The fundamental component synchrophasors are widely used nowadays, based on the use of phasor measurement units (PMUs), in several applications in electrical power systems. For the advancement of monitoring technology, harmonic synchrophasor estimation is still a challenge. To make it possible, it is necessary to correctly estimate the harmonic phasor (magnitude and phase of each harmonic component), mainly in scenarios with frequency deviations and noise. In this context, this present article proposes the use of B-spline-based interpolation as a preprocessing algorithm for the discrete Fourier transform (DFT). The performance is evaluated in terms of total vector error (TVE) considering the tests proposed by the IEEE C37.118.1 and IEC 60255-118-1 standards and compared with four different harmonic phasor estimation (HPE) methods, the HPE, sinc interpolation function-based estimator (SIFE), Taylor–Fourier transform (TFT), and flat-top finite impulse response (FT FIR). The proposed method can provide a TVE below 1% for all harmonic phasors, from the first to the fiftieth order, under significant frequency deviation. Also, for real-time validation, the proposed method was implemented in an industrial computer as a software routine capable of processing the signals in real time. The signals are acquired by a commercial merging unit (MU) and the samples are published using the IEC 61850 sampled values (SVs) protocol.

This article focuses on the hidden benefits of harvesting the wasted energy from undesirable components of electric signals in electric power systems for powering the transceivers of Internet of Things (IoT) devices. These components, mainly harmonics and interharmonics, occupy the ultranarrowband power line communication spectrum (i.e., frequencies below 3 kHz). In this context, we introduce a mathematical formulation that allows us to quantify the number of transceivers of IoT devices that this kind of wasted energy can power. Based on a measurement campaign in a building facility, we show that the wasted energy can be modeled as a cyclostationary random process on weekdays and a stationary one on weekends, mimicking the energy consumption profile in a building facility. Numerical results also highlight achievable data rates obtained in the building facility if the wasted energy is reused. This analysis shows that this kind of harvested energy from wasted energy is suitable for powering numerous transceivers of IoT devices in practical scenarios.