19-01-2013, 02:50 PM
ARTIFICIAL NEURAL NETWORKS FOR INTELLIGENT REAL TIME POWER QUALITY MONITORING SYSTEM
ARTIFICIAL NEURAL NETWORKS.pptx (Size: 521.5 KB / Downloads: 30)
COMPARISON WITH CONVENTIONAL NETWORKS
With traditional computers, processing is sequential--one task, then the next, then the next, and so on.
The idea of threading makes it appear to the human user that many things are happening at one time.
However, this is only an appearance; processes are not actually happening simultaneously
INTRODUCTION TO ANN(ARTIFICIAL NEURAL NETWORK)
A wide variety of real time quality monitoring systems are serving the power industry.
. A quick review will show that most of the monitoring systems are used just to track the quality of power supply and for load flow analysis.
. For the monitoring system to be more intelligent,we propose the use of ANN for predicting the trend of power factor,active power and reactive power.
. By predicting power factor and active power demand it is possible to auotmate the control of reactive power load and to better utilise the VA inflow.
IMPORTANCE OF REACTIVE POWER
The ratio of active power (P) measured in watts to the apparent power (S) in volt-amperes is termed the power factor
It has become a normal practice to say that the power factor is lagging when the current lags the supply voltage and leading when the current leads the supply voltage.
This means that the supply voltage is regarded as the reference quantity.
A majority of loads served by a power utility draw current at a lagging power factor.
When the power factor of the load is unity, active power equals apparent power (P = S).
But, when the power factor of the load is less than unity, say 0.6, the power utilized is only 60%. This means that 40% of the apparent power is being utilized to supply the reactive power, VAR, demand of the system.
Choosing the initial weights
:The learning algorithm uses a steepest descent technique, which rolls straight downhill in weight space until the first valley is reached. This valley may not correspond to a zero error for the resulting network. This makes the choice of initial starting point in the multidimensional weight space critical. However, there are no recommended rules for this selection except trying several different starting weight values to see if the network results are improved.