Some of the most remarkable issues related to interharmonic theory and modeling are presented. Starting from the basic definitions and concepts, attention is first devoted to interharmonic sources. Then, the interharmonic assessment is considered with particular attention to the problem of the frequency resolution and of the computational burden associated with the analysis of periodic steady-state waveforms. Finally, modeling of different kinds of interharmonic sources and the extension of the classical models developed for power system harmonic analysis to include interharmonics are discussed. Numerical results for the issues presented are given with references to case studies constituted by popular schemes of adjustable speed drives.

The harmonic and interharmonic analysis recommendations contained in the latest International Electrotechnical Commission (IEC) standards on power quality are considered. The spectral leakage problems due to synchronization errors of the analyzed waveforms with the window widths adopted are analyzed. Improvements of the signal processing recommended by the IEC, based on the utilization of the classical Hanning window in place of the rectangular window, are proposed and made compatible with the grouping technique introduced in the standards. Sensitivity analyses on simple case studies together with experimental results show the sensitivity of the signal processing recommended by the IEC to synchronization errors and the usefulness of the improvements proposed.

A Wind power forecasting method based on the use of discrete time Markov chain models is developed starting from real wind power time series data. It allows to directly obtain in an easy way an estimate of the wind power distributions on a very short-term horizon, without requiring restrictive assumptions on wind power probability distribution. First and Second Order Markov Chain Model are analytically described. Finally, the application of the proposed method is illustrated with reference to a set of real data

This paper presents the results of comprehensive testing and subsequent detailed analysis of the obtained test results, evaluating harmonic and interharmonic performances of photovoltaic inverters (PVInvs) for a range of different operating conditions. The presented results indicate significant power-dependent changes in harmonic and interharmonic emissions of tested PVInvs for different supply voltage conditions (presence of voltage waveform distortions and various source impedance values). To correctly quantify and describe these changes in PVInv performance, this paper discusses and applies measurement procedures and metrics for evaluating harmonic and interharmonic emission recommended in existing standards, as well as some additional metrics and indicators. For some operating conditions, tested PVInvs significantly increase both harmonic and interharmonic emissions, and this paper also discusses the impact of PVInv control (e.g., maximum power point tracking control) as a possible origin of the interharmonic distortion.

This paper represents the second part of a two-part article reviewing the state of the art of probabilistic aspects of harmonics in electric power systems. It includes tools for calculating probabilities of rectangular and phasor components of individual as well as multiple harmonic sources. A procedure for determining the statistical distribution of voltages resulting from dispersed and random current sources is reviewed. Some applications of statistical representation of harmonics are also discussed.

The importance of the accurate forecasting of power outputsof wind-based generation systems is increasing, as their contributions to the total system generation are rising. However, wind energy resource exhibits strong and stochastic spatio-temporal variations, which further combine with the uncertainties in WF operating regimes, i.e., numbers of wind turbines in normal operation, under curtailment, or that are faulty/disconnected. This paper presents a novel approach for efficient dealing with uncertainties in hour-ahead forecasted WF power outputs. It first applies Bayesian convolutional neural network-bidirectional long short-term memory (Bayesian CNN-BiLSTM) method, which allows for a more accurate probabilistic forecasting of wind speed, air density and wind direction, using the nearby WFs as additional input data. The WF operating regimes are also predicted using the same Bayesian CNN-BiLSTM structure. Afterwards, a high-dimensional Vine-Gaussian mixture Copula model is combined with Bayesian CNN-BiLSTM model to evaluate uncertainties in the WF outputs based on a cross-correlational conditioning of the forecasted weather variables and operating regimes. The proposed combined model is applied and validated using the actual field measurements from two WF clusters in close locations in Croatia and is also benchmarked against several other models.