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In this paper, the nonlinear dynamic model of a multi machine power system incorporated with Interline Power Flow Controller [IPFC] has been developed for improvement in damping of power system oscillations and transient stability. The IPFC performance is tested with PI controllers in comparison with fuzzy logic based controller under healthy and abnormal operating conditions. The IPFC fuzzy controller is design to coordinate two control inputs: change in voltage and change in capacitor voltage to improve the transient stability of the multimachine system. The Interline Power Flow Controller [IPFC] with fuzzy logic controller is designed with simple fuzzy rules to coordinate the additional damping signal. The proposed controller for IPFC is able to achieve improved designed performance of the power system. Digital simulations are carried out in MATLAB environment.

In a modern integrated power system, stability is of increasing importance for secure operation of large and complex systems. The high current semiconductor device based FACTS devices with proper control strategy can improve the transient stability of multimachine system. Many researchers present work on various nonlinear VSC based FACTS devices for stability improvement of the power system under various system conditions. Among these, the Unified Power Flow Controller (UPFC) is the most versatile FACTS device for improvement of transient stability and damping of oscillations [

Gomathi et al. [

Fuzzy Logic is a form of many-valued logic. Fuzzy logic controller is robust and easily modified. It can use multiple input and output sources. Fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, which is useful for the design of the controller. Also, it does not require precise numerical values of the control inputs and the system parameters. Furthermore, it allows the knowledge from experiences to be incorporated into the control scheme by means of logical reasoning. Hence, fuzzy logic control is an ideal and suitable approach for controlling IPFC. The coordination of control signals through a conventional technique is very difficult, where as fuzzy logic control allows the knowledge from experiences to be incorporated into the control scheme by means of logical reasoning. In multi machine system, the behavior of conventional IPFC controllers is required to coordinate parameters for better performance. Chandrakar et al. [

Dhurvey et al. [

In view of the available work presented by the authors, which is not covered the fuzzy logic based IPFC device with nonlinear modeling approach. Since fuzzy logic is proved the effective controller for nonlinear multi- machine system. Therefore, the main aim of this paper is to design fuzzy logic based IPFC controller with measurable components like change in voltage at IPFC location and change in capacitor voltage as input signal. The designed objective is to achieve improvement in first peak stability and damping of power system oscillations. The IPFC controller is tested under steady state condition as well as disturbed condition of the power system. The comparative performance of PI based controller and fuzzy logic based IPFC for improved power system performance is demonstrated. The results are validated in MATLAB environment.

Interline Power Flow Controller (IPFC) is VSC based FACTS controller, consists of two voltage-sourced converters (VSCs) inserted in series with lines. The active power can be transferred between the two VSCs through DC link as shown in

The power system and its detailed circuit model are shown in

For

Details of mathematical model for IPFC [

The complex power injected at n^{th} bus (n = y, z) is-

After simplification, the active power and reactive power injections at n^{th} bus are

With respect to the ac system, as IPFC neither absorbs nor injects active power, the active power exchange between the converters via the dc link is zero which is represented as

Here the superscript ^{*} denotes the conjugate of a complex number. Neglecting resistances of series transformers, equations (10) can be written as

In this section, PI Based IPFC [_{ref} − V_{m}) and the PI constants K_{p} and K_{i} are chosen by trial and error method. Also, additional damping signal POD can be applied for improvement in damping of PI controller. The block diagram is for control of the modulation index of the voltage source convertors.

In _{p} and K_{i} of the DC voltage regulators are chosen by trial and error method. Their values are given in Appendix.

PI controller performance deteriorates under varying system condition. Hence Fuzzy logic based IPFC controller

is proposed [

Fuzzification is a process whereby the input variables are mapped onto fuzzy linguistic variables. Each fuzzified variable has certain membership function. The first input (

The mechanism of the inference process is the search of input/output relationship to match the input conditions. Therefore, an integral part of the inference process is the rule-base (a list of rules that relate the input values to the output values). Control decisions are made on the basis of fuzzified linguistic variables. The rules [

The fuzzy inference system coordinates the linguistic input variables for IPFC control. The defuzzification process uses the applicable rules to derive a crisp or numerical output value from the output linguistic values. The fuzzy Controller uses the centroid method. The general function of the fuzzy Logic controller can be expressed as

Food Service | R | D | T | O |
---|---|---|---|---|

P | C | A | A | C |

G | C | G | A | A |

E | A | G | G | A |

M | C | A | A | A |

Variables | MF’s | α_{a} | α_{b} | α_{c} | α_{d} |
---|---|---|---|---|---|

Poor | -3.108 | 0.2024 | 2.318 | 5.628 | |

Good | 1.019 | 3.066 | 5.018 | 7.065 | |

Medium | 3.083 | 6.154 | 7.177 | 10.59 | |

Excellent | 6.406 | 8.132 | 9.941 | 11.67 | |

Ok | -0.364 | 1.68 | 2.2 | 4.24 | |

Rancid | 2.053 | 4.165 | 4.693 | 6.805 | |

Tasty | 4.49 | 6.36 | 6.83 | 8.7 | |

Delicious | 6.17 | 7.78 | 8.69 | 10.3 | |

Output signal (u) | Cheap | 0.5 | 4.5 | 5.5 | 9.5 |

Average | 10.6 | 14.6 | 15.6 | 19.6 | |

Generous | 20.75 | 24.75 | 25.75 | 29.75 |

where, f denotes the mapping defined by the rule base and α, β is the appropriate scaling, which depends on the scale of the X-axis and Y-axis of input and output variables. The fuzzy output is given by equation

For the simulation, three phase fault is considered at bus n_{2} for the fault duration of 0.05 sec. The simulation results are demonstrated in Figures 8-10.

The proposed PI and Fuzzy controllers performances are tested in multimachine system. _{1 }without IPFC, PI based IPFC, fuzzy based IPFC during abnormal condition. Result indicates that PI based IPFC damps the oscillations and Fuzzy based IPFC reduces first swing from 1.004 rad/sec to 1.0022 rad/sec with settling time 1 sec. Result indicates that fuzzy based IPFC significantly improves the transient stability. System is more amenable with fuzzy logic based IPFC than PI controller as indicated by the response for the speed ω_{1}.

_{2}) under 3 different conditions like without IPFC, with PI, with fuzzy, which depicts that with Fuzzy coordinated controller, response is better than PI controller. Fuzzy based IPFC reduces peak of speed deviation from 1.0022 rad/sec to 1.0015 rad/sec and settles within 1.5 sec. Hence Fuzzy logic based IPFC significantly improves transient stability of the multimachine power system.

The MATLAB result presents the inter area oscillations (dω_{1} − dω_{2}) during three phase fault of 0.05 sec. duration near bus 2 as shown in _{1} − dω_{2}) with Fuzzy coordinated controller reduces the first swing from

_{dc}) response. Under normal system conditions V_{dc} is stable and constant whereas during disturbance, V_{dc} variation is observed. V_{dc} helps to supply real power during disturbance condition to VSC_{2}. In fuzzy based IPFC, V_{dc} variation is little larger than PI based IPFC.

A fuzzy logic based controller for Interline Power Flow Controller (IPFC) has been proposed for multi-machine system to improve power system performance. The fuzzy rules have been designed with two inputs change in

voltage

Authors are thankful to Dept. of Electrical Engg., G. H. Raisoni College of Engineering, Nagpur for their constant support.

S. N. Dhurvey,V. K. Chandrakar, (2016) Improvement of Power System Performance Using Fuzzy Logic Based Interline Power Flow Controller [IPFC]. Journal of Power and Energy Engineering,04,67-77. doi: 10.4236/jpee.2016.44007

A.1. Synchronous Machine-I-2100MVA(M_{1})-Stator resistance-2.8544e−3, Inertia constant H(s) = 3.7, Line to line voltage (Vrms) = 13,800 V, frequency fn(HZ) = 60

Synchronous Machine-II-1400 MVA(M_{2})-Stator resistance-2.8544e−3, Inertia constant H(s) = 3.7, Line to line voltage (Vrms) = 13,800 V, frequency fn(HZ) = 60

A.2. Transmission line parameter-No. of phase-3

Length of transmission line-L1 = 280-km, Resistance per unit length (Ohms/km)-R_{1} = 0.01273*2, R_{0} = 0.3864, Inductance per unit length (H/km)-L_{1} = 0.9337e−3, L_{0} = 4.1264e−3

Capacitance per unit length (F/km)-C_{1} = 12.74e−9, C_{0} = 7.751e−9

L2 & L3 = 150 km-Resistance per unit length (Ohms/km)-R_{1} = 0.01273*2, R_{0} = 0.3864,

Inductance per unit length (H/km)-L_{1} = 0.9337e−3, L_{0} = 4.1264e−3,

Capacitance per unit length (F/km)-C_{1} = 12.74e−9, C_{0} = 7.751e−9

L3 = 50 km-Resistance per unit length (Ohms/km)-R_{1} = 0.01273*2, R_{0} = 0.3864,

Inductance per unit length (H/km)-L_{1} = 0.9337e−3, L_{0} = 4.1264e−3

Capacitance per unit length (F/km)-C_{1} = 12.74e−9, C_{0} = 7.751e−9

A.3. Rating of the loads

Three-phase parallel RLC load (star)-250 MW, Nominal phase-to-phase voltage = 500 e3V, Active power = 250 e6W, Three-phase parallel RLC load (star)-100 MW, Nominal phase-to-phase voltage = 500 e3V, Active power = 103 e6W

Three-phase Dynamic load-Nominal L-L voltage = 500 e3V, Active power = 2.2e+009 W, Reactive power = 1e+008 var

A.4. Rating of IPFC

1000 MVA, DC link nominal voltage = 40 KV, equivalent capacitance = 375 µF, equivalent impedance = 0.16 pu

A.5. Parameters of PI controller

AC regulator-[Kp Ki] = [0.00375 0.1875], DC regulator-[Kp Ki] = [0.1e−3 20e−3]