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Showing 2 results for Load Uncertainty
Using a New Method to Incorporate the Load Uncertainty into the SEP Problem
H. Kiani Rad, Z. Moravej,
Volume 15, Issue 3 (9-2019)
Abstract
In this paper, a new method is conducted for incorporating the forecasted load uncertainty into the Substation Expansion Planning (SEP) problem. This method is based on the fuzzy clustering, where the location and value of each forecasted load center is modeled by employing the probability density function according to the percentage of uncertainty. After discretization of these functions, the location and value of each of the new load centers are determined based on the presented fuzzy clustering based algorithm. A Genetic Algorithm (GA) is used to solve the presented optimization problem in which the allocations and capacities of new substations as well as the expansion requirements for the existing ones are determined. With the innovative presented method, the impact of uncertainty of the power and location of the predicted loads on the results of SEP is measured, and finally, it is possible to make a proper decision for the SEP. The significant features of this method can be outlined as its applicability to large-scale networks, robustness to load changes, the comprehensiveness and also, the simplicity of applying this method to various problems. The effectiveness of proposed method is demonstrated by application on a real sub-transmission system.

State Estimation in Electric Power Systems Based on Adaptive Neuro-Fuzzy System Considering Load Uncertainty and False Data
M. Ahmadi Jirdehi, V. Sohrabi-Tabar,
Volume 17, Issue 3 (9-2021)
Abstract
Control center of modern power system utilizes state estimation as an important function. In such structures, voltage phasor of buses is known as state variables that should be determined during operation. To specify the optimal operation of all components, an accurate estimation is required. Hence, various mathematical and heuristic methods can be applied for the mentioned goal. In this paper, an advanced power system state estimator is presented based on the adaptive neuro-fuzzy interface system. Indeed, this estimator uses advantages of both artificial neural networks and fuzzy method simultaneously. To analyze the operation of estimator, various scenarios are proposed including impact of load uncertainty and probability of false data injection as the important issues in the electrical energy networks. In this regard, the capability of false data detection and correction are also evaluated. Moreover, the operation of presented estimator is compared with artificial neural networks and weighted least square estimators. The results show that the adaptive neuro-fuzzy estimator overcomes the main drawbacks of the conventional methods such as accuracy and complexity as well as it is able to detect and correct the false data more precisely. Simulations are carried out on IEEE 14-bus and 30-bus test systems to demonstrate the effectiveness of the approach.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.