Abstract: High Voltage Direct Current (HVDC) power
transmission is employed to move large amounts of electric power.
There are several possibilities to enhance the transient stability in a
power system. One adequate option is by using the high
controllability of the HVDC if HVDC is available in the system. This
paper presents a control technique for HVDC to enhance the transient
stability. The strategy controls the power through the HVDC to help
make the system more transient stable during disturbances. Loss of
synchronism is prevented by quickly producing sufficient
decelerating energy to counteract accelerating energy gained during.
In this study, the power flow in the HVDC link is modulated with the
addition of an auxiliary signal to the current reference of the rectifier
firing angle controller. This modulation control signal is derived from
speed deviation signal of the generator utilizing a PD controller; the
utilization of a PD controller is suitable because it has the property of
fast response. The effectiveness of the proposed controller is
demonstrated with a SMIB test system.
Abstract: This paper presents an application of Artificial Neural
Network (ANN) algorithm for improving power system voltage
stability. The training data is obtained by solving several normal and
abnormal conditions using the Linear Programming technique. The
selected objective function gives minimum deviation of the reactive
power control variables, which leads to the maximization of
minimum Eigen value of load flow Jacobian. The considered reactive
power control variables are switchable VAR compensators, OLTC
transformers and excitation of generators. The method has been
implemented on a modified IEEE 30-bus test system. The results
obtain from the test clearly show that the trained neural network is
capable of improving the voltage stability in power system with a
high level of precision and speed.
Abstract: The objective of the Economic Dispatch(ED) Problems
of electric power generation is to schedule the committed generating
units outputs so as to meet the required load demand at minimum
operating cost while satisfying all units and system equality and
inequality constraints. This paper presents a new method of ED
problems utilizing the Max-Min Ant System Optimization.
Historically, traditional optimizations techniques have been used,
such as linear and non-linear programming, but within the past
decade the focus has shifted on the utilization of Evolutionary
Algorithms, as an example Genetic Algorithms, Simulated Annealing
and recently Ant Colony Optimization (ACO). In this paper we
introduce the Max-Min Ant System based version of the Ant System.
This algorithm encourages local searching around the best solution
found in each iteration. To show its efficiency and effectiveness, the
proposed Max-Min Ant System is applied to sample ED problems
composed of 4 generators. Comparison to conventional genetic
algorithms is presented.