However, this method has been tried for the systems presented in this thesis but could not yield reasonable results. This is because the method presented in [28] is for second and third order systems. However, the desired characteristic equation for higher order systems has no pre-defined standard formulas related to the specifications (settling time and undershoot). They rather depend on the choice of the desired closed loop poles, which by their turn depend on user experience. Moreover, not all the elements in the P matrix can be obtained by the comparison of both equations leaving behind many variables that need to be tuned based on trial and error for higher order systems, which leads to a very high cost function most of the time. Therefore, the best way to design **LQR** based controller for a higher order system is by creating an optimization code and tuning the system manually according to optimization results, which has been implemented in this thesis.

Show more
130 Read more

Performance of the uncontrolled system shown in Figure 1.3 is undesirable in terms of settling time, steady state error, and initial transient response. Proportional and Integral (PI), Proportional and Derivative (PD), or Proportional, Integral and Derivative (PID) **control** can be used to secure the system performance when changes occur on the **power** system parameters. There exist two stabilization techniques: Pole-Placement technique and Linear Quadratic Regulator (**LQR**) [6]. In this thesis, **LQR** technique is used to stabilize the system once a **control** is added. PID **control** is a powerful and well known tool to improve both transient and steady state performances. However, proper tuning of PID **control** can be a very complex task as there are three parameters ( š¾š¾ šš , š¾š¾ šš , š¾š¾ šš ) to be properly tuned to give the desired output response [7]. In [7], instructions of PID **control** tuning **using** MATLAB are explained in details. In [6], steady state performance of a two area interconnected thermal **power** system is considered after implementation of PI **control**.

Show more
107 Read more

L. A. Zadeh presented the first paper on **fuzzy** set theory in 1965. Since then, a new language was developed to describe the **fuzzy** properties of reality, which are very difficult and sometime even impossible to be described **using** conventional methods. **Fuzzy** set theory has been widely used in the **control** area with some application to **power** system [5]. A simple **fuzzy** **logic** **control** is built up by a group of rules based on the human knowledge of system behavior. Matlab/Simulink simulation model is built to study the dynamic behavior of converter. Furthermore, design of **fuzzy** **logic** controller can provide desirable both small signal and large signal dynamic performance at same time, which is not possible with linear **control** technique. Thus, **fuzzy** **logic** controller has been potential ability to improve the robustness of compensator.

Show more
There are some intelligent techniques, e.g. the **fuzzy** method, which are able to solve for more complex operating points [14]-[16]. Also in [17], the particle swarm optimization (PSO) method was used to optimize the parameters of the controller for the islanded microgrid. Although the results indicated good performance of this controller, incorrect selection of the corresponding coefficients can lead to oscillatory and instability in the system. Also in [18], a PSO algorithm with the proposed cost function for island microgrid consisting of solar **PV** and battery unit was implemented to reduce **power** and frequency oscillations levels. By comparing to the results of PI-based inverter **control**, this **control** strategy improved the performance of the controller over the time domain specification and reduced overshoot of **power** level in the transient conditions; however, there was still a problem similar to [17] here.

Show more
This concept in the case of a single-stage GCPPP. An issue that may show up in view of the deviation of the MPP during the voltage list is that, after the flaw being cleared, the dc-join voltage and ac currents may take a long time to come to the pre fault values, as appeared in Fig. 5(b) furthermore, (c). The reason is that the error in the dc-link voltage produces accumulation of **control** activity to the essential part of the proportional-integral (PI) controller. This **control** activity is constrained by the present limiter and in this way it has no impact on the lattice streams. Be that as it may, when the voltage list closes, the exorbitant **control** activity gathered in the necessary part of the controller must be remunerated by an input mistake the other way. As an outcome, the dc-link voltage is lessened beneath the reference value. For this situation, a critical diminishing of the dc-link voltage may lead inverter losing **control** and be disconnected. To conquer this issue, an anti-wind-up system is **connected** to stop the PI controller accumulating excessive **control** action when it exceeds a specified value. The schematic of the anti-wind-up method Is appeared in Fig.4. which V * dc and V dc are the reference also,

Show more
11 Read more

controller and hysteresis controller in APF-**PV** combination is studied, good steady state and fast dynamic response are achieved and the current THD is decreased to 3.24% in sliding mode. Authors are employed the direct **control** **power** DPC in APF-**PV** controller without voltage sensors [9], better compensation is observed in the line current and the THD value is equal to 1.5%. Artificial neural networks technics, **fuzzy** **logic** **control**, genetic are discussed in [10..12]. So far, the majority of **control** strategies for APF-**PV** combination are made in normal network conditions and have limited efficiency when the **grid** voltages are unbalanced or distorted which can degrade the electric system reliability and causes a poorer **power** quality. Therefore, the **control** methods of active **power** filter under unbalanced **grid** voltages have become an interesting concern [13, 14]. In this paper DPC is employed to **control** Shunt active **power** filter in **grid** **connected** **PV** system thanks to its simplicity and rapid dynamic response [15..18]. To be operating under unbalanced **grid** voltages, the proposed DPC controller is based on a new definition of reactive **power** called extended **power** theory developed by Komatsu [19].

Show more
10 Read more

MPPT charge controller use smart technologies, such as microcontrollers, to compute the highest possible **power** output at any given time. In this scenario the voltage will be monitored and regulated without **power** loss. Therefore in the same conditions as above, where the input voltage is higher than the output voltage, the MPPT charge controller will lower the voltage and simultaneously increase the current to the batteries. This results in higher **power** transfer efficiencies, which means less solar **power** is lost during the storage process [1].The peak **power** point tracking techniques vary in many aspects, such as: simplicity, convergence speed, digital or analog implementation, sensors required, cost, range of effectiveness, etc. [2] There are many MPPT algorithms that can be implemented in the MPPT **control** such as Perturb & observe , incremental conductance, constant voltage method, **fuzzy** **logic** **control**, neutral network etc. But, we are implementing P&O algorithm in the MPPT **control** because it can be implemented simply and the MPPT controllers available in the market are mostly programmed with this algorithm.

Show more
Inputs change in **power** and current and the output change in duty cycle are divided into four **fuzzy** subsets. Namely, negative big(NB), negative small(NS), positive small(PS), positive big(PB). Mamdaniās method with Max-Min is used for the **Fuzzy** combination [4],[5]. Defuzzification is the last stage of **fuzzy** controller. Center of Area Algorithm is used here for Defuzzification

voltage Ļwill drop. If it is higher than the lower value of the limiter Ļmin, the PLL can still operate normally, and the load voltage in Q-axis vCq will be zero. Otherwise, if it is fixed at Ļmin, the load voltage inQ-axis vCq will be negative. As the absolute values of vCd and vCq,at least the one of vCd, are raised, the magnitude of the load voltage will increase finally. The variation of the amplitude and frequency in the load voltage can also be explained by the **power** relationship mentioned before. When the islanding happens, the local load must absorb the extra **power** injected to the **grid**, as the output **power** of inverter is not changed instantaneously. According to (1), the magnitude of the load voltage Vm will rise with the increase of P load. At the same time, the angle frequency Ļ should drop, in order to consume more reactive **power** with (2). Therefore, the result through the **power** relationship coincides with the previous analysis.

Show more
13 Read more

This paper has introduced a new **control** of an existing **grid** interfacing inverter to improve the **power** quality at PCC for a 3-phase 4-WireDGsystem. The ability of the **grid**-interfacing inverter to be effectively used for the **power** conditioning without affecting itsnormal operation of real **power** transfer is also shown. The **grid**-interfacing inverter with the proposed technique can be utilized to: i) inject real **power** generated from RES to the **grid**, and/or, ii) operate as a shunt Active **Power** Filter (APF).This ap- proach helps to improve the quality of **power** at PCC with- out the need of additional **power** conditioning equipment. Extensive MATLAB/Simulink results have validated the proposed approach and have shown that the **grid**-interfac- ing inverter canbe utilized as a multi-function device. The simulation demonstrates that the PQ enhancement can be achieved under three different scenarios: 1) PRES = 0; 2) PRES < PLoad; and 3) PRES > PLoad. The current un- balance, current harmonics and load reactive **power**, due to unbalanced and non-linear load **connected** to the PCC, are compensated effectively such that the **grid** side cur- rents are always maintained as balanced and sinusoidal at unity **power** factor. The fourth leg of inverter prevents the load neutral current from flowing into the **grid** side by compensating it locally. When the **power** generated from RES is more than the total load **power** demand, the **grid**- interfacing inverter with the proposed **control** approach not only fulfills the total load active and reactive **power** demand (with harmonic compensation) but also delivers the excess generated sinusoidal active **power** to the **grid** at unity **power** factor.

Show more
The **control** diagram of **grid**- interfacing inverter for a 3- phase 4-wire system is shown in Fig. 2. To compensate the neutral current of load, a fourth leg is provided to the inverter. The proposed approach is mainly concerned about the regulation of **power** at PCC during three conditions like, when 1) PRES = 0; 2) PRES < total **power** (PL); and 3) PRES > PL. During the **power** management operation, the inverter is controlled in such a way that it always draws/ supplies fundamental active **power** from/ to the **grid**. If the load **connected** to the PCC is non-linear or unbalanced or the combination of both, the given **control** approach also compensates the harmonics, unbalance, and neutral current. By the **control**, duty ratio of inverter switches are varied in a **power** cycle in order to get the combination of load and inverter injected **power** to be appearing as balanced resistive load to the **grid**

Show more
213 **Fuzzy** **logic** is becoming popular for MPP tracking which overcomes the disadvantages of conventional methods. Many stand alone **PV** system and two-stage **grid** **connected** **PV** system use **fuzzy** **logic** controller for MPP that takes at least two input and generates the **control** output. The proposed **fuzzy** **logic** controlled modified Hill Climbing method for MPP tracking in micro **grid** stand- alone **PV** system. The algorithm generates change in duty ratio as an output with change in **power** and change in current as input. The two stage **grid** interactive **PV** system described in this paper supplies active and reactive **power** as well as provides the harmonic compensation during day time[6]-[7]. At night, the **PV** inverter still provides harmonic and reactive **power** compensation. Thus, the overall utilization of **PV** system is increased. The simulation results obtained **using** proposed algorithm gives the validity of the **grid** interactive **PV** system for reactive **power** and harmonic compensation features in addition to active **power** injection.

Show more
This paper presents the fault classification **using** **fuzzy** in a distributed generation, particularly photovoltaic **grid** **connected** system. The initial step in fault detection of **PV** system is recognition, investigation and classification of all possible faults that maybe occur in the system **using** **fuzzy** **logic** controller (FLC). Mainly the faults are identified as AC faults such as inverter side fault and DC fault such as fault in **PV** array and Dc link fault. A case study of a 100 kW array **connected** to a 25 kV **grid** via a DC-DC boost converter and a three-phase three-level Voltage Source Converter (VSC) system used to illustrate the proposed FLC **control** through MATLAB/Simulink software.

Show more
where ĪP is the **PV** array output **power** change, ĪI is the array output current change, and ĪD is the boost converter duty cycle change. To ensure that the **PV** output **power** does not diverge from the optimum point during varying weather conditions, ĪP passes through a gain controller to reverse its direction. The variable inputs and output are divided into four **fuzzy** subsets: positive big (PB), positive small (PS), negative big (NB), and negative small (NS). Therefore, the **fuzzy** rules algorithm requires 16 **fuzzy** **control** rules; these rules are based on the regulation of hill-climbing algorithm. To operate the **fuzzy** combination, Mamdaniās method with MaxāMin is used.

Show more
In this paper watched that a superior dynamic **power** **control** plot been constraining the greatest **power** which is nourish to the **PV** frameworks which is proposed there. The proposed arrangement ensure about the steady consistent **power** age operation. At the point when contrasted and the conventional techniques. Neuro **fuzzy** has many advantages i.e it can be stated that the normalized error obtained from neurofuzzy **logic** was lower. Here the proposed **control** methodology will powers the **PV** frameworks to work at the left half of the greatest **power** point, and which make to accomplish a steady operation alongside smooth changes. Neuro-**fuzzy** is a combination of ANN and **fuzzy** **logic**. By comparing all these techniques, the neuro- **fuzzy** controller is the lot of advantages, i.e the normalized error obtained from neuro **fuzzy** **logic** was lower . Reenactment have confirmed the viability of the proposed **control** arrangement with a specific end goal to limited **power** misfortunes, diminished overshoots and furthermore quick elements. In addition for single- arrange **PV** frameworks, same idea of CPG is likewise pertinent. In this way in such case, the **PV** voltage working extent will restricted and some little changes in the calculations which are important to ensure for a steady operation.

Show more
The **control** strategy for reactive **power** compensation for UPF mode of operation considers that the supply must deliver the dc component of direct-axis component of load current ( ) along with the active **power** component for maintaining the dc bus meeting the losses ( ). The dc link voltage of the VSC is maintained at a predetermined voltage level **using** a PI controller. This facilitates active **power** transfer from the **PV** array and determination of VSC losses. The PI controller generates a compensating current signal as,

[11] Jinhaeng Jang, Seokjae Choi, Byungcho Choi and Sungsoo Hong, "Average current mode **control** to improve current distributions in multi-module resonant dc- to-dc converters," **Power** Electronics and ECCE Asia (ICPE & ECCE), 2011 IEEE 8th International Conference, vol., no., pp.2312-2319, May 30 2011-June 3 2011. [12] Jaime CastellĆ³ Moreno, JosĆ© M. EspĆ Huerta, Rafael GarcĆa Gil and Sergio Alejandro GonzĆ”lez, ā Robust Predictive Current **Control** for Three-Phase **Grid**- **Connected** Invertersā. IEEE Transactions on Industrial Electronics, vol. 56, no. 6, June 2009.

Show more
11 Read more

A **fuzzy** **logic** controller (FLC) consists of four stages of interfacing mechanism in operation, which are a fuzzification interface, a rule base, an inference mechanism, and a defuzzification interface. It is a common practise to use error (e) and the rate of change of error (eā) as controller inputs. In **fuzzy** **logic** based DC voltage **control** across DC link, the capacitor voltage deviation and its derivative are considered as the inputs of the FLC and the real **power** (P) requirement for voltage regulation is taken as the output of the FLC. The input and output variables are converted into linguistic variables. The following seven variables are considered, they are NL (Negative Large), NM (Negative Medium), NS (Negative Small), ZE (Zero), PS (Positive Small), PM (Positive Medium) and PL (Positive Large).

Show more
11 Read more

There are many advantages of FL which comprise aspects that provide advantages to the **control** system. Firstly, is that the FL is easy to understand as there has numerous mathematical expression that are very simple and easy to comprehend. Its approach of ānaturalnessā and not complexity leads to the fact that FL is extremely interesting to study. Secondly, the FL can be created on besides taking into account the experience of experts; in other words means when in contrast to neural networks, which consider trainingdata and produce opaque, impenetrable models, on the peopleās experience who know the system can depend on FL. It is based on natural language and the foundations of **fuzzy** **logic** are parallel to that of human communication [4]. This paper describes a simulation modeling for the single-phase **grid**-**connected** DC/AC inverter for **PV** system. The inverter system model and the **control** strategies are carried out on MATLAB/SIMULINK environment. Utilizingfuzzy **logic** controller to manage **power** flow for the systemat three defferent loads profile. The inverter not only provides **power** to the local loads, but also feeds the **grid** with the available excess **power**. The **fuzzy** **logic** controller regulates real **power** for both modes of operations, namely isolated and **grid**-**connected** modes.

Show more