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UPS (Uninterruptible Power Supply) are the foundation of high-reliability power systems. They provide highly reliable psuedo-sinusoidal and filtered power to any downstream system. Unlike the electrical utility, UPS ensure that systems have conditioned power that won't be interrupted in the event of a power instability. If there is a drop in incoming power, a period of high harmonic distortion or the power is otherwise out of specification, it is the UPS's job to prevent everything downstream from failing. A loss of utility power, even for a single cycle, will drop most electrical loads - as will other power abberations. A diesel generator EPS takes a few minutes to spin up and deliver the power needed, and the UPS is there to manage that loss of power.

UPS are typically very reliable but they still require maintenance and should be watched continuously in a critical environment. Ideally a UPS should be watched in real-time to ensure that they will operate as needed. Monitoring should include all significant metering for the system including input voltage and current, bypass voltage, DC Bus voltage, output voltage and output current. Ideally UPS monitoring should be performed with statistical analysis in addition to instantaneous monitoring for 'under' or 'over' specification. The statistical analysis can be used to compare present values with historical values which provides a useful operating context. Additionally, statistical analysis can spot any problematic behavior. For example if modules are beginning to show signs that they aren't sharing well there may be something wrong with a module.

I wrote some R code to perform a basic statistical analysis of a UPS. R is used to perform the statistical analysis. RMarkdown is used to render the file as a webpage because RMarkdown provides beautiful output. It is very easy, then, to call the code periodically to provide a real-time render of UPS behavior. The only portion of a real-time system that I haven't written would involve compartmentalizing historical vs. current behavior. It is the historical characterization of the system that would flag any current issues. For instance, if we were to see the standard deviation of "A-phase output voltage" increase, we would have a very good idea of what UPS component may be drifting out of specification.

Note: The data being analyzed was created in Python for this purpose and is NOT real data.

Rmd file rendered as web page

# Building 001 UPS System A Report 
# R, RMarkdown code by eric@focusnumeric.net
# Note: UPS data is not from a real system.  The data was created using Python for illustrative purposes.

```{r echo=FALSE, results='hide'}   
	# Usage in R GUI:  render("UPS.Report.Rmd", output_format="pdf_document",output_file=paste("UPS_Report", Sys.Date(), ".pdf"))
	# set required packages 
	library(rmarkdown)
	library(xtable)
	library(knitr)

	# set number of UPS modules
	# This has been tested - and works.
	numModules <- 4
	
	# set input filename
	inputFileName <- "UPS A Data.csv"
	
	# set these to make indexing the datasets easier.  
	# IMPORTANT!  The numbers refer to the *csv numbers in the inputFileName file COLUMN #1
	# Note:  These numbers refer to rows in the data file I made.  The data file has an entire year of data on it so I just took a subset.  
	# Remember this is made up data anyway!
	SCC.FirstRow <- 2
	SCC.LastRow <- 54
	if (numModules >= 1){
	Mod1.FirstRow <- 678
	Mod1.LastRow <- 730
	}
	if (numModules >= 2){
	Mod2.FirstRow <- 1043
	Mod2.LastRow <- 1095
	}
	if (numModules >= 3){
	Mod3.FirstRow <- 1408
	Mod3.LastRow <- 1460
	}
	if (numModules >=4){
	Mod4.FirstRow <- 1773
	Mod4.LastRow <- 1825
	}
	
	# get file, setup dataframes for SCC, modules.
	UPS <- read.csv(inputFileName, header=TRUE, stringsAsFactors=FALSE)
	SCCData <- UPS[SCC.FirstRow:SCC.LastRow,]
	if (numModules >= 1){
	Mod1Data <- UPS[Mod1.FirstRow:Mod1.LastRow,]
	}
	if (numModules >= 2){
	Mod2Data <- UPS[Mod2.FirstRow:Mod2.LastRow,]
	}
	if (numModules >= 3){
	Mod3Data <- UPS[Mod3.FirstRow:Mod3.LastRow,]
	}
	if (numModules >= 4){
	Mod4Data <- UPS[Mod4.FirstRow:Mod4.LastRow,]
	}
	
	# For debugging
	#```{r, echo=TRUE}
	#print(SCCData$A.Out.V)
	#```
```


```{r, echo=FALSE}
	# Produce Power Factor vector for use later
	# if statement prevents division by zero
	PFLength <- length(SCCData$KVA)
	PF <- vector(length = PFLength)
	for (i in 1:PFLength){
		if (SCCData$KVA[i]==0){
		SCCData$KVA[i]=1
		}
		PF[i] = SCCData$KW[i] / SCCData$KVA[i]
		# print(PF) # For debugging
	}
```

```{r, echo=FALSE}
	# Get statistics for all sets. 
	# SYSTEM CONTROL CABINET
	# SCC Input Voltage
	SCCInputVoltage <- c(SCCData$A.In.V, SCCData$B.In.V, SCCData$C.In.V)
	meanSCCInputVoltage <- mean(SCCInputVoltage)
	minSCCInputVoltage <- min(SCCData$A.In.V, SCCData$B.In.V, SCCData$C.In.V)
	maxSCCInputVoltage <- max(SCCData$A.In.V, SCCData$B.In.V, SCCData$C.In.V)
	# SCC Bypass Voltage 
	SCCBypassVoltage <- c(SCCData$A.By.V, SCCData$B.By.V, SCCData$C.By.V)
	meanSCCBypassVoltage <- mean(SCCBypassVoltage)
	minSCCBypassVoltage <- min(SCCData$A.By.V, SCCData$B.By.V, SCCData$C.By.V)
	maxSCCBypassVoltage <- max(SCCData$A.By.V, SCCData$B.By.V, SCCData$C.By.V)
	# SCC Output Voltage
	SCCOutputVoltage <- c(SCCData$A.Out.V, SCCData$B.Out.V, SCCData$C.Out.V)
	meanSCCOutputVoltage <- mean(SCCOutputVoltage)
	minSCCOutputVoltage <- min(SCCData$A.Out.V, SCCData$B.Out.V, SCCData$C.Out.V)
	maxSCCOutputVoltage <- max(SCCData$A.Out.V, SCCData$B.Out.V, SCCData$C.Out.V)
	# SCC Output Current
	SCCOutputCurrent <- c(SCCData$A.Out.I, SCCData$B.Out.I, SCCData$C.Out.I)
	meanSCCOutputCurrent <- mean(SCCOutputCurrent)
	minSCCOutputCurrent <- min(SCCData$A.Out.I, SCCData$B.Out.I, SCCData$C.Out.I)
	maxSCCOutputCurrent <- max(SCCData$A.Out.I, SCCData$B.Out.I, SCCData$C.Out.I)
	# SCC KVA
	meanKVA <- mean(SCCData$KVA)
	minKVA <- min(SCCData$KVA)
	maxKVA <- max(SCCData$KVA)
	# SCC KW
	meanKW <- mean(SCCData$KW)
	minKW <- min(SCCData$KW)
	maxKW <- max(SCCData$KW)
	# SCC Power Factor
	meanPF <- mean(PF)
	minPF <- min(PF)
	maxPF <- max(PF)
	# SCC Three-Phase 
	meanVA <- mean(SCCData$A.Out.V)
	meanVB <- mean(SCCData$B.Out.V)
	meanVC <- mean(SCCData$C.Out.V)
	minVA <- min(SCCData$A.Out.V)
	minVB <- min(SCCData$B.Out.V)
	minVC <- min(SCCData$C.Out.V)
	maxVA <- max(SCCData$A.Out.V)
	maxVB <- max(SCCData$B.Out.V)
	maxVC <- max(SCCData$C.Out.V)
	allVoltageSCC <- c(SCCData$A.Out.V, SCCData$B.Out.V, SCCData$C.Out.V)
	sdVoltageSCC <- sd(allVoltageSCC)
	meanIA <- mean(SCCData$A.Out.I)
	meanIB <- mean(SCCData$B.Out.I)
	meanIC <- mean(SCCData$C.Out.I)
	minIA <- min(SCCData$A.Out.I)
	minIB <- min(SCCData$B.Out.I)
	minIC <- min(SCCData$C.Out.I)
	maxIA <- max(SCCData$A.Out.I)
	maxIB <- max(SCCData$B.Out.I)
	maxIC <- max(SCCData$C.Out.I)
	allCurrentSCC <- c(SCCData$A.Out.I, SCCData$B.Out.I, SCCData$C.Out.I)
	sdCurrentSCC <- sd(allCurrentSCC)
	
```
	
```{r, echo=FALSE}
	# Graphics Parameters 
	par(mfrow=c(3,1))
	par(xpd=FALSE)
	par(mar=c(5,5,5,5))
```

```{r fig.width=8, fig.height=3.5, echo=FALSE, results='asis'}

	# Plots SCC input voltage

	t <- 1:length(SCCData$A.In.V)
	minVIPlot <- min(c(SCCData$A.In.V, SCCData$B.In.V, SCCData$C.In.V)) - 5
	maxVIPlot <- max(c(SCCData$A.In.V, SCCData$B.In.V, SCCData$C.In.V)) + 5
	# minVIPlot <- 470
	# maxVIPlot <- 485
	
    plot(t, SCCData$A.In.V, main="System Control Cabinet Input Voltage, 2018", xlab = "Week", ylab = "Volts AC", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minVIPlot,maxVIPlot))
	lines(t, SCCData$A.In.V, lty=3)
	points(t, SCCData$A.In.V, pch=0, bg="brown")
	lines(t, SCCData$B.In.V, col="orange", lwd=3)
	lines(t, SCCData$B.In.V, lty=3)
	points(t, SCCData$B.In.V, pch=1, bg="orange")
	lines(t, SCCData$C.In.V, col="yellow", lwd=3)
	lines(t, SCCData$C.In.V, lty=3)
	points(t, SCCData$C.In.V, pch=2, bg="yellow")
    box()
```

    **Input Voltage Summary - System Control Cabinet**
  • *Mean Input Voltage :* $\mu(V_\text{in})=$ `r round(meanSCCInputVoltage,2)`
  • *Minimum Input Voltage :* $\text{min}(V_{\text{in}})=$ `r minSCCInputVoltage`
  • *Maximum Input Voltage :* $\text{max}(V_\text{in})=$ `r maxSCCInputVoltage`
```{r fig.width=8, fig.height=3.5, echo=FALSE, results='asis'} # Plots SCC bypass voltage t <- 1:length(SCCData$A.By.V) minVBPlot <- min(c(SCCData$A.By.V, SCCData$B.By.V, SCCData$C.By.V)) - 5 maxVBPlot <- max(c(SCCData$A.By.V, SCCData$B.By.V, SCCData$C.By.V)) + 5 # minVBPlot <- 470 # maxVBPlot <- 485 plot(t, SCCData$A.By.V, main="System Control Cabinet Bypass Voltage, 2018", xlab = "Week", ylab = "Volts AC", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minVBPlot,maxVBPlot)) lines(t, SCCData$A.By.V, lty=3) points(t, SCCData$A.By.V, pch=0, bg="brown") lines(t, SCCData$B.By.V, col="orange", lwd=3) lines(t, SCCData$B.By.V, lty=3) points(t, SCCData$B.By.V, pch=1, bg="orange") lines(t, SCCData$C.By.V, col="yellow", lwd=3) lines(t, SCCData$C.By.V, lty=3) points(t, SCCData$C.By.V, pch=2, bg="yellow") box() ```
    **Bypass Voltage Summary - System Control Cabinet**
  • *Mean Input Voltage :* $\mu(V_\text{in})=$ `r round(meanSCCBypassVoltage,2)`
  • *Minimum Input Voltage :* $\text{min}(V_{\text{in}})=$ `r minSCCBypassVoltage`
  • *Maximum Input Voltage :* $\text{max}(V_\text{in})=$ `r maxSCCBypassVoltage`
\pagebreak ```{r fig.width=8, fig.height=4., echo=FALSE, results='asis'} # Plots SCC output voltage t <- 1:length(SCCData$A.Out.V) minVOPlot <- min(c(SCCData$A.Out.V, SCCData$B.Out.V, SCCData$C.Out.V)) - 5 maxVOPlot <- max(c(SCCData$A.Out.V, SCCData$B.Out.V, SCCData$C.Out.V)) + 5 # minVOPlot <- 470 # maxVOPlot <- 485 plot(t, SCCData$A.Out.V, main="System Control Cabinet Output Voltage, 2018", xlab = "Week", ylab = "Volts AC", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minVOPlot,maxVOPlot)) lines(t, SCCData$A.Out.V, lty=3) points(t, SCCData$A.Out.V, pch=0, bg="brown") lines(t, SCCData$B.Out.V, col="orange", lwd=3) lines(t, SCCData$B.Out.V, lty=3) points(t, SCCData$B.Out.V, pch=1, bg="orange") lines(t, SCCData$C.Out.V, col="yellow", lwd=3) lines(t, SCCData$C.Out.V, lty=3) points(t, SCCData$C.Out.V, pch=2, bg="yellow") box() ```
    **Output Voltage Summary - System Control Cabinet**
  • *Mean Output Voltage :* $\mu(V_\text{Out})=$ `r round(meanSCCOutputVoltage,2)`
  • *Minimum Output Voltage :* $\text{min}(V_{\text{Out}})=$ `r minSCCOutputVoltage`
  • *Maximum Output Voltage :* $\text{max}(V_\text{Out})=$ `r maxSCCOutputVoltage`
```{r fig.width=8, fig.height=4., echo=FALSE, results='asis'} # Plots SCC output current t <- 1:length(SCCData$A.Out.I) minIOPlot <- min(c(SCCData$A.Out.I, SCCData$B.Out.I, SCCData$C.Out.I)) - 5 maxIOPlot <- max(c(SCCData$A.Out.I, SCCData$B.Out.I, SCCData$C.Out.I)) + 5 # minIOPlot <- 470 # maxIOPlot <- 485 plot(t, SCCData$A.Out.I, main="System Control Cabinet Output Current, 2018", xlab = "Week", ylab = "Amperage", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minIOPlot,maxIOPlot)) lines(t, SCCData$A.Out.I, lty=3) points(t, SCCData$A.Out.I, pch=0, bg="brown") lines(t, SCCData$B.Out.I, col="orange", lwd=3) lines(t, SCCData$B.Out.I, lty=3) points(t, SCCData$B.Out.I, pch=1, bg="orange") lines(t, SCCData$C.Out.I, col="yellow", lwd=3) lines(t, SCCData$C.Out.I, lty=3) points(t, SCCData$C.Out.I, pch=2, bg="yellow") box() ```
    **Output Current Summary - System Control Cabinet**
  • *Mean Output Current :* $\mu(I_\text{Out})=$ `r round(meanSCCOutputCurrent,2)`
  • *Minimum Output Current :* $\text{min}(I_{\text{Out}})=$ `r minSCCOutputCurrent`
  • *Maximum Output Current :* $\text{max}(I_\text{Out})=$ `r maxSCCOutputCurrent`
\pagebreak
    **3-Phase Voltage and Current Output Summary**
  • **Phase A:**
  • *Mean Voltage Output:* $\mu(V_A)=$ `r round(meanVA,2)`
  • *Minimum Voltage Output:* $\text{min}(V_A)=$ `r minVA`
  • *Maximum Voltage Output:* $\text{max}(V_A)=$ `r maxVA`
  • *Mean Current Output:* $\mu(I_A)=$ `r round(meanIA,2)`
  • *Minimum Current Output:* $\text{min}(I_A)=$ `r minIA`
  • *Maximum Current Output:* $\text{max}(I_A)=$ `r maxIA`
    **Phase B:**
  • *Mean Voltage Output:* $\mu(V_B)=$ `r round(meanVB,2)`
  • *Minimum Voltage Output:* $\text{min}(V_B)=$ `r minVB`
  • *Maximum Voltage Output:* $\text{max}(V_B)=$ `r maxVB`
  • *Mean Current Output:* $\mu(I_B)=$ `r round(meanIB,2)`
  • *Minimum Current Output:* $\text{min}(I_B)=$ `r minIB`
  • *Maximum Current Output:* $\text{max}(I_B)=$ `r maxIB`
    **Phase C:**
  • *Mean Voltage Output:* $\mu(V_C)=$ `r round(meanVC,2)`
  • *Minimum Voltage Output:* $\text{min}(V_C)=$ `r minVC`
  • *Maximum Voltage Output:* $\text{max}(V_C)=$ `r maxVC`
  • *Mean Current Output:* $\mu(I_C)=$ `r round(meanIC,2)`
  • *Minimum Current Output:* $\text{min}(I_C)=$ `r minIC`
  • *Maximum Current Output:* $\text{max}(I_C)=$ `r maxIC`
- - - - - - - - - - -
  • *Standard Deviation for voltage output (all phases):* $\sigma(V)=$ `r sdVoltageSCC`
  • *Standard Deviation for current output (all phases):* $\sigma(I)=$ `r sdCurrentSCC`
- - - - - - - - - - - # Summary Plots for UPS Output Voltage & Output Current ```{r fig.width=8, fig.height=10, echo=FALSE, results='asis'} # plots 4 figures arranged in 3 rows and 2 columns. These are summary statistics plots for UPS output plotAllVoltage <- 1:length(allVoltageSCC) plotAllCurrent <- 1:length(allCurrentSCC) bPhaseMark <- 1/3*length(allVoltageSCC)+1 cPhaseMark <- 2/3*length(allVoltageSCC)+1 par(mfrow=c(3,2)) plot(plotAllVoltage,allVoltageSCC, main="Scatterplot of SCC Output Voltage", col="cadetblue", ylab="Voltage", xlab="Phase A, B, C") abline(v=bPhaseMark, lty=3) abline(v=cPhaseMark, lty=3) plot(plotAllCurrent,allCurrentSCC, main="Scatterplot of SCC Output Current", col="coral3", ylab="Current", xlab="Phase A, B, C") abline(v=bPhaseMark, lty=3) abline(v=cPhaseMark, lty=3) hist(allVoltageSCC, main="Histogram of SCC Output Voltage", col="cadetblue", xlab="Recorded Voltage") hist(allCurrentSCC, main="Histogram of SCC Output Current", col="coral3", xlab="Recorded Current") boxplot(allVoltageSCC, main="Boxplot of SCC Output Voltage", col="cadetblue") boxplot(allCurrentSCC, main="Boxplot of SCC Output Current", col="coral3") ``` \pagebreak ```{r fig.width=8, fig.height=3., echo=FALSE, results='asis'} # Plots SCC Load (KVA) t <- 1:length(SCCData$KVA) minKVAPlot <- min(SCCData$KVA) - 5 maxKVAPlot <- max(SCCData$KVA) + 5 # minKVAPlot <- 470 # maxKVAPlot <- 485 plot(t, SCCData$KVA, main="System Control Cabinet Output Load (KVA), 2018", xlab = "Week", ylab = "Power (KVA)", type="l", col="darkorange", axes=TRUE, lwd = 4, bty='L', ylim=c(minKVAPlot,maxKVAPlot)) lines(t, SCCData$KVA, lty=3) points(t, SCCData$KVA, pch=0, bg="black") box() ```
    **Load (KVA) Summary - System Control Cabinet**
  • *Mean Load (KVA) :* $\mu(P_\text{KVA})=$ `r round(meanKVA,2)`
  • *Minimum Load (KVA) :* $\text{min}(P_{\text{KVA}})=$ `r minKVA`
  • *Maximum Load (KVA) :* $\text{max}(P_\text{KVA})=$ `r maxKVA`
```{r fig.width=8, fig.height=3., echo=FALSE, results='asis'} # Plots SCC Load (KW) t <- 1:length(SCCData$KW) minKWPlot <- min(SCCData$KW) - 5 maxKWPlot <- max(SCCData$KW) + 5 # minKWPlot <- 470 # maxKWPlot <- 485 plot(t, SCCData$KW, main="System Control Cabinet Output Load (KW), 2018", xlab = "Week", ylab = "Power (KW)", type="l", col="darkred", axes=TRUE, lwd = 4, bty='L', ylim=c(minKWPlot,maxKWPlot)) lines(t, SCCData$KW, lty=3) points(t, SCCData$KW, pch=0, bg="black") box() ```
    **Load (KW) Summary - System Control Cabinet**
  • *Mean Load (KW) :* $\mu(P_\text{KW})=$ `r round(meanKW,2)`
  • *Minimum Load (KW) :* $\text{min}(P_{\text{KW}})=$ `r minKW`
  • *Maximum Load (KW) :* $\text{max}(P_\text{KW})=$ `r maxKW`
\pagebreak # Summary Plots for UPS Load in KVA & KW ```{r fig.width=8, fig.height=8, echo=FALSE, results='asis'} # plots 4 figures arranged in 3 rows and 2 columns. These are summary statistics plots for UPS Load plotAllKVA <- 1:length(SCCData$KVA) plotAllKW <- 1:length(SCCData$KW) par(mfrow=c(3,2)) plot(plotAllKVA,SCCData$KVA, main="Scatterplot of SCC Load (KVA)", col="darkorange", ylab="Load (KVA)", xlab="Record") plot(plotAllKW,SCCData$KW, main="Scatterplot of SCC Load (KW)", col="darkred", ylab="Load (KW)", xlab="Record") hist(SCCData$KVA, main="Histogram of SCC Load (KVA)", col="darkorange", xlab="Recorded Load") hist(SCCData$KW, main="Histogram of SCC Load (KW)", col="darkred", xlab="Recorded Load") boxplot(SCCData$KVA, main="Boxplot of SCC Load (KVA)", col="darkorange") boxplot(SCCData$KW, main="Boxplot of SCC Load (KW)", col="darkred") ``` ```{r fig.width=8, fig.height=5., echo=FALSE, results='asis'} # Plots SCC Power Factor (calculated) t <- 1:length(PF) minPFPlot <- min(PF) - .05 maxPFPlot <- max(PF) + .05 # minPFPlot <- 470 # maxPFPlot <- 485 plot(t, PF, main="System Control Cabinet Power Factor (calculated), 2018", xlab = "Week", ylab = "Power Factor", type="l", col="purple", axes=TRUE, lwd = 4, bty='L', ylim=c(minPFPlot,maxPFPlot)) lines(t, PF, lty=3) points(t, PF, pch=0, bg="black") box() ``` \pagebreak ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 1 Voltage if (numModules >= 1){ t <- 1:length(Mod1Data$A.Out.V) minMod1VPlot <- min(c(Mod1Data$A.Out.V, Mod1Data$B.Out.V, Mod1Data$C.Out.V)) - 5 maxMod1VPlot <- max(c(Mod1Data$A.Out.V, Mod1Data$B.Out.V, Mod1Data$C.Out.V)) + 5 # minMod1VPlot <- 470 # maxMod1VPlot <- 485 plot(t, Mod1Data$A.Out.V, main="Module 1 Output Voltage, 2018", xlab = "Week", ylab = "Volts AC", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod1VPlot,maxMod1VPlot)) lines(t, Mod1Data$A.Out.V, lty=3) points(t, Mod1Data$A.Out.V, pch=0, bg="brown") lines(t, Mod1Data$B.Out.V, col="orange", lwd=3) lines(t, Mod1Data$B.Out.V, lty=3) points(t, Mod1Data$B.Out.V, pch=1, bg="orange") lines(t, Mod1Data$C.Out.V, col="yellow", lwd=3) lines(t, Mod1Data$C.Out.V, lty=3) points(t, Mod1Data$C.Out.V, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 1 Current if (numModules >= 1){ t <- 1:length(Mod1Data$A.Out.I) minMod1IPlot <- min(c(Mod1Data$A.Out.I, Mod1Data$B.Out.I, Mod1Data$C.Out.I)) - 5 maxMod1IPlot <- max(c(Mod1Data$A.Out.I, Mod1Data$B.Out.I, Mod1Data$C.Out.I)) + 5 # minMod1IPlot <- 470 # maxMod1IPlot <- 485 plot(t, Mod1Data$A.Out.I, main="Module 1 Output Current, 2018", xlab = "Week", ylab = "Amperes", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod1IPlot,maxMod1IPlot)) lines(t, Mod1Data$A.Out.I, lty=3) points(t, Mod1Data$A.Out.I, pch=0, bg="brown") lines(t, Mod1Data$B.Out.I, col="orange", lwd=3) lines(t, Mod1Data$B.Out.I, lty=3) points(t, Mod1Data$B.Out.I, pch=1, bg="orange") lines(t, Mod1Data$C.Out.I, col="yellow", lwd=3) lines(t, Mod1Data$C.Out.I, lty=3) points(t, Mod1Data$C.Out.I, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=3, echo=FALSE, results='asis'} # Module 1 Load (KVA / KW) if (numModules >= 1){ t <- 1:length(Mod1Data$KVA) loadM1All <- c(Mod1Data$KVA, Mod1Data$KW) minMod1PPlot <- min(loadM1All) - 5 maxMod1PPlot <- max(loadM1All) + 10 # minMod1PPlot <- 470 # maxMod1PPlot <- 481 plot(t, Mod1Data$KVA, main="Module 1 Output Load, 2018", xlab = "Week", ylab = "Load", type="l", col="darkorange", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod1PPlot,maxMod1PPlot)) lines(t, Mod1Data$KVA, lty=3) points(t, Mod1Data$KVA, pch=0, bg="darkorange") lines(t, Mod1Data$KW, col="darkred", lwd=3) lines(t, Mod1Data$KW, lty=3) points(t, Mod1Data$KW, pch=1, bg="darkred") legend("topleft", legend=c("KVA", "KW"), col=c("darkorange", "darkred"), lty=1:1, cex=0.8) box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 1 VDC plot if (numModules >= 1){ t <- 1:length(Mod1Data$VDC) minMod1VDC <- min(Mod1Data$VDC) - 5 maxMod1VDC <- max(Mod1Data$VDC) + 5 # minMod1VDC <- 470 # maxMod1VDC <- 485 plot(t, Mod1Data$VDC, main="Module 1 DC Bus Voltage, 2018", xlab = "Week", ylab = "Load", type="l", col="darkorchid", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod1VDC,maxMod1VDC)) lines(t, Mod1Data$VDC, lty=3) points(t, Mod1Data$VDC, pch=0, bg="darkorchid") } ``` \pagebreak ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 2 Voltage if (numModules >= 2){ t <- 1:length(Mod2Data$A.Out.V) minMod2VPlot <- min(c(Mod2Data$A.Out.V, Mod2Data$B.Out.V, Mod2Data$C.Out.V)) - 5 maxMod2VPlot <- max(c(Mod2Data$A.Out.V, Mod2Data$B.Out.V, Mod2Data$C.Out.V)) + 5 # minMod2VPlot <- 470 # maxMod2VPlot <- 485 plot(t, Mod2Data$A.Out.V, main="Module 2 Output Voltage, 2018", xlab = "Week", ylab = "Volts AC", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod2VPlot,maxMod2VPlot)) lines(t, Mod2Data$A.Out.V, lty=3) points(t, Mod2Data$A.Out.V, pch=0, bg="brown") lines(t, Mod2Data$B.Out.V, col="orange", lwd=3) lines(t, Mod2Data$B.Out.V, lty=3) points(t, Mod2Data$B.Out.V, pch=1, bg="orange") lines(t, Mod2Data$C.Out.V, col="yellow", lwd=3) lines(t, Mod2Data$C.Out.V, lty=3) points(t, Mod2Data$C.Out.V, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 2 Current if (numModules >= 2){ t <- 1:length(Mod2Data$A.Out.I) minMod2IPlot <- min(c(Mod2Data$A.Out.I, Mod2Data$B.Out.I, Mod2Data$C.Out.I)) - 5 maxMod2IPlot <- max(c(Mod2Data$A.Out.I, Mod2Data$B.Out.I, Mod2Data$C.Out.I)) + 5 # minMod2IPlot <- 470 # maxMod2IPlot <- 485 plot(t, Mod2Data$A.Out.I, main="Module 2 Output Current, 2018", xlab = "Week", ylab = "Amperes", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod2IPlot,maxMod2IPlot)) lines(t, Mod2Data$A.Out.I, lty=3) points(t, Mod2Data$A.Out.I, pch=0, bg="brown") lines(t, Mod2Data$B.Out.I, col="orange", lwd=3) lines(t, Mod2Data$B.Out.I, lty=3) points(t, Mod2Data$B.Out.I, pch=1, bg="orange") lines(t, Mod2Data$C.Out.I, col="yellow", lwd=3) lines(t, Mod2Data$C.Out.I, lty=3) points(t, Mod2Data$C.Out.I, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=3, echo=FALSE, results='asis'} # Module 2 Load (KVA / KW) if (numModules >= 2){ t <- 1:length(Mod2Data$KVA) loadM2All <- c(Mod2Data$KVA, Mod2Data$KW) minMod2PPlot <- min(loadM2All) - 5 maxMod2PPlot <- max(loadM2All) + 10 # minMod2PPlot <- 470 # maxMod2PPlot <- 485 plot(t, Mod2Data$KVA, main="Module 2 Output Load, 2018", xlab = "Week", ylab = "Load", type="l", col="darkorange", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod2PPlot,maxMod2PPlot)) lines(t, Mod2Data$KVA, lty=3) points(t, Mod2Data$KVA, pch=0, bg="darkorange") lines(t, Mod2Data$KW, col="darkred", lwd=3) lines(t, Mod2Data$KW, lty=3) points(t, Mod2Data$KW, pch=1, bg="darkred") legend("topleft", legend=c("KVA", "KW"), col=c("darkorange", "darkred"), lty=1:1, cex=0.8) box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 2 VDC plot if (numModules >= 2){ t <- 1:length(Mod2Data$VDC) minMod2VDC <- min(Mod2Data$VDC) - 5 maxMod2VDC <- max(Mod2Data$VDC) + 5 # minMod1VDC <- 470 # maxMod1VDC <- 485 plot(t, Mod2Data$VDC, main="Module 2 DC Bus Voltage, 2018", xlab = "Week", ylab = "Load", type="l", col="darkorchid", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod2VDC,maxMod2VDC)) lines(t, Mod2Data$VDC, lty=3) points(t, Mod2Data$VDC, pch=0, bg="darkorchid") } ``` \pagebreak ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 3 Voltage if (numModules >= 3){ t <- 1:length(Mod3Data$A.Out.V) minMod3VPlot <- min(c(Mod3Data$A.Out.V, Mod3Data$B.Out.V, Mod3Data$C.Out.V)) - 5 maxMod3VPlot <- max(c(Mod3Data$A.Out.V, Mod3Data$B.Out.V, Mod3Data$C.Out.V)) + 5 # minMod3VPlot <- 470 # maxMod3VPlot <- 485 plot(t, Mod3Data$A.Out.V, main="Module 3 Output Voltage, 2018", xlab = "Week", ylab = "Volts AC", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod3VPlot,maxMod3VPlot)) lines(t, Mod3Data$A.Out.V, lty=3) points(t, Mod3Data$A.Out.V, pch=0, bg="brown") lines(t, Mod3Data$B.Out.V, col="orange", lwd=3) lines(t, Mod3Data$B.Out.V, lty=3) points(t, Mod3Data$B.Out.V, pch=1, bg="orange") lines(t, Mod3Data$C.Out.V, col="yellow", lwd=3) lines(t, Mod3Data$C.Out.V, lty=3) points(t, Mod3Data$C.Out.V, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 3 Current if (numModules >= 3){ t <- 1:length(Mod3Data$A.Out.I) minMod3IPlot <- min(c(Mod3Data$A.Out.I, Mod3Data$B.Out.I, Mod3Data$C.Out.I)) - 5 maxMod3IPlot <- max(c(Mod3Data$A.Out.I, Mod3Data$B.Out.I, Mod3Data$C.Out.I)) + 5 # minMod3IPlot <- 470 # maxMod3IPlot <- 485 plot(t, Mod3Data$A.Out.I, main="Module 3 Output Current, 2018", xlab = "Week", ylab = "Amperes", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod3IPlot,maxMod3IPlot)) lines(t, Mod3Data$A.Out.I, lty=3) points(t, Mod3Data$A.Out.I, pch=0, bg="brown") lines(t, Mod3Data$B.Out.I, col="orange", lwd=3) lines(t, Mod3Data$B.Out.I, lty=3) points(t, Mod3Data$B.Out.I, pch=1, bg="orange") lines(t, Mod3Data$C.Out.I, col="yellow", lwd=3) lines(t, Mod3Data$C.Out.I, lty=3) points(t, Mod3Data$C.Out.I, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=3, echo=FALSE, results='asis'} # Module 3 Load (KVA / KW) if (numModules >= 3){ t <- 1:length(Mod3Data$KVA) loadM3All <- c(Mod3Data$KVA, Mod3Data$KW) minMod3PPlot <- min(loadM3All) - 5 maxMod3PPlot <- max(loadM3All) + 10 # minMod3PPlot <- 470 # maxMod3PPlot <- 485 plot(t, Mod3Data$KVA, main="Module 3 Output Load, 2018", xlab = "Week", ylab = "Load", type="l", col="darkorange", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod3PPlot,maxMod3PPlot)) lines(t, Mod3Data$KVA, lty=3) points(t, Mod3Data$KVA, pch=0, bg="darkorange") lines(t, Mod3Data$KW, col="darkred", lwd=3) lines(t, Mod3Data$KW, lty=3) points(t, Mod3Data$KW, pch=1, bg="darkred") legend("topleft", legend=c("KVA", "KW"), col=c("darkorange", "darkred"), lty=1:1, cex=0.8) box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 3 VDC plot if (numModules >= 3){ t <- 1:length(Mod3Data$VDC) minMod3VDC <- min(Mod3Data$VDC) - 5 maxMod3VDC <- max(Mod3Data$VDC) + 5 # minMod3VDC <- 470 # maxMod3VDC <- 485 plot(t, Mod3Data$VDC, main="Module 3 DC Bus Voltage, 2018", xlab = "Week", ylab = "Volts DC", type="l", col="darkorchid", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod3VDC,maxMod3VDC)) lines(t, Mod3Data$VDC, lty=3) points(t, Mod3Data$VDC, pch=0, bg="darkorchid") } ``` \pagebreak ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 4 Voltage if (numModules >= 4){ t <- 1:length(Mod4Data$A.Out.V) minMod4VPlot <- min(c(Mod4Data$A.Out.V, Mod4Data$B.Out.V, Mod4Data$C.Out.V)) - 5 maxMod4VPlot <- max(c(Mod4Data$A.Out.V, Mod4Data$B.Out.V, Mod4Data$C.Out.V)) + 5 # minMod3VPlot <- 470 # maxMod3VPlot <- 485 plot(t, Mod4Data$A.Out.V, main="Module 4 Output Voltage, 2018", xlab = "Week", ylab = "Volts AC", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod4VPlot,maxMod4VPlot)) lines(t, Mod4Data$A.Out.V, lty=3) points(t, Mod4Data$A.Out.V, pch=0, bg="brown") lines(t, Mod4Data$B.Out.V, col="orange", lwd=3) lines(t, Mod4Data$B.Out.V, lty=3) points(t, Mod4Data$B.Out.V, pch=1, bg="orange") lines(t, Mod4Data$C.Out.V, col="yellow", lwd=3) lines(t, Mod4Data$C.Out.V, lty=3) points(t, Mod4Data$C.Out.V, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 4 Current if (numModules >= 4){ t <- 1:length(Mod4Data$A.Out.I) minMod4IPlot <- min(c(Mod4Data$A.Out.I, Mod4Data$B.Out.I, Mod4Data$C.Out.I)) - 5 maxMod4IPlot <- max(c(Mod4Data$A.Out.I, Mod4Data$B.Out.I, Mod4Data$C.Out.I)) + 5 # minMod4IPlot <- 470 # maxMod4IPlot <- 485 plot(t, Mod4Data$A.Out.I, main="Module 4 Output Current, 2018", xlab = "Week", ylab = "Amperes", type="l", col="brown", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod4IPlot,maxMod4IPlot)) lines(t, Mod4Data$A.Out.I, lty=3) points(t, Mod4Data$A.Out.I, pch=0, bg="brown") lines(t, Mod4Data$B.Out.I, col="orange", lwd=3) lines(t, Mod4Data$B.Out.I, lty=3) points(t, Mod4Data$B.Out.I, pch=1, bg="orange") lines(t, Mod4Data$C.Out.I, col="yellow", lwd=3) lines(t, Mod4Data$C.Out.I, lty=3) points(t, Mod4Data$C.Out.I, pch=2, bg="yellow") box() } ``` ```{r fig.width=8, fig.height=3, echo=FALSE, results='asis'} # Module 4 Load (KVA / KW) if (numModules >= 4){ t <- 1:length(Mod4Data$KVA) loadM4All <- c(Mod4Data$KVA, Mod4Data$KW) minMod4PPlot <- min(loadM4All) - 5 maxMod4PPlot <- max(loadM4All) + 10 # minMod4PPlot <- 470 # maxMod4PPlot <- 485 plot(t, Mod4Data$KVA, main="Module 4 Output Load, 2018", xlab = "Week", ylab = "Load", type="l", col="darkorange", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod4PPlot,maxMod4PPlot)) lines(t, Mod4Data$KVA, lty=3) points(t, Mod4Data$KVA, pch=0, bg="darkorange") lines(t, Mod4Data$KW, col="darkred", lwd=3) lines(t, Mod4Data$KW, lty=3) points(t, Mod4Data$KW, pch=1, bg="darkred") legend("topleft", legend=c("KVA", "KW"), col=c("darkorange", "darkred"), lty=1:1, cex=0.8) box() } ``` ```{r fig.width=8, fig.height=2.5, echo=FALSE, results='asis'} # Module 4 VDC plot if (numModules >= 4){ t <- 1:length(Mod4Data$VDC) minMod4VDC <- min(Mod4Data$VDC) - 5 maxMod4VDC <- max(Mod4Data$VDC) + 5 # minMod4VDC <- 470 # maxMod4VDC <- 485 plot(t, Mod4Data$VDC, main="Module 4 DC Bus Voltage, 2018", xlab = "Week", ylab = "Volts DC", type="l", col="darkorchid", axes=TRUE, lwd = 3, bty='L', ylim=c(minMod4VDC,maxMod4VDC)) lines(t, Mod4Data$VDC, lty=3) points(t, Mod4Data$VDC, pch=0, bg="darkorchid") } ``` # Dataset check to verify that the correct set was analyzed ```{r echo=TRUE, results='asis'} print(c(SCCData$Bld[1:3], SCCData$Date[1:3],SCCData$Name[1:3],SCCData$AssetID[1:3])) if (numModules >= 1){ print(c(Mod1Data$Bld[1:3], Mod1Data$Date[1:3],Mod1Data$Name[1:3],Mod1Data$AssetID[1:3])) } if (numModules >= 2){ print(c(Mod2Data$Bld[1:3], Mod2Data$Date[1:3],Mod2Data$Name[1:3],Mod2Data$AssetID[1:3])) } if (numModules >= 3){ print(c(Mod3Data$Date[1:3], Mod3Data$Date[1:3],Mod3Data$Name[1:3],Mod3Data$AssetID[1:3])) } if (numModules >= 4){ print(c(Mod4Data$Date[1:3], Mod4Data$Date[1:3],Mod4Data$Name[1:3],Mod4Data$AssetID[1:3])) } ```

Links to datasets and Markdown code.
Bld001UPSA.Rmd file (this is a made up UPS)
Rmd file rendered as a webpage.
Depending on your browser you may have to 'save as.'

Some places to find out more:

  1. LaTeX installation: Obtaining LaTeX at latex-project.org
  2. The main Markdown site: R Markdown. The assumption is generally that you will be using R Studio. You can run Markdown in the regular R GUI without any complication. I can't use R Studio at work because of security restrictions but can use Markdown anyway.
  3. Pandoc is required for running Markdown. See http://pandoc.org/
  4. A great R resource! See: "R In Action" from Manning.com It is worth a little extra to get the second edition. (Markdown isn't even covered in the first edition.) I highly recommend this book.

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