Analysis and Verification of Islanding Detection Techniques for Grid-integrated PV Systems

Techniques for Grid-integrated PV Systems

Item Type Conference Paper

Authors AlSabban, Maha;Bertozzi, Otavio;Ahmed, Shehab

Citation Alsabban, M., Bertozzi, O., & Ahmed, S. (2023). Analysis and Verification of Islanding Detection Techniques for Grid-integrated PV Systems. 2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East). https://

doi.org/10.1109/isgtmiddleeast56437.2023.10078615 Eprint version Post-print

DOI 10.1109/isgtmiddleeast56437.2023.10078615

Publisher IEEE

Rights This is an accepted manuscript version of a paper before final publisher editing and formatting. Archived with thanks to IEEE.

Download date 2024-01-24 18:19:36

Link to Item http://hdl.handle.net/10754/690741

Analysis and Verification of Islanding Detection Techniques for Grid-integrated PV Systems

Maha Alsabban^∗, Otavio Bertozzi^∗, and Shehab Ahmed^∗

∗KAUST, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Abstract—The increase in solar energy installation capacity and the versatility of modern power inverters have enabled widespread penetration of distributed generation in modern power systems. Islanding detection techniques allow for fast detection and corrective action in the face of abnormal events.

Current standards specify the operational limits for voltage, frequency, and detection time. Grid codes specify the procedures for disconnection to establish safe network maintenance con- ditions. Passive and active techniques require voltage, current, and frequency measurements and the definition of thresholds for detection. Operational parameters such as load mismatch and quality factors influence the detection capabilities. False- positive triggering due to grid transients can lead to unneces- sary disconnection of distributed generation resources. In this paper, we analyze the performance of several islanding detection techniques presented in the literature and propose a modified 9-bus benchmark system to verify the robustness of passive and active methods against false-positive detections upon severe grid- side transients. Simulation results attest to the superiority of active methods and raise awareness of the susceptibility of all investigated techniques to false islanding detection.

Index Terms—Islanding Detection Techniques, Inverter-based Technologies, Grid-integrated PV Systems, False Islanding De- tection.

I. INTRODUCTION

A. Background and motivation

S

OLAR energy installation capacity is increasing yearly, and it has a significant share in many power gener- ation systems [1]. Crucial concerns have emerged due to the interconnections between photovoltaic (PV) panels and the utility grids due to abnormal events on the grid side, requiring attention from the electrical system operators. An islanding Detection Technique (IDT) is responsible for detect- ing abnormal events and disconnecting the PV side from the grid in compliance with grid codes. Otherwise, the system may become unstable, leading to device damage and further financial and operational losses [2].

B. Overview of islanding detection methods

Islanding events are categorized as intentional (scheduled) and unintentional (unscheduled). The former happens for dif- ferent reasons, such as testing a backup power system or main- tenance. The latter, however, occurs because of uncontrolled events taking place in the grid. For instance, blackouts, natural disasters, and increased demand [3]. We present a summary of the five main IDT categories illustrated in Fig. 1.

Fig. 1. Categorization of islanding detection techniques.

a) Passive IDT: monitors system parameters to detect any anomaly. After that, it compares the measured parameters to predefined thresholds. In case of violation, the deployed relays/switches in the system will open to isolate the grid from the installed Distributed Generation (DG) [4]. The downside of this technique is the existence of a large Non-detection Zone (NDZ) in which an IDT fails to detect an island.

b) Active IDT: relies on injecting a small magnitude disturbance signal with no transient effect on the system’s functionality at the Point of Common Coupling (PCC). In the case of islanding, the injected signal will cause a noticeable deviation in measurements, allowing the IDT to detect the event. Active IDT significantly improves the NDZ of passive IDT. The disadvantage is that the injected disturbance can introduce instability to the grid operation [2].

c) Hybrid IDT: combines both passive and active IDT to provide a faster and more accurate Islanding Detection (ID). Primary stages apply passive IDT, and active IDT is employed when it suspects the existence of an island. The combination also overcomes the disadvantages of both previ- ous techniques [5].

d) Data-driven IDT: is an advanced technique that uti- lizes data from the power system with beneficial contributions to help boost the accuracy and enhance the detection of islands.

It trains the developed model on different patterns for better decision-making on the system’s state [6].

e) Remote IDT: initiates and sustains direct commu- nication between the utility grid and the DG side. It is complex to set up the required implementation. Nevertheless, it provides a reliable and rewarding operation [4]. Remote IDT is advantageous as the control system monitors all the breakers, but installing telecommunication devices is costly and requires security against malicious cyber attacks [7].

C. Relevant literature review

ID has a significant impact on several aspects of the operating power system. For instance, it ensures grid pro- tection against different events such as degradation of power quality and out-of-phase re-closing. In addition, it provides

personnel safety in case of maintenance of transmission lines.

The following paragraphs review some of the approaches in the literature, highlighting the methodology, advantages and disadvantages, and detection time.

The authors of [8] proposed a passive IDT based on the logistic regression algorithm. The model is trained on a dataset considering frequency, Rate of Change of Frequency (ROCOF), Rate of Change of Power (ROCOP), and a binary output of the islanding state. The algorithm succeeded in achieving 100%accuracy. However, the authors cannot guar- antee the proper functioning of the algorithm in the case of complex structured grids. In [9], the authors proposed an active anti-islanding technique based on d-axis current injection, a sub-synchronous (20 Hz) signal disturbance corresponding to 1% of the reference current for the controllers. Authors in [10] proposed a remote IDT with real-time monitoring to generate control signals to the load, grid, and PV inverter. The method relies on inspecting different variables; current of the circuit breakers, voltage, and powers at different points of the electrical system. The measurements are compared to preset thresholds. Whenever under/over values are detected, a control signal opens the circuit breakers in real time. The detection time was one cycle with a frequency of 50 Hz, which is shorter than already existing techniques. The method provided a zero NDZ and a stable system operation since it is observed continuously in real-time. In [11], the authors presented a data- driven IDT with real-time operation capabilities that passes the voltage angle differences measured at different points of the power system to a developed model to detect islanding.

The captured data is pre-processed to cancel nonstationarity effects, followed by a probabilistic principal component anal- ysis model training phase. Hence, islanding is discriminated from other events based on the principal and residual spaces.

D. Contributions and organization

In this paper, we investigate the performance of one active and several passive IDTs presented in the literature in stan- dalone test models and the system-level behavior of a mod- ified 9-bus benchmark network containing solar PV sources equipped with active and passive methods. We summarize the simulation results for all scenarios analyzed and conclude by highlighting the findings and proposing future work and research directions.

The paper is organized as follows: Section II describes the test systems and their components. Section III provides details on the ID methods analyzed in this study. Section IV presents the simulation results and their discussion. Finally, Section V summarizes the findings and concludes the paper.

II. SYSTEMDESCRIPTION

This section describes the overall structure of the test system used to investigate IDTs in solar PV inverters. The test system is depicted in Fig. 2. We also introduce a modified version of the IEEE 9-bus benchmark system, as shown in Fig. 3 to analyze the susceptibility of active and passive IDTs to false detection upon severe grid-side transients.

Fig. 2. Single-line diagram of a grid-integrated PV power system.

Passive IDT

Active IDT

G1 G2

G3 Slack

Bus L1

L2 L3

T1

T2 T3

Fig. 3. Modified 9-bus system with IDT-equipped PV sources.

A. PV inverter

We model the PV inverter using a 3-phase 2-level Voltage Source Converter (VSC) averaged model. The DC-side is modeled as an ideal constant DC voltage sourceV_DC, capable of supplying the required DC currentI_DC to meet the power reference specified in the inverter controllers.

The VSC control is specified in the dq-frame, where the outer control loop provides current references to the inner loop through a proportional-integral (PI) compensation that regulates the converter’s active and reactive power output.

The inner current control loop with feedforward generates the corresponding dq-frame voltage references for the con- verter output. In the scope of this work, the converter is set to only supply active power, whileQ_ref = 0.

The converter’s output is connected to an RLC low-pass filter to provide fundamental 3-phase AC power at the PCC.

B. Local load

The local load is a parallel RLC bank that results in an equivalent Pload, Qload (1) demand at the PCC with a given quality factor Qf defined as the ratio between the energy stored in the system and the energy dissipated in one period [12].

P_load=P_DG+ ∆P

Qload =QDG+ ∆Q (1) The load can be altered by connecting an extra parallel RLC to emulate step changes in demand, allowing to verify the robustness of the IDT against false-positive detections.

C. Grid-side

The DG side is interfaced with the grid through a step-up transformer, and a circuit breaker emulates an islanding event

TABLE I

PARAMETERS OF INTERNATIONAL STANDARDS FORIDT Standard Time (s) Voltage (p.u.) Frequency (Hz) Qf

IEEE 1547

≤2

0.88−1.1 59.3−60 1

EEE 929 59.3−60.5 2.5

IEC 62116 0.88−1.15 59.3−60 UL 1741 0.88−1.1 59.3−60.5 1

through disconnection. The grid is modeled as a 3-phase swing generator with internal impedance.

In the proposed test system, the grid will complement any mismatch between the active demand from the load, Pload, and the active power from the converter. It will also supply the reactive demand of the local load, Qload, and from other reactive impedances in the system, such as the transformer.

D. Modified 9-bus benchmark system

We propose the modified version of the IEEE 9-bus bench- mark network seen in Fig. 3 in which we connect additional PV generation systems, each equipped with different IDTs.

We also add several contingencies at the generators Gi, transmission lines Ti, and loads Li, to emulate severe grid- side transients aiming to disturb the IDT methods.

III. DETECTIONTECHNIQUES

Renewable energy penetration in power systems demands standards to regulate operations and set the requirements for proper functioning. The standards define the allowable oper- ating range of voltage, frequency, quality factor, and detection time as key factors in IDT. Any newly-developed IDT must comply with the standards to reach commercialization. Table. I reviews a few of the relevant ID international standards [13].

A. Passive IDT (Over/Under Voltage (OV/UV), Over/Under Current (OC/UC), Over/Under Frequency (OF/UF), and ROCOF)

In the passive IDT, the voltage at the grid side, the current at the inverter side, and the frequency at the load side are monitored and compared to the permitted thresholds of the grid code [14]. The international standard adopted to simulate islands is IEEE 929-2000. The current maximum and mini- mum levels were found by simulating the model for sufficient time in normal operation to extract the nominal value of the current. The ROCOF threshold of 12 Hz/s was obtained from [14]. If the voltage, current, or frequency surpasses the maximum or minimum thresholds and ROCOF sustains a value greater than the specified, islanding is detected.

B. Active IDT (Q-axis Current Perturbation)

In the active IDT, a disturbance signal is injected into the q-axis current [15], resulting in the dq-axis currents in (2). The injected disturbance signalidisthas a magnitude of1%of the reference d-axis current and a frequency of 20Hz [16], [17]:

id

iq

=

i^ref_d i^ref_q +i_dist

=

"

i^ref_d i^ref_q + 0.01

i^ref_d

sin(ωdt)

# (2)

TABLE II

TESTSYSTEMPARAMETERS[9]

Parameter Value Parameter Value

P_ref 1M W Q_ref 0M V AR

Vrms 400V Q_f 2.5

Rf 1mΩ Lf 425µH

VDC 750V f0 50Hz

Rx 0.002pu Lx 0.06pu

Vgrid 25kV QC 225kV AR

wherei^ref_d is the reference d-axis current, ω_d = 2πf_d is the angular frequency, andf_d is the disturbance signal frequency.

The power system’s frequency changes dramatically after islanding with the injection of a q-axis signal [16]. Therefore, the frequency is monitored and used in the analysis and implementation of the active IDT. The mean of the Absolute Rate of Change of Frequency (AROCOF) is calculated as in (3) and compared to a threshold (95% ofAROCOFmean

in islanding condition). If the AROCOFmean surpasses the threshold for 80 ms, islanding is detected. The time delay was chosen such that the algorithm doesn’t falsely classify a transient event as islanding, as proved by simulations in [15].

AROCOFmean(t) = 1 T

Z T

t−T

df dt

dt (3) Wheret is the instantaneous time, andT is the period of the injected disturbance signal.

IV. SIMULATIONRESULTS

In this section, we compare passive IDTs with the q- axis injection active IDT. We provide a summary of the performance indices under various operating conditions. We then investigate the system-level behavior of a modified 9-bus benchmark system with PV generation systems equipped with IDT. The models are implemented in Matlab/Simulink, and the main simulation parameters are specified in Table II, where Pref andQref are the reference active and reactive powers to be generated from the VSC; Qf is the quality factor; VDC is the DC voltage supplied to the VSC input mimicking the PV array;Vrmsis the RMS AC voltage at the DG side;RfandLf

are the filter parameters; f0 is the synchronous frequency of the system;RxandLxare the total leakage impedance of the three-phase transformer;Vgrid is the grid side RMS voltage.

Finally, theQC stands for the reactive power compensation at the filtering stage.

The 1 M W grid-integrated PV system was simulated for 10 s. Islanding was introduced by opening the three-phase utility breaker at 5.5 s. Three scenarios were considered as described below:

1) Local load (1.5 M W) is greater than local generation;

2) Local load (1 M W) is equal to local generation (zero power mismatch);

3) Local load (0.5 M W) is less than local generation.

The grid balances the difference between the load demand and the DG supply. After islanding happens at 5.5 s, the grid is disconnected, and no power exchange occurs at its end. Hence, the mismatch forces the monitored parameters to deviate, allowing islanding detection.

-20 -10 0 10 20 30

" P -10

-5 0 5 10

" Q

NDZ

Simulated

Analytical

Fig. 4. Comparison of analytical and simulated NDZ of passive IDT

A. Passive IDTs

The detection time for the passive IDT of the three simulated scenarios is summarized in Table. III. Scenario two is the most challenging in Table. III. None of the passive IDTs detected the island since the local load is sufficiently supplied by the DG resource and has no power exchange with the utility grid.

This imposes a hazardous environment for the maintenance personnel, calling for further ID algorithm development.

1) NDZ: The analytical limits of the passive IDT NDZ have been calculated as in [18, eq. (9-10)], and the simulated NDZs have been found by running the model multiple times while assuring no detection of islanding with the corresponding active and reactive powers mismatch. The NDZ boundaries for analytical and simulated results are shown in Fig. 4, which illustrates a visual comparison of the NDZs. The discrepancy between the NDZs is due to simplifying assumptions and the cancellation of higher-order terms in the analytical definitions.

B. Active IDT

The three scenarios were simulated while holdingQ_f = 2.5 and setting the threshold at 2.584285. Additionally, the inter- national standards requireQ_f ≤2.5. Therefore, the frequency deviation will surpass the threshold for any Q_f ≤2.5 as the corresponding threshold was found to be the lowest [15]. The detection time of the three simulated scenarios is summarized in Table III.

1) NDZ: The NDZ is where voltage and frequency devia- tion post-islanding remain within the permitted limits for low percentages of active and reactive power mismatches. Table IV shows multiple testing cases for power mismatch between the DG and the local load. The mismatch values close to zero are the most challenging for IDTs; hence it was considered for multiple quality factors. The algorithm successfully detected all simulated cases of islanding. The active IDT presented satisfactory performance with zero NDZ.

C. Modified 9-bus Benchmark System

The model shown in Fig. 3 was simulated for several non-simultaneous disconnections (D) and 3-phase-to-ground faults (F) introduced at different system locations. The main objective is to investigate the robustness of the IDTs to severe grid-side disturbances without triggering false-positive islanding signals under the load mismatch scenarios proposed earlier. All contingencies are cleared within 0.1 s. For each

i ∈ {1,2,3} Generators G_i and transmission lines T_i are subject to D and F events, while the loads L_i are subject only to D events. We consider any trigger signal generated by the IDT due to disconnection, reconnection, fault, or fault clearance as a false islanding detection. Table V shows the detected (✓) and undetected (✗) events for the active IDT.

Table VI lists the passive techniques that presented a false trigger, and RF represents ROCOF.

Active and passive techniques presented many false detec- tions. All passive methods, except OF and OV, triggered false detections, and OC and UV are the most sensitive to grid-side disturbances. The DG local loading scenarios did not signifi- cantly impact the false detection profile of the IDTs. Possible solutions to reduce or eliminate false detections include a hybrid combination of IDTs, access to grid-side measurements to increase system observability and decision capabilities, and adaptive solutions to time delays and thresholds.

The false detection profile may change under different loading conditions and topologies of the grid. Bulk stochastic simulations, such as the Monte Carlo method, can provide deeper insight into necessary parameter adjustments and better confidence bounds on the capacity of IDTs to reject non- islanding events to avoid false detections.

From the operational standpoint, bulk simulation of low- probability high-impact grid-side events can also provide an understanding of the critical fault clearing times (CFCT) required to avoid unnecessary disruptions in DG due to false detections.

V. CONCLUSIONS

This paper presents a comprehensive analysis of contem- porary islanding detection techniques. An overview of the relevant literature summarized the main advantages and disad- vantages of the proposed methods applied to solar PV-based distributed generation. The main structure of a standalone test system for islanding detection was presented, and a modified version of the 9-bus benchmark system was proposed to verify the robustness of detection methods against false-positive trig- gers caused by grid-side contingencies. The relevant details of the current international standards for islanding detection were summarized, and the principles of the investigated active and passive techniques were presented. The simulation results were collected for several loading scenarios and load quality factor profiles. The discrepancy between the analytical and simulated non-detection zones for passive methods was discussed. The results attest to the strength of the active technique due to its zero non-detection zone. The results from the modified 9-bus system show that both active and passive techniques are subject to false-positive islanding detection due to severe grid-side contingencies. Future research directions include a deeper analysis of the susceptibility of detection methods to false positives through stochastic simulations to fine-tune pa- rameters such as the trigger delay and to optimize thresholds.

Grid loading and topology can influence the results as the critical fault clearance time varies, affecting the occurrence of false detection and calling for a more thorough investigation left for future work.

TABLE III DETECTIONTIME OFIDTS

Scenario Detection Time (s)

OC UC OV UV OF UF ROCOF Active

1 0.02575 - - 0.01845 - 0.33485 0.0413 0.12255

2 - - - - - - - 0.17705

3 - 0.01825 0.01825 - - - - 0.21255

TABLE IV

DETECTION TIME OF SEVERAL POWER MISMATCH CONDITIONS

Q_f Detection time for different power mismatches (s)

-15% -10% -5% -2.5% 0% 2.5% 5% 10% 15%

0.5 0.12475 0.11005 0.11395 0.11675 0.11715 0.11725 0.11725 0.11935 0.12185 1.0 0.12255 0.1103 0.11095 0.11585 0.11685 0.11725 0.12005 0.12255 0.12325 1.8 0.12395 0.11425 0.11315 0.11795 0.11765 0.1206 0.12175 0.12305 0.12335 2.5 0.12585 0.27345 0.17005 0.17555 0.17455 0.1244 0.12355 0.12345 0.12375

TABLE V

FALSE DETECTIONS UPON GRID-SIDE CONTINGENCIES(ACTIVEIDT)

Scenario

False detections

G1 G2 G3 T1 T2 T3 L1 L2 L3

D F D F D F D F D F D F D D D

1 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✓ ✓ ✗ ✓ ✗ ✓

2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✓ ✓ ✗ ✓ ✗ ✓

3 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✓ ✓ ✗ ✓ ✗ ✓

TABLE VI

FALSE DETECTIONS UPON GRID-SIDE CONTINGENCIES(PASSIVEIDTS)

Scenario

False detections

G1 G2 G3 T1 T2 T3 L1 L2 L3

D F D F D F D F D F D F D D D

1 OC/RF OC/UC/UV

UF/RF RF OC/UV OC/UC

UV/RF OC/UV RF OC/UV RF OC/UV - OC/UV OC - OC

2 RF OC/UC

UV RF OC/UV OC/UC

UV/RF OC/UV RF OC/UV RF OC/UV - OC/UV OC/RF - -

3 RF OC/UC

UV RF OC/UV OC/UC

UV/RF OC/UV RF OC/UV RF OC/UC

UV - OC/UV - - -

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