Optimal Location and Size of SVC Devices in Interconnected Electrical Power System Using Quadratic Adaptive Bacterial Foraging Algorithm

2018 ◽  
Author(s):  
Ayman K. Alhejji ◽  
Fawzan Salem ◽  
Mohamed I. Mosaad
Keyword(s):  
Power System ◽  
Electrical Power ◽  
Optimal Location ◽  
Electrical Power System ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

scholarly journals Optimal Placement of FACTS Controllers for Congestion Management in the Deregulated Power System

2018 ◽  
Vol 8 (3) ◽  
pp. 1336
Author(s):  
S. Surender Reddy
Keyword(s):  
Power System ◽  
Optimal Power Flow ◽  
Transmission Loss ◽  
Electrical Power ◽  
Congestion Management ◽  
Optimal Location ◽  
Optimal Placement ◽  
Total System ◽  
Electrical Power System ◽  
Facts Controllers

This paper proposes a methodology to determine the optimal location of Flexible AC Transmission System (FACTS) controllers for Congestion Management (CM) in the restructured electrical power system. An approach to find the optimum placement of Thyristor Controlled Phase Angle Regulators (TCPAR) and Thyristor Controlled Series Compensators (TCSC) has been proposed in this paper. The proposed methodology is based on the sensitivity of transmission loss which a controller is installed. The total system losses and the power flows are considered as the performance indices. The traditional optimal power flow (OPF) problem is modified to include the market players, who will compete and trade simultaneously, ensuring the system operation stays within the security limits. In this paper, pool and bilateral contracts are considered. Here, an integrated methodology is proposed that includes the FACTS Controllers in a bilateral contract framework to maintain the system security and to minimize the deviations from the contractual requirements. The simulation results on IEEE 30 bus system show that the sensitivity factors could be used effectively for the optimal location of FACTS controllers in response to the required objectives.

scholarly journals Optimal Placement of FACTS Devices Based on Whale Optimization Algorithm for Power System Security Enhancement

2020 ◽  
Vol 9 (3) ◽  
pp. 908-916
Keyword(s):  
Power System ◽  
Transmission Line ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Electrical Power ◽  
Optimal Location ◽  
Electrical Power System ◽  
Facts Devices ◽  
Whale Optimization ◽  
Line Overload

The present-day power system is vulnerable to instability and security threats due to the continuously changing load pattern. To enhance the security of the power system and to avoid the electrical power system from collapsing, the condition of the system security has to be inspected by security analysis tools and it can be enhanced by the proper integration of FACTS devices into the network. This paper presents a methodology in which the security of the system can be analyzed with the help of an index called Line Overload Severity Index (LOSI). Unified Power Flow Controller (UPFC) is preferred to improve the security of the power system. Owing to the cost involved in placing UPFCs it is obligatory to use minimum number of devices, by optimally placing them in the network. It is obligatory to recognize an ideal location to install UPFC. Considering the Line overload Sensitivity Index, the optimal location identification for UPFC is done. The paper also presents the formulation of a new severity function using transmission line loadings. The severity function combines the objectives of reducing transmission line loadings and improvement of voltage profile during multi line contingencies. In the event of multi-line contingencies, the objective function for reducing the fuel cost and the severity function are analyzed. Optimal power flow method is followed to analyze the security of the electrical power system during contingency situations. This optimal location identification procedure and the OPF are solved using a metaheuristic technique, Whale Optimization Algorithm (WOA). The whole methodology that is proposed is experimented on a standard IEEE-30 bus test system.

Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

2019 ◽  
Vol 14 ◽  
Author(s):  
Iyappan Murugesan ◽  
Karpagam Sathish
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Power System ◽  
Electrical Power ◽  
Daubechies Wavelet ◽  
Electrical Power System ◽  
Gradient Ascent ◽  
Neural Learning ◽  
Weight Value ◽  
Transmission And Distribution

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

scholarly journals Optical Voltage Transformer Based on FBG-PZT for Power Quality Measurement

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2699
Author(s):  
Marceli N. Gonçalves ◽  
Marcelo M. Werneck
Keyword(s):  
Power System ◽  
Power Quality ◽  
Distribution System ◽  
Electrical Power ◽  
Quality Measurement ◽  
Pockels Effect ◽  
Electrical Power System ◽  
Voltage Transformer ◽  
Technical Literature ◽  
Distribution Lines

Optical Current Transformers (OCTs) and Optical Voltage Transformers (OVTs) are an alternative to the conventional transformers for protection and metering purposes with a much smaller footprint and weight. Their advantages were widely discussed in scientific and technical literature and commercial applications based on the well-known Faraday and Pockels effect. However, the literature is still scarce in studies evaluating the use of optical transformers for power quality purposes, an important issue of power system designed to analyze the various phenomena that cause power quality disturbances. In this paper, we constructed a temperature-independent prototype of an optical voltage transformer based on fiber Bragg grating (FBG) and piezoelectric ceramics (PZT), adequate to be used in field surveys at 13.8 kV distribution lines. The OVT was tested under several disturbances defined in IEEE standards that can occur in the electrical power system, especially short-duration voltage variations such as SAG, SWELL, and INTERRUPTION. The results demonstrated that the proposed OVT presents a dynamic response capable of satisfactorily measuring such disturbances and that it can be used as a power quality monitor for a 13.8 kV distribution system. Test on the proposed system concluded that it was capable to reproduce up to the 41st harmonic without significative distortion and impulsive surges up to 2.5 kHz. As an advantage, when compared with conventional systems to monitor power quality, the prototype can be remote-monitored, and therefore, be installed at strategic locations on distribution lines to be monitored kilometers away, without the need to be electrically powered.

Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Diego A. Monroy-Ortiz ◽  
Sergio A. Dorado-Rojas ◽  
Eduardo Mojica-Nava ◽  
Sergio Rivera
Keyword(s):  
Power Systems ◽  
Power System ◽  
Model Reduction ◽  
Electrical Power ◽  
Schur Complement ◽  
Power Transfer ◽  
Electrical Power System ◽  
Admittance Matrix ◽  
Test Beds ◽  
Power Transfer Distribution Factors

Abstract This article presents a comparison between two different methods to perform model reduction of an Electrical Power System (EPS). The first is the well-known Kron Reduction Method (KRM) that is used to remove the interior nodes (also known as internal, passive, or load nodes) of an EPS. This method computes the Schur complement of the primitive admittance matrix of an EPS to obtain a reduced model that preserves the information of the system as seen from to the generation nodes. Since the primitive admittance matrix is equivalent to the Laplacian of a graph that represents the interconnections between the nodes of an EPS, this procedure is also significant from the perspective of graph theory. On the other hand, the second procedure based on Power Transfer Distribution Factors (PTDF) uses approximations of DC power flows to define regions to be reduced within the system. In this study, both techniques were applied to obtain reduced-order models of two test beds: a 14-node IEEE system and the Colombian power system (1116 buses), in order to test scalability. In analyzing the reduction of the test beds, the characteristics of each method were classified and compiled in order to know its advantages depending on the type of application. Finally, it was found that the PTDF technique is more robust in terms of the definition of power transfer in congestion zones, while the KRM method may be more accurate.

Sign in / Sign up

Export Citation Format

Share Document