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Microgrids are a highly efficient means of embedding distributed generation sources in a power system. However, if a fault occurs inside or outside the microgrid, the microgrid should be immediately disconnected from the main grid using a static switch installed at the secondary side of the main transformer near the point of common coupling (PCC). The static switch should have a reliable module implemented in a chip to detect/locate the fault and activate the breaker to open the circuit immediately. This paper proposes a novel approach to design this module in a static switch using the discrete wavelet transform (DWT) and adaptive network-based fuzzy inference system (ANFIS). The wavelet coefficient of the fault voltage and the inference results of ANFIS with the wavelet energy of the fault current at the secondary side of the main transformer determine the control action (open or close) of a static switch. The ANFIS identifies the faulty zones inside or outside the microgrid. The proposed method is applied to the first outdoor microgrid test bed in Taiwan, with a generation capacity of 360.5 kW. This microgrid test bed is studied using the real-time simulator eMegaSim developed by Opal-RT Technology Inc. (Montreal, QC, Canada). The proposed method based on DWT and ANFIS is implemented in a field programmable gate array (FPGA) by using the Xilinx System Generator. Simulation results reveal that the proposed method is efficient and applicable in the real-time control environment of a power system.

The Kyoto Protocol mandates that industrialized countries reduce their collective greenhouse gas emissions in 2012 to 5.2% below the levels in 1990 [

Many works have developed methods to implement the microgrid test bed. The Consortium for Electric Reliability Technology Solutions (CERTS) microgrid testbed was developed in the early 1990s [

A new issue associated with static switches in microgrids has emerged recently. A static switch is a fast electronic switch located at the secondary side of the main transformer near the point of common coupling (PCC). When a fault occurs inside or outside a microgrid, the static switch should immediately disconnect the microgrid from the power system of the utility [

This work presents a novel method for a static switch to detect/locate a fault in the first outdoor microgrid test bed in Taiwan. The generation capacity of this microgrid, including gas-turbine generators, wind-turbine generators (WTG), energy storage and high concentration photovoltaic (PV), is 360.5 kW. Numerous research projects have been undertaken to investigate key technologies, including power converters, wind turbines, PV modules, protection schemes, and monitoring systems. Preliminary studies of the operating mode, motor starting and voltage fluctuation have been published [

The rest of this paper is organized as follows: Section 2 briefly introduces the microgrid and presents the assumptions in the studied problem; Section 3 introduces the proposed method based on DWT, ANFIS and Opal-RT; Section 4 summarizes the simulation results; and Section 5 draws conclusions and offers recommendations for future research.

The studied microgrid has 11 AC buses.

2011: Buses 1–6 are constructed in Zone 1, which includes a 60 kW load, 65 kW gas turbine, 100 kW (60 kW h) battery and 31.5 kW PV;

2012: Buses 7–10 are located in Zone 2, which mainly consists of a 90 kW load, 25 and 4 kW wind turbines, and 10 kW PV;

2013: Bus 11 is located in Zone 3, which comprises a 60 kW PV and a 65-kW gas turbine.

Detailed models of components used in the microgrid are given in the

In order to study the problem, the following assumptions are made:

The voltage and current at the secondary side of the main transformer near the PCC are available. PT and CT are generally installed at the secondary side of the main transformer. The levels of fault voltage and current are reduced for the further usage of FPGA chip.

The time delay in the electronic switch is ignored in the simulation. If an IGBT-based switch is used, opening the circuit takes about 100 μs [

The latency of FPGA is ignored in the simulation. According to the results herein, the latency of FPGA is several nano seconds, which is significantly shorter than that of a DSP chip (a latency of the order of milli seconds).

The faulty zone is disconnected by the zonal isolating switch if a fault occurs in the microgrid.

The fault and the microgrid are balanced. Neither the single-phase grounded fault nor the phase-to-phase fault is discussed herein. Nine hundred and ninety-four balanced scenarios are studied herein using MATLAB/Simulink (see Section 4.1). Almost 3000 scenarios will be studied in case imbalance is concerned. The detection logic architecture is still the same in case of imbalance.

Fault detection and location problems for both distribution and transmission systems have been extensively studied [

The small disturbance caused by faults inside a microgrid is detected using DWT because DWT is a transient-sensitive means of processing a signal. Both the wavelet coefficient of the transient voltage and the sum of squared wavelet coefficients (called wavelet energy herein) of the fault current are utilized to enhance the proposed method. The proposed method avoids negative sequence of components [

Fuzzy reasoning provides a high-level linguistic inference engine that can tolerate uncertainty but lacks learning capability. An artificial neural network, by contrast, is like a black box but it can learn and tolerate imprecision. ANFIS is an intelligent system that integrates fuzzy reasoning with a neural network by considering their advantages. ANFIS builds a hybrid intelligent system that is capable of reasoning and learning in an uncertain and imprecise environment. However, three different neural networks, which are not subject to uncertainty and lack reasoning capability, were developed in [

The use of double detection/location logics (wavelet coefficient of transient voltage and ANFIS plus wavelet energy of transient current) enhances the reliability of the proposed method. The proposed method, therefore, is able to use only the transient voltage and current near PCC to detect and locate a fault. This capability is essential in case the plug-and-play and peer-to-peer implementations are concerned and the decentralized control is applied. However, references [

The microgrid test bed is studied using the real-time simulator eMegaSim developed by Opal-RT Technology Inc. [

A signal can be represented as a sum of wavelets and scale functions with coefficients at different time shifts and scales (frequencies) using DWT. DWT is able to extract the features of transient signals by decomposing signal components overlapping in both time and frequency [

According to DWT, a time-varying function (signal) ^{2}(_{0} and _{j}_{0} could be any integer. The translated and scaled (dilated) version of the wavelet, ^{−}^{j}

The small scales represent high-frequency ranges of a transient signal. Thus, the wavelet coefficient (_{j}

On the other hand, the Parseval's Theorem [

The second term of

In summary, this work utilizes the wavelet coefficients of the fault voltage and the sum of the squared wavelet coefficients of the fault current to activate the electronic switch to open once a fault occurs inside or outside the microgrid. All wavelet coefficients are computed using 1/4 of a cycle of sampled points of both the voltage and current. Traditional effective (rms) values of the fault voltage and current are not suitable to be indicators for detecting the fault occurrence because the faults may occur at zero crossings. However, the DWT is able to detect changes of instantaneous voltage/current by using the detailed coefficients obtained at their corresponding scales (

Some mother wavelets are available in the wavelet theory [

This subsection describes the use of three inputs (_{i}^{3}) fuzzy rules. If A1–A5, B1–B5, and C1–C5 and _{i}_{i}

The structure of ANFIS can be explained as follows: the first layer in _{i}_{i}_{i}_{i}_{i}_{i}_{i}

In the training stage for developing the proposed ANFIS, the unknowns _{i}_{i}_{i}_{i}_{0}, _{i}_{1}, _{i}_{2} and _{i}_{3} of _{i}

The occurrence of a fault inside/outside the microgrid is detected and located using two logics:

First, the occurrence of a fault is detected using the MRA of DWT implemented by FPGA. Wavelet coefficients at Scale 2 are the most appropriate for this role (see

Second, because the voltage transient may be related to other disturbance (e.g., capacitor switching), the wavelet energies that are calculated using wavelet coefficients of 1/4 cycle of the fault current at Scales 1, 2 and 3 are also used to identify faulty zone by ANFIS.

If the values of both logics are unities, then the electronic switch is activated to open the circuit.

Real-time simulation was conducted in this work using Opal-RT eMegaSim. eMegaSim, which is composed of software (RT-Lab, Montreal, QC, Canada) and hardware (target computer; Intel i7 965 Extrem), can be integrated entirely with the MATLAB\Simulink software. Parallel computation is implemented in Opal-RT-Redhat OS. In a host PC, the user can perform preliminary design and integrate the above models constructed in the studied case in the Simulink environment (

In this work, the Xilinx FPGA chip and the Xilinx System Generator (XSG) are used to implement the proposed method. The XSG toolbox can build a system model to be a hardware circuit in a Matlab/Simulink environment. _{1}, d_{2}_{3}

The proposed ANFIS was trained using the MATLAB ANFIS Editor GUI. Following convergence, _{i}_{i}_{i}_{i}_{0}, _{i}_{1}, _{i}_{2} and _{i}_{3} of _{i}

The performance of the proposed method is evaluated using the microgrid described in Section 2. This microgrid test bed is described in detail in [

This section uses various wind speeds (11, 10, 9, 8 and 0 m/s), irradiations (1000, 800, 600, 400 and 0 W/m^{2}), load levels (150 kW multiplied by 100%, 85% and 70%) and short circuit capacities (1714.2 MV A multiplied by 100% and 70%) at PCC. The minimum generation of a gas-turbine generator is 37 kW. The power generation in the microgrid is not allowed to be injected into the main grid. Thus, there are a total of 142 base cases, which include normal and extreme meteorological limits as well as possible operating conditions. The faults are assumed to occur individually at Buses 1, 4–6, or 9–11. Considering these faults yields 994 scenarios: the results of simulating 663 and 331 scenarios are used for training and testing ANFIS, respectively. In order to enhance the proposed method, the 663 training scenarios are selected at random. The simulations are conducted using the MATLAB/Simulink SymPowerSystem.

_{2} is the most suitable wavelet coefficient for detecting the occurrence of a fault transient. Specifically, a sufficiently large _{2} occurs at _{2}. However, the value of _{1} at _{3} oscillate after _{1} and _{3} are inappropriate for the usage of detection. Any transient and noise may be included in the measured signals. However, the DWT can still identify the most appropriate scale (

Unlike traditional methods in which multiple sensors are used in the power systems, the proposed method only uses the transient voltages and currents near the PCC.

The accuracy of the proposed ANFIS is still promising although only signals near the PCC are used, as shown in

In total, 452,172 slices, 812,007 FFs (flip-flops), 52 BRAMs (block RAM, kB) and 651,915 LUTs (look-up table, 16 × 1 RAM) are required in the FPGA for the proposed ANFIS. Moreover, DWT requires 920 slices, 80 FFs, 0 BRAMs and 1200 LUTs in the FPGA.

This subsection discusses a three-phase balanced fault occurring at Bus 4 at 2 s. Suppose that the wind speed, irradiation and system load are 8 m/s (corresponding to 11 and 1.5 kW from the two wind turbines at Bus 10), 600 W/m^{2} (corresponding to 17, 6 and 30 kW at Buses 6, 10 and 11) and 150 kW, respectively. After 1/4 of the cycle, the proposed method activates the static switch at PCC and the microgrid becomes islanded. Meanwhile, the following actions are carried out according to the assumptions described in Section 2:

Disconnect all loads, the gas-turbine generator, PV and the energy storage at Bus 4.

The gas-turbine generator at bus 11 becomes the master generation source with the V/f control. The other distributed generations are maintained in their P/Q mode.

The time step is 1 μs in the above simulation. The simulation is run for 4 s.

This work presents a novel method for designing the fault detection/location module of a static switch in a microgrid. The contributions of the proposed method are summarized as follows:

Both the wavelet coefficient of fault voltage and the “wavelet energy” of fault current incorporating with ANFIS are utilized. The small fault current from the inverter-based distributed generations can therefore be effectively and reliably detected and located.

Only the transient voltage and current near the PCC are utilized. No complicated communication system and other sensors at other buses inside the microgrid are required. This may enable peer-to-peer and plug-and-play to be realized in the microgrid.

The proposed ANFIS is capable of reasoning and learning to detect/locate a fault inside or outside the microgrid. Fifteen membership functions for three “wavelet energies” are evaluated by training a six-layered ANFIS.

Not only is the simulation performed, the detection/location module is also implemented using the Xilinx FPGA chip. The accuracy of the proposed method, applied to the studied problem, is favorable. The performance of the designed FPGA indicates that the proposed method is highly promising for applications in a real-time environment.

Future work will consider imbalanced faults and resynchronization between the main grid and the islanded microgrid.

The authors gratefully acknowledge the contributions of Robert H. Lasseter at University of Wisconsin at Madison and Dave Klapp, Dolan Technology Center, American Electric Power for their comments on designing/operating the microgrid test bed. The authors would like thank the financial support from the Institute of Nuclear Energy Research under the Grant NL1020402.

The authors declare no conflict of interest.

This appendix provides the model of components used in the microgrid for studies.

The characteristics of HCPV implemented with the III-V family (GaAs) are measured and tested according to IEC 62108. Each HCPV panel (1.5 kW or 5 kW) incorporates an inverter to perform maximum power point tracking (MPPT). ^{2}). HCPV is modeled with a controlled current source. MPPT is applied to the DC/DC converter.

Model of high concentration PV (HCPV) panel and its power converters with maximum power point tracking (MPPT).

The specifications of WTG adhere to the IEC 61400-1 Class-1A. Its essential technologies comprise active turbine sliding control, pitch control of the blade, intelligent stalling control and MPPT-based power converter.

Control structure of 25-kW wind-turbine generators (WTG).

The microgrid has one energy storage device, which is a Li-based battery system. This battery system is 100 kW, 60 kW h, and 696 Vdc. This battery system has two control modes: (a) master mode operates with both V and f controls once the microgrid is islanded; and (b) slave mode operates with the P/Q controls if the microgrid is grid-tied.

Control of discharge from energy storage device.

The Capstone 65 kW gas-turbine generator is adopted in the microgrid. It is a high-speed single-shaft design with the compressor and turbine mounted on the same shaft as the permanent magnet synchronous generator [

Model of gas turbine.

One-line diagram of studied microgrid with converters. PV: photovoltaic.

Proposed structure of ANFIS.

Wavelet coefficients d_{1}–d_{3} of voltages at point of common coupling (PCC): (

Overall architecture of the two logics. MRA: multi-resolution analysis; DWT: discrete wavelet transform; and ANFIS: adaptive network-based fuzzy inference system.

Calculation of wavelet coefficients _{1}, _{2} and _{3} in Xilinx System Generator (XSG).

15 membership functions (A1–A5, B1–B5, and C1–C5) of ANFIS used to identify faults in Zone 1.

System responses caused by fault at Bus 4: (

Accuracy of proposed ANFIS.

Main grid | 0.012389 | 100% |

Zone 1 | 0.037720 | 100% |

Zone 2 | 0.077971 | 96.37% |

Zone 3 | 0.087307 | 98.19% |

CPU times required using the three approaches.

Simulink (normal) | PC | 36:36 |

Simulink (accelerator) | PC | 4:13 |

Simulink (Opal-Lab) | eMegaSim | 0:12 |