produced from the synchronous generator unit 137 Figure 4.32 Active power sharing performance of micro-grid units 138 Figure 4.33 Reactive power response of DGs following change load 138 Figure 4.34 Frequency deviation (Hz) and time response 139 Figure 4.35 Frequency time response from transient to steady- state 139 Figure 4.36 Frequency error during step load change 140 Figure 4.37 Rotor speed oscillation in island MG 140 Figure 4.38 Rotor speed response by proposed FLTC and PI control 141 Figure 4.39 Rotor speed oscillation at step load change in island mode 141 Figure 4.40 The rotor speed oscillations for the different adaptation gain (a.g)
Fuzzy logic is mathematical tool which provides solutions for a complex problem where there is no well-defined mathematical formulation. Mamdani and Takagi Sugeno interface fuzzy systems are commonly used for control applications. An application of a mamdani interface system is found in  where a fuzzy-based speed governor was designed for a mini hydro power plant that supplies varying load. In  an adaptivefuzzy logic controller was designed using a mamdani interface system for loadfrequencycontrol of a small hydro plant. Some applications of Takagi Sugeno fuzzy systems are presented in [13,14]. In , a Takagi Sugeno fuzzysystem is designed to efficiently control static synchronous compensator (STATCOM) installed in a power system network under various disturbances. The voltage and frequency of an isolated wind turbine controlled by a Takagi Sugeno fuzzy logic controller considering variation in wind speed and load power can be found in . A fuzzy logic system integrated with an optimization technique to provide fast and robust results has been presented in [15–18]. In , a fuzzy-logic–genetic-algorithm model is developed to estimate monthly solar radiation. The results presented in  indicate that the fuzzy–genetic model more accurately estimates the monthly solar radiation than an artificial neural network (ANN) and neuro fuzzy model. The application of a fuzzy-basedcontroller to tune the PI control parameters for a hybrid electric vehicle application is found in . A cuckoo-search-optimization-basedfuzzy logic controller is developed in  to operate a hybridsystem consisting of PV, battery and diesel generator in standalone mode. PSO used to tune the membership function of a fuzzycontroller for solar PV MPPT control is carried out in . Application of fuzzy logic to solve diverse engineering problems can be found in the literature; however, the application of fuzzy to find the PI controller parameters for a DG used in a power management problem is very limited.
Scientists have great attention about the adaptive neuro fuzzy interference system because it deals with nonlinearities and does not require exact numerical modeling of the system . ANFIS controller has the potential to give an enhanced execution even to a system with wide parameter varieties . The ANFIS controller has advantages of robust, basic, simple to be changed, usable for multi information. Utilization of ANFIS control rather than PI conventional controller can be a powerful app roach to explain the issue of variation in the system parameters
Therefore, variable structure controller [Benjamin et al., 1982; Sivaramaksishana et al., 1984; Tripathy et al., 1997 & Shayeghi et al., 2004] has been proposed for AGC. For designing controllers based on these techniques, the perfect model is required which has to track the state variables and satisfy system constraints. Therefore it is difficult to apply these adaptivecontrol techniques to AGC in practical implementations. In multi-area power system, if a load variation occurs at any one of the areas in the system, the frequency related with this area is affected first and then that of other areas are also affected from this perturbation through tie lines. When a small load disturbance occurs, power systemfrequency oscillations continue for a long duration, even in the case with optimized gain of integral controllers [Sheikh et al., 2008 & Demiroren, 2002]. So, to damp out the oscillations in the shortest possible time, automatic generation control including SMES unit is proposed.
• The system should have least maintenance requirement and minimum cost of installation. • The DC microgrid should be able to adapt changes even after initial sizing and installation. These may be changes in storage or generation capacity, and changes in the load pattern. III. NEED FOR ENERGY MANAGEMENT Energy management algorithm and power converters together provide the necessary control to the system . WTG and photovoltaic panel can be controlled to extract maximum power from the available natural sources. Energy storage system requires management for deciding which storage should be used in case of a hybrid energy storage system, and for deciding the charge-discharge cycles of chosen storage. The DC-link voltage must be maintained constant for balanced flow of energy among the multiple sources and loads, in a DC microgrid. Also, a variation of DC-link voltage would disrupt normal
The proposed adaptive AFNN control mechanism is applied on the 50 kW SOFC power plant system that has been modeled in MATLAB/Simulink. In order to check the performance and validation of the proposed controller, a short-duration active power transient study has been conducted considering SOFC stack under constant fuel flow. To estimate response of SOFC power system as grid real power demand change, step increase and decrease transients were applied on the system. The SOFC stacks have slow response to rapid and sustained load transients, observed throughout simulation. When a step change of power was experienced by utility grid, the power electronic inverter circuitry sensed these perturbations and the robust VSI control signal was effectively conditioned to able the SOFC plant to ramp up its output to meet required load demand. The results are compared with conventional PI controller to show the faster response time of proposed control strategy.
Turbine governors are systems for the control and adjustment of the turbine power output and for evening out deviations between the power and the gridload as quickly as possible. Two main governors are used to automatically control the frequency of the generating unit). First, it could remain constant by action on the gate opening position to produce just the necessary power according to the connected\ load. Second, electronic load controllers (ELC) govern the frequency by adjusting the electrical load connected to the alternator. Therefore, they maintain a constant electrical load on the generator in spite of changing users' load. In this case, the turbine gate opening is kept in a specific position that guarantees a nominal mechanical power at the generator shaft. It permits to use turbine with no flow regulating devices. The former governor takes a long time to stabilize the output and it becomes insufficient in case of large load variations where the stability of the system could be completely lost. ELC is used in order to simplify the MHPP control. The stabilizing time is short even for large load variations.
LFC systems basically use simple PI controller and I controller, whose parameters are usually tuned based on classical control or trial and error approaches. The controller provides zero steady-state frequency deviation but it exhibits poor dynamic performance. The basic approaches to design controllers are not effective to obtain good dynamic performance for various load changes scenarios and disturbances in an interconnected or an isolated power system. PI controllers are very often used in industry, especially when speed response is not an issue. Control strategies used for LFC include linear feedback optimal control, artiﬁcial neural networks, fuzzy logic control techniques, adaptive self-tuning and decentralised control techniques.
In th is paper, the basic ABC frame is chosen for the controller strategy. In this frame, each phase can be controlled separately, while it would still wo rk p roperly in unbalanced conditions. For better tracking and rejecting of sinusoidal signals resonant controllers are used. This controller has an infinite gain at a certain frequency called resonant frequency and almost zero gain at other frequencies. The theory beyond this kind of controller is discussed in more details in . To avoid stability problems associated with an infinite gain at the resonant frequency, some damping can be added to have a quasi-resonant integrator as follows:
The growth in size and complexity of electric power system due to nonlinear load characteristics and variable operating points has necessitated the use of fuzzybased methods to address satisfactorily the performance under small perturbations. A jaya algorithm optimized fuzzy pi controller , Adaptive Neuro Fuzzy Interface Systemcontroller containing SMES-TCPS , a Firefly algorithm (FA) optimized fuzzy PID controller , Fuzzy gain scheduled PI controller . It has been observed in literature survey that most of the researchers adopt thermal-thermal or thermal hydro systems in LFC studies. The bulk power transmission through HVDC lines connected with AC lines possesses many advantages like fast controllability of HVDC lines through convertor control, ability to reduce the transient stability problem of AC lines and other economical and technical operation of power system.
frequencycontrol in a complex and competitive environment is a tedious process, the control technique must be more intelligent and adaptive for a changing environment. More research work has been carried on Frequencycontrol in restructured power system -. Possible issues in Frequencycontrol in energy market were dis- cussed in . Vertically integrated structure and bilateral based scheme modeling were discussed in  . Different approaches like pluralistic and hierarchical methods were discussed and quoted in . A decentralized approach based on control theory was formulated and analyzed in . Some researchers have proposed a fre- quency control logic based on fuzzy logic controller , reinforcement learning  and artificial neural network  in interconnected power systems.
Up to the present time, numerous studies have been done on the controller design to enhance the frequency fluctuations of hybrid mini/micro-grids. The traditional proportional-integral (PI) controller was widely been under consideration of many researchers to control the frequency of hybridmicro-grids [9-11]. In Refs. [9, 10], particle swarm optimization (PSO) algorithm is used to the optimal design of PI controller for the frequency of a hybridmicro-grid include energy storage systems. Authors in Ref.  represent the application of dispersed generation (DG) resources to achieve the power balance conditions. The results of numerous investigations on the frequencycontrol of microgrids over the past three decades have been reviewed in . This literature includes sixteen optimization methods and programming tools such as HOMER (hybrid optimization model for electric renewables), HOGA (hybrid optimization using genetic algorithm), etc. Additionally, design, optimization and evaluation of photovoltaic, solar-wind, combined systems have been evaluated in a comprehensive review. Given that, the increase in the number of renewable energy resources and their uncertainties in mini/micro-grids is unavoidable, the use of traditional control methods does not have the ability to damp the frequency oscillations . Therefore, the need for adaptive, robust and efficient control mechanisms are more feeling day by day , [14-16]. Here in [1, 14, 15] new frequencycontrol methods based on the optimization algorithms and fuzzy logic for a micro-grid integrated with renewable and storage systems along with electric vehicles, are considered. Khalghani et al. in  have represented a controller to control the frequency of a micro-gridbased on the emotional learning procedure of the human brain. According to the results, it's evident that although these controllers have a better dynamic response than traditional controllers, they also have a relatively complex structure. Therefore, their design is difficult and it is impossible to obtain their optimal structure for more complex systems. Consequently, a training-based intelligent control strategy is required to control the frequency of the hybrid mini/micro-grid, so that it can adapt itself to the system's variable conditions and always perform the optimal control policy [17, 18]. Reinforcement learning (RL) is a computational method
ABSTRACT: This paper proposes power conditioning scheme based on fuzzy logic for load side inverter. In this configuration a single-phase transformer less inverter for grid-tied photovoltaic (PV) system because of minimal effort, high efficiency, light weight, and so forth. In this way, numerous transformer less topologies have been proposed and confirmed with genuine power infusion as it were. As of late, practically every worldwide control has forced that an unequivocal measure of reactive power ought to be taken care of by the grid-tied PV inverter The new topology structure and detail operation principle with reactive power flow is portrayed. The high frequency common mode (CM) model and the control of the proposed topology are broke down. The inalienable circuit structure of the proposed topology does not lead itself to the turnaround recuperation issues notwithstanding when infuse reactive power which permit using MOSFET changes to support the general efficiency. The CM voltage is kept consistent at midpoint of dc input voltage, comes about low spillage current. There has been an expanding enthusiasm for transformer less inverter for grid-tied photovoltaic (PV) system because of ease.
input to the generator which will transform this mechanical energy into electrical energy. The reheater makes the system more efficient as it reheats the steam to keep the same high temperature of the steam that entered the governor.  In this section the specific mathematical model of each area has been presented along with the connection of these two areas in one system and the response of this system without controllers. The final model is shown with the photovoltaic system connected to it. This PV system has been designed separately based on [ 3] but is not the focus of this paper. The integral controller is required in both areas since one of the criteria to be met is a steady state error equal to zero. Since the integral controller adds one state variable to the model of the system, it has been included to the models of both areas for which the FL controller is designed.
The LoadFrequencySystem (LFC) system investigated is composed of an interconnection of two areas under open market system. Area 1 comprises of a reheat system and area 2 comprises of hydro system. Fig. 1 is the block diagram of two-area hydrothermal system under open market scenario where ACE of each area is fed to the corresponding controller. The accurate control signal is generated for every incoming ACE at that particular load change. A performance index given by J t f f P tie
Changes in electrical load in each system lead to frequency and voltage deviations, and if these changes do not resolve quickly and efficiently, major problems such as customer failure and power outage will happen. Automatic production control is usually used to counteract these deviations in frequency and voltage and reduce them to acceptable levels. In this paper, a new frequencycontroller for multi-region power systems is designed based on direct and indirect adaptivefuzzycontrol. Frequency controllers for each area will be designed based on the frequency deviation of each area and the deviation of the power line between the zones. The ability of the fuzzysystem approximation to develop appropriate adaptivecontrol rules and algorithms for updating parameters are used to reduce the uncertainty effect in the communication lines of the frequencycontrol regions of the load. The proposed comparative fuzzycontroller efficiency is evaluated during load disturbance for a three-zone power system. The simulation results on this power system show the effectiveness of the proposed comparative fuzzyloadfrequencycontroller and is clearly demonstrated by comparison with the classic PID controller and the conventional fuzzycontroller.
algorithms give a better solution for some particular problems than others do. The intelligent controllers such as Particle Swarm Optimization based PID controller , Differential Evolution Algorithm based PID controller , Differential Evolution Particle Swarm Optimization based PID controller , Firefly optimized PID , Teaching Learning Based Optimization (TLBO) optimized PID . The growth in size and complexity of electric power system due to nonlinear load characteristics and variable operating points has necessitated the use of fuzzybased methods to address satisfactorily the performance under small perturbations. A jaya algorithm optimized fuzzy pi controller , Adaptive Neuro Fuzzy Interface Systemcontroller containing SMES-TCPS , a Firefly algorithm (FA) optimized fuzzy PID controller , Fuzzy gain scheduled PI controller .
The Multi area hybridcontroller is designed in MATLAB Simulink for loadfrequencycontrol . 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 fuzzysystem 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 . 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 . 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 loadfrequency is important variable that effect the overall system efficiency is also considered.
V. PROPOSED STRATEGY TO CONTROL THEGENERATED POWER IN THE MICROGRID In stand-alone and distributed renewable energy systems, there is no commercial or conventional grid to absorb any surplus power generated internally in the microgrid. Therefore, the generated power needs to be controlled when the load power is less than the amount of power that could be generated by the energy sources. This is necessary to keep the energy balance in the microgrid under control and to keep the battery bank voltage below or equal its maximum allowable value. This is necessary since voltages higher than the gasification voltage can decrease the life span of batteries or even damage them irreversibly .In the proposed control strategy, the GFC verifies the battery bank voltage to know if it reached the maximum allowed charging voltage and, if so, change the microgridfrequency to inform the other sources that they must reduce their generated power. Based on the microgridfrequency, the control systems of the power generation sources connected to the microgrid decide whether to restrict the power generated by each of them.
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 basedfrequencycontrol 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