Economic load dispatch is the process of allocating the required load demand between the available generators in power system while satisfying all units and system equality and inequality constraints. Economic Load Dispatch solutions are found by solving the conventional methods such as lambda iteration, Gradient search method, Linear Programming and Dynamic Programming while at the same minimizing fuel costs, but convergence is too slow, so in order to get fast convergence and accurate results we are using artificial neural network. Artificial neural network is well-known in the area of power systems. It is a very powerful solution algorithm because of its rapid convergence near the solution. This property is especially useful for power system applications because an initial guess near the solution is easily attained. In this paper a three generator system is considered and by using lambda iteration method Economic Load Dispatch is determined and 150 patterns for different loads will be derived from same method to train neural network. As it is too slow method, we proposed a soft computing based approach i.e. Back Propagation Neural Network (BPNN) for determining the optimal flow. This method provides fast and accurate results when compared with the conventional method.
A novel Extreme learning Machine (ELM) algorithm based tuning of the parameters of the SVC FACTS controller was implemented in order to control voltage at various buses over a wide range. The ELM algorithm is a non-iterative method which forecasts the parameters of SVC FACTS controller quickly and effectively while Back Propagation Neural Network (BPNN) algorithm is an iterative method which takes a long time for training as well prediction of parameters. The load perturbation is one of the nonlinear disturbances which is considered to investigate the operational capability of the control methodology. Standard IEEE 5 and 30 bus systems are considered as test systems and operation of two models of SVC observed with the BPNN and ELM controllers. The weakest bus is identified using L-Index method which is the optimal location of the SVC FACTS. Results show that the novel ELM method expeditiously and efficiently tunes the parameters of the SVC FACTS controller online such that the voltage regulated to desired value when there is a perturbation in load.
Voltage stability is the most vital phenomena in power systems which may be mainly disturbed by a mismatch in the reactive power generation and load. Not only reactive power imbalance sometimes due to internal faults of the equipments and short circuit faults there may be voltage collapse at the buses. Voltage stability can be enhanced using shunt devices such as Static VAR Compensator (SVC). It can generate or absorb reactive power in a controlled manner such that it can able to enhance Voltage Stability. Voltage Stability Index method is used to determine Voltage sensitivity at each bus and the bus having highest Voltage stability index value can be considered as weak bus which is the optimal location of facts controller. In this paper investigation is made to observe how susceptance model and firing angle model of SVC is used to enhance the voltage at each bus under chaotic load case is observed. IEEE 5-bus and 30-bus systems are considered as test systems and simulations are carried out in Matlab environment.
In this paper a Power System Stabilizer (PSS) is developed with Artificial Intelligent techniques to damp the low frequency oscillations thereby improve the stability of multi machine power system. To damp the low frequency oscillations lead-lag, fuzzy and artificial neural network power system stabilizers were designed for single machine connected to infinite bus and 4-machine, 11-bus system. From the result it was observed that conventional controllers such as lead-lag PSS cannot be applied at all operating points which also gives a slow response. Fuzzy Logic PSS (FLPSS) will give better and faster response compared to the conventional controller. Artificial Neural Networks (ANN) give the superior damping characteristics compared to remaining controllers. The performance of the each and every individual controller is analyzed in terms of Integral absolute error, Integral squared error, peak value and settling time of the response. ANN gives better response in all aspects and the simulation is carried out in MATLAB environment.
Keywords:power system stabilizer, fuzzy logic controller, artificial neural network controller, Integral Absolute Error(IAE), Integral Squared Error (ISE)
Voltage stability is the most vital phenomena in power systems which may be disturbed by the mismatch between the reactive power supply and demand. The occurrence of internal faults in the equipment and short circuit faults also there may be voltage collapse at the buses. Voltage stability can be improved using Static VAR Compensator (SVC) which is a shunt device. It can generate or absorb reactive power in a controlled manner such that it can enhance voltage stability of the system. LIndex method is used to determine voltage sensitivity at each bus and the bus having highest L- index value can be considered as a weak bus which is the optimal location of FACTS controller. The investigation is made to observe how susceptance in susceptance model and firing angle in firing angle model of the SVC is predicted to enhance the voltage at each bus by the artificial neural network under chaotic load. Standard IEEE 5 bus and 30 bus systems are considered as test systems and simulations are performed in MATLAB software.
The paper presents an adaptive Load Frequency Controller (LFC) based on a neural network for the interconnected multi-area systems. When there is an imbalance between active power generation and demand there will deviation in the frequency from the reference value. Major disturbances that lead to the variation in frequency beyond the allowable limits are variation in load demand and faults, etc. Initially PID based LFC which is a conventional controller is used to bring back the variations in frequency when there is a disturbance. But these conventional controllers will operate certain operating points only, very slow and, are less efficient for nonlinear systems. To avoid the flaws in the conventional controller the artificial intelligent controllers such as neural network and fuzzy logic controllers are designed. The three, two area, and single area systems are considered as the test systems. The response of all the test systems is observed without and with PI, fuzzy, and neural network controllers. It was observed that the neural network controller is outperforming in damping the variation in the frequency due to the disturbances.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.