are a kind of stationary processes in such a way that there is always a statistical long range dependency between the current value and values in different times of the series. Motivated by this, we introduce a new hybrid model combining Seasonal Auto Regressive Fractionally Integrated Moving Average with high order Fuzzy Time Series (SARFIMA-FTS). We present comparative results of SARFIMA-FTS with other two methodologies solutions in microgrid data. The computational results show that the performance of the proposed SARFIMA-FTS method is quite competitive with other presented methods in literature using less parameters, hence it is an important tool for prediction in smart grids.
Abstract - The efficient integration of Renewable Energy Sources (RES) in the actual electrical grid has gained recently a high attention in the smart grids (SGs) research topic. The evolution of existing electric distribution networks into SGs can be accomplished gradually and conveniently through the installation of local grid-connected Microgrids (MGs), usually installed nearby the RESs and provided by Energy Storage Systems (ESSs). Each MG is in charge to manage connected RES, assuring the local power demand, as well as the safety and stability of the electric grid. To this aim, the Energy Management System (EMS) must provide intelligent decision making in fixing both MG configuration and energy flows between each subsystem in real time, according to some objective functions. In this work, it is proposed a MG EMS based on a Fuzzy Inference System (FIS) optimized through a custom implementation of Multi Objective Genetic Algorithm (MO-GA). In particular, the EMS is based on a three inputs FIS and it has been designed in order to reduce the fluctuations of energy exchanged with the grid (i.e. the grid stress) and to maximize the energy auto-consumption by employing an efficient utilization of the ESS. Results show that it is possible to improve considerably the auto-consumption performance, and at the same time to reduce grid stress, improving peak shaving concerning the maximum power request from the main grid.
Abstract - Advantages of the recurrent fuzzy cerebellar model articulation controller (RFCMAC) include local generalization capability, fast learning, simplicity of computation, and capability of solving dynamic problem. Normally, the tracking error and the derivative of the tracking error are fed into the controller to perform the control of command tracking. Firstly, in this study, a two- dimensional RFCMAC (2D-RFCMAC) is adopted to approach the command control of the active and reactive power for performing the low-voltage ride-through (LVRT) operation of a single-stage photovoltaic (PV) system. However, the 2D- RFCMAC requires larger memory due to the memory size increases exponentially with the number of inputs. Thus, a one-dimensional RFCMAC (1D-RFCMAC) with signed distance and input space repartition mechanisms is proposed to replace the 2D-RFCMAC to perform the LVRT operation of the single-stage PV system. The reduced input dimension and computation complexity make the 1D-RFCMAC more practical. Moreover, some experimental tests are presented to illustrate the effectiveness of the proposed controller. The results show that the control performance of the proposed 1D-RFCMAC is not only slightly better than 2D- RFCMAC but also has lower memory size and computation complexity.
Abstract - The roll out of electricity grid assets with advanced communications capabilities enables new ways to steal energy, such as false data attacks and remote meter disconnection. On the other hand, data communicated by these devices has the potential to improve utilities ability to combat fraud through computational intelligence techniques. We propose a clustering-based novelty detection scheme to uncover electricity theft. The scheme starts by extracting easily interpreted consumption indicators from data collected by smart meters. Fuzzy clustering is then used to capture the structure of the data that consists of indicators from benign consumers. The extracted clusters provide the basis for a distance-based novelty detection model to uncover abnormal data sent by consumers. The results for the developed use case show that the proposed scheme using Gustafson-Kessel fuzzy clustering best captures the behavior of consumers, achieving good performance with a low number of clusters, in comparison to euclidean distance-based hard and fuzzy C-means.
Abstract - Non-Intrusive Appliance Load Monitoring has drawn increasing attention in the last few years. Many existing studies that use machine learning for this problem assume that the analyst has access to the actual appliances states at every sample instant, whereas in fact collecting this information exposes consumers to severe privacy risks. It may, however, be possible to persuade consumers to provide brief samples of the operation of their home appliances as part of a "registration" process for smart metering (if appropriate financial incentives are offered). This labeled data would then be supplemented by a large volume of unlabeled data. Hence, we propose the use of semi-supervised learning for non-intrusive appliance load monitoring. Furthermore, based on our previous work, we model the simultaneous operation of multiple appliances via multi-label classification. Thus, our proposed approach employs semi-supervised multi-label classifiers for the monitoring task. Experiments on publicly-available dataset demonstrate our proposed method.
Abstract - This paper outlines the potential role of Fuzzy Logic Control (FLC) for voltage-rise mitigation in power distribution networks in the presence of grid-connected photovoltaic (PV) systems. In particular, after analyzing the main performances of the traditional techniques currently adopted for voltage rise mitigation by reactive power compensation, a decentralized approach based on local fuzzy controllers is proposed to regulate the reactive power injected into the grid by the distributed PV systems. The proposed solution is based on a closed control loop, where the local voltage at the point of common coupling (PCC) is fed at input to the FLC to decide the amount of reactive power generated by the local PV systems. The results obtained on a realistic case study are presented and discussed in order to assess the benefits deriving by the application of the proposed approach.