# Fault Diagnosis on Medium Voltage (MV) Electric Power Distribution Networks: The Case of the Downstream Network of the AES-SONEL Ngousso Sub-Station

^{*}

## Abstract

**:**

## 1. Introduction

- i)
- the company recorded 131,544 kWh of undistributed energy
- ii)
- the D113 circuit breaker tripped 20 times during this period
- iii)
- this led to 120 hours of power cuts, an average of 6 hours per power cut [2].

## 2. Types of Faults, Characteristic Network Parameters and Identification of Faults on Electric Power Networks

#### 2.1. Various types of faults

**Short-circuit**is the contact between two live conductors or between a live conductor and the earth conductor. It results in a rapid flow of current between the source and the fault. This current is limited only by the impedance of the line.

**Over voltage**is any disturbance superimposed on the nominal voltage of the network, and can appear either between phases or between a phase and earth. The operating mode of the surge arresters gives rise to a high flow of current.

**Overload**is generally due to power demand by the load which is more than the rated capacity of the line. Overload does not always lead to tripping of the circuit breaker because certain overloads are tolerated for reasons of the continuity of supply. For the distribution network, the acceptable level of overload is 140% during 0.3 s. Overloads can be detected by the measurement of the forwarded power. Since the network voltage is fixed, an overload on the network is equivalent to an overflow of an over current.

**Loss of phase**occurs when a live phase ruptures. If the part under voltage touches another live wire or earth, then we have a short-circuit. The identification of these faults relies on two guiding principles: recording data on the networks, analyzing them, and then drawing conclusions about the state of the network.

#### 2.2. Collecting characteristic parameters of the network

**The voltage between phases is sampled**as follows: the effective value of the voltage is obtained by getting the average of the squares of samples taken at regular intervals. Then the square root of this average is calculated. A sample is taken every 20/N ms, the square is calculated and kept in memory. After 20 ms, which corresponds to one period, the average of all the samples taken is calculated, then the square root of this average gives us an approximate value of the effective phase-to-phase voltage. For N=10 and $u(t)=15000\sqrt{2}\mathrm{sin}100\pi t$ the following table can be obtained:

**Table 1.**Evaluation of the effective value of phase-to-phase voltage using voltage samples taken every 2 ms.

I | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

U[i] (kV) | 0 | 12,469 | 20,174 | 20,174 | 12,469 | 0 | -12,469 | -20,174 | -20,174 | -12,469 |

Square (U[i]) | 0 | 155,475 | 406,990 | 406,990 | 155,475 | 0 | 155,475 | 406,990 | 406,990 | 155,475 |

average | 224,986 | |||||||||

U (kV) | 14,9994 |

**Sampling of phase current I:**Given that the current signal period is identical to that of the voltage, the methodology is exactly the same as that used in sampling the voltage. By replacing U with I in the calculation algorithm for the value of U, one obtains the calculation algorithm for the value of I (Figure A5 in the Appendix).

**Sampling of active power P:**This is a signal with a period of 40 ms. This requires 40 samples of the current and the voltage to be obtained simultaneously. The sampling interval is therefore 1 ms. The sampling method is that of two wattmeters, since the neutral is not being distributed. An approximate value of the active power is given by the average of the products of samples taken. The current is taken on phases 1 and 3. The voltage is taken between phases 1 and 2, then 2 and 3. The power P12 is calculated between phases 1 and 2 and then the power P23 between phases 2 and 3. The power P = P12 + P23. The algorithms for the calculation of P12 and P are established (Figure A6 of the Appendix).

**Sampling of power factor cosφ:**This follows from three preceding measurements, if none of them is not zero, because:

**Sampling of apparent power S:**This follows from the measurements of P and cos φ

#### 2.3. Identification of faults on electric power network

**Identifying line overload faults**: We must define the various thresholds of current intensity and duration at the end of which a fault indication must be issued. We have three thresholds of overload. The first corresponds to an operating current higher than 140% of the nominal intensity for 500 ms; the second is that in which the current is higher than 180% of the nominal intensity for 100 ms; the third corresponds to the current of minimal short-circuit during an instantaneous time. The algorithm for the identification of an overload fault is shown in Figure A1 of the appendix

**Identifying homopolar current faults**: Homopolar current is the algebraic sum from the instantaneous values of electrical currents in the three phases. The threshold of the homopolar current is selected between 130% from I

_{0}and 10% of the nominal phase current. I

_{0}is the value of the current due to the ground-phase capacitance at the upper level of the network. The algorithm for the identification of homopolar current fault is shown in Figure

**A2**of the appendix.

**Identifying loss-of-phase faults**: There is loss of phase when at the upper level all the three phases are live, whereas only two phases have current. The algorithm for the identification of loss-of-phase fault is shown in Figure A3 of the appendix.

## 3. Proposal of a System for the Detection and Localization of Faults on an Electric Power Network

- -
- Be able to forward to the NLC the exact geographical position of the faults which they detect;
- -
- Be able to disconnect and accurately isolate the defective portion of the network, and as such limit the number of customers not having electricity;
- -
- Be able to delimit the portion where technicians shall look for the faults.

#### 3.1. Localizing the faults

#### 3.2. Current and voltage sensors

## 4. System for Data Processing and diagnosis

#### 4.1. System for the data processing

- Recording current and voltage for that part of the network
- Calculating the other parameters of operation of the part of network
- Deducing the operating condition of the part of network (be able to indicate if the portion is under normal operation or if a fault was detected thereon )
- Identifying the fault in the part of network and communicating it to the NLC.

#### 4.2. Implementation of the diagnostic system

## 5. Proposals for the implementation of the system by AES-SONEL

N ° | Designation | Quantity | Complete price |
---|---|---|---|

1 | PLC TSX P57 | 1 | 554 € |

2 | current transformer | 3 | 3,077 € |

3 | Voltage transformer | 3 | 3,077 € |

4 | MODEM | 1 | 1,536 € |

5 | Converter 110 V / 48 V DC | 1 | 385 € |

6 | Relay | 3 | 33 € |

7 | Other components | 2,108 € | |

TOTAL | 10,770 € |

## 6. Conclusions

## APPENDIX

## References

- Tamo Tatietse, T.; Villeneuve, P.; Ndong, E.P.; Kenfack, F. Interruption modelling in medium voltage electrical networks. Electr. Power Energy Syst.
**2002**, 24, 859–865. [Google Scholar] [CrossRef] - Moïse, E. Gestion en temps réel des défauts sur le réseau de distribution de l’énergie électrique : cas du réseau AES-Sonel, réseau aval du poste de Ngousso; Mémoire de fin des études d’ingénieurs, 5
^{ème}année, Ecole Polytechnique de Yaoundé: Yaoundé, Cameroon, juillet; 2006. [Google Scholar] - Données NLC, AES-Sonel, juin 2006.
- Chetate, B.; Khodja, D. Système automatique de diagnostic des défaillances des systèmes électromécaniques par application de la technique des réseaux de neurones artificiels. Available online: http://www.umbb.dz/Bibumbb/magisfsi.htm (accessed on 10 October 2006).
- Zwingelstein, G. Diagnostic des défaillances - Théorie et Pratique pour les systèmes Industriels; Hermes Science Publications, 1995; Available online: http://www.priceminister.com/offer/buy/571508/Zwingelstein-Gilles-Diagnostic-Des-Defaillances-Livre.html#info.
- AL-Alani, T. Introduction au diagnostic des défaillances. Laboratoire, Laboratoire A2SI, ESIEE-Paris. Available online: www.esiee.fr/~alanit/cours_diagnostic/diagnostic/diagnostic.pdf.
- Schéma d’exploitation réseau MT urbain Yaoundé, AES-SONEL/DRCSE/DT. 20 November 2004.
- Manuel de Référence PL7 Micro/Junior/Pro Description du logiciel PL7. Schneider Electrics. Available online: www.gel.usherbrooke.ca/ste/ste/Schneider/PL7/Manuel_de_reference_Pl7_ V44/35009568_K01_001_00.pdf (accessed on 20 Mars 2005).
- Automates Premium TSX P57/ TSXDSY/DEY/DMY processeurs Entrées/Sorties TOR. Schneider Electrics. available online: www.gel.usherbrooke.ca/ste/ste/Schneider/PL7/Autre/Processeurs_ Instruction_de_service.pdf (accessed on 20 March 2005).
- Automates Premium TSX 57 / PCX 57 Analogique et Pesage Manuel de mise en œuvre. Tome 5, Schneider Electrics. Available online: www.gel.usherbrooke.ca/ste/ste/Schneider/PL7/ Manuel_de_mise_en_oeuvre/Tome5_Analogique_et_Pesage.pdf (accessed on 20 March 2005).

© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Tatietse, T.T.; Voufo, J.
Fault Diagnosis on Medium Voltage (MV) Electric Power Distribution Networks: The Case of the Downstream Network of the AES-SONEL Ngousso Sub-Station. *Energies* **2009**, *2*, 243-257.
https://doi.org/10.3390/en20200243

**AMA Style**

Tatietse TT, Voufo J.
Fault Diagnosis on Medium Voltage (MV) Electric Power Distribution Networks: The Case of the Downstream Network of the AES-SONEL Ngousso Sub-Station. *Energies*. 2009; 2(2):243-257.
https://doi.org/10.3390/en20200243

**Chicago/Turabian Style**

Tatietse, Thomas Tamo, and Joseph Voufo.
2009. "Fault Diagnosis on Medium Voltage (MV) Electric Power Distribution Networks: The Case of the Downstream Network of the AES-SONEL Ngousso Sub-Station" *Energies* 2, no. 2: 243-257.
https://doi.org/10.3390/en20200243