Algorithm-driven Intelligent Component Recommendation for Medium Voltage Air-Insulated Switchgear Using Python

Medium voltage switchgear systems form the backbone of electrical distribution networks. These systems are responsible for protection, isolation, and operational control of power circuits. The process of selecting appropriate components for switchgear assemblies is critical, as improper selection may lead to system failure or safety hazards.

In traditional engineering practice, component selection is carried out manually by referring to design standards, manufacturer data, and prior experience. This approach is often time intensive and may lead to inconsistencies, especially when dealing with complex configurations.

To address these challenges, this work proposes an algorithm-based selection framework that transforms engineering rules into programmable logic.

RELATED WORK

Conventional Design Practices

Existing methods rely heavily on: Manual calculations, Spreadsheet tools, Vendor catalogs. These methods lack adaptability and automation.

Need for Intelligent Systems

With the rise of digital engineering:

Automated design tools are becoming essential, Decision-making must be standardized, Engineering time must be reduced.

Research Gap

There is limited work on:

• Rule-based automation in switchgear design
• Integration of programming tools in electrical design

METHODOLOGY

Input Variables

The system considers:

• Rated voltage
• Load current
• Short-circuit capacity
• Cable length

Decision Algorithm

The algorithm follows structured logic:

• Classification of voltage levels
• Matching fault ratings
• Current-based component size

Mathematical Evaluation

Voltage drop across the cable is determined using:

Vd=3⋅I⋅R⋅L1000

This ensures that selected components operate within permissible limits.

Short-Circuit Withstand Check

For cable/busbar safety:

I2t=K2S2

You can validate if selected cable survives fault.

Standards-Based Logic

We embed simplified rules inspired by:

• IEC 62271
• IEC 60044
• IS 7098

System Workflow

• User inputs system parameters
• Algorithm processes input
• Components are selected
• Output is displayed

IMPLEMENTATION

The framework is implemented using Python, leveraging object-oriented programming for modularity.

# Medium Voltage AIS Component Recommendation System

class SwitchgearRecommender:

    def __init__(self, voltage_kv, load_current_a, fault_level_ka, cable_length_m):

        self.voltage_kv = voltage_kv

        self.load_current_a = load_current_a

        self.fault_level_ka = fault_level_ka

        self.cable_length_m = cable_length_m

    # Circuit Breaker Selection

    def select_circuit_breaker(self):

        if self.voltage_kv <= 11:

            voltage_class = "12 kV"

        elif self.voltage_kv <= 24:

            voltage_class = "24 kV"

        else:

            voltage_class = "36 kV"

        if self.fault_level_ka <= 25:

            breaking_capacity = "25 kA"

        elif self.fault_level_ka <= 31.5:

            breaking_capacity = "31.5 kA"

        else:

            breaking_capacity = "40 kA"

        return f"Vacuum Circuit Breaker ({voltage_class}, {breaking_capacity})"

    # CT Selection

    def select_ct(self):

        primary = int((self.load_current_a * 1.25) // 10 * 10)  # rounding

        return f"{primary}/1 A, Class 5P10"

    # Busbar Selection

    def select_busbar(self):

        if self.load_current_a <= 800:

            return "Aluminum Busbar: 50x6 mm"

        elif self.load_current_a <= 1600:

            return "Aluminum Busbar: 75x10 mm"

        else:

            return "Copper Busbar: 100x10 mm"

    # Protection Relay Selection

    def select_relay(self):

        if self.fault_level_ka > 25:

            return "Numerical Relay with OCR + E/F + S/C Protection"

        else:

            return "Numerical Relay with OCR + E/F Protection"

    # Cable Selection (simplified)

    def select_cable(self):

        if self.load_current_a <= 200:

            size = "70 sq.mm"

        elif self.load_current_a <= 400:

            size = "120 sq.mm"

        elif self.load_current_a <= 800:

            size = "240 sq.mm"

        else:

            size = "400 sq.mm"

        return f"XLPE Cable: {size}"

    # Final Recommendation

    def generate_recommendation(self):

        return {

            "Voltage Level (kV)": self.voltage_kv,

            "Load Current (A)": self.load_current_a,

            "Fault Level (kA)": self.fault_level_ka,

            "Recommended Circuit Breaker": self.select_circuit_breaker(),

            "Recommended CT": self.select_ct(),

            "Recommended Busbar": self.select_busbar(),

            "Recommended Relay": self.select_relay(),

            "Recommended Cable": self.select_cable()

        }

# ---------------------------

# USER INPUT

# ---------------------------

def main():

    print("⚡ MV Switchgear Component Recommendation System ⚡")

    voltage = float(input("Enter System Voltage (kV): "))

    current = float(input("Enter Load Current (A): "))

    fault = float(input("Enter Fault Level (kA): "))

    length = float(input("Enter Cable Length (m): "))

    recommender = SwitchgearRecommender(voltage, current, fault, length)

    result = recommender.generate_recommendation()

    print("\n???? Recommended Components:")

    for key, value in result.items():

        print(f"{key}: {value}")

if __name__ == "__main__":

    main()

Example Input:

Voltage = 11 kV 

Current = 630 A 

Fault Level = 25 kA 

Cable Length = 100 m

Output:

Vacuum Circuit Breaker (12 kV, 25 kA)

CT: 800/1 A

Busbar: Aluminum 50x6 mm

Relay: OCR + E/F

Cable: 240 sq.mm

IMPLEMENTATION RESULTS

Python Program: Component Recommendation System

Python Program Run Result: Component Recommendation System

FUTURE SCOPE

• Integration with machine learning
• Real-time database connectivity
• Web-based application (Streamlit / Power Apps)
• Advanced protection coordination

CONCLUSION

The proposed algorithm-driven framework provides an efficient and scalable solution for switchgear component selection. By integrating engineering rules into a programmable structure, the system enhances design accuracy and reduces engineering effort.

REFERENCES

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