Gitanjali Saha

Bio: Gitanjali Saha is an academic researcher from Tripura Institute of Technology. The author has contributed to research in topics: Electric power system & Artificial neural network. The author has an hindex of 2, co-authored 3 publications receiving 8 citations.

Off-line voltage security assessment of power transmission systems using UVSI through artificial neural network

Abstract: Coming days are becoming a much challenging task for the power system researchers due to the anomalous increase in the load demand with the existing system. As a result there exists a discordant between the transmission and generation framework which is severely pressurizing the power utilities. In this paper a quick and efficient methodology has been proposed to identify the most sensitive or susceptible regions in any power system network. The technique used in this paper comprises of correlation of a multi-bus power system network to an equivalent two-bus network along with the application of Artificial neural network(ANN) Architecture with training algorithm for online monitoring of voltage security of the system under all multiple exigencies which makes it more flexible. A fast voltage stability indicator has been proposed known as Unified Voltage Stability Indicator (UVSI) which is used as a substratal apparatus for the assessment of the voltage collapse point in a IEEE 30-bus power system in combination with the Feed Forward Neural Network (FFNN) to establish the accuracy of the status of the system for different contingency configurations.

Voltage Stability Prediction on Power Networks using Artificial Neural Networks

Abstract: The objective of this paper is to predict the secure or the insecure state of the power system network using a hybrid technique which is a combination of Artificial Neural Network (ANN) and voltage stability indexes. Voltage collapse or an uncontrollable drop in voltage occurs in a system when there is a change in the condition of the system or a system is overloaded. A Transference Index (TI) which acts as a voltage stability indicator has been formulated from the equivalent two-bus network of a multi-bus power system network, which has been tested on a standard IEEE 30-bus system and the result is validated with a standard Fast Voltage Stability Index (FVSI). FACTS devices in the critical bus have been considered for the improvement of the voltage stability of the system. An ANN based supervised learning algorithm has been conferred in this paper alongside Contingency Analysis (CA) for the prediction of voltage security in an IEEE 30 - bus power system network.

Recent Developments in Machine Learning for Energy Systems Reliability Management

Abstract: This article reviews recent works applying machine learning (ML) techniques in the context of energy systems’ reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of ML. The objective is to foster the synergy between these two fields and speed up the practical adoption of ML techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, microgrids, and multienergy systems.

Voltage collapse point evaluation considering the load dependence in a power system stability problem

Abstract: Voltage Stability has emerged in recent decades as one of the most common phenomena, occurrence in Electrical Power Systems. Prior researches focused on the development of algorithm indices to solve the stability problem and in the determination of factors with most influence in voltage collapse to solve the stability problem. This paper evaluates the influence that the load dependence has with the voltage on the phenomenon of the voltage stability and especially on the characteristics the collapse point or instability point. Load modeling used is detailed and comparisons of the results obtained are made with those described in the bibliography and those obtained with commercial software. The results of the load margin are also compared when a constant load or a voltage-dependent load is considered as well as the values obtained at the maximum load point and the point of voltage instability.

Intelligent based technique for under voltage load shedding in power transmission systems

Abstract: The increasing demand of electric power energy and the presence of disturbances can be identified as the factors of voltage instability condition in a power system. A secure and reliable power system should be considered to ensure smooth delivery of electricity to the consumers. A power system may experience undesired event such as voltage instability condition leading to voltage collapse or cascading collapse if the system experiences lack of reactive power support. Thus, to avoid blackout and cascaded tripping, load shedding is the last resort to prevent a total damage. Under Voltage Load Shedding (UVLS) scheme is one of the possible methods which can be conducted by thepower system operators to avoid the occurrence of voltage instability condition. This paper presents the intelligent based technique for under voltage load shedding in power transmission systems. In this study, a computational based technique is developed in solving problem related to UVLS. The integration between a known computational intelligence-based technique termed as Evolutionary Programming (EP) with the under-voltage load shedding algorithm has been able to maintain the system operated within the acceptable voltage limit. Loss and minimum voltage control as the objective function implemented on the IEEE 30-Bus Reliability Test System (RTS) managed to optimally identify the optimal location and sizing for the load shedding scheme. Results from the studies, clearly indicate the feasibility of EP for load shedding scheme in loss and minimum voltage control in power system.

Online Learning Artificial Neural Network Controller for a Buck Converter

Abstract: This paper presents an Online Learning Artificial Neural Network Controller (OLANNC) for a DC-DC Buck converter. The proposed control scheme uses a Perceptron Online Learning algorithm to stabilize the output voltage. The OLANNC obtains the appropriate duty cycle of the PWM signal that determines the switching operation of the semiconductor device. To verify the effectiveness of the proposed method, a simulation results are presented with some operations such as reference voltage variations. Comparison with a typical controller is also presented to denote it advantages.

Prediction of electricity sales using neural based inverse distance weighting method

Abstract: Prediction of electricity sales becomes important for State Electricity Company of Indonesia (PLN) in order to estimate the Statement of Profit and Loss in next year. To obtain good predictive results may require many variables and data availability. There are many available methods that do not require so many variables to get predicted results with a good approximation. The aim of this study was to predict electricity sales by using an interpolation method called IDW (Inverse Distance Weighting). Several data samples are mapped into Cartesian coordinates. The data samples used are power connected to the household (X-axis), to industry (Y-axis), and electricity sales (Z value). Firstly, the sampled data clustered by using SOM algorithm. The Z value in each cluster is predicted by using the IDW method. The prediction results of IDW method are then optimized using ANN-BP (Artificial Neural Network Back Propagation). The trained net structure is then used to predict the electricity sale in next year.