A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement. Issue 1 (5th November 2021)
- Record Type:
- Journal Article
- Title:
- A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement. Issue 1 (5th November 2021)
- Main Title:
- A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement
- Authors:
- Laouid, Abdelkader Azzeddine
Mohrem, Abdelkrim
Djalab, Aicha - Abstract:
- Abstract : Purpose: This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line. Design/methodology/approach: An efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network ("GW-NN") to place the PMUs in power grids optimally. Findings: Following extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches. Practical implications: The efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems. Originality/value: This paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the costAbstract : Purpose: This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line. Design/methodology/approach: An efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network ("GW-NN") to place the PMUs in power grids optimally. Findings: Following extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches. Practical implications: The efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems. Originality/value: This paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases. … (more)
- Is Part Of:
- Compel. Volume 41:Issue 1(2022)
- Journal:
- Compel
- Issue:
- Volume 41:Issue 1(2022)
- Issue Display:
- Volume 41, Issue 1 (2022)
- Year:
- 2022
- Volume:
- 41
- Issue:
- 1
- Issue Sort Value:
- 2022-0041-0001-0000
- Page Start:
- 187
- Page End:
- 208
- Publication Date:
- 2021-11-05
- Subjects:
- Particle swarm optimization -- Power transmission systems -- Multi-objective optimization -- Phasor measurement units (PMUs) -- Zero injection bus (ZIB) -- Grey wolf optimizer (GWO) for training multi-layer perceptron (MLP) -- Redundancy of measurement
Electrical engineering -- Data Processing -- Periodicals
Electrical engineering -- Mathematics -- Periodicals
Electrical engineering -- Periodicals
Electronics -- Data Processing -- Periodicals
Electronics -- Mathematics -- Periodicals
621.3 - Journal URLs:
- http://www.emeraldinsight.com/0332-1649.htm ↗
http://www.emeraldinsight.com/ ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1108/COMPEL-01-2021-0018 ↗
- Languages:
- English
- ISSNs:
- 0332-1649
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3363.924000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25400.xml