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The expansion of renewable energy is continuing powerfully. Electrical system ought to transmit power with diminished loss, improved power quality, and reliability while pleasing the need of customer’s load demand. Nevertheless, owing to the exhaustion of fossil fuels and their environmental impact, the availability of quality, stable, and reliable power in developing countries is worrying. Integrating a solar-wind based microgrid to the distribution network is the more feasible and best alternative solution to gratify the customer intensifying power demand while seeing the strict environmental regulations of generating power. However, the microgrid system connected in a distribution network has diverse problems and challenges. The problems comprise the development of voltage sag and swell, voltage unbalance, and power losses because of the intermittent nature of PV and wind resources. The objective of this study is to integrate microgrid system with STATCOM (static synchronous compensator) controller to ensure the higher power flow with enhanced voltage profile and reduced power loss. MATLAB/PSAT is used to model microgrid and STATCOM controller connected to the grid. Proportional integral (PI) and fuzzy logic controllers (FLC) are also applied to control the STATCOM. The effectiveness of STATCOM with microgrid integration is tested by connecting to the main distribution system using standard IEEE 30-bus system. Finally, it was observed that STATCOM raises the capacity of the distribution line and contributes to voltage profile improvements and power loss reduction.

Today’s energy consumption has steadily increased, yet the investments in conventional power system and usage of fossil fuels have declined correspondingly. Thus, electrical energy systems worldwide are working under a stressed condition resulting in less reliability, poor power quality, frequent power outages, and high-energy price [

By applying several control techniques, the increased penetration of solar and wind micro sources at different locations changes the structure of the conventional power system into an interconnected two-way, high-efficiency, high-reliability, and a stable network system termed as microgrid [

Renewable energy sources, particularly PV and wind, have intermittent and unpredictable nature depending on weather and climatic changes. Along with this, the network system produces a high fluctuation in their outputs and integrating the microgrid system alone in distribution network is not entirely safe in meeting the demand for the load [

Implementing reactive power compensation devices is an embryonic need for steady-state operation of the system. Reactive power causes a negative impact on effectiveness and capacity of the power system and involves energy losses and high costs as well as rising of power quality problems [

In order to tackle the power system operational challenges related to monitor, operation, and control, Flexible Alternating Current Transmission System (FACTS) devices were introduced and established in the late 1980s [

By increasing the transmission lines capacity, STATCOM eliminates the need to build new lines. Various control strategies are used to allow operation of the power system within the operating limits required.

The most commonly utilized controllers of STATCOM are Proportional-Integral (PI), Proportional-Integral-Derivative (PID), fuzzy logic controller (FLC), and Artificial Neural Networks- (ANN-) based controllers [

In recent years, various researches have been presented on STATCOM. Research on STATCOM for Reactive Power Flow Control in microgrid system has been presented. But it only worked for current control of the microgrid integrated distribution system and did not deal with voltage fluctuation control [

The penetration of microgrid system to the radial distribution network to minimize the power loss has been reported in [

Voltage control by means of reactive power support for a hybrid power system network has been proposed in [

Reactive power control and power factor improvement using STATCOM, SVC, and UPFC alternatively for wind energy based microgrid integration to the network have been reported in [

The literature review shows that there is very little research on STATCOM controller-based voltage fluctuations and loss minimizations caused by solar-wind based grid connected microgrid systems. With the growing installation of PV and wind based microgrid system, the FACTS controllers still need enhancements by tuning of controller’s gain and broad investigations have to be made under various operating conditions.

In the STATCOM controller, the controller gains were usually designed based on a linearized system equation for the system under a nominal load condition [

Artificial Neural Networks (ANNs) have been proposed to adapt to the controller gains of the STATCOM [

In this study, the objective is to increase the reliable operating limit of the microgrid integrated power system network by incorporating fuzzy logic based STATCOM controller for reactive power compensation. Also, it is aimed to reduce the voltage fluctuation and power loss occurring due to the varying nature of renewable energy sources.

To the best of the authors’ knowledge, a study dealing with the grid connected microgrid system voltage fluctuation improvement and power loss minimization using fuzzy logic based STATCOM controllers has not been published.

This paper is organized as follows. Section

The objective of this study is to build a single line diagram of the IEEE 30 standard bus system model for conducting power flow analysis using MATLAB/PSAT. Furthermore, the paper aims to find out the improved voltage profile, reduced loss, and increased power system performance by integrating microgrid and FACTS devices to the main grid.

Today, the most preferred generators for wind energy conversion systems are doubly fed induction generator (DFIG) [

DFIG has two progressive converters as rotor side control and grid side control as realized in [

The aerodynamic model of the power of the rotor as a function of air flow on the blades has been shown in [

where

^{3}).

Photovoltaic (PV) cells can convert photons of light into electrical energy. PV cells are connected in parallel to increase the current and in series to increase voltage. A photovoltaic cell for its forward-biased characteristics using a one-diode model is detailed in [_{ph}), series (_{s}), and shunt (_{sh}) resistors is modelled in [^{−23} J/K), ^{−19} C), _{pv} is the output voltage of the solar cell, and Io shows the dark saturation current value. The mathematical model of the photovoltaic system with perturb and observe (P&O) algorithm used for the maximum power point tracking (MPPT) is presented in [

The current and voltage measurements of the network in each bus and active and reactive power flow in each line are analyzed using power flow analysis method. Newton-Raphson (N.R), Gauss-Seidel (G.S), and fast-decoupled (FD) power flow methods are the most widely used strategies of power flow analysis. N.R is the most preferred because of its quadratic convergence, more computation time per iteration, and being efficient for large networks [

Static synchronous compensator (STATCOM) is a parallel-connected DC to AC converter. If it is overexcited, it acts as a capacitor which provides reactive power and acts as an inductor when underexcited and absorbs reactive power. Figure

Modified equivalent circuit of STATCOM.

Based on Figure

The role of the control system is to increase or decrease the DC voltage of the capacitor, so that the produced AC voltage has the appropriate amplitude for the reactive power needed. The control system must also keep the voltage produced by the AC in phase with the system voltage in the STATCOM link bus to either produce or absorb reactive power. PLL (phase-locked loop) synchronizes the system voltage and provides a reference angle to the measuring device. The STATCOM voltage and current positive-sequence elements are measured, using phase-to-dq transformation and a running-window average. The voltage regulator block (outer loop) measures the reactive current reference Iqref used by the current regulator block (inner loop) from the calculated voltage (Vmeas) and the reference voltage (Vref). The current regulator output is the

Proposed control scheme diagram of STATCOM.

Linear PI controllers are entrenched in traditional control system and it is regularly used as a benchmark against different kinds of controllers [

Hence, challenges urged the creation of an effective technique to solve such problems. One of these methods is adaptive tuning of the PI controller which is used to adjust the gain of integral controller at any disturbance that leads to an adjustment in device condition using fuzzy logic control. Fuzzy logic controller (FLC) is a versatile controller and suits complex environments. Like Boolean or crisp logic, fuzzy logic tackles vagueness and ambiguity. Fuzzy logic is a rule-based operation, which is simple to design for any number of inputs and outputs [

FLC’s structure consists of mainly five parts, namely, knowledge base, fuzzification, inference, rule base, and defuzzification, as shown in Figure

Basic structure of fuzzy logic control system.

In

The

The

The

The structure of the fuzzy-PI controller used for STATCOM controller simulation is shown Figure

Structure of fuzzy-PI controller.

_{i} and _{p} are the integral and proportional gains, and

The control output of fuzzy is multiplied with a PI controller output gain factor (Kp) to get proportional derivative and with another gain (Ki) of PI controller again multiplied and integrated to get proportional-integral. Then, both values are multiplied to get a combined proportional-integral-derivative action from fuzzy logic controller (FLC).

In this study, we preferred triangular membership function for both input and output fuzzy sets. The membership function of error (

Triangular membership functions: (a) and (b) for input variables and (c) for output variable.

The fuzzy-PI controller rule base and surface viewers are illustrated in Table

Rule base for fuzzy-PI controller.

d | NB | NS | Z | PS | PB |
---|---|---|---|---|---|

NB | PB | PB | PB | PB | PB |

NS | PB | PB | PB | PB | PB |

Z | Z | Z | Z | Z | Z |

PS | PB | PB | PB | PB | PB |

PB | PB | PB | PB | PB | PB |

Surface viewer for rule base.

The simulation study is done for changing of Iqref from 0 A to +200 A at 0.5 sec., from +200 A to −200 at 0.8 sec., and from −200 A to 0 A at 1.1 sec. Performance of fuzzy-PI controlled STATCOM is evaluated for changes in Iqref. The following are considered as preconditions for normal operation of the proposed system.

At the moment of disturbance, when the voltage magnitude at bus is far towards positive from reference value, the error value is also increased. If the error value is more than +0.025, the change of integral gain must be large for damped in voltage magnitude.

At the moment of disturbance, when the voltage magnitude at bus is far towards negative from reference value, the error value is also increased. If the error value is less than −0.025, the change of integral gain must be large for damped voltage magnitude.

The error value is limited within +0.025 >

This work considers the IEEE 30-bus system. The bus, line, and transformer data of the IEEE 30-bus standard system are taken from [

The open-source MATLAB interfaced freely downloadable system called PSAT 2.1.10 (Power system Stability Analysis Toolbox) software is used to model the systems. It is a graphical user interface (GUI) and Simulink editor toolbox used for power system model, control, analysis, and operation. PSAT can conduct simulations of continuation power flow (CPF), small-signal stability analysis (SSSA), power flow (PF), and time-domain simulation (TDS).

The IEEE 30-bus system and microgrid were modelled using MATLAB/PSAT software for integration system. The weakest buses identification methods are continuous power flow (CPF), voltage stability indices (VSI), line stability index (LSI), fast voltage stability index (FVSI), new voltage stability index (NVSI), line voltage stability index (LVSI), and voltage collapse proximity indicator (VCPI). From those techniques, we proposed continuation power flow (CPF) method for identifying the collapsed bus of the IEEE 30-bus system. So, the continuation power flow was applied to the IEEE 30-bus system using MATLAB/PSAT software and the voltage profiles of the buses were evaluated. From the results, we observed that bus 30 has the lowest voltage magnitude of 0.88341569 p.u. as shown in Table

Flow diagram of simulation using MATLAB/PSAT.

The simulation was conducted on the IEEE 30-bus system and the performance was assessed by applying power flow on the system. Figure

IEEE 30-bus system model using PSAT.

The microgrid, which is connected to the IEEE 30-bus system, consists of a 6 MW wind and 7.5 MW PV energy resources as optimally modelled in [

Integration of microgrid system in IEEE 30-bus test.

The integration of the microgrid system into the network is, therefore, an exceptionally challenging task with power quality issues such as voltage fluctuations, power factor reduction, and high-power losses. Hence, adding STATCOM controller to the system is a significant solution to solve such problems.

The lowest voltage is found in bus 30 of the IEEE 30-bus system including the microgrid buses as described above. Thus, bus 30 with a magnitude of 0.64993 p.u. is the weakest and most collapsed bus, which can be an appropriate location of STATCOM.

Because of its advantages mentioned in [

Moreover, the fluctuation of voltage and reduction of power angle due to the integration of microgrid cause power loss, which lowers the performance of the power system.

Figure

Base case with microgrid and STATCOM controller.

For instance, voltage magnitudes for buses 29, 30, 31, 32, and 33 are increased from per-unit values of 0.721993, 0.64992, 0.65994, 0.668909, and 0.668918 to per-unit values of 0.934573, 0.954784, 1.009995, 1.009995, and 1.01, respectively (see Table

Power flow results of voltage in IEEE 30-bus system including microgrid and STATCOM controller.

Voltage magnitude in per unit | |||
---|---|---|---|

Buses | Base case | With MG | With MG & FACTS |

Bus 1 | 1.06 | 1.06 | 1.06 |

Bus 2 | 1.043 | 1.043 | 1.043 |

Bus 3 | 1.0109026 | 1.006818 | 1.009946 |

Bus 4 | 1.0000539 | 0.99518 | 0.998932 |

Bus 5 | 1 | 1 | 1 |

Bus 6 | 1.00040024 | 0.994788 | 0.99947 |

Bus 7 | 0.99187422 | 0.988475 | 0.99947 |

Bus 8 | 1.01 | 1.01 | 0.99104 |

Bus 9 | 1.00516937 | 1.003427 | 1.01 |

Bus 10 | 1 | 1 | 1.004794 |

Bus 11 | 1 | 1 | 1 |

Bus 12 | 1.00681984 | 1.003427 | 1 |

Bus 13 | 1 | 0.987513 | 0.993152 |

Bus 14 | 0.99124229 | 0.980787 | 0.988467 |

Bus 15 | 0.98654462 | 0.994828 | 0.996998 |

Bus 16 | 0.99637152 | 0.992823 | 0.993503 |

Bus 17 | 0.99332895 | 0.974721 | 0.979772 |

Bus 18 | 0.9785084 | 0.974457 | 0.977923 |

Bus 19 | 0.97705653 | 0.980006 | 0.982618 |

Bus 20 | 0.98196494 | 0.988266 | 0.992154 |

Bus 21 | 0.99071004 | 0.985527 | 0.991603 |

Bus 22 | 0.98954412 | 0.960667 | 0.978467 |

Bus 23 | 0.97395893 | 0.943003 | 0.974167 |

Bus 24 | 0.96601939 | 0.873522 | 0.965257 |

Bus 25 | 0.94069283 | 0.852789 | 0.946585 |

Bus 26 | 0.92151129 | 0.841286 | 0.969968 |

Bus 27 | 0.93422492 | 0.981543 | 0.997718 |

Bus 28 | 0.9982865 | 0.721993 | 0.934573 |

Bus 29 | 0.88459486 | 0.64992 | 0.954784 |

Bus 30 | 0.88341569 | 0.65994 | 1.009995 |

Bus 31 | … | 0.668909 | 1.009995 |

Bus 32 | … | 0.668508 | 1.009954 |

Bus 33 | … | 0.668918 | 1.01 |

The details of wind power, photovoltaic power, and STATCOM controller parameters and values are given in Appendix A, Table

The per-unit value of voltage magnitude is increased after incorporating STATCOM controller in the interconnected microgrid system. However, without the STATCOM controller, the per-unit voltage magnitude is fluctuating/decreased in a microgrid-integrated network system. This is because of the randomness nature of wind and solar irradiation. The per-unit voltage comparison of the base case with microgrid and base case with both microgrid and STATCOM is represented clearly in Figure

On the Other hand, the real power loss for the base case is 0.127933 p.u. and it increased to 0.230291 p.u (see Table

By introducing STATCOM controller, the real power loss is decreased from 0.397967 p.u. to 0.35933 p.u (see Table

The wave forms of DC link capacitor voltage, the per-unit voltage measurement, and reactive power using proportional-integral (PI) control and fuzzy-PI controller for STATCOM are shown in Figures

From Figure

From Figure

The peak overshoot values for reactive power are 65 Mvar and 110 Mvar for PI and fuzzy-PI controllers, respectively (see Figure

Base case, with microgrid, and STATCOM controller.

The DC voltage with (a) PI control and (b) fuzzy-PI control.

Voltage measurement: (a) with PI and (b) with fuzzy and PI controllers.

Reactive power: (a) with PI controller and (b) with fuzzy-PI controller.

Total generation, power consumption, and total power losses in the IEEE 30-bus test system with and without microgrid and FACTS.

Voltage magnitude in per unit | |||
---|---|---|---|

IEEE 30-bus system | Base case (p.u.) | With MG (p.u.) | With MG & FACTS (p.u.) |

Total generation real power (p.u.) | 12.8056 | 13.236 | 13.23677 |

Total generation reactive power (p.u.) | 2.87686 | 3.292914 | 3.197345 |

Total load real power (p.u.) | 12.51 | 12.99579 | 13.0362281 |

Total load reactive power (p.u.) | 2.73345 | 2.779339 | 2.7999962 |

Total loss real power (p.u.) | 0.29560 | 0.397967 | 0.359338 |

Total loss reactive power (p.u.) | 0.14340 | 0.245767 | 0.129539 |

Hence, from the above figures, we can conclude that the performance of fuzzy logic with proportional-integral (fuzzy-PI) controller for STATCOM device gives low peak overshoot and quick settling time. In addition, the proposed controller is effective in settling the final value and in damping the oscillations quickly. Thus, fuzzy-PI controller is proved to give improved performance compared to PI controller in STATCOM applications.

The modelling of a 6 MW DFIG wind and 7.5 MW photovoltaic based microgrid generation system was integrated and configured into the weak bus 30 in the IEEE 30-bus test system using MATLAB/PSAT environment. The power flow using N.R method was performed for the base case and with microgrid-integrated system with STATCOM controller. The microgrid-integrated system reduced the high voltage fluctuation and high-power losses. The parallel connection of the controller in the integrated system improved reactive power compensation and enhance the voltage profiles of the buses as well as minimizing line losses. Additionally, fuzzy-PI controller gives a good result compared to PI controller alone. Generally, this work demonstrates that the integration of microgrid with fuzzy-PI based STATCOM controller has played a significant role in increasing the system capacity while minimizing voltage fluctuation and line losses.

The parameters of the wind components of the microgrid system in the distribution network are provided in Table

Value of the wind component in the microgrid system.

No. | Component | Values |
---|---|---|

1 | Power rating | 5 MVA |

2 | Voltage rating | 69 kv |

3 | Rated frequency | 60 Hz |

4 | Stator resistance (Rs) | 0.01 p.u. |

5 | Stator reactance (Xs) | 0.1 p.u. |

6 | Rotor resistance (Rr) | 0.01 p.u. |

7 | Rotor reactance (Xr) | 0.08 p.u. |

8 | Magnetization reactance (Xm) | 3.00 p.u. |

9 | Pitch control gain (Kp) | 10 p.u. |

10 | Time constant (Tp) | 3 sec |

11 | Voltage control gain (Kv) | 10 p.u. |

The parameters of the PV components of the microgrid system in the distribution network are provided in Table

Parameters of the PV component of microgrid.

No. | Component | Values |
---|---|---|

1 | Power rating | 7.5 MVA |

2 | Voltage reference | 1.045 p.u. |

3 | Inverter response time (Tp, Tq) | 0.015, 0.015 |

4 | Voltage PI controller gains (Kv, Ki) | 0.0868, 50.9005 |

Different parameters in STATCOM controller are provided in Tables

Value of STATCOM components.

No. | Component | Values |
---|---|---|

1 | Power | 100 MVA |

2 | Voltage | 230 kv |

3 | Frequency | 60 Hz |

4 | Gain and time constant of the current control (Kr, Tp) | 50 p.u., 0.1 sec |

5 | Max and min current | 1.2 p.u., 0.8 p.u. |

Parameter values of STATCOM controller.

No. | Component | Values |
---|---|---|

1 | Reference voltage | 1 p.u. |

2 | Droop (p.u./100 MVA) | 0.03 |

3 | Voltage regulator gains (kp, ki) | 12, 300 |

4 | Iq regulator gains (kp, ki) | 5, 4 |

5 | DC-link voltage gain | 6.5 |

6 | Active power gain | 200 |

7 | Reactive power gain | 800 |

The data used to support the findings of this work are accessible from the corresponding author upon request.

The authors declare that there are no conflicts of interest regarding the publication of this paper.