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  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.
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) . 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 ( 𝐾𝐾 𝑔𝑔 , 𝐾𝐾 𝑖𝑖 , 𝐾𝐾 𝑑𝑑 ) to be properly tuned to give the desired output response . In , instructions of PID control tuning using MATLAB are explained in details. In , steady state performance of a two area interconnected thermal power system is considered after implementation of PI control.
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 . A simple fuzzylogiccontrol 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 fuzzylogic controller can provide desirable both small signal and large signal dynamic performance at same time, which is not possible with linear control technique. Thus, fuzzylogic controller has been potential ability to improve the robustness of compensator.
There are some intelligent techniques, e.g. the fuzzy method, which are able to solve for more complex operating points -. Also in , 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 , 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  here.
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,
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 controlpower DPC in APF-PV controller without voltage sensors , better compensation is observed in the line current and the THD value is equal to 1.5%. Artificial neural networks technics, fuzzylogiccontrol, 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 gridconnectedPV 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 .
Grid voltage is taken as Peak voltage 300V. Grid impedance is taken as R-L series branch with R=0.5ohm, L=1mH.. Active power injection into the grid is controlled by MPPT technique. Depending upon duty cycle the active power injected into Grid either increases or decreases.Active power flowing from port one to port two is given by
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 .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.  There are many MPPT algorithms that can be implemented in the MPPT control such as Perturb & observe , incremental conductance, constant voltage method, fuzzylogiccontrol, 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.
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). Mamdani’s method with Max-Min is used for the Fuzzy combination ,. Defuzzification is the last stage of fuzzy controller. Center of Area Algorithm is used here for Defuzzification
voltage ωwill drop. If it is higher than the lower value of the limiter ωmin, the PLL can still operate normally, and the load voltage in Q-axis vCq will be zero. Otherwise, if it is fixed at ωmin, 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 ω should drop, in order to consume more reactive power with (2). Therefore, the result through the power relationship coincides with the previous analysis.
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.
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
213 Fuzzylogic is becoming popular for MPP tracking which overcomes the disadvantages of conventional methods. Many stand alone PV system and two-stage gridconnectedPV system use fuzzylogic controller for MPP that takes at least two input and generates the control output. The proposed fuzzylogic 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-. 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.
This paper presents the fault classification usingfuzzy in a distributed generation, particularly photovoltaic gridconnected 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 usingfuzzylogic 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.
where ΔP is the PV array output power change, ΔI is the array output current change, and ΔD 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, ΔP 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 fuzzycontrol rules; these rules are based on the regulation of hill-climbing algorithm. To operate the fuzzy combination, Mamdani’s method with Max–Min is used.
In this paper watched that a superior dynamic powercontrol 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 fuzzylogic. By comparing all these techniques, the neuro- fuzzy controller is the lot of advantages, i.e the normalized error obtained from neuro fuzzylogic 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.
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,
 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.  Jaime Castelló Moreno, José M. Espí Huerta, Rafael García Gil and Sergio Alejandro González, “ Robust Predictive Current Control for Three-Phase Grid- Connected Inverters”. IEEE Transactions on Industrial Electronics, vol. 56, no. 6, June 2009.
A fuzzylogic 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 (e’) as controller inputs. In fuzzylogic 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).
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 ―naturalness‖ 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 people‘s experience who know the system can depend on FL. It is based on natural language and the foundations of fuzzylogic are parallel to that of human communication . 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 fuzzylogic controller regulates real power for both modes of operations, namely isolated and grid-connected modes.