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2013 IEEE GCC Conference and exhibition, November 17-20, Doha, Qatar

Matlab/Simulink Implementation & Simulation of Islanding Detection using Passive Methods Marwa Ashour, Lazhar Ben-Brahim, Adel Gastli, Nasser Al-Emadi, Yara Fayyad Qatar University, Department of Electrical Engineering Doha, Qatar passive techniques seems to be sufficient in detecting an islanding case. However, when the mismatch is very small, it is difficult to detect the islanding state because the variations in voltage or frequency at the PCC are also very small. The area where the mismatch percentage is small to reach a specific level islanding is not detectable by passive methods is called None Detection Zone (NDZ). The NDZ limits are defined according to particular values of voltage’s amplitude and frequency which are considered as the maximum/minimum allowable limits that voltage’s amplitude and frequency should not exceed. The passive islanding detection methods are known to be inefficient in the NDZ. That is why they were replaced with active methods which are based on injecting small disturbances to the network at the PCC and watching the response of the system accordingly. Even though most of active method has almost zero NDZ, they have the disadvantage of being more complex and also they may affect the delivered power quality. While the passive methods are usually simpler and do not disturb the network. This paper presents and discusses two passive methods which are Over/Under voltage, over/ under frequency and wavelet based. The paper is organized as follows: section 2 presents the two algorithms, section 3 describes the Matlab/Simulink modeling and simulation blocks, section 4 presents and discusses the simulation results and finally section 5 concludes the paper.

Abstract— The extensive use of Distributed Generators in Electrical and power systems made it a must to explore its functionality and the issues related to their connection to the grid. One of the main issues is unintentional islanding, which has been considered and studied for many years since it has serious consequences on electric systems and line workers safety. Therefore, islanding detection methods is a motivating topic to be discussed by many scientists and engineers. Islanding detection methods can be divided into two main categories, remote methods and local methods; the later method is classified into passive techniques and active techniques. This paper represents two methods of the passive islanding techniques and a simple comparison between both of them. The selected two methods are the over/under voltage and over/under frequency detection method and the wavelet based method. These two were implemented and simulated using Matlab/Simulink toolboxes. The simulation results proved that the two studied methods have a good performance for parallel RLC loads having quality factor of 2.5. The simulated passive techniques have no negative impact on the power quality. Keywords- Distributed generator, Islanding detection, passive methods, over under voltage/frequency, Wavelet transform.

I.

INTRODUCTION

Distributed Generation (DG) is an electric power source placed in the distribution network in a direct manner or in the customer side of the meter. It may be understood in simple term as small-scale electricity generation. It can be defined also as a generating resource, other than standalone generating power plants. DG and load that consumes the power generated by them are usually connected close to each other. The increase usage of DG in distribution systems has many advantages as they can avoid transmission and distribution (T&D) capacity upgrades, reduce transmission and distribution line losses, improve power quality, improve voltage profile of the system, etc [1]. Energy exhaustion and the recent environmental issues forced many countries to introduce DG in different distribution systems. There are some known types of distributed generation systems such as wind power generation, photovoltaic power generation, fuel cell power generation, and micro-turbine power generation. Islanding detection is a key issue when a DG works in connection with the power grid. A passive method which can be defined as monitoring the output parameters of the DG such as the variation of voltage and frequency at the PCC (point of common connection) [2]. When the mismatch between the generated power and the size of the load is large,

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II.

ALGORITHMS

Fig.1 shows the system model was used to test the performance of the proposed islanding detection technique. The modeled circuit is the same as the anti-islanding testing circuit defined in UL 1741 (Standard for Inverters, Converters, Controllers and Interconnection System Equipment for Use with Distributed Energy Resources) and IEEE 929 [3].The testing procedure requires that the active and reactive power supplied from the DG match the power required by the test load. Because the load is very close to the DG compared with the grid, almost all the power required by the load is taken from the DG. Therefore, when islanding takes place, the detection is difficult. 1. OUV/OUF passive islanding technique The first discussed passive method is the Over/under voltage and over/ under frequency (OUV/OUF), which is one of the most used passive anti-islanding detection technique. These

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techniques basically monitor the system’s voltage and frequency in order to decide whether or not an islanding has taken a place [4]. L

jQload

Ipcc Pload +

Vpcc

Vpv

∆P + j∆Q

PPV + jQPV

Grid

Figure 2: None- detection zone for UOV and UOF passive techniques

Va Vb Vc Gating signals

Θ PLL

Id* Va* Sin-PWM modulation scheme

dq

Vd*

abc

Vq*

+

Vb* Vc*

-

Id

Ia

abc

Ib

+

Iq

dq

Ic

The shaded area in Fig. 2 is defined as the NDZ where the islanding is not detectable. In fact the efficiency of islanding detection methods are categorized according to the area of the non-detective zone (NDZ), defined in power mismatch space (∆P versus ∆Q) at the Point of Common Coupling (PCC). ∆P is the real power output of the grid, ∆Q is the reactive power output of the grid, PDG and QDG are the real output power and reactive output power of the distributed generation respectively. Pload and Qload are the real output power and reactive output power of load respectively.

Iq* OUV/OUF islanding technique Fault Islanding

Pload=PDG + ∆P Qload=QDG + ∆Q

Voltage and frequency measurement

OUV/OUF Relay

Islanding

Wavelet based islanding technique Compare with the normal operation

Calculate the Standard deviation of the details

Wavelet Transform

One cycle window

Figure 1: Circuit diagram of designed circuit

Thresholds for UOV and UOF can be calculated as follows: ∆ )2 − 1 ≤ ≤( )2 − 1 (1) ( . (1 − (

)2 ) ≤

∆

≤

. (1 − (

)2 )

As the islanding occurred the change in active power and reactive power leads to changes in voltage and frequency. Considering the proportional relationship between the active power and voltage and the reactive power and frequency respectively; a large mismatch in power results a drift in voltage and frequency to exceed the limits of the NDZ and to detect an islanding.

(2)

Where Vmax, Vmin, fmax and fmin are the UOV and UOF thresholds. Typically, Vmax=110% and Vmin=88% of the nominal voltage. fmax= 60.5 Hz and fmin=95.3 Hz

2.

Then for Qf= 2.5: −17.36% ≤ −4.22% ≤

∆

∆

The behavior of the system at the time of utility disconnection will depend on ∆P and ∆Q at the instant before the breaker open to form island. Active power is directly proportional to the voltage. After the disconnection of the grid, the active power of the load is forced to be the same with the power generated by the distributed generation; hence the grid voltage changes. The change in reactive power corresponds to the change in frequency and the amplitude of the voltage. The worst case for islanding detection is represented by a condition of balance of the active and reactive power in which there is no change in amplitude and frequency, i.e. ∆P=0 and ∆Q=0 [5].

≤ 23.46% ≤ 4.12%

Wavelet based passive Islanding technique

2.1 Wavelet transform Wavelet transform (WT) is an effective mathematical tool which has been widely used in many engineering applications such as speech and image processing. WT has found many numerous applications in the power systems field some of the applications are power system protection, power quality, and partial discharge.

(3) (4)

These limits define the non detection zone shown in Fig1.

Unlike Fourier transform (FT) which transforms the signal from the time domain to the frequency domain. The WT

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9]. In the this paper , db1 wavelet (with two filter coefficients) has been used as the mother wavelet to extract the standard deviation of the detail coefficient of voltage waveforms. db1 is a short wavelet and therefore it can efficiently detect transients. The signal was decomposed for 12 wavelet levels. Table 1 gives the frequency band information of the wavelet analysis. The sampling frequency is 100 kHz.

extract the frequency components of the signal while preserving the time domain properties [6]. Similar to FT which breaks the signal into sinusoidal waves of different frequencies; WT breaks the signal into shifted and dilated version of a short term waveform called mother wavelet. Mathematically, the continuous wavelet transform (CWT) of a signal can be represented by (5): ( )

( , )=

1 √

∞

∫−∞ ( ).

∗

(

−

)

Table 1: Frequency bands of Wavelet Details

(5)

Where: a is the scale, b is the translation or position, ( )is the analyzed signal, and is the mother wavelet described in (6).

,

( )=

1

−

(6)

√

The definition of CWT shows that the wavelet analysis is a measure of the resemblance between the wavelet and the original signal. The calculated coefficient refers to the correlation or similarity between the function and the wavelet at the current scale. If the coefficient is relatively large then the signal is similar to the wavelet at this point in time-scale plane. In practical implementation of CWT there will be redundant information. Therefore, for the ease of computational purposes the scale and translation variables are discretized. The discrete wavelet transform is described in (7). ( , )= ∑ Where,

,

( )

∗

,

( )

Wavelet level

Frequency Band (Hz)

Wavelet level

Frequency Band (Hz)

1 – D1

25000-50000

7 – D7

390.625-781.25

2 – D2

12500-25000

8-D8

195.3-390.625

3 – D3

6250-12500

9 – D9

97.65-195.3

4 – D4

3125-6250

10-D10

48.825-97.65

5 – D5

1562.5-3125

11-D11

24.4125-48.825

6 – D6

781.25-1562.5

12-D12

Dc- 24.4125

The standard deviation of the details of the measured voltage signal at the point of common coupling were used to differentiate between the normal operation - the grid is connected- and the islanded situation [10]. III.

MATLAB/SIMULINK MODELING

The software design of the circuit implemented using MATLAB/Simulink toolboxes as shown on Fig. 3.

(7)

is the discretized mother wavelet given by (8): ,

( )=

1

− 0 0

0

0

(8)

Where ao > 1 and bo > 0 are fixed real values, m is the scale and n is the translation are positive integers. 2.2 Feature extraction

Figure 3: Simulink model of system

Transients in power systems are usually aperiodic, short term, and nonstationary waveforms. Since Wavelet transform is capable of extracting the frequency components of a signal without affecting the time domain properties it can be defined as an efficient tool in islanding detection [7-8]. A transient signal can be fully decomposed into smoothed signals and detailed signals for L wavelet levels. Islanding conditions were detected with the help of wavelet transform. Using the properties of WT, Important features can be extracted from the decomposed waveforms [9]. A standard deviation curve at different resolution levels was introduced as a feature to classify the occurrence of islanding. This feature can be used to detect the occurrence of islanding [10]. In fact, using a proper wavelet mother has a significant role in the analysis. Daubechies wavelet family is commonly used in analyzing power system transients as investigated in [6, 7,

The model in Fig. 3 represents the grid to inverter connection and the load is an RLC load, it is shown that the opening the circuit breaker would form an island consisting of the inverter and the load. The load parameters are determined to maintain a quality factor of 2.5. The model consists of three parts; the grid and the circuit breaker side, the RLC load and the three phase inverter representing the DG side. The parameters of the system are presented in Table. 2. Table 2: SYSTEM PARAMETERS Parameter Value Grid voltage 600 V Nominal frequency 60 Hz Vdc 900 V Rload 1.6 Ω Lload 1.69 mH Cload 4.14 mF

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In the grid side a circuit breaker is attached to it and opening of this breaker would form the islanding case. The RLC load is connected to the grid and the DG where a circuit breaker is added to stop feeding power to the load in the case of islanding. The three phase inverter is controlled and synchronized to the grid using a Phase Locked-Loop (PLL). The input of the PLL Vabc is the sensed grid voltage which is converted in to DC components using transformation block build in Simulink “abc-dq0 transformation” and the PLL gets locked by setting Vd* to zero. The loop filter PI is a low pass filter. It is used to suppress high frequency component and provide DC controlled signal to voltage controlled oscillator (VCO) which acts as an integrator. The output of the PI controller is the inverter output frequency that is integrated to obtain inverter phase angle θ. When the difference between grid phase angle and inverter phase angle is reduced to zero PLL becomes active which results in synchronously rotating voltages Vd= 0 and Vq gives magnitude of grid voltage [11].

Figure 4: Output voltage at PCC in case of Under Voltage

The control unit consists of voltage and frequency measurement blocks which compare the output values to a pre determined standards. As a result if any measurement exceeds the limit, a control signal is generated to open the circuit breaker connected to the load. IV.

Figure 5: Output frequency at PCC in case of over frequency

SIMULATION RESULTS

In this section, three cases corresponding to OUV/OUF are demonstrated according to the value of ∆P and ∆Q. It should be noted that in simulation the islanding occurs at 0.1 s. A. Case 1: ∆P > ∆Plimit and ∆Q > ∆Qlimit (under voltage and over frequency) x Figure 4 illustrate the case of under voltage where ∆P >∆Plimit. . The islanding occurs at 0.1s and the trip signal is generated at 0.116 s to shut down the inverter at 0.15s. x

Figure 5illustrate the case of over frequency where ∆Q >∆Qlimit. . The islanding occurs at 0.1s and the trip signal is generated at 0.148s.

Figure 6: Output frequency at PCC in case of under frequency

B. Case 2: ∆P > ∆Plimit and ∆Q > ∆Qlimit (over voltage and under frequency) x Figure 6 illustrate the case of over voltage where. . ∆P > ∆Plimit. The islanding occurs at 0.1s and the trip signal is generated at 0.13 s to shut down the inverter at 0.15s. x

Figure 7 illustrate the case of under frequency where ∆Q > ∆Qlimit. . The islanding occurs at 0.1s and the trip signal is generated at 0.148s.

Figure 7: Output voltage at PCC in case of Over Voltage

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C. Case 3: ∆P ≤ ∆Plimit and ∆Q ≤ ∆Qlimit In this case islanding occurs at 0.1 s; however this case is included in the None-detective zone and there is no change in the amplitude of neither the voltage nor the frequency.

Figure 11: Islanding situation voltage signal

Fig. 10 and fig.11 shows an example of the acquired voltage system for both non-islanding –normal operation- and islanding condition. The islanding occurs at t=0.01 sec. Fig. 12 and fig. 13 gives an example of the first two details (D1 and D2) for both non-islanding and islanding situations. It is illustrated from both fig.12 and fig. 13 that some differences can be noticed between the two events when analyzing the voltage waveforms using DWT.

Figure 8: Output voltage at PCC in case of non-detection zone

Figure 9: Output frequency at PCC in case of non-detection zone

Regarding the wavelet based technique; one power cycle of the voltage signal was measured from the PCC. Then WT was carried on and the standard deviation of the details was calculated and compared to the non-islanding situation. Figure 12: Detail 1 for both non-islanding and islanding situation

Figure 10: Non-islanding situation voltage signal Figure 13: Detail 2 for both non-islanding and islanding situation

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V.

The Standard deviations of the 12 details are then calculated. Fig. 14 shows the standard deviation curve for both non-islanding (normal) and islanding operation.

CONCLUSION

This paper discussed two passive islanding techniques; OUV/OUF and the wavelet based islanding detection, these techniques have shown no effect on power quality. Though, OUV/OUF is easy and simple to implement it has the disadvantage of having large NDZ. In contrast, the wavelet based technique showed potential in detecting islanding even if the power mismatch is smaller.

Fig. 15 shows the standard deviation curve for three different cases: non-islanding, islanding, and an islanding event in case of power mismatch. It is shown in the graph that the standard deviation curve differs in the cases of power mismatch and power match.

REFERENCES [1]

N. Acharya, P. Mahat, and N.Mithulananthan, “An analytical approach for DG allocation in primary distribution network,” International journal of Electrical power & Energy systems, Volume 28, Issue 10, December 2006, Pages 669̄678. [2] Byung-Yeol Bae, Jong-Kyou Jeong “Islanding Detection Method for Inverter-based Distributed Generation Systems using a Signal Crosscorrelation Scheme”, Journal of Power Electronics, Vol. 10, No. 6, November 2010. [3] W. Xu, K. Mauch, S. Martel “An assessment of distributed generation islanding detection methods and issues for Canada” CANMET energy technology center, 2004. [4] H. Zeineldin, E. El-Saadany, M. Salama, “Impact of DG Interface Control on Islanding Detection and Nondetective Zones,” IEEE Trans. on Power Del., Vol. 21, Issue 3, July 2006, pp.1515 - 1523. [5] R. Teodorescu , M. Liserre and P. Rodriguez Grid Converters for Photovoltaic and Wind Power Systems, 2011 :IEEE-Wiley [6] R. Kunte “A wavelet transform-based islanding detection algorithm for inverter assisted distributed generators” Ms.c. thesis, Tennessee Technological University, 2009. [7] N. W. A. Lidula, N. Perera, “Investigation of a Fast Islanding Detection Methodology Using Transient Signals” power and energy society general meeting, 2009. [8] W. Xu, K. Mauch, S. Martel “An assessment of distributed generation islanding detection methods and issues for Canada” CANMET energy technology center, 2004. [9] I. Daubechies, Ten Lectures onWavelets. Montpelier, VT: Capital City Press, 1992 [10] A.M. Gaoudam M. Salama, M. Sultan, A. Chikhani, “ Power Quality Detection and Classification Using Wavelet-Multiresolution Signal Decomposition” IEEE Trans. on Power Del., Vol. 14, No. 4, October 1999, pp.1469 - 1476. [11] Nandurkar, Miss Sangita R., and Mrs Mini Rajeev. "Design and Simulation of three phase Inverter for grid connected Photovoltic systems."

Figure 14: Standard deviation curve for both non-islanding and islanding operation

Figure 15: Standard deviation curve for non-islanding, operation when power match, and islanding operation when power mismatch

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