Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations
Abstract In existing power system networks, the positioning and sizing of multi-DG is critical at the optimum locations for effective energy management. Initially optimal power flow is assessed using the NR method (without DG) in which performance parameters such as real power loss, accuracy, select...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-03-01
|
| Series: | Protection and Control of Modern Power Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41601-022-00230-5 |
| _version_ | 1818137730422407168 |
|---|---|
| author | Anwar Shahzad Siddiqui Prashant |
| author_facet | Anwar Shahzad Siddiqui Prashant |
| author_sort | Anwar Shahzad Siddiqui |
| collection | DOAJ |
| description | Abstract In existing power system networks, the positioning and sizing of multi-DG is critical at the optimum locations for effective energy management. Initially optimal power flow is assessed using the NR method (without DG) in which performance parameters such as real power loss, accuracy, selectivity and MSE are obtained, but in an undesirable manner. To meet load demand; multi-DGs are placed and their optimal locations are assessed by the proposed heuristic probability distribution methodology and an ANN because existing techniques provides poor performance parameters for selecting the location and sizing of DGs. The optimal positions of multi-DGs are estimated in terms of performance parameters including real power loss of transmission network, accuracy, selectivity and MSE, while the performance parameters obtained with the ANN are better than the heuristic pdf. Then, the sizing of multi-DGs is evaluated in relation to active and reactive power. It is found that that sizes of multi-DGs are smaller with ANN than with heuristic pdf. It is preferable to connect the buses having lowest real power losses with the smallest multi-DGs. The performance analysis is tested in the standard IEEE 9- bus and IEEE 57- bus systems on Simulink. To improve the distortion level in real and reactive power, multi-FACTS namely TCSC, TSC and STATCOM are used. The switching of TCSC and TSC is done by SPWM while STATCOM switching is controlled with ANFIS. The locations of multi-FACTS devices are chosen for buses having larger distortion and the sizing of multi-FACTS devices is also optimally decided. The application of multi-FACTS devices helps to improve power quality and fulfill load demand with minimal size in order to make the system economical. |
| first_indexed | 2024-12-11T10:00:57Z |
| format | Article |
| id | doaj.art-d6dd1ccab4ef475ba862ff55a7148f0c |
| institution | Directory Open Access Journal |
| issn | 2367-2617 2367-0983 |
| language | English |
| last_indexed | 2024-12-11T10:00:57Z |
| publishDate | 2022-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Protection and Control of Modern Power Systems |
| spelling | doaj.art-d6dd1ccab4ef475ba862ff55a7148f0c2022-12-22T01:12:08ZengSpringerOpenProtection and Control of Modern Power Systems2367-26172367-09832022-03-017112510.1186/s41601-022-00230-5Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System OperationsAnwar Shahzad Siddiqui0Prashant1Department of Electrical Engineering, Faculty of Engineering & Technology, Jamia Millia IslamiaDepartment of Electrical Engineering, Faculty of Engineering & Technology, Jamia Millia IslamiaAbstract In existing power system networks, the positioning and sizing of multi-DG is critical at the optimum locations for effective energy management. Initially optimal power flow is assessed using the NR method (without DG) in which performance parameters such as real power loss, accuracy, selectivity and MSE are obtained, but in an undesirable manner. To meet load demand; multi-DGs are placed and their optimal locations are assessed by the proposed heuristic probability distribution methodology and an ANN because existing techniques provides poor performance parameters for selecting the location and sizing of DGs. The optimal positions of multi-DGs are estimated in terms of performance parameters including real power loss of transmission network, accuracy, selectivity and MSE, while the performance parameters obtained with the ANN are better than the heuristic pdf. Then, the sizing of multi-DGs is evaluated in relation to active and reactive power. It is found that that sizes of multi-DGs are smaller with ANN than with heuristic pdf. It is preferable to connect the buses having lowest real power losses with the smallest multi-DGs. The performance analysis is tested in the standard IEEE 9- bus and IEEE 57- bus systems on Simulink. To improve the distortion level in real and reactive power, multi-FACTS namely TCSC, TSC and STATCOM are used. The switching of TCSC and TSC is done by SPWM while STATCOM switching is controlled with ANFIS. The locations of multi-FACTS devices are chosen for buses having larger distortion and the sizing of multi-FACTS devices is also optimally decided. The application of multi-FACTS devices helps to improve power quality and fulfill load demand with minimal size in order to make the system economical.https://doi.org/10.1186/s41601-022-00230-5DGLocationSizingFACTSANNANFIS |
| spellingShingle | Anwar Shahzad Siddiqui Prashant Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations Protection and Control of Modern Power Systems DG Location Sizing FACTS ANN ANFIS |
| title | Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations |
| title_full | Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations |
| title_fullStr | Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations |
| title_full_unstemmed | Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations |
| title_short | Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations |
| title_sort | optimal location and sizing of conglomerate dg facts using an artificial neural network and heuristic probability distribution methodology for modern power system operations |
| topic | DG Location Sizing FACTS ANN ANFIS |
| url | https://doi.org/10.1186/s41601-022-00230-5 |
| work_keys_str_mv | AT anwarshahzadsiddiqui optimallocationandsizingofconglomeratedgfactsusinganartificialneuralnetworkandheuristicprobabilitydistributionmethodologyformodernpowersystemoperations AT prashant optimallocationandsizingofconglomeratedgfactsusinganartificialneuralnetworkandheuristicprobabilitydistributionmethodologyformodernpowersystemoperations |