International Journal For Multidisciplinary Research

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Multibus Power Transmission System’s Voltage Stability Limit Prediction Using Artificial Neural Network

Author(s) Kabir Chakraborty, Apurba Kumar Das
Country India
Abstract The objective of this paper is to predict the voltage stability limit of a multi-bus power transmission system using Artificial Neural Networks (ANN). When a system is overloaded or a change in the systems condition, there is an uncontrollable drop or collapse of voltage occurs. The weakest, weaker, and weakest bus in the IEEE - 30 bus systems can be identified by using the Newton Raphson method of load flow analysis. The IEEE 30 bus system is simulated using MATLAB programming. In the Jacobian matrix of the said system, there are four sub matrixes that are J1, J2, J3 and J4. By using the values of diagonal elements of J4 matrix, dq / dv of the load buses has been calculated and inverted these values to find the dv/dq index for finding out the weak segment of the network. Finding the critical bus voltage magnitude and active power loading values is the goal of the study's second phase. Plotting the PV curve of the matching buses is how this is accomplished. For this reason, the weakest bus active power loading has been gradually increased while the active power loading of the other buses has remained constant. The goal of the paper's final page was to use an artificial neural network (ANN) to discover the crucial bus voltages for any unforeseen loads. In this case, a multilayer feed forward network and a back propagation algorithm were used to determine the critical voltage magnitude values.
Keywords Voltage Stability, Reactive power sensitivity indicator, Artificial Neural Network
Field Engineering
Published In Volume 5, Issue 4, July-August 2023
Published On 2023-07-09
Cite This Multibus Power Transmission System’s Voltage Stability Limit Prediction Using Artificial Neural Network - Kabir Chakraborty, Apurba Kumar Das - IJFMR Volume 5, Issue 4, July-August 2023. DOI 10.36948/ijfmr.2023.v05i04.4259
DOI https://doi.org/10.36948/ijfmr.2023.v05i04.4259
Short DOI https://doi.org/gsgntt
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