Abstract: Low frequency power oscillations may be triggered
by many events in the system. Most oscillations are damped by the
system, but undamped oscillations can lead to system collapse.
Oscillations develop as a result of rotor acceleration/deceleration
following a change in active power transfer from a generator. Like
the operations limits, the monitoring of power system oscillating
modes is a relevant aspect of power system operation and control.
Unprevented low-frequency power swings can be cause of cascading
outages that can rapidly extend effect on wide region. On this regard,
a Wide Area Monitoring, Protection and Control Systems
(WAMPCS) help in detecting such phenomena and assess power
system dynamics security. The monitoring of power system
electromechanical oscillations is very important in the frame of
modern power system management and control. In first part, this
paper compares the different technique for identification of power
system oscillations. Second part analyzes possible identification
some power system dynamics behaviors Using Wide Area
Monitoring Systems (WAMS) based on Phasor Measurement Units
(PMUs) and wavelet technique.
Abstract: This paper presents Genetic Algorithm (GA) based
approach for the allocation of FACTS (Flexible AC Transmission
System) devices for the improvement of Power transfer capacity in an
interconnected Power System. The GA based approach is applied on
IEEE 30 BUS System. The system is reactively loaded starting from
base to 200% of base load. FACTS devices are installed in the
different locations of the power system and system performance is
noticed with and without FACTS devices. First, the locations, where
the FACTS devices to be placed is determined by calculating active
and reactive power flows in the lines. Genetic Algorithm is then
applied to find the amount of magnitudes of the FACTS devices. This
approach of GA based placement of FACTS devices is tremendous
beneficial both in terms of performance and economy is clearly
observed from the result obtained.
Abstract: In this paper, multilayered coreless printed circuit
board (PCB) step-down power transformers for DC-DC converter
applications have been designed, manufactured and evaluated. A set
of two different circular spiral step-down transformers were
fabricated in the four layered PCB. These transformers have been
modelled with the assistance of high frequency equivalent circuit and
characterized with both sinusoidal and square wave excitation. This
paper provides the comparative results of these two different
transformers in terms of their resistances, self, leakage, mutual
inductances, coupling coefficient and also their energy efficiencies.
The operating regions for optimal performance of these transformers
for power transfer applications are determined. These transformers
were tested for the output power levels of about 30 Watts within the
input voltage range of 12-50 Vrms. The energy efficiency for these
step down transformers is observed to be in the range of 90%-97% in
MHz frequency region.
Abstract: Analytical expression for maximum power transfer
through a transmission line limited by voltage stability has been
formulated using exact representation of transmission line with
ABCD parameters. The expression has been used for plotting PV
curve at different power factors of a radial transmission line.
Limiting values of reactive power have been obtained.
Abstract: A wireless power transfer system can attribute to the
fields in robot, aviation and space in which lightening the weight of
device and improving the movement play an important role. A
wireless power transfer system was investigated to overcome the
inconvenience of using power cable. Especially a wireless power
transfer technology is important element for mobile robots. We
proposed the wireless power transfer system of the half-bridge
resonant converter with the frequency tracking and optimized
power transfer control unit. And the possibility of the application
and development system was verified through the experiment with
LED loads.
Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.
Abstract: The Inter feeder Power Flow Regulator (IFPFR)
proposed in this paper consists of several voltage source inverters
with common dc bus; each inverter is connected in series with one of
different independent distribution feeders in the power system. This
paper is concerned with how to transfer power between the feeders for
load sharing purpose. The power controller of each inverter injects
the power (for sending feeder) or absorbs the power (for receiving
feeder) via injecting suitable voltage; this voltage injection is
simulated by voltage drop across series virtual impedance, the
impedance value is selected to achieve the concept of power exchange
between the feeders without perturbing the load voltage magnitude of
each feeder. In this paper a new control scheme for load sharing using
IFPFR is proposed.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.