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Mitigation of hotspots in electrical components and equipment using an adaptive neuro-fuzzy inference system

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Abstract

The poor management of hotspots in electrical systems often leads to very devastating consequences; an extremity is fire outbreak which could result in loss of lives and/or properties. Hence, the necessity to effectively manage or handle electrical hotspots cannot be overstated. This paper proposes a model for arresting the occurrence of the destructive activities of electrical hotspots in industries by controlling the temperature, humidity and dust density within electrical components/equipment. To this end, a thermal imaging camera is employed for the detection of various locations and magnitudes of hotspots within electrical systems. Based on the globally approved industrial standards for the prevention of thermally induced electrical systems failure, each of the electrical components and equipment [whose thermal excursion is beyond the allowable temperature rise under measured load values (i.e. ΔTcorr)] is identified and treated by adopting the recommended actions. Additionally, a fuzzy logic control (FLC) system is designed. This is further developed into an adaptive neuro-fuzzy inference system (ANFIS) for the control of the operation of the air handling unit (AHU) and the aspirator suction speed. This arrangement, thus, leads to heat reduction and dust elimination within the electrical components/equipment in industrial space, thus preventing the destructive effects of the occurrence of hotspots. However, for the sake of graphical representations of this scheme, the MATLAB environment is created for the generation of the optimum temperatures at various locations within the electrical systems. From this development, it is established that the framework has very high potential to eliminate the catastrophic effects of hotspots in electrical systems.

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Abbreviations

ACO:

Ant colony optimization

AHU:

Air handling unit

ANFIS:

Adaptive neuro-fuzzy inference system

CA:

Cultural algorithm

COG:

Center of gravity

COMP:

Component

CS:

Cuckoo search

EQPT:

Equipment

FCS:

Fuzzy control system

FIS:

Fuzzy inference system

FLC:

Fuzzy logic control

GA:

Genetic algorithm

IR:

Infrared

IRT:

Infrared thermography

MATLAB:

Matrix laboratory

MIMO:

Multiple input multiple output

MF:

Membership function

PSO:

Particle swamp optimization

RMSE:

Root-mean-square error

\( T_{\text{comp}} \) :

Continuously permissible temperature of components (connector, breaker, panel or insulated conductor/wire) (°C)

\( T_{\text{amb}} \) :

Ambient temperature (°C)

\( \Delta T_{\text{corr}} \) :

Maximum temperature rise of components under measured load (°C)

\( \Delta T_{ \max } \) :

Maximum temperature rise of components under rated load (°C)

\( I_{\text{r}} \) :

Rated load (A)

\( I_{\text{m}} \) :

Measured load (A)

n:

Number of conductors

\( R_{0} \) :

Radius of boundary surface of conductors (m)

\( R_{1} \) :

Outer radius of insulation (m)

\( R_{2} \) :

Outer radius of outer sheath (m)

\( R_{\text{w}} \) :

Conductor radius (m)

S:

Power loss per unit length (W/m)

\( \rho \) :

Electrical resistivity (ohm-m)

I:

Electrical current (A)

\( \pi \) :

pi or 3.142

N:

Number of sub-areas

\( A_{i} \) :

The area of ith sub-area (m2)

\( \bar{x}_{i} \) :

Centroid of area of ith sub-area

\( x^{ *} \) :

Defuzzified value (output)

\( {\text{x}}, {\text{y}} \) :

Inputs to node i

\( A_{i} ,B_{i} \) :

Linguistic label (membership function)

\( O_{i}^{1} \) :

Membership function of Ai and Bi that specifies the degree to which the given x and y activate Ai and Bi, respectively

\( \omega_{i} \) :

Firing strength of the rules

\( \left\{ {p_{i} , q_{i} , r_{i} } \right\} \) :

Consequent parameter set

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, University of Lagos, Lagos, Nigeria

    Peter O. Oluseyi, Jamiu A. Adeagbo, Demilade D. Dinakin & Tolu O. Akinbulire

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  1. Peter O. Oluseyi
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  2. Jamiu A. Adeagbo
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  3. Demilade D. Dinakin
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Correspondence to Peter O. Oluseyi.

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Oluseyi, P.O., Adeagbo, J.A., Dinakin, D.D. et al. Mitigation of hotspots in electrical components and equipment using an adaptive neuro-fuzzy inference system. Electr Eng 102, 2211–2226 (2020). https://doi.org/10.1007/s00202-020-01028-0

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