A spectral estimation method for nonstationary signals analysis with application to power systems

Adamo, Francesco; Attivissimo, Filippo; Nisio, Attilio Di; Savino, Mario; Spadavecchia, Maurizio

doi:10.1016/j.measurement.2015.04.023

Cited by 18 publications

(3 citation statements)

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“…In the case where mono-component signals can not be extracted by filtering, more sophisticated approaches are required such as Empirical Mode Decomposition (EMD) [74] and its extensions or Discrete Wavelet transform (DWT) [75][76][77]. For transient and nonstationary environments, time-frequency and time-scale approaches are more appropriate and are extensively discussed in this paper [78][79][80]. These approaches allow retrieving the evolution of signal frequency content and amplitudes over time and allow abnormal operating condition tracking over time.…”

Section: Ref Contributionsmentioning

confidence: 99%

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

et al. 2021

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Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.

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Section: Ref Contributionsmentioning

confidence: 99%

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

et al. 2021

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“…If the change of the statistic is time independent, the signal is called stationary signal. If the statistic changes with time, the signal is called nonstationary signal [1][2][3]. e random signal in engineering is usually nonstationary signal.…”

Section: Introductionmentioning

confidence: 99%

Signal Nonstationary Degree Evaluation Method Based on Moving Statistics Theory

Cheng

Zhang

2021

Shock and Vibration

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Nonstationary signal refers to the signal whose statistics change with time, and its nonstationary degree evaluation can provide effective support for the evaluation of the operating state of the signal source. This paper introduced a variety of typical signal global and local nonstationary degree evaluation methods and compared the applicable scope of different evaluation methods. In view of the limitations of the existing evaluation methods in the scope of application, considering the influence of adjacent signal points, this paper proposed the concepts and calculation methods of the moving mean, moving standard deviation, moving variation coefficient, and moving Hurst exponent based on the theory of moving statistics. According to different nonstationary degree evaluation methods, three different fields of signals (sinusoidal signal, mechanical fault signal, and ECG signal) are analyzed. The results show that, compared with the existing nonstationary degree evaluation methods, the signal nonstationary degree evaluation method proposed in this paper can reveal the time-varying details of the nonstationary signals, with high precision and strong stability, and has unique advantages in nonstationary signal processing.

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“…[3] suitable windows and interpolation algorithms have been examined in order to reduce undesirable effects due to spectral leakage caused by a sampling process that is not synchronized, and in Ref. [4] the signal is weighted before the Discrete Fourier Transform (DFT) is calculated. Frequencies and complex amplitudes of the various components of the signal are obtained from the DFT by interpolation.…”

Section: Introductionmentioning

confidence: 99%

Correction for the deviation of power system frequency in the measurement of power by digital techniques

Volodarskyi

Voloshko

2019

Metrolog. and instr.

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The saturation of power supply systems with nonlinear and powerful sources both from sources and consumers of electricity has led to the fact that signals in the network become non-stationary, nonlinear, and with significant frequency fluctuations. The purpose of the work is to reduce the methodological error of determining the parameters of the mode of electricity consumption due to the frequency deviation in the electrical network. The article proposes methods based on the determination of the frequency deviation value and the introduction of correction coefficients (for example, the calculation of active power and voltage in the electric network). The proposed methods are applied to both sinusoidal and non-sinusoidal signal forms. The results show that these methods have high accuracy, even with signals containing harmonics, and for a frequency deviating from the nominal value.

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A spectral estimation method for nonstationary signals analysis with application to power systems

Cited by 18 publications

References 50 publications

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

Signal Nonstationary Degree Evaluation Method Based on Moving Statistics Theory

Correction for the deviation of power system frequency in the measurement of power by digital techniques

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