• We analyzed cuts at the power plants in Turkey between 2012-2018.
• We had in total 73313 observations with 8 variables
• We mutated new observations from the existing ones: Plant.Type, Duration of Cut, Capacity Ratio at the cut and reason of the cut.
• We tidied the raw data using regular expressions and stringr package.
• We used tidy text mining to analyze count of words and which word is following which word.
• We divided cuts into two category, Malfunctions and Planned Activities and looked for their distributions.
• We looked at differences between malfunctions and planned activities in terms of duration of the cut.
• We looked at malfunction types, malfunction reasons and durations according to plant type.

Yearly Incidents are way higher at 2018.

It was not easy

cuts\(Plant.Name <- cuts\)Plant.Name %>% str_replace_all("[ı]", "i") %>% str_replace_all("enerj.sa", "enerjisa") %>% str_replace_all("yenikoy ts", "yenikoy tes") %>% str_replace_all("ienikoi tes", "yenikoy tes") %>% str_replace_all("^ova elektrik", "gebze ova elektrik") %>% str_replace_all("yatagan .", "yatagan tes") %>% str_replace_all("kokluce$", "kokluce hes") %>% str_replace_all(". entek", "entek") %>% str_replace_all("kurtun-hes", "kurtun hes") %>% str_replace_all("^rwe_turcas_guney", "denizli rwe_turcas_guney") %>% str_replace_all("tekirdag santrali.", "modern enerji tekirdag santrali") %>% str_replace_all("karadag", "karadag res") %>% str_replace_all(".?menzelet( hes)?", "menzelet hes") %>% str_replace_all("\.", "") %>% str_replace_all("hidro(\s?elektrik santral[ıi]| e\.?s)", " hes") %>% str_replace_all("(termik santral[ıi]|\sts\s?)", " tes") %>% str_replace_all("tuncbilektes", "tuncbilek tes") %>% str_replace_all("d.(k.)c.(s.)?", "dgkc") %>% str_replace_all("jeotermal (e.s.*)", "jes")

We've categorised power plants by their type, doing analysis by plant name would not yield much useful results.

*HES: Hydroelectricity Plant

*TES: Thermal Energy Plant

*RES: Wind Energy Plant(Wind Turbines)

*DGKC: Natural Gas Combined Cycle Plant

*JES: Geothermal Energy Plant

## Warning: The plyr::rename operation has created duplicates for the
## following name(s): (`x`)

• Most time consuming part was data transformation and cleaning.

• Especially in 2018, number of data entries have drastically increased.

• While in average Thermal plants produce higher amounts of power, on total Hydroelectric plants' throughput is the highest.

• Each type of plant have a different leading reason for shutdowns.