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Optimal Power Flow Using Adaptive Droop Coefficients and Considering the Probability Approach of Renewable Sources and Load
S. Rajamand,
Volume 18, Issue 2 (June 2022)
Abstract
Fair distribution of generated power has a significant impact on the performance of the power system. Many methods have been proposed for the safe and secure operation of power systems under the uncertainties of distributed generators and system load. In this paper, we present an optimal power distribution algorithm for distributed generators against uncertainties and load changes of direct-current and alternating-current transmission systems. In this optimal algorithm, considering the stable-state constraints for all uncertainties is performed. In order to establish these constraints at the lowest cost, the adaptive droop coefficients are employed to optimize the power sharing, reloading and modifying the power coefficient of each distributed generator in the power system. Simulation results show the efficiency of the proposed method to improve the performance of the system and reduce the total cost. The voltage/power deviation from reference value in the proposed method is about 1-1.5% where in the conventional droop control, it is more than 2-3%. In addition, in the same uncertainty of the load/distributed generator power in the test system, proposed method requires 20% less power redistribution compared to the conventional droop method. Also, total cost increasing (due to uncertainty increasing) in the conventional droop method is higher than the proposed method (about 10-15%) which shows the robustness of the suggested method against uncertainty changes.

Noise Covariance Matrix Estimation in Target Tracking in Three Approaches: n-step Prediction, Kalman Gain Covariance and Gamma-distribution of Noise Statistics
Sahbasadat Rajamand, Abdulhamid Zahedi,
Volume 22, Issue 3 (September 2026)
Abstract
Noise parameters in many target tracking projects are assumed as known factors which is a main challenge because of uncertainty in measurement and state-model noise. Thus, many papers are focused on the accurate estimation of noise statistics. This paper is concentrated on this subject where it is tried to present three simple efficient methods in this regard. Estimation using n-step prediction, applying Kalman filter covariance and using Gamma distribution for noise parameters are the main concepts of the three proposed methods. Simulation results show the efficiency of all methods compared to other methods in the literature where the Gamma-distribution-based method is the most efficient work among other suggested ones in term of estimation error.
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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.