Network Optimization
Mohammad Saberi; Behzad Taghipoor
Abstract
This paper presents a methodology for detecting and classifying the errors occurring on smart power transmission lines. In the proposed method, the voltage and current phases are estimated by the phasor measurement unit (PMU) installed in the generator bus, and then the equivalent voltage and current ...
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This paper presents a methodology for detecting and classifying the errors occurring on smart power transmission lines. In the proposed method, the voltage and current phases are estimated by the phasor measurement unit (PMU) installed in the generator bus, and then the equivalent voltage and current angles are obtained. These angles are analyzed by fast fourier transform (FFT) and used to detect of transmission line errors. Detection of the transmission line error is performed using the nerve- fuzzy inference system methodology, and the diagnostic error classification is performed using support vector machine (SVM). Validation of the proposed method for the IEEE 14 system is also tested in the MATLAB software environment.
Network Optimization
Mohammad Saberi; Mehdi Hatef
Abstract
The purpose of Transmission expansion planning (TEP) is to find the required network lines with the lowest investment cost So that the future burden will be provided economically by observing the system security indicators. Due to the uncertainty of the load, Distributed wind power and Responsive resources ...
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The purpose of Transmission expansion planning (TEP) is to find the required network lines with the lowest investment cost So that the future burden will be provided economically by observing the system security indicators. Due to the uncertainty of the load, Distributed wind power and Responsive resources to load and competitive markets for Transmission expansion planning, Faced with challenges that require new models to be felt more than ever. In this paper, a multi-objective TEP model is presented taking into account investment costs, Responsive resources to load, along with an index for determining system security. These target functions are optimized for obtaining a non-dominant solution set based on operator priorities (cost or risk), using pareto power evolutionary algorithms based on multi-objective particle pool optimization (SPEA2-MOPSO). The proposed model is numerically verified on the modified IEEE RTS 24- bus and 118-bus systems. According to the simulation results, the proposed model can provide information regarding variants of risks and coordinate the optimum planning and DR solutions.