Novel Modeling and Simulation Concepts for Power Distribution Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks

Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessing the reliability of power grids. Such models cannot avoid taking into account the variability that is inherent to the electrical system and users’ behavior. In this paper, we present a solution for the generation of a compressed surrogate model of the electrical state of a realistic power network that is subject to a large number (on the order of a few hundreds) of uncertain parameters representing the power injected by distributed renewable sources or absorbed by users with different consumption profiles. Specifically, principal component analysis is combined with two state-of-the-art surrogate modeling strategies for uncertainty quantification, namely, the least-squares support vector machine, which is a nonparametric regression belonging to the class of machine learning methods, and the widely adopted polynomial chaos expansion. Such methods allow providing compact and efficient surrogate models capable of predicting the statistical behavior of all nodal voltages within the network as functions of its stochastic parameters. The IEEE 8500-node test feeder benchmark with 450 and 900 uncertain parameters is considered as a validation example in this study. The feasibility and strength of the proposed method are verified through a systematic assessment of its performance in terms of accuracy, efficiency, and convergence, based on reference simulations obtained via classical Monte Carlo analysis

Real Time Monitoring and Supervisory Control of Distribution Load Based on Generic Load Allocation: A Smart Grid Solution

Our work is the small part of the smart grid system. This is regarding the check and balance of power consumption at the consumer level. It is a well known fact that the consumers are allocated a fixed load according to their requirement at the time of application for the electricity connection. When the consumer increases its load and does not inform the power company, the result is the overloading of the system. This paper presents a solution regarding distribution and load allocation to each customer. If the customer uses power greater than the load allocated, further power is not provided and consequently that appliance is not turned on unless the total load must not be decreased than the allocated load. This is achieved by designing a processor controlled system that measures the power on main line and also the power taken by each device. Now when a device is turned on, its power is measured by the controller and compares it with the main line power, and when the device consumes some power consequently main line power will also be increased thus this main line power is monitored and if it exceeds particular limit that device is turned off through its rela

An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain

This paper presents an alternative solution for the power-flow analysis of power systems with distributed generation provided by heterogeneous sources. The proposed simulation approach relies on a suitable interpretation of the power network in terms of a nonlinear circuit in the phasor domain. The above circuit interpretation can be solved directly in the frequency-domain via the combination of a standard tool for circuit analysis with an iterative numerical scheme, providing directly the steady-state solution of the power-flow of a generic distribution network. At each iteration, the resulting circuit turns out to be composed by two decoupled subnetworks, a large linear part and a set of smaller nonlinear pieces accounting for the load characteristics, with evident benefits in terms of the computational time. The feasibility and strength of the proposed simulation scheme have been verified on a large benchmark consisting of the IEEE 8500-node test feeder. Then it is applied to the statistical simulation of a power network accounting for the variability effects of renewable generators. According to the results, the proposed tool provides an effective alternative to the state-of-the-art approaches for power-flow analysis further highlighting the benefits of the application of well-established tools for circuit analysis to power-flow problems

Single Image Super Resolution Technique: An Extension to True Color Images

The super-resolution (SR) technique reconstructs a high-resolution image from single or multiple low-resolution images. SR has gained much attention over the past decade, as it has significant applications in our daily life. This paper provides a new technique of a single image super-resolution on true colored images. The key idea is to obtain the super-resolved image from observed low-resolution images. A proposed technique is based on both the wavelet and spatial domain-based algorithms by exploiting the advantages of both of the algorithms. A back projection with an iterative method is implemented to minimize the reconstruction error and for noise removal wavelet-based de-noising method is used. Previously, this technique has been followed for the grayscale images. In this proposed algorithm, the colored images are taken into account for super-resolution. The results of the proposed method have been examined both subjectively by observation of the results visually and objectively by considering the peak signal-to-noise ratio (PSNR) and mean squared error (MSE), which gives significant results and visually better in quality from the bi-cubic interpolation technique