961 research outputs found
Control techniques for power system stabilisation
The conventional PSS was first proposed earlier based on a linear model of the power system to damp the low frequency oscillations in the system. But they are designed to be operated under fixed parameters derived from the system linearized model. Due to large interconnection of power system to meet the load demand brings in deviations of steady-state and non-linearity to power system. The main problem is that PSS includes the locally measured quantities only neglecting the effect of nearby generators. This is the reason for the advent of Wide area monitoring for strong coupling between the local modes and the inter-area modes which would make the tuning of local PSSs for damping all modes nearly impossible when there is no supervisory level controller. Wide area control addresses these problems by proposing smart topology changes and control actions. Dynamic islanding and fast load shedding are schemes available to maintain as much as possible healthy transmission system. It is found that if remote signals from one or more distant locations of the power system can be applied to local controller design, system dynamic performance can be enhanced. In order to attain these goals, it is desirable to systematically build a robust wide area controller model within an autonomous system framework
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Multi particle swarm optimisation algorithm applied to supervisory power control systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs.
The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems
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