Analysis Of Electrical Load Behaviour Of 100Kva On Save Life Foundation Hospital

ABSTRACT

When a disturbance occurs in a power system, the value of load power will change in response to the voltage and frequency changes. Step changes in power generation, for example, will induce speed changes in the system frequency and this induces speed changes in any induction motors or other dynamic loads present in the load, and these speed changes will be seen as power changes to the load. Dynamic loads consume 60-70% of the energy from the power system. It is important to have good knowledge about Dynamic loads, because these type of loads draw large reactive currents that can slow voltage recovery after a fault. In this paper the effect of frequency changes (f) on a various electrical loads real (P) and reactive (Q) power changes have been modeled.  Among the parameters of different electrical loads, the behavior is largely characterized by active power (P) and the reactive power (Q). The model of electrical loads in these frequency and power relations that has been developed can be used to estimate P and Q.

CHAPTER ONE

1.0                                                            INTRODUCTION

This work analyses models used to simulate various power quality behavior of loads. The models presented include fault in distribution line, starting of induction motor , and energizing of transformer that are used to simulate various types of voltage sag event. Capacitor bank switching model used to simulate oscillatory transient event, lightning impulse model used to simulate impulsive transient event, nonlinear load models used to simulate triple harmonic and voltage notching disturbances generated from the load side, and lastly electric arc furnace model used to simulate flicker disturbance are also presented.

Electrical power system is expected to deliver distorted sinusoidal rated voltage and current continuously at rated frequency to the consumers. In recent years, grid users have detected an increasing number of drawbacks caused by electric power quality (PQ) variations and PQ problems have sharpened because of the increased number of loads sensitive to PQ and have become more difficult to solve as the loads themselves have become important causes of degradation of quality [1].Therefore, these days, customers demand higher levels of PQ to ensure the proper and continued operation of such sensitive equipments. According to IEEE standard 1159-1995 [2], the PQ disturbances include wide range of PQ phenomena namely transient (impulsive and oscillatory), short duration variations (interruption, sag and swell), power frequency variations, long duration variations (sustained under voltages and sustained over voltages) and steady state variations (harmonics, notch, flicker etc.) with time scale ranges from tens of nanoseconds to steady sate. A number  of causes of transients can be identified: lightning strokes, planned switching actions in the distribution or transmission system, self- clearing faults or faults cleared by current limiting fuses, and the switching of end-user equipment. Transient phenomena are extremely critical since they can cause over voltages leading to insulation breakdown or flashover. These failures might trip any protection device initiating a short interruption to the supplied power. Excess current produced by transients may lead to complete damage to system equipment during the transient period. Moreover, if such disturbances are not mitigated, they can lead to failures or malfunctions of various sensitive loads in power systems and may be costly.

In electricity market scenario, now electricity consumers can shift to the new service providers, if power quality is not good. Moreover, these customers can demand a higher quality of service. The utilities or other electric power providers have to ensure a high quality of their service to remain competitive and to retain/ attract the customers. Therefore the Power Quality has been a challenge for power system planners and researchers. The main task of PQ analysis involves detection, identification, recognition and classification of various types of PQ behavior. In this work, an analysis of PQ issues, types of PQ behavior, automatic power quality recognition system, feature extraction techniques and artificial intelligence based classification methods proposed by the researchers recently are presented.

1.1                                BACKGROUND OF THE STUDY

The electricity market in Save Life Foundation in India is undergoing dramatic changes. Legal, social and political drivers for a more carbon efficient electricity network, along with the dramatically increased flow of data from households through the deployment of smart meters, requires a transformation of existing practices. In particular, the change of the frequency of sampling of electricity usage, by using smart meters, alters the level of understanding of households’ behaviour that is possible [1].

One approach to address the pressures on the electricity network is the application of Demand Side Management (DSM) techniques to achieve changes in consumer behaviour. DSM is defined as “systematic utility and government activities designed to change the amount and/or timing of the customer’s use of electricity” for the collective benefit of society, the utility company, and its customers [2]. The peak time for electricity usage in the India is during the early evening and the successful application of techniques to reduce, or move, the peak usage would improve the overall efficiency of the electricity network.

To allow selection of appropriate DSM interventions, a good understanding of the existing behaviour of hospitals is needed. Firstly, knowledge is needed on an individual household that can be deduced from house-wide electricity metering. Secondly, a method is required to group large numbers of hospitals into a manageable number of archetypal groups where the members display similar characteristics. This approach allows for cost effective targeting of the most appropriate subset of customers whilst allowing the company management to deal with a manageable number of archetypes [3].

There is an extensive body of work on clustering the hospital which includes comparing or combining timed meter readings to create additional attributes that contribute to the quality of the clustering [4]. However, little work has focused on how the daily activity patterns of the hospital vary from day to day and how this can be used for clustering.  Ellegard and Palm [5] have investigated the variability of behaviour using diaries and interviews but have not used analysis of meter data.

Clustering households using their degree of variability in behaviour, as shown by electricity consumption, provides a way of identifying the subset of electricity users who may be most receptive to an intervention to influence their activity patterns. The intervention may be to reward hospital for NOT changing their current pattern of usage if it is already as desired by the utility company.

This paper addresses the question of whether making use of the variability of  behaviour (as shown by the electricity meter data) provides “better” groupings of households for the purpose of  DSM than those provided by using daily load profiles. The judgement of “better” is measured by implementing a number of different clustering techniques and measuring the degree of overlap between the clusters found. A consistent set of clusters across the different clustering algorithms implies a better, and more useful, approach to generating the clusters.

The investigation of household electricity load profiles is an important area of research given the centrality of such patterns in directly addressing the needs of the electricity industry, both now and in the future. This work extends existing load profile work by taking electricity meter data streams and developing new ways of representing the household that can be used as the basis for clustering using existing data mining techniques. The identification of repeating motifs and the investigation of how the timing of the motifs varies from day to day, as a key behavioural trait of the hospital, is a novel area of research.

1.2                                                      AIM OF THE PROJECT

The aim of this work is to analyse the behavior of supply electrical energy or power to customers.  Non linear loads, utility switching and fault clearing produce disturbances that affect the quality of this delivered power.  In the present scenario, electric power is viewed as an integral product with certain characteristics, which can be measured, predicted, guaranteed and improved. And this work was carried out on save life foundation hospital.

1.3                                                    SCOPE OF THE PROJECT

Power quality means the quality of the normal voltage supplied to our homes, factories, etc.  It is based on the extent of variation of the voltage and current wave forms from the ideal pure sinusoidal wav eforms of fundamental frequency.  To improve the power quality, it is necessary to know what kind of behavior occurred.  A power quality monitoring system that is able to automatically detect, characterize and classify disturbances on electrical lines is thus required.

1.4                                            SIGNIFICANCE OF THE PROJECT

This paper presents a comprehensive overview of different techniques used for feature extraction and classification of power disturbance behavour. This paper helps the researchers to know about the different methods presented so far for power quality behavior classification or recognition, so that further work on power quality improvement can be carried out for better results.