Antonio Bracale

Measurement of IEC Groups and Subgroups using Advanced Spectrum Estimation Methods

The International Electrotechnical Commission (IEC) standards characterize the waveform distortions in power systems with the amplitudes of harmonic and interharmonic groups and subgroups. These groups/subgroups utilize the waveform spectral components obtained from a fixed frequency-resolution discrete Fourier transform (DFT). Using the IEC standards allows for a compromise among the different goals, such as the needs for accuracy, simplification, and unification. In some cases, however, the power-system waveforms are characterized by spectral components that the DFT cannot capture with enough accuracy due to the fixed frequency resolution and/or the spectral leakage phenomenon. This paper investigates the possibility of a group/subgroup evaluation using the following advanced spectrum estimation methods: adaptive Prony, estimation of signal parameters via rotational invariance techniques, and root multiple-signal classification (MUSIC). These adaptive methods use variable lengths of time windows of analysis to ensure the best fit of the waveforms; they are not characterized by the fixed frequency resolution and do not suffer from the spectral leakage phenomenon. This paper also presents the results of the applications of these methods to three test waveforms, to current and voltage waveforms obtained from simulations of a real DC arc-furnace plant, and to waveforms measured at the point of common coupling of the low-voltage network supplying a high-performance laser printer.

Adaptive Prony Method for the Calculation of Power-Quality Indices in the Presence of Nonstationary Disturbance Waveforms

The presence of the new liberalized markets has increased the interest in power-quality (PQ) disturbances due to their economic effect. In particular, in the case of disturbances caused by a single event (such as a capacitor switching or voltage sag), the waveform assessment can be difficult due to the rapid variations in waveform spectral component characteristics; these difficulties require a suitable choice of signal-processing techniques for spectral analysis and, in particular, the resort to time-frequency representations. In this paper, the adaptive Prony method is proposed to calculate PQ indices based on a time-frequency analysis of waveforms. Numerical applications on a simulated transient due to capacitor switching, a measured voltage sag, and a test waveform are also presented and discussed in order to investigate the validity of the proposed method.

A Bayesian-Based Approach for a Short-Term Steady-State Forecast of a Smart Grid

Future distribution networks are undergoing radical changes, due to the high level of penetration of dispersed generation and information/communication technologies, evolving into the new concept of the Smart Grid. Dispersed generation systems, such as wind farms and photovoltaic power plants, require particular attention due to their incorporation of uncertain energy sources. Further and significant well-known uncertainties are introduced by the load demands. In this case, many new technical considerations must be addressed to take into account the impacts of these uncertainties on the planning and operation of distribution networks. This paper proposes novel Bayesian-based approaches to forecast the power production of wind and photovoltaic generators and phase load demands. These approaches are used in a probabilistic short-term steady-state analysis of a Smart Grid obtained by means of a probabilistic load flow performed using the Point Estimate Method. Numerical applications on a 30-busbar, low-voltage distribution test system with wind farms and photovoltaic power plants connected at different busbars are presented and discussed.

Site and System Indices for Power-Quality Characterization of Distribution Networks With Distributed Generation

The problem of developing a definition that adequately assesses power-quality (PQ) levels in the presence of distributed generation (DG) is addressed by using proper probabilistic indices for distribution networks. In the planning of new DG installations, these indices take the variation of PQ level into account and use weighting functions properly. They are useful in quantifying the impact of the installation of DG units because they can consider several PQ disturbances simultaneously. Also, they indicate how each PQ disturbance may affect the decision concerning the installation of DG units. Several tests on real distribution networks were performed and discussed in order to show the usefulness of these indices in evidencing the impact of DG on PQ levels. The analysis of the electrical distribution systems on the basis of the considered indices is valuable also to help decide the best allocation and size of distributed generators.

Analysis of the Italian distribution system evolution through reference networks

This paper presents initial results of a three-year research project entitled ATLANTIDE, which is aimed at developing a comprehensive digital archive of reference models of Italian distribution networks, including forecasted load and generation development evolving towards future smart grid scenarios. Such reference network models and evolutionary scenarios should provide a useful benchmark for testing and comparing different control methodologies, distribution schemes and operation strategies for dealing with the new challenges caused by the envisaged widespread diffusion and integration of distributed generation, renewable generation and distribution storage devices. The paper focuses on the criteria adopted for defining a set of evolutionary scenarios for the reference networks, accounting for current drives and future trends, valuable to a wide range of the stakeholders of the distribution business.

Accurate methods for signal processing of distorted waveforms in power systems

A primary problem in waveform distortion assessment in power systems is to examine ways to reduce the effects of spectral leakage. In the framework of DFT approaches, line frequency synchronization techniques or algorithms to compensate for desynchronization are necessary; alternative approaches such as those based on the Prony and ESPRIT methods are not sensitive to desynchronization, but they often require significant computational burden. In this paper, the signal processing aspects of the problem are considered; different proposals by the same authors regarding DFT-, Prony-, and ESPRIT-based advanced methods are reviewed and compared in terms of their accuracy and computational efforts. The results of several numerical experiments are reported and analysed; some of them are in accordance with IEC Standards, while others use more open scenarios.

Point estimate schemes for probabilistic load flow analysis of unbalanced electrical distribution systems with wind farms

In this paper, the point estimate method is applied to account for the uncertainties that affect the evaluation of the steady state operating conditions of an unbalanced three-phase power system in the presence of wind farms. The accuracy of the proposed technique is tested on the three-phase unbalanced IEEE 34-bus test system adequately modified to take into account the presence of wind farms; the results obtained by applying the Monte Carlo simulation are assumed as a reference. The main conclusion is that the point estimate method gives good solutions in terms of accuracy and computational efforts.

Probabilistic Index for Increasing Hourly Transmission Line Ratings

The steady state thermal rating of overhead transmission lines is limited by the conductor`s maximum design temperature, which is related to the maximum sag and the loss of tensile strength of the conductors. Traditionally, this overhead transmission lines rating is computed using a deterministic approach, with reference to severe weather conditions. Thus, the application of this method leads to conservative results resulting in under-utilization of conductors. In this paper, a new method based on an hourly probabilistic index is proposed to predict the line thermal rating for each hour of the day; this index is evaluated using the conductor current limit probability density function (pdf). The method uses Bayesian time series models for the weather parameters (ambient temperature, wind speed and wind direction) and calculates the conductor current limit pdf using a Monte Carlo simulation. The probabilistic index is applied by considering measured weather data of both hot and cold seasons; the corresponding lines ratings are reported and analyzed.

On some advanced methods for waveform distortion assessment in presence of interharmonics

One of the main problems for waveform distortion assessment that is to reduce the effects of the spectral leakage due to fundamental and harmonics on interharmonic components. In the framework of DFT approaches, line frequency synchronization techniques or algorithms to compensate desynchronization are necessary; alternative approaches as those based on the Prony methods are not sensitive at all to desynchronization but can require not negligible computation burden. In the paper, different proposals by the same authors regarding both DFT based and Prony based advanced methods are recalled and compared to each other. The results of several numerical experiments are reported and analyzed; some of them are in accordance with IEC guidelines and some others are related to more open scenarios

A Probabilistic Competitive Ensemble Method for Short-Term Photovoltaic Power Forecasting

Photovoltaic systems are expected to play a key role in the planning and operation of future distribution systems due to the benefits associated with their use. Unfortunately, a great problem is involved in photovoltaic power utilization, i.e., the unpredictability of the solar source. Thus, many forecasting methods have been developed in order to provide tools with adequate consistency, quality, and value. The methods can provide either deterministic or probabilistic forecasts; the latter seem to be the most appropriate for taking into account the unavoidable uncertainties of the solar source. In this paper, a new probabilistic method based on a competitive ensemble of different base predictors is proposed for the short-term forecasting of photovoltaic power. Three probabilistic methods were selected and trained as base predictors in order to obtain an ensemble of the predictive distribution with optimal characteristics of sharpness and reliability. Numerical applications based on actual data were performed to test the effectiveness of the proposed method with respect to single predictors and to a benchmark method.