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Use of waste activated carbon and wood ash mixture as an electrical grounding enhancement material

  • Mahmoud Wahba ORCID logo EMAIL logo , Mazen Abdel-Salam , Mohamed Nayel and Hamdy A. Ziedan
Published/Copyright: September 25, 2023
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International Journal of Emerging Electric Power Systems
From the journal International Journal of Emerging Electric Power Systems Volume 25 Issue 6

Abstract

The grounding scheme is one of the main elements for protection system to mitigate the effect of unwanted lightning strikes or operational failures due to faults in generation, transmission and distribution systems. Desert sand soil has a very low electric conductivity, causing weakness in grounding system. To mitigate problems, the soil is supported with a high conductivity agent to adjust the soil conductivity to acceptable levels. A high-cost and non-renewable commercial product can be added to soils to increase their conductivity. This study brings innovation to conventional soil-enhancement materials. A newly developed mixture is proposed, which is composed of waste-activated carbon received from water purification industries and wood ash from agricultural wastes. First, mixture samples with different compositions of available waste materials were prepared. Then, experimental tests were performed and optimized with a combined genetic algorithm (GA) and fuzzy ranking method to estimate the optimal percentage volume value of each material involved in the developed mixture. To validate the effectiveness of the developed mixture, the results were compared with a commercial product available in the market. Also, the obtained results using GA are compared with those obtained by particle swarm optimization (PSO) to appreciate the best GA solutions. The effectiveness of using the developed mixture and the commercial product in reducing the resistance-to-ground of a rod driven in high and low resistivity soils is evaluated. Finally, a sample of the developed mixture was checked to be non-corrosive material for copper grounding rods.

Keywords: grounding system; mixture design; waste activated carbon; wood ash; genetic algorithm; fuzzy ranking
Corresponding author: Mahmoud Wahba, Aqua Paris for Natural Water Company, New-Valley, Egypt, E-mail:

Acknowledgments

The authors would like to acknowledge the reviewers for their valuable comments which enhanced the clarity of the paper.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: This research received no specific grant from any funding agency in the public, commerical, or not-for-profit sectors.

  5. Data availability: Not applicable.

Appendix A

The cost coefficients that used in equation (5) can be summarized in the following Table A as:

Table A:

Cost values in USD of waste materials.

Symbol Value (USD) Reference
Cost of commercial product/kg GEM-25A 5.8 [85]
Cost of excavation per m3 C 1 40 [86]
Cost of WAC/kg C 2 1.5 Locally
Cost of WA/kg C 3 0.75 Estimated
Appendix B
Figure B: Flow chart of genetic algorithm.
Figure B:

Flow chart of genetic algorithm.

The steps procedure of genetic algorithm optimizer in the flow chart in Figure B.

Appendix C

The step procedure of the GA and pareto-fuzzy ranking are listed in following as:

  1. Define the multi-objective functions MOF (tri-objective function), variables and parameters of the GA.

  2. Generate initial populations.

  3. Applied the main operator of the GA as:

    1. Generation

    2. Selection

    3. Cross over

    4. Mutation

  4. Convergence check and stop iterations according to the stoppage criteria.

  5. Generate pareto-front figure and Sort the pareto optimal solutions according to the GR (R g) values.

  6. Find fuzzy ranking of each one solution of the pareto set solutions in the following expression as [74]:

(10) μ ( F i ) = { F i ≤ F i min   1 F i max − F i F i max − F i min F i min ≤ F i ≤ F i max F i ≥ F i max   0

where F min and F=max are expected maximum and minimum values of each objective function (R g, PH and cost)and ith is the number of objective function.

  1. Generate µ (R g), µ (PH) and µ (cost) for each solution of the pareto set.

  2. Compute the minimum value for each solution according to [74]:

(11) F MIN = { min [ μ ( F j ) k ] }

  1. Select the more unique optimal solution as:

(12) μ best   solution = Max . ( F MIN )

where j is number of objectives to be minimized and k are number of pareto-optimal solutions obtained

  1. Display the more optimal solution with corresponding values of generations and values of functions.

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Received: 2023-04-08
Accepted: 2023-09-06
Published Online: 2023-09-25

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. California’s electric grid nexus with the environment
  4. Research Articles
  5. Adaptive centralized energy management algorithm for islanded bipolar DC microgrid
  6. Distributed new energy information acquisition model of distribution network based on Beidou communication
  7. A single phase modified Y-source inverter with high voltage gains and reduced switch stresses
  8. Technical assessment of power interface to utilize untapped power of decentralized solar pumps for positive impact in livelihoods
  9. An improved method for monitoring the junction temperature of 1200V / 50A IGBT modules used in power conversion systems
  10. Stochastic uncertainty management in electricity markets with high renewable energy penetration
  11. Power quality disturbances classification using autoencoder and radial basis function neural network
  12. Use of waste activated carbon and wood ash mixture as an electrical grounding enhancement material
  13. Double-layer optimal energy management of smart grid incorporating P2P energy trading with smart traction system
  14. Performance analysis of SRFT based D-STATCOM for power quality improvement in distribution system under different loading conditions
  15. Power quality improvement of utility-distribution system using reduced-switch DSTATCOM in grid-tied solar-PV system based on modified SRF strategy
https://doi.org/10.1515/ijeeps-2023-0120
Keywords for this article
grounding system; mixture design; waste activated carbon; wood ash; genetic algorithm; fuzzy ranking

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. California’s electric grid nexus with the environment
  4. Research Articles
  5. Adaptive centralized energy management algorithm for islanded bipolar DC microgrid
  6. Distributed new energy information acquisition model of distribution network based on Beidou communication
  7. A single phase modified Y-source inverter with high voltage gains and reduced switch stresses
  8. Technical assessment of power interface to utilize untapped power of decentralized solar pumps for positive impact in livelihoods
  9. An improved method for monitoring the junction temperature of 1200V / 50A IGBT modules used in power conversion systems
  10. Stochastic uncertainty management in electricity markets with high renewable energy penetration
  11. Power quality disturbances classification using autoencoder and radial basis function neural network
  12. Use of waste activated carbon and wood ash mixture as an electrical grounding enhancement material
  13. Double-layer optimal energy management of smart grid incorporating P2P energy trading with smart traction system
  14. Performance analysis of SRFT based D-STATCOM for power quality improvement in distribution system under different loading conditions
  15. Power quality improvement of utility-distribution system using reduced-switch DSTATCOM in grid-tied solar-PV system based on modified SRF strategy
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