INTRODUCTION
With the increasing demand of power and the incapacity of grid power supply,
power failure occurs from time to time. In order to keep the balance between
surplus power and the power shortage in power consumption peak times, a lot
of large EPS power is applied. As large EPS power has big power, when it is
accessed into the grid, it will lead to some power quality problems such as
the fluctuation and flicker of the voltage (Li and Sun,
2006; Fengjuan, 2006). On the other hand, when a large
EPS power controller is connected into grid voltage, as there is disturbance
in grid, it will send incorrect controlling signals to IGBT. As a result, it
is hard to regulate and control the inverter output voltage. Therefore, it becomes
particularly important to detect and control the accessed voltage fluctuation
and flicker of the large EPS power. This paper focused on the detection of the
grid accessing voltage fluctuation and flicker of large EPS power.
At present, there are many detection methods for accessing voltage fluctuation.
The top three frequently used detection methods (Shiping and
Guiying, 2008) are square demodulation detection, rectification demodulation
detection and effective value detection. Additionally, modern detection methods
includes Fourier transform, Wavelet transform (Zhaojing
and Wenhui, 2001; Mingde and Zhigang, 2011; Sharma
and Agarwal, 2012), Hilbert transform (Zhiqun et
al., 2004), instantaneous reactive power theory (Xiaopu
et al., 2009; Ge and Song, 2011), Kalman
filter technology, minimum of absolute value of state estimation (Jiasheng
et al., 2012), Teager power operator technology, Genetic algorithm
technology (Shanghua et al., 2004; Xiaohua,
2007) etc. Because square demodulation detection is suitable for standalone
by using the method of digital signal processing to form a new flicker instrument,
therefore, we use square demodulation detection on the IEC recommended flicker
instrument (Lixia et al., 2005). Based on the
square modulation detection algorithm recommended by IEC, this paper proposed
an analyzing method which can detect amplitude signals without filtering.
ANALYSIS ON ALGORITHM PRINCIPLE
Comparison analysis on various algorithms: For the three classical detection
algorithms mentioned in Literature (Shiping and Guiying, 2008),
no matter which demodulation method can detect the amplitudemodulated wave.
In order to remove DC component and doubling frequency component but remain
amplitudemodulated wave, effective value detection has an average effect and
can be easily affected by fundamental wave voltage and fundamental frequency.
When going through the integrator, reference voltage U_{o} = A^{2}/2
should be subtracted. But it is impossible that there is no DC component. Therefore,
it is still necessary to block DC and filter wave. For square demodulation detection
and rectifier demodulation detection, strict low pass filter or band pass filter
should be designed to properly filter DC, power frequency and power frequency
harmonic wave and remain the amplitudemodulated wave. It is suitable to use
artificial circuit to realize rectification detection.
In Literature (Zhaojing and Wenhui, 2001), the detection
algorithm of wavelet transform is proposed. Its fundamental principle is to
replace the low pass filter in traditional synchronous demodulator with wavelet
multiresolution signals decomposition filter which can detect the envelop signal
of voltage flicker and the sudden change time of voltage flicker signal. This
method is effective for detecting fluctuation signals which include one or two
or above kinds of frequency. But the sync signal and carrier signal should be
of the same phase and frequency. What’s more, the frequency should be strictly
fractioned. In order to improve the accuracy of the power focused wavelet base,
we usually take Db4 wavelet. It has 4 arc converters and frequency in the center
is high, which makes it easy to extract a wide frequency range of transient
signal and inhibit the interfusion of low frequency carrier. With eight filter
coefficient, the calculation amount of fast wavelet transformation is smaller.
The HilbertHuang Transformation (HHT) detection put forwarded in literature
(Zhiqun et al., 2004) is a kind of nonstationary
signal processing method, that is, HHT method can be used to detect typical
power quality disturbance signals related to timedomain analysis. For example,
voltage flicker and fluctuation signals. This method is composed by two parts:
Empirical mode decomposition method (EMD) and Hilbert transform. Firstly, we
make use of EMD to extract signal’s Intrinsic Mode Function (IMF) component.
Then to Hilbert transform IMF for instantaneous frequency and amplitude. This
method has the advantage of wavelet multiresolution, which enables us to analyze
signals from two aspects: time domain and frequency domain so that we can precisely
detect the time, frequency and amplitude information of sudden change, nonstationary
harmonic wave and voltage flicker signals. Meanwhile, it overcomes the difficulty
of extracting wavelet base from wavelet transform and the signal decomposition
is based on the signal’s own properties, so that it has good local characteristics.
The detection method proposed in literature (Xiaopu et
al., 2009) is based on instantaneous reactive power theory. Its fundamental
principle is as follows. If the singlephase voltage signals delay 60°,
we can get the threephase voltage, then replace the three current with three
phase voltage. We can take advantage of the reactive power theory to calculate
the instantaneous active and then calculate the voltage signal envelope.
New algorithm principle: If you intend to make valid detection of the
voltage fluctuation and flicker, the top priority is to extract fluctuation
signals precisely. As a usual, we regard fluctuation voltage as amplitudemodulated
wave which takes power frequency rated voltage as carrier and the voltage’s
amplitude is modulated by the voltage fluctuation component whose frequency
range from 0.05 to 0.35 Hz. For the simplicity of analysis and do not break
generality, the instantaneous expression of the power frequency voltage u (t)
is written as (Xiaohua, 2007):
In this expression, A is the amplitude of the power frequency carrier voltage, ω is the angular frequency of it, m_{i }is the ratio between the amplitude and the carrier amplitude of amplitudemodulated wave i, Ω_{i} is the angular frequency of amplitudemodulated wave i, ψ_{i} is the epoch angular of amplitudemodulated wave i, θ is the epoch angular of power frequency carrier voltage.
When doing research on the voltage fluctuation detection, we can assume that the voltage to be measured only has the modulation of one certain single frequency’s amplitude wave to power frequency carrier. Then the instantaneous expression for the to be measured voltage signal u(t) with voltage fluctuation is:
In this expression, m is the ratio between the voltage amplitude and the carrier amplitude of the amplitudemodulated wave, which represents the amplitudemodulated wave’s controlling degree by modulating signals. Ω is the angular frequency of the amplitudemodulated wave voltage.
First, we detect the carrier wave in sample signals and according to the test
results we send reverse carrier signals to offset the carrier waves in the original
signals (Li et al., 2010; Xiaohua,
2007). Then process the remaining signals in two ways. According to the
And Angle formula principle, the two processed signals minus each other and
the influence of fundamental carrier frequency cancels each other out. In this
way the detection of amplitude modulated signals can be realized. The overall
frame diagram is shown in Fig. 1. Different from filtering
wave in frequency level, this method gets a satisfying result by analyzing from
another perspective.
Firstly, detect the carrier wave’s frequency ω, amplitude A, phase angle θ in signals and send out a reverse signal through the signal generator:
Subtract expression 3 from expression 2, offset the fundamental wave component and get:
Then process expression 4 from two ways. φ is arbitrary constant.
• 
Way 1: Multiply expression 4 with cos (ωt+θ+φ),
then we get: 

Fig. 1: 
Overall frame diagram 
• 
Way 2: Multiply expression 4 with cot(ωt+θ)
sin(ωt+θ+φ), then we get: 
Deduct expression 6 from expression 5, we get:
We can get from expression 7 that this method not only omits the filter but also gets rid of the influence of DC component and doubling frequency component in square demodulation detection. This algorithm can better show its superiority when it is in multifrequency amplitude signals.
SIMULATED VERIFICATION AND ANALYSIS
In order to testify the validity of this algorithm, this algorithm was tested
and analyzed in MATLAB R2009. In simulation experiment, we set 311 V to A, 50
Hz to ω, 0.1 to m, 5 Hz to Ω, π/3 to φ and 0 to θ. Figure
2 is the simulation diagram of the to be measured voltage u(t) and p(t),
which produce voltage fluctuation. Figure 3 is the voltage
fluctuation signal wave form of p(t), u(t) and the preadded voltage with DC
component. Figure 4 is the simulation diagram of voltage fluctuation
detection.

Fig. 2: 
The simulation diagram of u(t) and p(t) 

Fig. 3(ac): 
The waveform of (a) p(t), (b) u(t) and (c) The preadded voltage
with DC component 

Fig. 4: 
The simulation diagram of voltage fluctuation detection 

Fig. 5: 
The voltage fluctuation signal of the detection 
In this figure, module out 1 is the submodule of p(t). Figure
5 is the voltage fluctuation signal of the detection. From this figure,
we can get that the detected voltage fluctuation is nearly the same as the preadded,
except for different amplitudes. From Fig. 4 we can see that
this method not only omits filter but also gets rid of the influence of the
DC component and doubling frequency component in square demodulation detection.
To further illustrate the effectiveness and superiority of this algorithm,
we also made simulation research in the circumstance of multifrequency amplitude
signal, that is, the voltage fluctuation which has several different frequencies.

Fig. 6: 
The simulation diagram of voltage fluctuation u(t) with several
different frequencies 

Fig. 7(ab): 
The waveform of (a) The preadded multifrequency amplitude
signal voltage fluctuation and (b) The measured voltage u(t) 
Therefore, expression 7 would convert into:
In the simulation research, four voltage fluctuation signals are added. Their
frequencies are 5, 8, 10 and 12 Hz and the m value for they are 0.1, 0.15, 0.2,
0.05. For simplify of the process, 5, 8, 10 and 12 Hz voltage fluctuation signals
will be produced to build a subsystem. Figure 6 is the simulation
diagram of voltage fluctuation u(t) with several different frequencies and out
1 is separately the subsystem modules of four different frequency voltage fluctuation
signal. Figure 7 is the waveform of preadded multifrequency
amplitude signal voltage fluctuation and the to be measured voltage u(t). Figure
8 is the simulation diagram of detected voltage fluctuation by using the
detecting algorithm proposed in this paper. Figure 9 is the
detected voltage fluctuation signal.

Fig. 8: 
The simulation diagram of detected voltage fluctuation 

Fig. 9: 
The detected voltage fluctuations 
From this figure, we can see the signal detected in Figure 9
is nearly the same with the preadded voltage fluctuation which has several
different frequency signals in Figure 7.
CONCLUSIONS
In voltage fluctuation detection, the algorithm proposed in this paper works
easily and quickly. The data got from calculation is small in amount and high
in accuracy. It gets rid of not only filters but also the influence of DC component
and doubling frequency in square demodulation detection (Xiaoli,
2004). In the detection of voltage fluctuation with several different frequencies,
this algorithm has more advantage. It is very suitable for the detection of
voltage fluctuation and flicker in shore power grid accessing.
ACKNOWLEDGMENT
This project has gained support from Science and Technology Plan Project of Hunan Province. The project number is 2010GK3179.