The increasing pressure on the power system increases the complexity that is becoming a concern for the **stability** of the power system and mainly for transitory **stability**. To operate the system in the event of faults, Flexible AC Transmission System (FACTS) devices are used that provide opportunities to control power and vibrations damping. This paper deals with the two control strategies of the Unified Power Flow **Controller** (**UPFC**) to damping the system oscillations **stability**. The **stability** of the Multi-Machine Power System (**MMPS**) was analysed with the presence of **UPFC**. The first strategy is the traditional **PI** **controller** (**PI**-C) with **UPFC**, and the second strategy is the proposed **Fuzzy** **Logic** **controller** (FL-C) proposed for **UPFC** device along with **PI** **controller**. The MATLAB R2014a was used in all simulations. Based on the results, FL-C for **UPFC** device along with the **PI** **controller** has proven its superiority by has enhanced **response** to the system, thus minimized in the transitions overshoot and undershoot, and has lower ripple compared to traditional **PI**-C, both with and without **UPFC**.

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A proportional–integral–derivative **controller** (**PI** **controller**) is a generic control loop feedback mechanism widely used in industrial control systems – a **PI** is the most commonly used feedback **controller**. A **PI** **controller** calculates an "error" value as the difference between a measured process variable and a desired set point. The **controller** attempts to minimize the error by adjusting the process control inputs. In the absence of knowledge of the underlying process, **PI** controllers are the best controllers. However, for best performance, the **PI** parameters used in the calculation must be tuned according to the nature of the system – while the design is generic, the parameters depend on the specific system. The **PI** **controller** calculation involves three separate parameters, and is accordingly sometimes called three-term control: the proportional, the integral and derivative values, denoted P, I, and D. The proportional value determines the reaction to the current error, the integral value determines the reaction based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element. Heuristically, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction of future errors, based on current rate of change. By tuning the three constants in the **PI** **controller** algorithm, the **controller** can provide control action designed for specific process requirements. The **response** of the **controller** can be described in terms of the responsiveness of the **controller** to an error, the degree to which the **controller** overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system **stability**. The PID **controller** is probably the most-used feedback control design. PID is an acronym for Proportional-Integral-Derivative, referring to the three terms operating on the error signal to produce a control signal. If u(t) is the control signal sent to the system, y(t) is the measured output and r(t) is the desired output, and tracking error e(t) = r(t) − y(t), a PID **controller** has the general form.

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identified some of the limitation of each of them, such as the steady state error presented by ordinary **fuzzy** **controller**. The application of IT2FLC **controller** is the main focus of this paper. The simulation results for disturbed terminal voltage values and transmission lines faults shows a very sharp reduction in settling time, over shoot, rise time and zero steady state error. It could be observed in study **using** MATLAB simulation, an excellent **response** of the IT2FLC **controller** and with no oscillation, while the AVR **response** presented a ripple in both studies and some oscillations before reaching the steady state operation point. It is shown that an excellent performance of the IT2FLC control over the conventional AVR one for the excitation control of synchronous machines could be achieved. It has been shown that the genetically evolved interval type-2 FLCs can lead to superior performance in **comparison** to interval type-1 FLCs and the manually designed interval type-2 FLCs. The results indicate that the genetic evolution of interval type-2 MFs can provide valid and high-performance interval type-2 FLCs without relying on any a priori knowledge such as logged data or a previously existing model, making it suitable for control problems where no such a priori data is available such as in **transient** **stability** control in power system. This work will be a step towards overcoming the problem of manually specifying pseudo-optimal MFs for interval type- 2 FLCs, which to date is one of the main obstacles when designing interval type-2 FLCs.

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Abstract – In gas turbine system, Governor **controller** plays an important role in the dynamic behavior of turbine system for automatically regulating the supply of fuel with respect to load and although to limiting the speed. Need of gas turbines and its applications nowadays more prominently increasing due to technology improvement towards their countries development is the main cause for it. So the **controller** system used in gas turbine system in various applications effective **response** must be required, for that concern governor system could be model with **PI**- **controller**. In which additionally another two methods are used such as Feed-forward and Back-tracing methods to improving the dynamic behavior and **response** time and also its load rejection **response**. In order to get better **response** to improving the **stability** and load rejection the **Fuzzy** **Logic** **controller** used in the place of **PI**-**controller**. So entirely in this paper the comparative **response** behavior of Heavy-Duty gas turbine system would be observe between **PI**-**controller** and **Fuzzy** **Logic** **controller** of governor system model design in MATALAB-SIMULINK platform.

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Simulated model of the synchronous generator is connected to an AC system with all parameters from experimental setup. The behavior of the **fuzzy** **logic** excitation **controller** is simulated and compared with **PI** voltage **controller** for two characteristic operation conditions. In the first simulation voltage reference is changing from 100% to 80% and then back to 100% with 80% of active power. On the fig. 5. Is presented active power **response** with **fuzzy** **logic** stabilizing **controller** and with classical **PI** regulator. A modern excitation system contains components like automatic voltage regulators (AVR), Power System stabilizers (PSS), and filters, which help in stabilizing the system and maintaining almost constant terminal voltage. These components can be analog or digital depending on the complexity, viability, and operating conditions. The final aim of the excitation system is to reduce swings due to **transient** rotor angle instability and to maintain a constant voltage. To do this, it is fed a reference voltage which it has to follow, which is normally a step voltage. The excitation voltage comes from the transmission line itself. The AC voltage is first converted into DC voltage by rectifier units and is fed to the excitation system via its components like the AVR, PSS etc.

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This paper reviewed the various techniques employed in synchronous generator terminal voltage control. Such techniques are the CAVR, MV, GMV, PP and ordinary **fuzzy** **controller**. It identified some of the limitations of each of them, such as the steady state error presented by ordinary **fuzzy** **controller**. The application of EIT2FLC **controller** is the main focus of this paper. The simulation results for disturbed terminal voltage values and transmission lines faults shows a very sharp reduction in settling time, over shoot, rise time and zero steady state error. It could be observed in study **using** MATLAB simulation, an excellent **response** of the EIT2FLC **controller** and with no oscillation, while the AVR **response** presented a ripple in both studies and some oscillations before reaching the steady state operation point. It is shown that an excellent performance of the EIT2FLC control over the conventional AVR one for the excitation control of synchronous machines could be achieved. It has been shown that the genetically evolved interval type-2 FLCs can lead to superior performance in **comparison** to interval type-1 FLCs and the manually designed interval type- 2 FLCs. The results indicate that the genetic evolution of interval type-2 MFs can provide valid and high-performance interval type-2 FLCs without relying on any a priori knowledge such as logged data or a previously existing model, making it suitable for control problems where no such a priori data is available such as in **transient** **stability** control in power system. This work will be a step towards overcoming the problem of manually specifying pseudo-optimal MFs for interval type-2 FLCs, which to date is one of the main obstacles when designing interval type-2 FLCs.

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Sensor less control of induction motor **using** vector control technique has been proposed. Sensor less control gives the benefits of vector control without **using** any shaft encoder. The mathematical model of the drive system has been developed. Vector controlled induction motor by employing the different speed controllers like **PI**, sliding mode **controller** and **fuzzy** **logic** controllers is presented. The performance of the sliding mode **controller** and **fuzzy** **logic** **controller** for the indirect vector controlled induction motor drive has been verified and compared with that of conventional **PI** **controller** performance. It can be concluded that the **fuzzy** **logic** controllers performance is better in **comparison** with that of **PI** and SMC in terms of the **transient** **response**. The dynamic performance of SMC is found to be the best out of the three controllers. The robustness of the SMC and **Fuzzy** **logic** **controller** during sudden changes in load has been seen but SMC gives better performance than **fuzzy** **logic** **controller** with load conditions. **PI** **controller** is very simple to implement, but its steady state **response** and dynamic performance are not very satisfactory. Its robustness to load disturbances is also relatively poor.

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Power transmission networks have become narrower due to the increased demand for power. A result of many **stability** problems such as overloading some transmission lines after a disturbance. Therefore, the problem of transitory **stability** after the main defect is a special transmission-limiting factor [1]. **Transient** **stability** refers to the capacity of the power system to preserve synchronization when exposed to severe **transient** disturbances such as sudden change of load and faults [2]. Include system **response** resulting from large fluctuations in generator speed and rotor angle. **Transient** **stability** of the complex power system can be improved **using** FACTs devices [3]. The FACTs controllers are able to control network condition very quick. This allows the existing network to obtain used efficiently and thus avoid the need for constructing newlines [4]. The optimal tuning and modelling of various FACTs devices for a dynamic **stability** enhancement of **MMPS** studied in [5]. **UPFC** is the most widely deployed device in FACTs that can provide effective control of power system parameters such by way of transmission voltage, line impendence, and phase angle. Moreover, **UPFC** can provide either positive or negative power injections positive or interactive. Therefore, it can enhance the operation of the system because it allows for extra efficient control of power flow, super-control system, and **stability** [6]. The **PI** **controller** becomes been used in recent years to improve both temporary and fixed performance, as well as to reject disturbances caused by startup events [7], [8], [9].

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the hysteresis band current **controller** for pulse width modulation. This transformation results in reduction of output voltage obtained from the three phase voltage source inverter and applied to the stator input terminals of induction motor drive, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the reduced motor losses. The drive system is simulated **using** Matlab/Simulink models and simulation results based on total losses and efficiency are compared with changing load torque.

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This Paper gives a complete modeling and simulation of a two inverter fed six phase permanent magnet synchronous motor drive system, Then **response** based comparative analysis is done on starting torque ,settling time, Steady state current at various speed levels and torque levels by changing proportional-integral (**PI**) **controller** to **Fuzzy** **logic** **controller**. The **PI** **controller** has some disadvantages like, more settling time, sluggish **response** due to sudden change in load torque etc. So an intelligent **controller**, based on **fuzzy** **logic** is introduced which replaces the **PI**-**controller** and its drawbacks. The performance of both the **controller** has been investigated and studied by comparing the different plots obtained by setting various speed level both incremented and decremented speed, at different load conditions like No- load, fix load and dynamic load through Matlab / Simulink environment. Finally it is concluded from the result that **fuzzy** **logic** based **controller** is robust, reliable gives quick **response** with high starting torque and more effective than the conventional **PI** **controller**. It is also observed that both the proposed model can also run above rated speed significantally.

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Frequency Control is a technical requisite for the appropriate setup of an interconnected power system and it is the precondition for a stable electricity grid and guarantees secure supply at a frequency of 50Hz. An interconnected power system comprises of interconnected control areas. When load changes or abnormal conditions arises like outages of generation and varying system parameters, mismatches in frequency can be began. These incompatibilities can be improved by controlling the frequency. Automatic Generation Control is used to retain the schedule system frequency (1)(2-4)..Next importance is given to the usage of High Voltage DC transmission (HVDC)(1) link in the system instead of high Voltage Alternating Current (HVAC) transmission only. HVDC is a forecast technology due to huge growth of this transmission system and due to its economic, environmental and performance advantages over the other options. Therefore it is planned to have a dc link in parallel with HVAC link interconnecting control areas to get an enhanced system dynamic performance. Those studies are conceded out considering the nominal system parameters. Practically system parameters vary considerably with changing operating conditions. Intelligent controllers can be hired to elucidate this problem. The conventional control technique does not provide control problems including AGC of interconnected power system. **Fuzzy** **logic** based **controller** can be implemented to scrutinize the load frequency control of three area interconnected power system with HVAC and HVDC parallel link taking parameter uncertainties into account. In the system working under deregulated environment, a Wind Turbine Generator(WTG) or other locally generating plants can be replicated **using** in the to carry out all the planned operations and to control the frequency of the system **using** AGC and **fuzzy** **Controller** with PID(7-9)

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One FACTS **controller** in particular, the Unified Power Flow **Controller** (**UPFC**), is capable of concurrently or selectively controlling transmission line power flows, voltage magnitudes and phase angles in a power system. Here in fig.1 given below **UPFC** is shown with both PWM and Phase Control strategy.

Abstract—FACTS devices are available, which can help Power Engineers to deal with problems like Large Signal **Stability** and economic Factors that gives strong incentives to raise the **Stability** limit of the System, depending upon the diverse conditions. Among the converter based FACTS devices Static Synchronous Compensator (STATCOM) and Unified Power Flow **Controller** (**UPFC**) are the popular FACTS devices. Unified Power Flow **Controller** (**UPFC**) is a power electronic based device that has capability of controlling the power flow through the line by controlling appropriate its series and shunt parameter. **UPFC** is a versatile FACTS **controller** that can regulate the Power Flow through the Line by controlling its Series and shunt parameters. In shunt it is having STATCOM and in series SSSC are employed can independently control the Line Power Flow. Two Converters improves the flexibility and provide additional degree of freedom in Power System. With the application of **UPFC**, **Transient** **stability** of the system can be improved. This Paper shows that how **Transient** **Stability** of the Inter Area system is Improved. In this paper, we analyze the **transient** **stability** of simple two area system of 11 bus test system of two area system with and without **UPFC** by comparing the rotor angles, voltage at buses, and angular frequency plots **using** the Time Domain feature in PSAT software.

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As every power system is constantly being subjected to disturbances, we should see that these disturbances do not make the system unstable. Therefor additional signals derived from speed deviation, excitation deviation and accelerating power are injected into voltage regulators. The device to provide these signals is referred as power system stabilizer. The use of power system stabilizers has become very common in operation of large electric power systems. The conventional PSS which uses lead-lag compensation, where gain settings designed for specific operating conditions, is giving poor performance under different loading conditions. Therefore, it is very difficult to design a stabilizer that could present good performance in all operating points of electric power systems. In an attempt to cover a wide range of operating conditions, **Fuzzy** **logic** control has been suggested as a possible solution to overcome this problem. In this paper, a systematic approach to **fuzzy** **logic** control design is proposed. The study of **fuzzy** **logic** power system stabilizer for **stability** enhancement of a single machine infinite bus system is presented. In order to accomplish the **stability** enhancement, speed deviation and acceleration of the rotor synchronous generator are taken as the inputs to the **fuzzy** **logic** **controller**. These variables take significant effects on damping the generator shaft mechanical oscillations. The stabilizing signals were computed **using** the **fuzzy** membership function depending on these variables. The performance of the system with **fuzzy** **logic** based power system stabilizer is compared with the system having conventional power system stabilizer and system without power system stabilizer

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Abstract—Owing to increase in power demand, many instability problems are experienced by power system in every year. Voltage **stability** is one of the main problems in power systems that must be treated **using** suitable mitigation techniques. Under heavy load conditions due to insufficient reactive power causes the voltage drop and under lightly loaded condition due to surplus amount of reactive power causes voltage rise, finally it leads to voltage instability. The enhancement of voltage **stability** in transmission line is done by Unified Power Flow **Controller**. In this project voltage **stability** improvement **using** **UPFC** is analyzed. Voltage **stability** improvement is shown and analyzed **using** MATLAB/ S imulink.

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Besides the advantages, there are some limitations of **Fuzzy** **Logic** Controllers. The tuning of an FLC is a very difficult task. To make the tuning easy and efficient, there are several structures of **fuzzy** **logic** controllers are available. This paper considers new structures of **fuzzy** **logic** controllers. Two structures, self-tuning FLC and **fuzzy** supervised conventional **controller** are considered. In **Fuzzy** **Logic** control rules and scaling factors play very important role. In this paper, a self-tuning **PI**-like FLC is used for the tuning of output scaling factor. In FSPI, we tune the **PI** **controller** parameters with FLC. So, it adds the advantages of PID **controller** and FLC. The design of these controllers is discussed. These are implemented to control three non-linear example systems. The concert of FLC, STFLC and FSPI **controller** is compared with each other. It is found that STFLC and FSPI **controller** give better concert than the conventional **PI** **controller** or simple FLC.

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Abstract: The paper here states, the comparative study of frequency control of MGP system with convectional **PI** and **fuzzy** **logic** **controller** is done. The electrical power generation has done by non renewable energy sources in most parts of the world. The carbon emissions during the production of electricity challenge the environmental issues which lead to pollution. So, there is a necessity for the interconnection of renewable energy sources with grid for the reduction in usage of non renewable energy sources. But, interfacing of renewable sources to the grid is not an easy task. The reduction of inertia in the power system, the frequency instability problems may limit the usage of renewable sources in the interfacing with the grid. The motor generator pair can deal with all the above problems very effectively. The source grid characteristics are analyzed by simulation **using** convectional **PI** **controller** and **fuzzy** **logic** **controller**. The **fuzzy** **logic** **PI** **controller** gives better results when compared with convectional **PI** **controller**. The results were compared by simulation whether the **fuzzy** **PI** **controller** gives best results or convectional **PI** **controller**.

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This project presents a non isolated, high boost ratio hybrid dc–dc converter with applications for low-voltage renewable energy sources The system is designed considering solar-PV panels of 750W to feed an average load demand of 250W for a rural household. The system includes series-parallel combination of solar panels, MPPT (maximum power point tracking) **controller**, a dc-dc SEPIC converter, an energy storage system .A dc-dc converter is providing a constant dc bus voltage and its duty cycle is controlled by the MPPT **controller**. A P&O approach is utilized for MPPT. In this approach, MPPT **controller** automatically generates a PWM signal for the dc-dc converter to extract maximum power. To maintain the power quality a feedback control is used. The complete system is designed, and modeled to evaluate its performance. Simulated results are presented to demonstrate the performance of the MPPT **controller** and designed system for varying atmosphere conditions and load disturbances.

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The area of DC motor speed control and analysis in very wide, but Proportional-Integral-Derivative (PID) controllers have gained wide popularity in the control of DC motors. Their performances, though require some degree of manual tuning by the operator, are still satisfactory but a means of auto-tuning is desirable. In this paper, the performance of a select dc motor controlled by a proportional-integral-derivative (PID) **controller** and by a proportional integral (**PI**) **controller** is investigated. An overshoot is observed with an accompanied large settling time thereby confirming the behavior of a typical PID **controller** and **PI** **controller**. It is therefore a matter of necessity to tune the PID **controller** and **PI** **controller** in order to obtain the desired performance. On the other hand, a **fuzzy** **logic** based **controller** applied to the dc motor is investigated. With the application of appropriate expert rules, there is no overshoot and the settling time is within the desired value. With **fuzzy** **logic** **controller**, manual tuning is eliminated and intelligent tuning takes the Centre stage with satisfactory performance. Therefore in this paper we are comparing the performance of a select DC motor with the application of PID and **PI** **controller** and an auto **fuzzy** **logic** **controller** and observing the best result.

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