Voltage Sag Assessment of Distribution Network with Distributed Generations

Shuangyin Dai, Qionglin Li, Shuming Liu Henan Electric Power Research Institute

Zhengzhou, China daishuangyin@163.com

Abstract—Voltage sag assessment of distribution network with DGs based on Monte Carlo method is researched in this paper. Models of synchronous machine type DG and inverter type DG are established respectively; then a typical distribution network with DGs is simulated based on PSCAD/EMTDC. Impacts of type, control strategy, output power and location of DG on voltage sags are discussed based on several simulation examples. The results show that inverter type DG has certain capability of suppressing voltage sags and can reduce the frequency of voltage sags in distribution network and the impacts are depend on its type, control strategy, output power and location. These conclusions can provide references for configuration of DGs and mitigation of voltage sags.

Keywords- Voltage Sags; Distributed Generation; Distribution Network; Monte-Carlo Method

I. INTRODUCTION Distributed Generation (DG) arouses wide attention

increasingly for its advantages in saving investment, improving energy efficiency and environmental protection. DGs are mainly interconnected with distribution network and would cause impact on it in many aspects, such as power quality, relay protection and power supply reliability etc [1]-[3]. With wide application of modern sensitive power equipments, voltage sags have become one of the most prominent power quality problems and have caused great concern from power supply enterprises and users. Therefore, it is necessary to study voltage sag assessment for distribution network with DGs.

According to research status at home and abroad, the studies on voltage sag assessment are increasing [4]-[10], but the research on voltage sag assessment of distribution network with DGs is less [11]-[12]. In this paper voltage sags of distribution network with DGs are assessed based on Monte Carlo method. Simulation models of synchronous machine type DG and inverter type DG are established respectively; then a typical distribution network with DGs is simulated based on PSCAD/EMTDC, and SARFI is used as the index to evaluate voltage sag. Impacts of type, control strategy, output power and location of DG on voltage sags are analyzed based on several simulation examples.

II. SIMULATION MODELS OF DGS Distributed Generation is the use of small electrical power

generation equipment (typically less than 30 MW) located near consumers and centers of electricity demand [13], and it may

include the following technologies: combined heat power, microturbines, reciprocating engines, fuel cells, photovoltaic systems, wind power systems.

Two typical DG models, i.e. synchronous machine type DG and inverter type DG are established respectively in this paper. Synchronous machine type DGs connect to distribution network by using Synchronous machines as interface, inverter type DGs connect to distribution network by using inverters as interface.

DG usually is not requested to participate in voltage regulation of distribution network and will be disconnected from the network quickly enough when a fault occurs in it for eliminating DG's negative influence according to most of standards for interconnecting DGs [14]. But in this way it will limit the capability of DG to suppress voltage sag of distribution network in faulty condition. With rapid increase of the proportion of DG in power system, DG is requested to have low voltage ride through capability. So the following two control strategies are considered in the simulation based on the above analyses: (1) PQ control: active power and reactive power of DG should keep at reference values when a fault occurs; (2) PV control: active power and bus voltage of DG should keep at reference values when a fault occurs.

Synchronous machine type DG is modeled using synchronous generator as interface. PQ control strategy is realized by keeping its mechanical torque and exciting voltage in constant and PV control strategy is realized by using exciting control system and keeping its output voltage in constant. Inverter type DG is simulated using three-phase voltage source inverter as interface. Control signal is generated based on SPWM technology, as shown in Figure 1. M is the amplitude of modulated sine wave and δ is the phase angle of modulated sine wave in Figure 1.

refP +

−P

δ+

−

δΔ

0δ (a) Control system of active power

refQ +

−Q

M+

−

MΔ

0M (b) Control system of reactive power

978-1-4577-1600-3/12/$26.00 © 2012 IEEE

refV +

−V

M+

−

MΔ

0M (c) Control system of voltage

Figure 1. Block diagram of control system of inverter type DG

III. VOLTAGE SAG ASSESSMENT BASED ON MONTE CARLO METHOD

A. Voltage Sag Assessment Procedure The Monte Carlo method is a widely used technique for

analyzing multidimensional complex systems, such as power system. It can be used for solving both stochastic and deterministic problems. This technique is based on an iterative procedure that is repeated using in every new step a new set of values of the random variables involved in the process, being these values generated according to the probability density function associated to each variable.

The procedure assumes that sags are due only to faults caused in the distribution network. The main features of voltage sag assessment procedure can be summarized as follows: (1) The test system is simulated as many times as required to achieve the convergence of the Monte Carlo method. (2) Every time the system is run, fault characteristics are randomly generated using fault probability distribution model as described in the next section. (3) Voltage sag characteristics (retained voltage, duration and phase-angle jump) at the nodes of concern are recorded during every run. (4) Once the procedure is finished, the recorded information is manipulated to obtain indices such as voltage sag density functions, the number of sags per year and the number of trips of sensitive equipment.

SARFI (System Average Root Mean Square Variation Frequency) is used as the index of voltage sags in this paper. SARFI gives the average number of events (sags, swells, short interruptions) over the assessment period, usually one year, per customer served [16]. A SARFI value is obtained by means of the following expression:

1

T

i n

ii

x

NSARFI

N

=

==∑

(1)

where n is the number of events, Ni is the number of customers experiencing an event, and NT is the number of customers served from the section to be assessed.

B. Fault Probability Distribution Model Fault characteristics (location of the fault, type of fault,

fault impedance and duration) are randomly generated using the following distributions.

• The fault location is selected by generating a uniformly distributed random number, since it is assumed that the probability is the same for any point of the distribution system.

• The representation of the fault impedance is a difficult subject since the fault arc varies with time and depends on the type of fault. The fault impedance has a normal distribution with a mean value of 5Ω, and a standard deviation of 1Ω [5].

• The initial time of the fault is uniformly distributed within a power frequency period between 0.1s and 0.12s.

• The duration of the fault also has a normal distribution, with a mean value of 0.06s, and a standard deviation of 0.01s [5].

• The type of the fault is generated according to the frequency of each type of the fault. The following types of faults are considered in the simulation: single-phase-to-ground fault (LG), two-phase fault (2L), two-phase-to-ground fault (2LG), and three-phase-to-ground fault (3LG).

The type of the fault FType is generated by means of the following expression:

3L

3L 3L 2LType

3L 2L 3L 2L 2LG

3L 2L 2LG

1,2,3,4,

x PP x P P

FP P x P P PP P P x

<⎧⎪ ≤ < +⎪=⎨ + ≤ < + +⎪⎪ + + ≤⎩

(2)

where x follows the uniform distribution between 0 and 1, P3L, P2L, P2LG, PLG are the probabilities of each type of the fault, as shown in Table Ⅰ.

TABLE I. PROPORTION OF FAULT TYPES

Fault type L-G 2L-G L-L 3L-G Proportion /% 72 20 5 3

C. Modeling Based on PSCAD/EMTDC Voltage sag is caused by a transient process in an electrical

network. Although several types of tools have been used to simulate voltage sags, only those based in a time-domain solution can obtain the main characteristics of voltage sags with high accuracy and reproduce their effects. PSCAD/EMTDC is used for voltage sag analysis in this paper. It is a powerful electromagnetic time domain transient simulation environment and study tool and Built-in models and capabilities allow users to represent accurately most power equipments such as generators, lines, transformers, loads, and short-circuit faults. A constant impedance model has been chosen for representing loads.

According to voltage sag assessment procedure the following custom-made modules are developed taking advantage of capabilities available in PSCAD/EMTDC: (1) a module for generating random variables, according to the probability distribution functions above presented;(2) a multiple run module for simulating the test system as many times as needed; (3) a module for monitoring and recording voltage sag characteristics at the selected nodes.

IV. TEST SYSTEM

3

2

6

5 9

8 7

11 13

12 14

17 19 16

18 20

23 24 22

26 21 25 27

15

1

4 10

Figure 2. Diagram of the test system

Figure 2 shows the diagram of a test system used in this paper. It is a medium size distribution network with two feeders and 27 nodes. The parameters of the main components in the test system are presented in literature [5]. Node 3, 8, 13, 14, 19 and 27 are selected as monitoring points.

The test system is simulated 1000 times in all of the studies. Although many more runs are needed to obtain a very accurate solution of the Monte Carlo method, this quantity can provide good enough results for the present test system. On the other hand, if the frequency of occurrence is 12 faults per year and 100 km of overhead lines, then 1000 runs are equivalent to analyze the performance of the system during 214 years.

V. SIMULATION RESULTS AND ANALYSIS

A. Voltage Sag Assessment Simulation Results

0.6

0.4

0.2

0.0 27 19 14 13

3 8 0.1 0.3 0.5 0.7 0.9

Node Voltage/p.u.

Freq

uenc

y/(e

vent

/yr)

Figure 3. Probability distribution of voltage sag magnitudes

TABLE II. SARFI VALUES AT SELECTED NODES

SARFIx Node

3 8 13 14 19 27 90% 2.117 2.126 2.126 2.126 2.140 2.149 70% 1.400 1.648 1.648 1.648 1.550 1.606 50% 1.241 1.311 1.33 1.339 1.274 1.325

Figure 3 shows the probability distribution of voltage sag magnitudes at selected nodes after 1000 runs. SARFI values at selected nodes are shown in Table Ⅱ.

It can be seen from Table Ⅱ that SARFI values at selected nodes are almost the same and mainly depend on electric distance between the node and source node. The closer the electric distance is, the smaller SARFI index is, and the lower the frequency of voltage sags is. SARFI value at node 27 is bigger than the value at other node and this means sensitive equipments at node 27 will experience more voltage sags.

B. Impacts Of DG on Voltage Sags in Distribution Network

1) Impact of type and control strategy of DG on voltage sags

It is assumed that a DG connects to the test system at node 13. The test system with DG using different connecting schemes is simulated. Table Ⅲ describes four connecting schemes with different types and control strategies of DG, and Pref, Qref and Uref in Table Ⅲ are the reference value of active power, reactive power and voltage of DG. Table Ⅳ shows SARFI70 values using different connecting schemes at selected nodes.

TABLE III. CONNECTING SCHEMES OF DG

Scheme 1 2 3 4

Type synchronous machine

synchronous machine inverter inverter

Control strategy PQ PV PQ PV Pref/MW 1.0 1.0 1.0 1.0 Qref/Mvar 0.1 — 0.0 — Uref/p.u. — 1.0 — 1.0

TABLE IV. SARFI VALUES WITH DIFFERENT CONNECTING SCHEMES

Scheme Node 3 8 13 14 19 27

Without DG 1.400 1.648 1.648 1.648 1.550 1.606 1 1.400 1.634 1.625 1.630 1.550 1.606 2 1.400 1.634 1.629 1.634 1.550 1.606 3 0.197 0.628 0.314 0.384 0.637 0.852 4 0.141 0.525 0.215 0.262 0.548 0.782

From Table Ⅳ it can be concluded: (1) SARFI70 values using scheme 1 or scheme 2 at selected nodes are almost the same as the values without DG. This indicates that synchronous machine type DG has little impact on distribution network voltage sags. (2) SARFI70 values using scheme 3 or scheme 4 at selected nodes are smaller than the values without DG. This shows that inverter type DG has certain capability of suppressing voltage sags and can reduce the frequency of voltage sags in distribution network. (3) SARFI70 values using scheme 4 at selected nodes are smaller than the values using scheme 3. This shows that inverter type DG with PV control strategy can suppress voltage sags in distribution network more effectively.

By comparing the reactive power output of DG with different connecting schemes it can be found: (1) whatever synchronous machine type DG use PQ or PV control strategy the change of reactive power output of DG is very small, so synchronous machine type DG has little impact on voltage

sags; (2) inverter type DG can generate more reactive power especially using PV control strategy during fault period, so inverter type DG can suppress voltage sags in distribution network effectively.

2) Impact of output power of DG on voltage sags It is assumed that a DG using connecting scheme 3

connects to the test system at node 13. The impact of output power of DG is researched by only changing the reference value of active power and keeping other parameters unchanged. Figure 4 shows SARFI70 values at selected nodes when the reference value of active power is set to 0.5MW, 0.75MW and 1.0MW.

Pref =0.5 MW； Pref =0.75 MW；

Pref =1.0 MW。

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 3 8 13 14 19 27

Node

SARF

I70/

(eve

nt/y

r)

Figure 4. SARFI70 values with different output power of DG

It can be seen from Figure 4 that SARFI70 values at selected nodes decrease in different degrees as the active power of DG increases and the capability of suppressing voltage sags can be improved by increasing the active power of DG.

3) Impact of location of DG on voltage sags It is assumed that a DG using connecting scheme 3

connects to the test system. The impact of location of DG is studied by changing the location of DG. SARFI70 values at selected nodes when DG connects to the test system at node 13 and node 8 are shown in Table Ⅴ.

TABLE V. SARFI70 VALUES WITH DIFFERENT LOCATIONS OF DG

Location Node 3 8 13 14 19 27

Without DG 1.400 1.648 1.648 1.648 1.550 1.606 Node8 0.187 0.342 0.562 0.562 0.581 0.801 Node13 0.197 0.628 0.314 0.384 0.637 0.852

It can be found from Table Ⅴ that different DG locations result in different SARFI70 values at selected nodes. When the nodes are located close to the DG sites, the number of voltage sags that they experience reduces. For example SARFI70 value at node 13 changes from 0.562 per year to 0.314 per year when the DG is moved from node 8 to node13. The results show that the impact of DG on voltage sags is depend on its location and is mainly limited to the nearby nodes.

VI. CONCLUSIONS Voltage sags of distribution network with DGs are assessed

based on Monte Carlo method and impacts of type, control

strategy, output power and location of DG on voltage sags are analyzed based on several simulation examples in this paper. The following conclusions are obtained from simulation results: (1) inverter type DG has certain capability of suppressing voltage sags and can reduce the frequency of voltage sags in distribution network; (2) DG’s capability of suppressing voltage sags can be improved by increasing the active power of DG; (3) the impacts of DG on voltage sags are depend on its location and is mainly limited to the nearby nodes. These conclusions can provide references for configuration of DGs and mitigation of voltage sags.

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