stations are located at remote locations from the load centers. However, for economical and technical reasons, most of the systems are electrically interconnected into vast power grids which are subdivided into regional operating groups called power pools. Each individual power system within such a pool operates technically and economically independently, but they are dependent on other pool members in respect to generation and scheduling aspects for which they have binding through a mutual contract. There are numerous advantages associated with operating in interconnected or pool fashion like; provision of construction of larger and more economical generating units and the transmission of large blocks of energy from the generating sources to major loads, reduced reserves requirements by sharing of capacity between areas and sectors, capacity savings from time zone and random diversity, capacity savings by seasonal exchange of capacity between areas which have opposing winter and summer needs, transmission facility of off-peak energy, flexibility to meet unforeseen emergency demands and importantly to have technical benefits out of the uses of the variability in generation mixes and load patterns.
In Fig. 8, the responses are shown with +10% changes in nominal system parameter values at SLP of 1% in area 1 to area 4. It indicates that the changes in frequency in area 1, area 2 and change in generated power of areas 4 and 5 are getting settled down at their steady state values within reasonably good time. Similarly with same amount of disturbance in areas 1 to 4, it is observed that the system is settled down quite fast with ─ 10% changes in system parameter values as shown in Fig. 9. This justifies the robustness of the fuzzy logic controller which is capable to withstand the changes in dynamic parameters of the five unequal area power system. It also depicts the effectiveness of the HVDC link in parallel with existing HVAC tie-line to suppress frequency, tie-line power, and generated power oscillations under various load perturbations in different areas of the system, as also depicted in Table II. From Table II we concluded that settling time, taken in a 5% band and peak overshoots of the deviation in frequency of area 1 (i.e. ∆ f 1 )
on load frequency control, and in particular AGC systems, is studied in . A robust controller that copes with communica- tion delays and other problems in the communication network and ensures good performance of load frequency control is given in . The analysis of the system behavior in the case an attacker gains access to the AGC signal and injects undesirable inputs to the system is studied in ; in this work the authors propose the design of an optimal control strategy to destabilize a two-area power system in the case such a cyber attack occurs. In , the authors propose a stochastic optimal relaxed AGC system, which takes into account NERC’s frequency performance standards, and reduces control cost by tuning the relaxation factors online. In , the authors formulate the frequency regulation problem by viewing future electric power networks as a general dynamical system driven by disturbances, and propose a modified AGC system that better responds to fast disturbances. A method to determine the impact of random load perturbations on system stability by calculating the evolution of the probability density function of system states with the Fokker-Planck equation is presented in . In , the authors motivate the need for stochastic models in power system analysis and propose a systematic approach that describes power system behavior as continuous stochastic differential-algebraic equations. In , the authors propose a framework to study the impact of stochastic power injections (e.g., arising from renewable-based generation) on power system dynamics. In , a study that investigates how individual wind turbines affect the wind farm performance under the AGC set-point operation is presented.
Applying flexible alternating current transmission system (FACTS) based controllers in interconnectedpowersystems is an effective solution to improve the dynamic performance of the AGC system . Due to fast dynamic responses, the series FACTS controllers such as thyristor controlled phase shifter (TCPS), static synchronous series compensator (SSSC), and interline power flow controller (IPFC), and thyristor controlled series capacitor (TCSC) have been employed in the tie-line of interconnectedpower sys- tems to damp the area frequency and tie-line power oscillations [14–16]. The TCSC is a high-performance and cost-effective series FACTS that has a significant practical background. Recently, a new dynamic modeling and control method of the TCSC applicable in AGC is presented in . It is proved that when the TCSC is added in series with the tie-line along with the AGC, the dynamic performance of the system is improved greatly. In order to imple- ment the TCSC-based damping controller, a 5th order approxima- tion of the Taylor series expansion has been used in [15,16]. Deciding on an appropriate approximation order is challenging. In fact, a compromise is established between the complication of the controller realization and its accuracy. This can be interpreted as a drawback for the suggested TCSC damping controller since the employed method uses approximation and lacks any precise model for the TCSC damping controller. In [17,18], a precise dynamic modeling and control of the TCSC in the AGC studies have been presented which has some good points over the approximated method. However, the contribution of the TCSC in the tie-line power flow exchange has not been presented independently. In the current paper, an attempt is made to present a new dynamic model and control method for the TCSC in which the tie-line power flow exchange is stated clearly in the mathematical formulations with and without the TCSC.
A PID 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 through use of a manipulated variable. The PID controllers are widely used in power system and controlsystems to damp system oscillations, increase stability and reduce steady state error as they are simple to realize and easily tuned. It is seen that if the proper tuning of parameter of PID controller is done, the area frequencies could brought back to its predefined value or very nearer to its predefined value with acceptable tolerance so as the tie line power in minimum time, when the is sudden change in load occurs .
In this work, controller tuning for automaticgenerationcontrol (AGC) of two-area interconnected thermal power system is carried out. The parameters of the studied system are listed in Appendix A. A classical PID controller with derivative filter is used. As it is already discussed any change of load in one area affects other area too as both areas are connected with each through tie-line. Ant lion optimization (ALO) algorithm is used to tune the controller parameters. To verify the controller performance, various sets of step load disturbances are created in both areas. The proposed controller is compared to particle swarm optimization (PSO) based controller to validate its superiority. The time-domain simulations are carried out to further validate the superior performance of the proposed controller. The social and cognitive parameter for PSO are taken as 2 and 2, respectively while inertia weight is taken as 0.9. A total of 50 iterations and 10 population size is considered for both ALO and PSO algorithm. All simulations are carried out in MATLAB environment. The boundary conditions of the controller parameters used in this work are defined as follows:
Abstract- This paper presents the GRC & AGC techniques which are useful for the study for the methods of artificial intelligence for the automaticgenerationcontrol of interconnectedpowersystems. In the given paper, a control line of track is established for interconnected three area power system using generation rate constraints (GRC) &Artificial Neural Network (ANN). The ANN controller is simulated using MATLAB/SIMULINK technique. The waveforms of both (i.e. with & without) controllers are compared with 1% step load conditions.
An efficient electromagnetic energy harvester featured with mechanical motion rectifier (MMR) is designed to recover energy from the vibration-like railroad track deflections induced by passing trains. Trackside electrical infrastructures for safety and monitoring typically require a power supply of 10-100 Watts, such as warning signals, switches, and health monitoring systems, while typical existing vibration energy harvester technologies can only harvest sub-watts or milliwatts power. The proposed harvester is designed to power major track-side accessories and possibly make railroad independent from national grid. To achieve such a goal we implement the MMR, a patented motion conversion mechanism which transforms pulse-like bidirectional linear vibration into unidirectional rotational motion at a high efficiency. The single-shaft MMR design further improved our previously developed motion mechanism, increased energy harvester efficiency and expanded power harvesting potential. The proposed new design improved reliability, efficiency, and provided steadier power output. Bench test of the harvester prototype illustrated the advantages of the MMR based harvester, including up to 71% mechanical efficiency and large power output.
Simulations were performed using Matlab®Simulink Fuzzy Logic Toolbox and Neural Network Toolbox. The parameters of the power system are given in appendix. The step load disturbance of 0.01 p.u. was applied in area-1 for all the cases and the deviations in frequency and tie-line power flows were investigated. The AGC performance with conventional optimized integral and Ziegler-Nichols tuned PID controller is compared with that of proposed fuzzy tuned PID the ANN controllers. The change in frequency deviation f1, f2 and the deviation in the tie -line power flow Ptie under the load disturbances of 0.01 p.u. in area - 1, are shown in Figures.4,5 and 6. It is observed that the performance is superior in case of ANN and Fuzzy controllers as compared to conventional controller.
4.1. Dynamic Response of Three Area Thermal-Thermal-Thermal Uncontrolled System Simulink model of thermal-3 uncontrolled system is studied in terms of size variation besides transient perfor- mance of the system. 1000 Mw is selected as base quantity and various parameters used in models are calculated on 1000 Mw basis. The system is first considered as uncontrolled, i.e. no supplementary controllers are consi- dered. 1% step load perturbation is given to one area. Frequency deviation and tie-line analysis shows that rating change has far less effect on system performance if control areas consist of same rating generators. As shown in Graphs 1-8, oscillations in terms of overshoot and undershoot produced on step unit change in load are low and settling time is also low. Settling time for frequency deviation and tie-line deviation stabilization is around 6 - 7 seconds for both systems with same and different ratings.
Feedwater control problems affect plant availability and challenge plant protection systems. The loss of feedwater is considered as a design basis accident. Many of the challenges associated with feedwater control in conventional Light Water Reactors are anticipated in advanced reactor designs and the IRIS reactor concept adds additional challenges to the feedwater controlproblem as a result of a steam generator design where neither level nor steam generator mass inventory can be measured directly. In addition, the flow is predominantly horizontal, and any pressure drop measurement across the secondary side of the tube bundle would be dominated by flow losses and only weakly correlated to the liquid mass inventory, even at low power.
In this case, the higher fault rating of the inverter means the inverter is better able to support its terminal voltage. Consequently, there are fewer fault locations that result in the inverter sympathetically tripping before the adjacent faulted feeder’s protection. At the electrically “closest” faults i.e. fault locations 10 and 11, the fault current is high enough to trip the feeder protection before the inverters on the non-faulted feeder disconnect. At ‘distant’ faults i.e. fault locations 16-18, the undervoltage at the various inverters’ terminals is between 0.8 pu and 0.87 pu so the inverter would disconnect according to the upper G83/2 setting of 2.5 s (long after the feeder protection operates, so sympathetic tripping will not occur). At the fault locations furthest along feeder 1 i.e. 19-26, the voltage at the terminals of the inverters remains above 0.87 pu, so the inverter will remain connected regardless of the fault duration. For fault locations 12-15 the voltage at the inverters’ terminals is below 0.8 pu so the inverters’ undervoltage protection operate within 0.5s and before the feeder protection, i.e. a manifestation of the sympathetic tripping problem. In summary, increasing inverter fault current capacity will reduce the risk of sympathetic tripping as shown in Table 5. This could be achieved by increasing the number of inverters (although this is not practicable, and the sympathetic tripping would still occur at lower penetrations) or by modifying the inverter control algorithm. In the latter case the limiting factors in increasing the inverters fault current contribution would be the availability of the primary energy source supplying the inverter (in an urban network this is likely to be solar radiation) and the rating of the inverter itself.
through the globe. Grid-connected three- phase voltage source inverters encourage power spill out of the photovoltaic cluster to the heap and the connected power grid. The enormous increment of grid-connected PV generation system can posture genuine difficulties in keeping up grid solidness, power quality; power confuses, powercontrol, energy administration and furthermore proficient security undertakings, and so on. Different active and reactive power stream control strategies for the three- phase grid-connected PV cluster have been accounted for by a few specialists as of late. The most famous strategy received by the majority of the analysts incorporate high bandwidth current controllers for the voltage source converter interface in which direct- and quadrature-hub currents acquired with asynchronously turning reference frame are utilized. A three-phase reference frame plan to wipe out the voltage swell at dc connection and yield voltage distortion of VSC systems in the micro-grid application have been presented. A large portion of the controllers has a place with either PI or hysteresis band write controllers. Additionally prescient current controller] had been before proposed and actualized in asynchronously pivoting reference frame. It is notable that the performance of the PI controllers intended for one working condition deteriorates when the working state of the PV cluster is changed.
demand. In large interconnectedpower system networks regulation by using manual methods is not feasible. So automatic equipments for regulation are installed on each generator. In AGC controllers are set to operate for a particular operating condition and keep the frequency within the specified limits by taking care of small changes in load demand. If there is any variation in the load demand on a generating unit, it results in unbalance between real power input and output. The difference between real power input and output is supplied by the stored kinetic energy of the rotating parts of the unit.
Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Kennedy and Dr. Eberhart in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. The detailed information will be given in following sections. Compared to GA, the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust. PSO has been successfully applied in many areas: function optimization, artificial neural network training, fuzzy system control, and other areas where GA can be applied.
For example, a rule is make up that says- if temperature is low and humidity is low then room is cool. So here we have membership functions that define by low temperature (input1), low humidity (Input 2 ) and cool room (output1) . Taking temperature as an input known to be process of fuzzification, and defining “and”/ “or” by means of fuzzy rules is known as combination of fuzzy. Rules for the mamdani membership function in ANFIS modeling are easy and understand -able for learning of process model. due to its human friendly behavior most of times it uses in the systems as compare to sugeno fuzzy membership functions, which needs computational nature in order to designing system model designing and rules creation.
Currently system became too complex with addition of more utilities, which may leads to a condition where supply and demand has got a wide gap. Due to heavy load condition in tie-lines by electric power exchange results in poor damping which may leads to inter-area oscillation. Since the loading conditions are unpredictable, this makes the operation more complex. It has been a topic of concern, right from the beginning of interconnectedpower system operation. The block diagram of interconnected generating system is shown in fig 4. These three generating areas are interconnected by tie lines.
Maintaining a nominal frequency and rated voltage within allowable limits is the most important requirement of power system operation. This ensures proper power system operation avoiding blackouts. Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR) Control loops are mostly used in powersystems to ensure quality power with rated frequency and voltage to the customer -. The scheme in which generation is adjusted automatically to restore the frequency to nominal value, as the system load changes continuously is called as AutomaticGenerationControl (AGC) which can make the interconnectedpower system more economic and reliable. The role of AGC is to divide the loads among system stations and generators so as to achieve maximum economy and to correctly control the scheduled interchanges of tie-line powers while maintaining a reasonable uniform frequency -.
Noble excellence of electrical power system means both the voltage and frequency to be fixed at desired values irrespective of change in loads that occurs randomly. It is in fact impossible to maintain both active and reactive power without control which would result in variation of voltage and frequency levels. To cancel the effect of load variation and to keep frequency and voltage level constant a control system is required. Though the active and reactive powers have a combined effect on the frequency and voltage, the controlproblem of the frequency and voltage can be separated. 
In PSO algorithm, the system is initialized with a population of random solutions, which are called particles, and each solution is also assigned a randomized velocity. Each particle is treated as a valueless particle in g-dimensional search space and keeps track of its coordinates in the problem space associated with the best solution and this value is called pbest. The overall best value and its location obtained so far by any particle in the group that was tracked by the global version of the particle swarm optimizer gbest. The PSO concept consists of changing the velocity of each particle toward its pbest and gbest locations at each time step. As example, the jth particle is represented as xj = (xj,1 , xj,2 , . . . ,xj,g) in thegdimensional space. The best previous position of the jth particle is recorded and represented as pbestj = (pbestj,1 , pbestj,2 , . . , pbestj,g). The selection of best particle among all particles in the group is represented by the gbestg.