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Nowadays, the study and implementation of microgrids (MGs) are increasing rapidly. However, MG implementation faces great technical challenges, such as control, monitoring, and protection of MG. Conventional signal estimation methods present not well performance in MG application due to asymmetry, harmonic and decaying dc component of the signal during transient and faulted periods. In this paper, a new Phasor Estimation Algorithm (PEA) for MGs applications is proposed. This method able to handle the dynamic characteristics as well as the decaying DC components so as to improve the accuracy of phasor estimation.

The exponential functions of the fundament frequency and decaying dc offset component are replaced by Taylor series. Then, the Least Square technique is used to estimate the magnitudes and the time constants of decaying components. The performance of the proposed algorithm is evaluated by real-time simulation of a MG by MATLAB software. The simulation results indicate that the proposed method has good performance and can be used in MG phasor estimation protection, control, and monitoring.

Nowadays considering the global warming, low dependency on fossil fuels, and being more environmentally friendly, using new energy sources is becoming a pivotal necessity [1]–[3]. The idea of using a MG has been developed as a solution to need for DG sources, reduced power dissipation in transmission lines, enhancing the voltage profile, and increasing the reliability. Some advantages of MGs are eliminating the cost of constructing new power stations, enhancing the grid reliability, and being more environmentally friendly.

Dependency on weather conditions, introducing harmonics due to power convertors, and high launching costs are some of MGs disadvantages. Moreover, because of limited power, MGs cannot provide high consumption loads which cause frequency and voltage fluctuations [4]. Under certain circumstances that MG disconnects from the main grid because of any kind of fault, in order to prevent instability, frequency and voltage must be controlled by a suitable controller and the primary way to do this is to prevent imbalance between the generation and consumption [5]. Since signals characteristic in fault condition is very different from normal condition, MG behavior under fault condition and its protection is very important [6].

In MG coupled rotating-machine-based micro sources directly will increase short circuit currents while power electronic interfaced micro sources cannot normally provide any significant levels of short-circuit currents require. Many micro sources are generally connected to the MG by means of a power electronic inverter, either because their output is not compatible with the grid voltage or because of the flexibility provided by power electronics in the energy extraction management [7].

Due to the low thermal inertia of semiconductor switches, inverters are actively current limited and, because of their small fault current contribution, they lead unavoidably to different problems that have to be considered by the protection system [8], [9]. Because of importance MG protection, control, and monitoring, a lot of papers deal it [9], [10]. In [11] a protection scheme based on digital relays with a communication network is presented. The increased reliability of adding an additional line to form a loop structure is explored.

Also to show how the protection scheme can protect a novel method for modelling high impedance faults is demonstrated. The simulation of the protection scheme shows that this approach is able to quickly detect and clear all faults including high impedance faults with current of at least 10% of the nominal current, in all locations. In [12] has been discussed how advanced MG protection can be performed utilizing phasor measurement units (PMUs) and other measurement systems that can be deployed throughout the MG.

By using such systems, an increase in reliability and security for the MG can be achieved. Wide area impact of on angular stability in MGs after sudden load drop has been studied in [13]. In this paper phasor measurement sensor (PMS) powered real time stability monitoring technique and stability retention technique through optimized MG operational control with the aid of topological genetic algorithm (TGA) are proposed. Researchers in [14] have been proposed an improved discrete Fourier transform (DFT)-based phasor estimation algorithm with degraded owing to presence of DC offset in fault current signals and have overcome the undesired influence of DC offset, in this letter is used an auxiliary signal which is generated by down-sampling of the fault current.

An accuracy analysis of known estimation techniques based on interpolated discrete Fourier transform (IpDFT) methods, under the impaired conditions of a low-voltage MG is proposed in [15]. A brief discussion of expected impairments is provided, then IpDFT is applied for the estimation of voltage phasors in the neighborhood of abrupt voltage amplitude variations. Many studies investigate MG protection and control. The estimation method that used for phasor estimation in these papers operated in normal condition. It is clear that in fault and transient state voltage or current signals contain distortion and DC decaying component [16], [17] that by normal method accurate estimation cannot be achieved.

Several researchers have been studied the phasor estimation. However, providing a suitable method for phasor estimation is an important challenge in MGs because accurate phasor estimation accounts as a basis for grid operation, control and protection. Conventional signal estimation methods present not well performance in MG application due to asymmetry, harmonic and dc component of signal during transient and faulted periods. Hence, this paper is proposed new method for phasor estimation in MGs.

In fault condition the fault current is combination of a decaying DC offset, a decaying fundamental frequency component, and harmonics fault current signal is used for phasor estimation. The main problem in MGs control monitoring and protection is accurate estimation of signals. Hence, in this paper has been proposed a new Phasor Estimation Algorithm (PEA) method for accurate estimation of signals in MG. The remainder of this paper is as follows. A hybrid MG configuration is described in section 2. Afterwards, in section 3 control and protection in MG is defined. Then PEA method that used for signals estimation is described in 4. Finally simulations are done in section 5. Simulation results show that by using this method accurate phasor estimation in transient condition can be achieved that can be used in MG protection, control or monitoring.

Three-phase fault in MG create faulted signals composed fundamental frequency component, harmonics, and DC offset so it is essential to brief study MG protection and signals specification in fault conditions. Fig. 1 shows a MG with protection system. As demonstrated in Fig. 1, if a fault occurs at the MG side of the separation device, the two feeders A & B have protection to allow isolation of the minimum number of generators using the line breakers.

For example, a fault in Zone-4 should activate the nearest breaker isolating the fault with minimum disturbance to the rest of the loads. For a fault in Zone-3, all loads on feeder A would be isolated and shutdown. Faults in Zone-5 would isolate feeder B because of the response of these protective devices while the MG will vary dramatically depending on the complexity of the MG [21]. An isolated MG with only one energy source may be able to employ a protection scheme similar to a conventional radial distribution system. More complex MGs with a number of distributed energy resources (DER) will require more complex protection schemes. Decisions about the cost and complexity of protection schemes are depended on the needs of the MG.

Under the impaired conditions, fault protections in a MG network differ from those of conventional grids which are the reference targets of the IEEE C37.118 standard for synchro-phasor measurements [20]. Applied stresses are actually induced by known, sometimes extreme grid faults, for example due to a very large load connection, short-circuits, etc. Moreover, typical loads and systems connected to a MG may induce particular supply operating conditions that end–user devices must carefully take into account. In this paper has been used three-phase fault in MG for created fault condition and estimated faulted signals.

A MG usually consists of small segments of a distribution network connected to local DG units and loads. Block diagram of the proposed micro grid is illustrated in Fig. 2. Power generators systems in this hybrid configuration includes Wind Turbine Generator (WTG)/Diesel Generator (DG)/Ni–Cd Battery based Energy Storage System (BESS) [21], [22].

Diesel Only (DO) and Wind Diesel (WD). In DO mode the DG supplies the active and reactive power demanded by the consumer load. The speed governor controlling the DE, performs frequency regulation and voltage regulation is performed by the automatic voltage regulator in the SM. In WD mode, the WTG also supplies active power and the same regulators as in DO mode are in charge of the frequency and the voltage control. The DE speed control in Fig. 2 is isochronous so the diesel speed governor will command the necessary fueling rate to make the DE run at constant speed.

The DG under the control of the speed governor performs the frequency regulation by maintaining an instantaneous balance between the consumed and produced active power. Therefore, the DE behaves as a controlled source of active power. The WTG in Fig. 2 consists of a Wind Turbine (WT) driving an Induction Generator (IG) directly connected to the autonomous grid conforming a constant speed stall-controlled WTG. The mechanical power produced by a WT is:

Where ρ is the air density, v is the wind speed, A is the area swept by the turbine blades and CP is the power coefficient. Since the WTG used in here has no pitch control, CP is only a function of tip speed ratio. In addition, the IG speed range variation in the WTG is very limited and thus CP can be considered as a function only of the wind speed. As the wind speed is quasi-random there is no way to control the WTG active power, so the WTG behaves as an uncontrolled source of active power. The IG consumes reactive power so a capacitor bank has been added to compensate the power factor.

In WD mode, the WTG produced power PT can be greater than the load consumed power PL, so in this case the outgoing active power from the isolated power plant PL −PT is negative. This situation means that the DG power must be negative (DG power inversion) to balance active powers (consumption = production) in order to keep frequency constant. Since the speed governor cannot order the DE to consume power, the DG is unable to regulate frequency when PL −PT < 0. To avoid the DG power inversion, a Dump Load (DL) must be incorporated to the system. The system control will order to the DL to dump the necessary power to keep the needed DG produced power positive, so that the DG can regulate the frequency.

The DL in Fig. 1 consists of a set of power switches and a bank of resistors. By closing/opening these power switches, the DL consumed active power can be controlled and thus the DL behaves as a controlled sink of active power. An Energy Storage System (ESS) can also be used to prevent the DG power inversion in WD mode. Additionally ESS can be used in both DO and WD modes to reduce the spinning reserve needs, to increase the loading of the DGs in order to improve their performance and to improve the dynamics of the system.

In this section, proposed PEA method is explained. As already mentioned the fault current delivered from distributed generators can be considered as the sum of a decaying DC offset component, a decaying fundamental frequency component and harmonics with constant amplitude. This wave form can be mathematically expressed as (2).

Where and are the amplitude and the time constant of the decaying dc component, is the fundamental frequency of the system, , , and are the constant amplitude, the decaying amplitude, the phase angle and the time constant of the decaying fundamental frequency component, respectively, and are the amplitude and the phase angle of the kth harmonic component, M is the highest order of the harmonic component present in the signal. It is possible to expand by using the Taylor series (3).

Higher order harmonics can be blocked by the signal conditioning equipment such as an analog low-pass filter. Using the Taylor series expansion in (3) and assuming that harmonic components higher than fifth order are effectively blocked by a low-pass filter and that even harmonics are hardly present in the power system signals, (2) can be represented as (4).

The equations of are presented in Appendix. If the current is sampled with a specific time interval, ∆t, (4) can be expanded to each current sample and the equations can be written in the matrix forms as (5).

The elements of the matrix [A] depend on the time references and the sampling interval and those can be predetermined in an off-line mode. The matrix [S] made up of the sampled current data is also known. If the current sampling (m) is more than the number of unknown variables which is 13; then by using matrix [X] through least squares (LS) method we can calculate the unknown variables as (6).

Finally, the dynamic phasor of the fundamental frequency component can be obtained as (7)

To verify the algorithm performance and impaired signal creation, the MG that is presented in Fig. 2 is simulated in the MATLAB software. In this study, first a three-phase fault is generated in wind turbine bus at t=13s. At first, the fault resistance is considered zero. The wind turbine currents and voltages are shown in Figs. 3 and 4. It can be seen that during the fault, the current of line increases and its voltage decreases. Fault current inclusive a decaying DC offset component and traditional estimation method such as derivative based cannot be used to current estimation.

Wind turbine power is given in Fig. 5. Voltage and frequency deviations are shown in Figs. 6 and 7. It is clear that after the fault, control system has good operation and voltage and frequency deviations are in acceptable interval. Phasor estimation of fault current by PEA method is shown in Fig. 8. To compare the results with other method, partial sum discrete Fourier transform (PS-DFT) is used [23]. The reason of using the phasor estimation to compare is that this method is an effective techniques which is included damped DC component. Due to the most conventional DFT methods are not be able to estimate signals including damped DC component and harmonics accurately, consider it as a comparison is not correct.

On the second simulation, another fault is created in wind turbine bus at time 11s to 11.1s. In this case the fault resistance is increased which makes reducing in fault current that is considerable for protective purpose. The fault current is shown in Fig. 9. The phasor estimation result is also shown in Fig. 10 and compared with DFT-Ps method. It can be seen that in fault condition, the current contains harmonic and decaying DC offset component DFT-Ps cannot estimate phasor as well as PEA method. It can be seen that by using this method in MG signals estimation, an acceptable estimation is yielded. Because when MG is operated in isolated mode, signal variations are more than traditional network so this way is a good method for various signal phasor estimation.

For the third case, a single phase fault on A-phase is created and here fault current is very small and this fault current is shown in Fig. 11. Fig. 12 shows phasor estimation of this condition. It can be concluded that PS-DFT cannot estimate phasor well, but MDPEA method has good result. It should be noted that phasor estimation by this method takes about 1.7 cycle time.

It is clear that here by using this method, the results of the estimation compared to PS-DFT has improved because the PS-DFT technique is only included the damped DC component while in this method both the damped DC component and ac harmonic are considered. Thus, the accuracy of phasor estimation is high. This is done because the transient and faulted signals especially in MG include harmonic and the damped DC component and due to that in phasor calculations the mentioned signals are presented the suitable results are yielded.

In this paper PEA method for MG phasor estimation in islanded mode is proposed. The correct and fast phasor estimation is one of the important issues in MGs controlling and protection. Based on the simulations result, it is clear that the fault signals in the MGs are different from conventional grids and have different components. To verify the prosed algorithm, fault has been created in an isolated MG and fault current estimation has to be done and compare with another method. The simulation results indicate that the proposed method has good performance and can be used in MG phasor estimation protection, control, and monitoring. This method also can be used in LV network and PMU systems because main goal in this application is correct phasor and frequency estimation.

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