learning (RL). Some efforts are addressed in [3, 4, 5, 7, 16, 17]. RL based controllers learn and are adjusted to keep the **area** **control** error small enough in each sampling time of a LFC cycle. Since, these controllers are based on learning methods; they are independent of environment conditions and can learn a wide range of operating conditions. The RL based **frequency** **control** design is a model-free design and can easily scalable for large scale systems and suitable for **frequency** variation caused by wind turbine fluctuation. **Using** conventional linear **control** methodologies for the LFC design in a modern **power** **system** is not more efficient, because they are only suitable for a specific operating point in a traditional structure. If the dynamic/structure of **system** varies; they may not perform as expected. Most of conventional **control** strategies provide model based controllers that are highly dependent to the specific models, and are not useable for large-scale **power** systems concerning the integration of RES units with nonlinearities, undefined parameters and uncertain models. If the dimensions of the **power** **system** increase, then these **control** design may become more different as the number of the state variables also increases, significantly. Therefore, design of intelligent controllers that are more adaptive and flexible than conventional controllers is become an appealing approach. When WTGs are introduced to the **power** **system**, as they generate a part of **power** **system** loads, much portion of conventional nominal **power** can be available for **using** in supplementary **control**. However, as the variable wind farms **power** output may or may not be available during peak demand and abnormal periods, due to unpredictable nature

In this model shown by Fig.3, the application of **fuzzy** **controller** [3] for **load**-**frequency** **control** in **power** systems is used. For the same, interconnected **power** **system** having **three** **control** areas including same turbine units. In the **system**, non-reheat turbines are used for each **area**. So, same properties and physical constants of the areas above are considered in the simulation. In the simulation, a step **load** increment in the **three** areas of **power** **system** is considered [7]. The **area** **control** error for each **area** is controlled with FLC to optimize the integral coefficient and hence to achieve the zero **frequency** steady state error. The **fuzzy** model used is Mamdani and bisector is used as defuzzification method. In the decision-making stage, total twenty five rules are used, which are specified by a set of IF–THEN statements [9] define the **controller** behavior. The **fuzzy** simulation model in Simulink is shown in Fig. 3.

Since **power** **system** dynamic characteristics are complex and variable, conventional **control** methods cannot provide desired results. Intelligent controllers can be replaced with conventional controllers to get fast and good dynamic response in **load** **frequency** **control** problems [12]. If the **system** robustness and reliability are more important, **fuzzy**- **logic** controllers can be more useful in solving a wide range of **control** problems since conventional controllers are slower and also less efficient in nonlinear **system** applications [8, 13, 14]. **Fuzzy** **logic** **controller** is designed to minimize fluctuation on **system** outputs [15]. There are many studies on **power** **system** with **fuzzy** **logic** **controller** [16-18]. FLC designed to eliminate the need for continuous operator attention and used automatically to adjust some variables the process variable is kept at the reference value. A FLC consists of **three** sections namely, fuzzifier, rule base, and defuzzifier as shown in Fig 3.

generation **control** (AGC) is a **system** for balancing the **power** output of multiple generators at different **power** plants, in response to changes in the **load**. In an interconnected **power** **system**, fluctuations in **frequency** caused due to **load** variations and penetration of renewable resources. **Load** variations occur in either one **area** or all areas of the **system** causes change in **system** **frequency** and tie line **power**. Due to high **frequency** deviation in interconnected **power** **system** could result in **system** collapse. **Load** **frequency** **control** is one of the most efficient method to solve these kinds of problems. In the proposed method a **three**-**area** **system** is considered i.e., **area**-1 with thermal **power** plant, **area**-2 with hydro **power** plant and **area**-3 with distributed generation (i.e., wind **power** plant, solar **power** plant etc.). In order to analyze the performance of a **three**-**area** **system**, the **system** responses are comparing the values of undershoot and settling time for each case **using** conventional **control** and **Fuzzy** **logic** **control** techniques separately for 1% disturbance in either **area**. **Load** **frequency** **control** (LFC) including conventional **controller** is proposed in order to suppress **frequency** deviations and **area** **control** error (ACE) for a **power** **system** involving wind, hydro and thermal plants. A **three**-**area** **system** involving thermal plants, a wind farm and a hydro plant will be modeled **using** MATLAB. The **controller** performances are simulated **using** MATLAB/SIMULINK simulation software.

In recent years electricity has been used to **power** more sophisticated and technically complex manufacturing processes, and a variety of high-technology consumer goods. These products and process are sensitive not only to the continuity of **power** supply but also on the quality of **power** supply such as voltage and **frequency**. In **power** **system**, both active and reactive **power** demands are never steady they continuously change with the rising or falling trend. The changes in real **power** affect the **system** **frequency**, while reactive **power** is less sensitive to changes in **frequency** and is mainly dependent on Changes in voltage magnitude [1]. **Load** **Frequency** **Control** (LFC) as a major function of Automatic Generation **Control** (AGC) is one of the important **control** problems in electric **power** **system** design and operation. It is becoming more significant today because of the increasing size, changing structure, emerging new uncertainties, environmental constraints and the complexity of **power** systems. A large **frequency** deviation can damage equipment, corrupt **load** performance, reason of the overloading of the transmission lines and can interfere with **system** protection schemes, ultimately leading to an unstable condition for the electric **power** **system** [2]. Although the active **power** and reactive **power** have combined effects on the **frequency** and voltage, the **control** problem of the **frequency** and voltage can be decoupled. The **frequency** is highly dependent on the active **power** while the voltage is highly dependent on the reactive **power**. Thus the **control** issue in **power** systems can be decoupled into two independent problems. One is about the active **power** and **frequency** **control** while the other is about the reactive **power** and voltage **control** [3].Many investigations in the **area** of LFC of an isolated **power** **system** have been reported and a number of **control** schemes like integral (I), Proportional and Integral (PI), Proportional, Integral and Derivative (PID) **control** have been proposed to achieve improved performance [4-7].**Fuzzy** -PI controllers have been proposed to solve **Load** **Frequency** **Control** problems, and developed different **fuzzy** rules for the proportional and integral gains separately. in this paper **three** case studies of **Fuzzy** -PI controllers different tuning of PI **controller**. The comparison results suggest that the overshoots and settling time with the proposed **Fuzzy** -PI controllers’ **controller** was better.

ABSTRACT: In this paper the **load** **frequency** **control** of the **three** **area** **system** consisting of thermal, hydro and photovoltaic **system**. **Using** modified hill climbing algorithm, the maximum **power** point tracking is performed in photovoltaic. Combining **fuzzy** **logic** with hill climbing algorithm the modified hill climbing is performed. Boost converter is used for this analysis. **Using** single phase inverter the obtained DC output is converted to AC output. By **using** sinusoidal pulse width modulation the Grid connection is performed. Transfer function model of the thermal , hydro is interfaced with the photovoltaic **system**. **Load** **frequency** **control** of this **three** **area** **system** and simulation of the entire **system** is performed in the MATLAB SIMULINK.

generation **control** (AGC) is a **system** for balancing the **power** output of multiple generators at different **power** plants, in response to changes in the **load**. In an interconnected **power** **system**, fluctuations in **frequency** caused due to **load** variations and penetration of renewable resources. **Load** variations occur in either one **area** or all areas of the **system** causes change in **system** **frequency** and tie line **power**. Due to high **frequency** deviation in interconnected **power** **system** could result in **system** collapse. **Load** **frequency** **control** is one of the most efficient method to solve these kinds of problems. In the proposed method a **three**-**area** **system** is considered i.e., **area**-1 with thermal **power** plant, **area**-2 with hydro **power** plant and **area**-3 with distributed generation (i.e., wind **power** plant, solar **power** plant etc.). In order to analyze the performance of a **three**-**area** **system**, the **system** responses are comparing the values of undershoot and settling time for each case **using** conventional **control** and **Fuzzy** **logic** **control** techniques separately for 1% disturbance in either **area**. **Load** **frequency** **control** (LFC) including conventional **controller** is proposed in order to suppress **frequency** deviations and **area** **control** error (ACE) for a **power** **system** involving wind, hydro and thermal plants. A **three**-**area** **system** involving thermal plants, a wind farm and a hydro plant will be modeled **using** MATLAB. The **controller** performances are simulated **using** MATLAB/SIMULINK simulation software.

The Multi **area** hybrid **controller** is designed in MATLAB Simulink for **load** **frequency** **control** [12]. The model is shown in fig. 4. **Area** 1 has a thermal turbine, **Area** 2 has thermal turbine with re-heater and **area** **three** has hydro turbine. Value of physical constant is taken appropriately. The simulation is done for the 1.5% step **load** change. The **fuzzy** **system** has used “mamdani” inference and having 25 if-then rule set. The fuzzification and de-fuzzification method is shown in fig. 5. This is based on the equation (9) derived above [13]. All the simulation result is compared with the conventional PI **control** methodology. **Area** **control** error is processed through the **fuzzy** interface with appropriate derivative and proportional constant. Triangular membership function has a range from -0.4 to 0.4 for input and output which is deduced from the optimisation of **controller** [14]. The graph has been plotted for **three** **area** **frequency** deviation and the graph for comparing the resultant deviation with the intelligent **controller** and orthodox controller.Tie line **power** variation with the **load** **frequency** is important variable that effect the overall **system** efficiency is also considered.

**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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Conventional Proportional plus Integral **controller** (PI) provides zero steady state **frequency** deviation, but it exhibits poor dynamic performance (such as number of oscillation and more settling time), especially in the presence of parameters variation and nonlinearity [10].In PI **Controller** Proportionality constant provides simplicity, reliability, directness etc. The disadvantage of offset in it is eliminated by integration but this **system** will have some oscillatory offset. The **control** signals can be written as:

Fig -4: Change of **frequency** of both areas (for the **system** with integral **controller** only) for reasonable **load**. It is observed that without **using** the integral **controller** the steady state **frequency** error does not disappear, which is undesirable. When including the integral **controller**, the steady state error requirement is met, however the settling time is much larger than the required 3s (it is more than 18s in both cases) and the undershoot is also much larger than the required 0.02 Hz. The second case of a sudden increase in **load** equal to 50% is an extreme case. However, even in the first case (i.e. reasonable change in **load** **power**), neither the undershoot nor the settling time criteria were met with integral **controller** only. Thus, FL **controller** is designed to enhance the **system** performance in terms of **system** **frequency**.

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**Fuzzy** **logic** **controller** has been used in both the thermal- thermal and hydro-thermal inter connected areas. Attempt has been made to examine with five number of triangular membership functions (MFs) which provides better dynamic response with the range on input (error in **frequency** deviation and change in **frequency** deviation) i.e. universe of discourse is -0.25 to 0.25. The number of rules is 25. The dynamic response are obtained and compared to those obtained with conventional integral controllers [3]. Further, several inputs have been tried out and dynamic responses are examined in order to decide suitable inputs to the **fuzzy** **logic** **controller** (FLC).

generation **control** scheme in electric **power** systems. Among the various types of **load** **frequency** controllers, the most widely employed is the conventional proportional integral (PI) **controller**. The PI, PID and **Fuzzy**-PID and ANFIS controllers are very simple for implementation and gives better dynamic response, but their performance deteriorate when the complexity in the **system** increases due to disturbance like **load** variation . Therefore, there is need of controllers which can overcome these problems. The artificial intelligent controllers like **fuzzy** and neural **control** approaches are more suitable in this respect. Literature survey shows that most of earlier work in the **area** of LFC pertains to interconnected reheat thermal **power** **system**. In this paper, the performance evaluation based on ANFIS, **Fuzzy**-PID and conventional PID for two **area** thermal interconnected **system**.

To be effective in **load** **frequency** **control** application, the energy storage **system** should be fast acting i.e. the time lag in switching from receiving (charging) mode to delivering (discharging) mode should be very small. For damping the swing caused by small **load** perturbations the storage units for LFC application need to have only a small quantity of stored energy, though its **power** rating has to be high, since the stored energy has to be delivered within a short span of time. However, due to high cost of superconductor technology, one can consider the use of non-superconducting of lossy magnetic energy storage (MES) inductors for the same purpose. Such systems would be economical maintenance free, long lasting and as reliable as ordinary **power** transformers.

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The Fig.2 shows the basic configuration of a SMES unit in the **power** **system**. The superconducting coil can be charged to a set value (which is less than the full charge) from the utility grid during normal operation of the grid. The DC magnetic coil is connected to the AC grid through a **Power** Conversion **System** (PCS) which includes an inverter/rectifier. Once charged, the superconducting coil conducts current, which supports an electromagnetic field, with virtually no losses. The coil is maintained at extremely low temperature (below the critical temperature) by immersion in a bath of liquid helium.

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Due to energy crisis and environmental issues such as pollution and global warming effect, photovoltaic (PV) systems are becoming a very attractive solution. Unfortunately the actual energy conversion efficiency of PV module is rather low. So to overcome this problem and to get the maximum possible efficiency, the design of all the elements of the PV **system** has to be optimised. In order to increase this efficiency, MPPT controllers are used. Such controllers are becoming an essential element in PV systems. A significant number of MPPT **control** schemes have been elaborated since the seventies, starting with simple techniques such as voltage and current feedback based MPPT to more improved **power** feedback based MPPT such as the perturbation and observation (P&O) technique or the incremental conductance technique [1-2]. Recently intelligent based **control** schemes MPPT have been introduced. Some converters operate at very high **frequency** with fast transient response. The main switch is fabricated from an integrated **power** process, the layouts can be changed to vary the parasitic, however design of switch layout is complex, fixed **frequency** and constant duty ratio must be maintained [5]. This converter provides high voltage gain and can be employed for high **power** applications however the duty ratio is limited to 0.85 [4]. In this, the energy of the leakage inductor is recycled to the output **load** directly, limiting the voltage spike on the main switch. To achieve a high step-up gain, it has been proposed that the secondary side of the coupled inductor can be used as boost and buck-boost converters [3-4].

For large scale **power** systems which normally consist of interconnected **control** areas, **load** **frequency** **control** (LFC) is important to keep the **system** **frequency** and the inter-**area** tie **power** as near to the scheduled values as possible. Because loading of a given **power** **system** is never constant and to ensure the quality of **power** supply, a **load** **frequency** **controller** is needed to maintain the **system** **frequency** at the desired nominal value. In a deregulated **power** **system**, each **control** **area** contains different kinds of uncertainties and various disturbances due to increased complexity, **system** modeling errors and changing **power** **system** structure Therefore, a **control** strategy is needed that not only maintains constancy of **frequency** and desired tie-**power** flow but also achieves zero steady state error and inadvertent interchange. Among the various types of **load** **frequency** controllers, the most widely employed is the conventional proportional integral (PI) **controller**.

Damping torque is produced to overcome rotor oscillation. To achieve better performance **fuzzy** **logic** can be implemented in a more effective way for **load** **frequency** **controller**. The **fuzzy** **controller** considered, has two phases, first being the **fuzzy** **system** unit where the **Area** **Control** Error (ACE) and its derivative (dACE) are set as input parameters. Before being connected to the output a rule base is created in the Mamdani **controller**. It is a common practice to build a rule base from terms such as s, z, and b representing labels of **fuzzy** sets. An input family may consist of those **three** terms. Consequently, with two inputs it is possible to build 3 x 3 = 9 rules. Nine rules is a manageable amount often used in practice, and the same is used here. The **fuzzy** codes are written in the .fis file in MATLAB **using** AND function in the Mamdani inference **using** triangular membership functions. The rules highly depend on the membership function, the rules are set in appropriate collection of input and output parameters. The **fuzzy** **controller** is implemented with and without an SMES unit. Both the results are separately shown in the results section.

Fuzzification: The fuzzification procedure consists of finding appropriate membership functions to describe crisp data. For the design of the proposed FLC, **power** mismatch, ∆p, and firing angle of thyristor, α, are selected as the input and output, respectively. Triangular membership functions for are shown in Figure 3, in which the linguistic variables N, Z, and P stand for negative, zero, and positive, respectively. The membership functions have been determined by the trial and error approach in order to obtain the best **system** performance. The equation of the triangular membership function used to determine the grade of membership values is as follows 3 :

------------------------------------------------------------------------***------------------------------------------------------------------------- Abstract - In this paper, **load** **frequency** **control** is one of the efficient ways to solve various problems in **power** **system**. The different configuration of models and **control** techniques are applied for **load** **frequency** **control** have been addressed which are applicable for generation **system**. An interconnected **system** for two areas is designed and simulated by **using** **fuzzy** **logic** **controller** for improved performance parameter. Like:-setting time, overshoot value, and undershoot value and maximum range over the conventional PID **controller**. The **control** methodology assures that the steady state error of **frequency** and exchange of tie-line **power** of **area** maintain within prescribed limit. The working of the two **area** **system** incorporating these controllers are simulated **using** MATLAB/Simulink packages.