PID tuning through SCADA in SCADA systems - Step-by-Step Execution

Process Flow - PID tuning through SCADA

Start: Read Process Variable (PV)

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Calculate Error = Setpoint - PV

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Apply PID Formula: P + I + D terms

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Output Control Signal to Actuator

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Process Responds, PV Changes

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SCADA Monitors PV and Output

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Adjust PID Parameters (Kp, Ki, Kd) if needed

↩Back to Calculate Error

The SCADA system continuously reads the process variable, calculates the error, applies the PID formula, sends control signals, monitors responses, and adjusts PID parameters to tune the system.

Execution Sample

SCADA systems

PV = 50
Setpoint = 60
Error = Setpoint - PV
Output = Kp*Error + Ki*Integral(Error) + Kd*Derivative(Error)
Send Output to Actuator
Adjust Kp, Ki, Kd based on response

This code snippet shows the basic PID control loop steps executed through SCADA.

Process Table

Step	Process Variable (PV)	Setpoint	Error	PID Output	Action	Notes
1	50	60	10	Calculate P=Kp*10, I=Ki*0, D=Kd*0	Send output to actuator	Initial error calculation, integral and derivative zero
2	52	60	8	Calculate P=Kp*8, I=Ki*10, D=Kd*(8-10)	Send output to actuator	PV increased, error decreased, integral accumulates
3	55	60	5	Calculate P=Kp*5, I=Ki*18, D=Kd*(5-8)	Send output to actuator	Error smaller, integral grows, derivative negative
4	58	60	2	Calculate P=Kp*2, I=Ki*23, D=Kd*(2-5)	Send output to actuator	Error close to zero, integral high, derivative negative
5	60	60	0	Calculate P=Kp*0, I=Ki*25, D=Kd*(0-2)	Send output to actuator	Error zero, integral max, derivative negative
6	61	60	-1	Calculate P=Kp*(-1), I=Ki*24, D=Kd*(-1-0)	Send output to actuator	PV overshoot, error negative, integral decreases
7	60	60	0	Calculate P=Kp*0, I=Ki*24, D=Kd*(0--1)	Send output to actuator	PV back to setpoint, system stabilizing
Exit	60	60	0	Stable output	No further adjustment	System tuned, error minimized

💡 System reaches setpoint with zero error and stable output, tuning complete

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	After 6	After 7	Final
PV	50	52	55	58	60	61	60	60	60
Error	10	8	5	2	0	-1	0	0	0
Integral(Error)	0	10	18	23	25	24	24	24	24
Derivative(Error)	0	-2	-3	-3	-2	-1	1	0	0
PID Output	Calc	Calc	Calc	Calc	Calc	Calc	Calc	Calc	Stable

Key Moments - 3 Insights

Why does the integral term keep increasing even when the error is getting smaller?

The integral term sums all past errors, so even if the current error is small, the accumulated sum can still grow, as seen in rows 2 to 5 of the execution_table.

What causes the derivative term to be negative and then positive?

The derivative term measures error change rate. When error decreases quickly, derivative is negative (rows 2-6). When error starts increasing again (overshoot), derivative becomes positive (row 7).

Why does the system output stabilize even when PV slightly overshoots the setpoint?

The PID controller adjusts output based on error and its history, correcting overshoot by reducing output, leading to stabilization as shown in the last steps of the execution_table.

Visual Quiz - 3 Questions

Look at the execution table at step 3. What is the error value?

A 5

B 8

C 2

D 0

💡 Hint

Check the 'Error' column in row 3 of the execution_table.

At which step does the process variable (PV) first reach the setpoint?

A Step 4

B Step 5

C Step 6

D Step 7

💡 Hint

Look at the 'PV' column and find when it equals the 'Setpoint' in the execution_table.

If the integral term was not accumulated, how would the PID output change over steps?

A It would stay constant

B It would only depend on current error and derivative

C It would increase faster

D It would become zero

💡 Hint

Refer to the 'Integral(Error)' column in variable_tracker and its role in PID output.

Concept Snapshot

PID tuning through SCADA:
- Continuously read process variable (PV)
- Calculate error = setpoint - PV
- Compute PID output: P + I + D terms
- Send output to actuator
- Monitor PV and output via SCADA
- Adjust Kp, Ki, Kd to improve control
- Repeat until system stabilizes

Full Transcript

This visual execution shows how a SCADA system performs PID tuning. It starts by reading the process variable and calculating the error from the setpoint. Then it applies the PID formula combining proportional, integral, and derivative terms to compute the control output. This output is sent to the actuator to influence the process. The SCADA system monitors the process variable and output response, adjusting the PID parameters as needed. The execution table traces each step's values, showing how error decreases and the system stabilizes at the setpoint. Key moments clarify common confusions about integral accumulation and derivative sign changes. The quiz tests understanding of error values, setpoint reaching, and the role of integral term in PID output.

Practice

1. What is the main purpose of PID tuning in a SCADA system?

easy

A. To adjust how a machine controls a process to keep it steady

B. To change the color scheme of the SCADA interface

C. To increase the speed of the SCADA software

D. To backup SCADA data automatically

Solution

1. Step 1: Understand PID control basics

   PID tuning changes how the machine reacts to keep a process stable by adjusting proportional, integral, and derivative settings.

2. Step 2: Identify the role of PID tuning in SCADA

   SCADA systems allow easy adjustment of these PID settings to improve process control.

3. Final Answer:

   To adjust how a machine controls a process to keep it steady -> Option A

4. Quick Check:

   PID tuning controls process stability = A [OK]

Hint: PID tuning controls process stability, not UI or speed [OK]

Common Mistakes:

• Confusing PID tuning with UI customization
• Thinking PID tuning speeds up software
• Assuming PID tuning is for data backup

2. Which of the following is the correct way to change the proportional gain (P) in a SCADA PID controller interface?

easy

A. Set P value to a negative number to reduce output

B. Set P value to zero to speed up the system

C. Decrease P value below zero to stabilize the system

D. Increase P value to make the system respond faster

Solution

1. Step 1: Understand proportional gain effect

   Increasing the proportional gain makes the system respond faster to errors.

2. Step 2: Identify correct adjustment

   Setting P to a negative or zero value is incorrect and can cause instability or no response.

3. Final Answer:

   Increase P value to make the system respond faster -> Option D

4. Quick Check:

   Higher P means faster response = C [OK]

Hint: Increase P to speed response; never use negative P [OK]

Common Mistakes:

• Using negative values for P gain
• Setting P to zero thinking it speeds system
• Confusing P with integral or derivative gains

3. After increasing the integral gain (I) in a SCADA PID controller, what is the most likely effect on the system output?

medium

A. The system will eliminate steady-state error faster but may oscillate

B. The system will respond slower and may never reach the target

C. The system output will become constant and unchanging

D. The system will ignore errors and keep output fixed

Solution

1. Step 1: Understand integral gain role

   Integral gain helps remove steady-state error by accumulating past errors and adjusting output accordingly.

2. Step 2: Predict effect of increasing I

   Increasing I speeds error correction but can cause oscillations if too high.

3. Final Answer:

   The system will eliminate steady-state error faster but may oscillate -> Option A

4. Quick Check:

   Higher I removes steady error but risks oscillation = B [OK]

Hint: Higher I removes steady error but watch for oscillations [OK]

Common Mistakes:

• Thinking higher I slows system response
• Assuming output becomes constant after increasing I
• Ignoring oscillation risk with high I

4. You set the derivative gain (D) too high in a SCADA PID controller. What problem will most likely occur?

medium

A. The system will become very slow to respond

B. The system output will become noisy and unstable

C. The system will stop controlling the process

D. The system will ignore sudden changes in error

Solution

1. Step 1: Understand derivative gain effect

   Derivative gain reacts to the rate of error change and helps reduce overshoot.

2. Step 2: Identify effect of too high D

   Too high derivative gain amplifies noise causing output to become unstable and noisy.

3. Final Answer:

   The system output will become noisy and unstable -> Option B

4. Quick Check:

   High D causes noise and instability = D [OK]

Hint: Too much D gain causes noisy, unstable output [OK]

Common Mistakes:

• Thinking high D slows system
• Assuming high D ignores error changes
• Believing system stops controlling process

5. You want to tune a PID controller in SCADA to reduce oscillations and improve stability. Which combination of changes is best?

hard

A. Set all gains to zero and restart the system

B. Increase P gain sharply, increase I gain sharply, decrease D gain

C. Decrease P gain slightly, increase D gain moderately, keep I gain low

D. Increase I gain sharply, decrease P and D gains

Solution

1. Step 1: Understand oscillation causes

   High P gain can cause oscillations; D gain helps dampen them; I gain affects steady error.

2. Step 2: Choose tuning to reduce oscillations

   Decreasing P reduces aggressive response; increasing D adds damping; keeping I low avoids integral windup.

3. Final Answer:

   Decrease P gain slightly, increase D gain moderately, keep I gain low -> Option C

4. Quick Check:

   Lower P + higher D = less oscillation = A [OK]

Hint: Lower P and raise D to reduce oscillations [OK]

Common Mistakes:

• Increasing P sharply causing more oscillations
• Ignoring derivative gain's damping effect
• Setting all gains to zero stopping control