Artificial Neural Network - 1

Only available on StudyMode
  • Download(s) : 351
  • Published : September 30, 2010
Open Document
Text Preview



This is to certify that ,

Mr. Ankur Gupta

Class : B.E. ELECTRICAL Roll No . 10

has completed Seminar work Satisfactorily in the Department of Electrical engineering as prescribed by The Bharati Vidyapeeth Deemed University in the academic year 2004-05.


Seminar Guide H.O.D.

Electrical Engg. Dept.



The aim of this seminar is to introduce a new technique to control the voltage and reactive power in power systems based on Artificial Neural Network (ANN). Feed-forward ANN with Back Propagation training algorithm is used and the training data is obtained by solving several abnormal conditions such as increase in load, increase in losses etc. using Linear Programming. Considering generator voltages, reactive power sources and transformer taps as control variables, and load bus voltages and generator reactive powers as dependent variables, the relations are derived according to relations based on Newton-Raphson load flow equations. The load increase and system losses are chosen as objective function so that the approach minimizes system losses and enhances voltage profile. This technique is tested by computer simulation on IEEE 6-bus power system and satisfactory results are obtained.


One of the main requirements in power system is to keep the load bus voltage within limits specified for the proper operation of equipment. Any changes to the System configuration or in power demands can result in higher or lower voltages in the system. The operator can improve this situation by reallocating reactive Power generations in the system by control devices (transformer tap settings, generator voltage magnitudes and switching VAR sources). In this respect, an efficient control technique is needed. The main objective function of reactive power control is: • To improve the voltage profiles.

• To minimize the system losses.
In the past, several techniques have been employed to overcome this complex problem. Those techniques give the approximate changes in bus voltages for a given control action. In these approaches, the bus voltage violations are alleviated one by one. So these methods can be used in small number of violations. In case of many violations, the method may run into an infinite number of iteration. To avoid these difficulties linear programming approach has been proposed to yield the control actions. In most of these studies, the LP problem has been formulated using real valued control variables in order to reduce the computational effort. However these methods are complex and require significant computational effort to determine the required adjustments to control variables. Artificial intelligence methods have also been applied to control the reactive power and voltage to be within acceptable limits. The expert system (ES) techniques are applied to identify the system operating conditions, detect the bus or buses at which certain constraints have been violated, and select the appropriate control actions to alleviate the voltage violations. Therefore, ES decides and gives proper signals to perform the control actions of the power system. However, in the case of large power system, these techniques have problems, because the excepted time saving will be decreased, in this respect the knowledge base is larger and consequently the search time will be increased. Artificial Neural Network (ANN) is applied to control the voltage and reactive power in power systems, a three layer Feed-forward ANN with Back Propagation training algorithm is trained to give the proper control action...