Power System ModelThe training data sets of an ANN were obtained from simulation results basedon a typical distribution system as shown in Fig. 2. It depicts the sample studysystem of two radial distribution feeders with linear and nonlinear loads, voltagecorrection capacitor banks and equivalent HIF arc model. Since disturbancesresulting from HIFs may resemble those from capacitor switching and transformertap changing, it is therefore necessary to include cases with these contingencies toensure that the ANNs will not be confounded even under the high level of ambientharmonics so generated.Digital simulations were performed using an electro-magnetic transientprogram PSCAD/EMTDC [17] for different types of faults, fault location andother contingencies such as capacitor switching, signal phase load switching, nonload transformer switching etc.

DESIGN OF NEURAL NETWORK HIF DETECTOR

An ANN-based scheme is proposed as a high impedance faultdetection module. The proposed module processes the current and voltage signalsat the relay location at the beginning of the distribution line. Block diagram of theproposed approach is shown in Fig. 3.

3.1 Inputs and OutputsIn this study, six input signals are considered at the input layer of the proposed

ANN. The neural network processes lower order harmonics of the voltage andcurrent signals. The input signals are prepared at the preprocessing stage.The six input signals required at the input layer are namely Ir2, Ir3, Vr2, Vr3, Zr2and Zr3. Ir2 and Ir3 are the second and third harmonics of the residual current.Similarly, Vr2 and Vr3 are the second and third harmonics of residual voltage.The residual current and voltage are defined as:

Vr= Va + Vb + VcIr= Ia + Ib + Ic

where Va , Vb and Vc are the voltages at the relay location and Ia, Ib and Ic are thecurrents through the relay.Zr2 and Zr3 are the second and third harmonics of residual apparent impedanceof the faulted distribution line. They are measured at the relay location using (2):

The ANN unit processes its six inputs and makes a suitable decision based onthe power system state. Its output is activated for a high impedance fault andremains stable for other system states.

SHORT-TERM LOAD FORECASTING USING ANN TECHNIQUE

INTRODUCTION Multiple layer perceptrons have been applied successfully to solve some

difficult diverse problems by training them in a supervised manner with a highly popular algorithm known as the error back-propagation algorithm. This algorithm is based on the error-correction learning rule. It may be viewed as a generalization of an equally popular adaptive filtering algorithm- the least mean square (LMS) algorithm. Error back-propagation learning consists of two passes through the different layers of the network: a forward pass and a backward pass. In the forward pass, an input vector is applied to the nodes of the network, and its effect propagates through the network layer by layer. Finally, a set of outputs is produced as the actual response of the network. During the forward pass the weights of the networks are all fixed. During the backward pass, the weights are all adjusted in accordance with an error correction rule. The actual response of the network is subtracted from a desired response to produce an error signal. This error signal is then propagated backward through the network, against the direction of synaptic connections. The weights are adjusted to make the actual response of the network move closer to the desired response

APPROACH A broad spectrum of factors affect the system‟s load level such as trend effects, cyclic-time effects, and weather effects, random effects like human activities, load management and thunderstorms. Thus the load profile is dynamic in nature with temporal, seasonal and annual variations. In our project we developed a system that predicted 24 hour at a time load demand. As inputs we took the past 24 load and the day of the week. Melbourne was chosen for Victorian region and used the daily temperature, humidity and wind speed as input parameters. The city chosen was the major city in Victoria and as such gave sufficient representation to the change in weather parameter across the state. The inputs were fed into our Artificial Neural Network (ANN) and after sufficient training were used to predict the load demand for the next week. A schematic model of our system is shown in Fig 5.1. The inputs given are: 1. Hourly load demand for the full day.

2. Day of the week.

3. Min/Max/ Average daily temperature (Melbourne).

4. Min/Max daily Humidity (Melbourne).

5. Daily wind speed (Melbourne).

And the output obtained was the predicted hourly load demand for the next day. The flow chart is shown below.