Artificial Neural Network (ANN)-Based Voltage Stability Prediction of Test Microgrid Grid

Abstract

The Power Grid Initiative is currently engaged in persistent endeavors to convert the conventional power grid into a smart grid with the objective of enhancing the operation of the power system. As per the United States Department of Energy, the contemporary power grid comprises a range of constituents such as information management systems, communication technologies, field devices, and other related components. The optimal functioning of said components yields enhanced power quality, thereby reducing power losses and augmenting the dependability of the power provision. During the operational process, the interdependence of interconnected components can present various challenges, including load forecasting, voltage stability evaluation, and power grid security. The present investigation involves the utilization of an artificial intelligence network (ANN) to facilitate the training and prediction of the nodal voltage level pertaining to the IEEE 4-bus system. Four discrete models have been postulated to authenticate the efficacy of the Artificial Neural Network (ANN) methodology. The findings indicate that the ensemble of models exhibits superior performance compared to other models in forecasting the nodal voltage stability of the aforementioned electrical system. The ensemble model attains the highest accuracy rate of 98.73%. Additionally, the mean squared error and mean absolute error exhibit the best (lowest) values of 0.0095 and 0.0141, respectively. The outcomes obtained indicate the efficacy of the Artificial Neural Network (ANN) methodology, particularly in the ensemble configuration, for predicting the voltage stability of the electrical power grid. INDEX TERMSVoltage stability, machine learning, artificial neural networks, forecasting electric power system, ensemble learning. I. INTRODUCTIONThe conventional electric power grid is being converted into a network that is controlled electronically as part of the Energy Grid Initiative. The traditional grid was previously controlled by an electromechanically operated system. According to the Power Grid System Report, [1] published by the US Department of Energy, the components that make up Power Grid systems include information management, control technology, digital sensing, communication technologies, and field devices. These components work in conjunction with each other to control a number of distinct electrical processes. These technologies collaboratively participate in aThe associate editor coordinating the review of this manuscript and approving it for publication was Youngjin Kim .

Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges

“…Standard computer processing takes place in a fundamentally different way from how the human brain works, which must be taken into account. The human brain can execute some tasks better than even the most sophisticated conventional computers, such as pattern recognition and vision, because of its complex, nonlinear, and parallel working [34]. Artificial intelligence has improved computer capabilities by modeling the biological information-processing system of humans.…”

Convolutional Long Short-Term Memory (ConvLSTM)-Based Prediction of Voltage Stability in a Microgrid

“…These solutions use different criteria for voltage instability to determine how close they are to the voltage collapse threshold. The precise collapse points and voltage security margins were also determined using continuation techniques [9][10][11]. The concept of an energy margin, which serves as a measure of the security of system voltage, is easily comprehensible from an intuitive point of view.…”

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

“…The authors of [90] considered the problem of preserving VS in isolated EPS. For the numerical experiment, the simplest IEEE4 model was used.…”