mmi {CNAIM}

Maximum and Multiple Increment (MMI) Technique

Description

This function returns a combined factor using a maximum and multiple increment (MMI) technique (cf. CNAIM, 2021. page 54, section 6.7.2). The function can be used to derive the health score factor for EHV and 132kV transformers. For deriviation of the health score factor for all other assets see health_score_excl_ehv_132kv_tf. To derive the health score factor for EHV and 132kV transformers one needs to use mmi() to derive the health score factor for the main transformer and for the tapchanger respectively. The constants factor_divider_1, factor_divider_2 and max_no_combined_factors are all available in the lookup table 10 and 11 on page 57 and 58 in CNAIM (2021). For an in dept description see also section 6.8 on page 57 in CNAIM (2021). The mmi() can also be used in the derivation of observed and measured condition factors for all assets, using measured and observed input factors. The constants factor_divider_1, factor_divider_2 and max_no_combined_factors can be found in table 13 on page 63 (observed condition factors) and in table 15 on page 67 (measured condition factors).

Usage

mmi(factors, factor_divider_1, factor_divider_2, max_no_combined_factors)

Arguments

factors

Numeric vector. Factors to me combined.

factor_divider_1

Numeric. Constant that specifies the degree to which additional “good” or “bad” factors are able further drive the combined factor.

factor_divider_2

Numeric. Constant that specifies the degree to which additional “good” or “bad” factors are able further drive the combined factor.

max_no_combined_factors

Numeric. Specifies how many factors are able to simultaneously affect the combined factor.

Value

Numeric. Combined factor.

Source

DNO Common Network Asset Indices Methodology (CNAIM), Health & Criticality - Version 2.1, 2021: https://www.ofgem.gov.uk/sites/default/files/docs/2021/04/dno_common_network_asset_indices_methodology_v2.1_final_01-04-2021.pdf

Examples

mmi(factors = c(1,
1.5),
factor_divider_1 = 1.5,
factor_divider_2 = 1.5,
max_no_combined_factors = 1)