Smart grids

3rd Renewable Power Generation Conference (RPG™)

24 - 25 September 2014 - Ramada Naples, Naples, Italy

A Nonlinear and Non-Stationary Signal Analysis for Accurate Power Quality Monitoring in Smart Grids

Silvano Vergura*, Giulio Siracusano+, Mario Carpentieri*, Giovanni Finocchio+

*Technical University of Bari +University of Messina

Italy

DIPARTIMENTO DIELETTROTECNICAED ELETTRONICA

A Nonlinear and Non-Stationary Signal Analysis for Accurate Power Quality

Monitoring in Smart Grids

Silvano Vergura Ramada Naples, 25/09/2014

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Analysis of the power disturbances in an active line, i.e. line which can

either absorb either feed the active power.

Use of Wavelet Transform (WT)

Use of Hilbert-Huang Transform (HHT)

Application to two different scenarios: line fed by large PV plants

(power indicated as PPV) and line with no PV generators (power

indicated as P ).

AIMS





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ISSUES


Non-uniform spatial distribution of the electrical power gives rise to

multi-modes and intermittent non-stationary solicitations.

For SGs with high penetration of DGs, a significant amount of

conventional generation is replaced with distributed PV resources with

the result of the lack of reactive power and reduced system inertia.

Unexpected fluctuations introduce anomalies: short circuit manifests

itself as a high-frequency component, whereas a load variation gives

rise to a low-frequency component.




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Comparison of signal processing techniques


Fourier STFT HHT

Basis A priori A priori Adaptive

Frequency Convolution:

global, uncertainty

Convolution: regional,

uncertainty

Differentiation: local, certainty

Presentation Energy-

frequency Energy-time-

frequency Energy-time-

frequency

Nonlinear No No Yes

Nonstationary No Yes Yes

Feature Extraction No Yes Yes

Theoretical Base Theory

complete Theory complete Empirical




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PROPOSED APPROACH


1. WT and HHT have been utilized for the detection of non-

stationary behaviour and recognition of anomaly patterns,

specifically on negative active power conditions.

2. WT identifies the time evolution of the modes of the electrical

power.

3. Computations based on HHT is able to separate the time domain

traces related to the harmonics and the steady states.

4. Finally, they allow to detect and locate the irregular operating

conditions.




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WAVELET TRANSFORM


For a time-domain signal x(t), the continuous wavelet transform is a linear function

given by:

with s and u the scale and translation parameters of the mother wavelet ψ(t) :

We have used the complex Morlet wavelet mother (with fB=30 and fC=1 ):

*1,

t uW u s x t dt

ss

2

2 /

,

1 C B

t u t uj f f

s su s

B

e es f

,

1u s

t ut

ss




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HILBERT-HUANG TRANSFORM 1/2


By HHT, complex sets of nonlinear and non-stationary data can be decomposed into a

finite collection of Intrinsic Mode Functions (IMF), through the Empirical Mode

Decomposition (EMD). The IMFs have well-defined instantaneous frequencies and

represent the intrinsic oscillatory modes embedded in the original signal.

HHT consists of two parts: Hilbert Transform (HT) and EMD.

Given a time-domain function x(t), its HT (with P the Cauchy principal value):

The HT computes the instantaneous power and frequency of a mono-component signal.

A generalization to a multi-component signal is possible by using the EMD method,

applied to decompose non-linear and non-stationary signals.

x sP

y t dst s




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HILBERT-HUANG TRANSFORM 2/2


It extracts a series of IMFs from the analyzed signal by means of an iterative process

which is known as sifting and consists in three steps:

1. starting from the original signal x(t), set , extract the local minima and

local maxima from ;

2. interpolate the local minima and local maxima with a cubic spline to form upper and

lower envelopes, respectively;

3. obtain the mean of the upper and lower envelopes and subtract it from to

determine a new ProtoMode Function (PMF)

The above procedure is repeated until satisfies the ending criteria: the number

of maxima and minima and the number of zero-crossings differs only by one and the

local average is zero.

ih t x t

ih t

ih t

1i ih t h t m t

1ih t




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DESCRIPTION OF THE SYSTEM UNDER TEST


Several passive and active lines located near Bari (Italy).

Lines feed both residential and commercial users.

Each line absorbs a peak mean power in the range [50÷350] kW over a length

which varies from 248 to 472 meters.

We have chosen two power lines: a passive one with a peak power of about 50 kW

and an active one of about 70 kW of absorbed power.

Power measurements have a sampling period of 10 minutes and have been

captured between September 2013 and February 2014 for a total of 154 days

with 144 samples per day (154x144=22176 events recorded).

The passive line has no PV plants, whereas the active line has 18 grid-connected

PV plants with a total rated peak power of about 108 kW




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NUMERICAL RESULTS 1/3


Mean active power signal: line with no DGs (solid red

line) and line fed by large PV plants (solid black line).

P1 is the main mode of the power dynamics of the

two lines; the amplitude of PPV(t) is double with

respect to the P(t) signal.

P2 mode is associated with the alternation between

daytime and can explain its larger amplitude if

compared with no PV line.

P3 mode is mainly related to seasonal events that

cause a change of the energy demands and load

curves of both power lines. P3 mode is substantially

invariant for the two signals of interest.

fP1=11.4μHz≈24h-1 fP2=23.3μHz≈12h-1

fP3=34.7μHz≈8h-1




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Time-frequency representation for P(t) (a) and

PPV(t) (b) aims us to better evaluate the intermittent

behaviour of the P2 mode.

In (b) we observe a telegraphic signal appearing

and disappearing which suggests an irregular active

power absorption due to the PV power

We performed the HHT on the signal PPV(t) to

extract the independent oscillations and to

investigate the P2 mode deeply. Once extracted by

means of HHT [24], we applied the previous Morlet

wavelet and computed the Wavelet scalogram to

evaluate the dynamics.




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This figure provides evidence of highly non-

stationary behaviour of the P2 mode, as extracted

from the signal PPV(t). Note the strong temporal

coherence between the occurrence of events

wherein no power has been measured (solid white

stars in the upper part of the Fig.) which follows

the nearest local maxima of the P2 mode. This

indicates a possible relationship between the most

of the unexpected faults in the line and the

nonlinear amplitude of this disturbance.




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A combined Wavelet and HHT-based analysis is proposed, which demonstrates

to be a valuable framework to investigate the impact of DG penetration on the

power quality in SGs.

Both steady state and dynamic behaviour of distribution lines with and without

PV plants contribution are studied and compared to identify the effects of PV

systems on the power line.

The results of steady state analysis reveal that increasing the amount of power

due to the DGs leads to larger fluctuations of the active power.

The procedure has shown the ability to study non-stationary power system

waveforms and non-linear dynamical signals.

Conclusions


Engineering

Smart grids