Clear explanations of quantum computing and power plant insurance terminology
The process of setting up QPPIP on your local system or server. QPPIP requires Python 3.8+ and automatically installs quantum computing libraries (Qiskit) and data visualization tools (Matplotlib, Plotly).
pip install -r requirements.txt in the QPPIP directory to install all dependencies.
A streamlined guide to running your first QPPIP analysis. Execute the main script to generate a complete risk assessment for your power plant, including quantum simulations and insurer quotes.
python qppip.py --plant combined_cycle --capacity 500
Customizable parameters that control QPPIP behavior: number of qubits (default 26), simulation shots (default 16,384), plant type, and capacity. Higher shots increase accuracy but require more computation time.
Risk assessment for pressure-containing equipment including boilers, heat recovery steam generators (HRSG), and pressure vessels. Factors include age, operating pressure, inspection history, and material condition.
Risk evaluation for rotating machinery including gas turbines, steam turbines, and generators. Critical factors are operating hours, vibration levels, and overhaul history.
Assessment of electrical infrastructure including transformers, switchgear, and protective relays. Key concerns are transformer oil condition, dissolved gas analysis (DGA), and arc flash potential.
Environmental liability exposure including emissions, spill history, and permit compliance. Coal and oil plants have higher environmental risk than gas or renewable facilities.
Financial loss from unplanned plant outages. Calculated based on capacity, market prices, and outage duration. BI coverage typically includes a waiting period before claims begin.
Risks from external sources including natural catastrophes (hurricane, earthquake, flood), cyber attacks on SCADA/DCS systems, and grid failures from transmission events.
QPPIP's quantum circuit uses 26 qubits to simultaneously model multiple risk domains. Qubits can exist in superposition, allowing the circuit to evaluate millions of risk scenarios in parallel.
The process of converting classical risk data into quantum states. QPPIP uses RY rotation gates where the rotation angle is proportional to the risk level (0 = no risk, π = maximum risk).
Quantum entanglement used to model risk dependencies. For example, boiler failure increases business interruption risk. QPPIP uses CX (CNOT) gates to create these correlations between qubit groups.
Grover-like amplitude amplification to boost the probability of measuring high-risk scenarios. This helps identify rare but severe loss events that traditional Monte Carlo simulation might miss.
The maximum value that can be claimed under a property insurance policy. For power plants, TIV typically ranges from $1.2M to $1.8M per MW of capacity.
The amount you pay out-of-pocket before insurance kicks in. Higher deductibles lower premiums but increase retention. QPPIP optimizes deductibles based on your risk tolerance and cash flow.
The time delay before business interruption coverage begins. Shorter waiting periods cost more in premium. Typical range is 14-60 days.
Fee paid to insurance brokers, typically 15-25% of premium. QPPIP eliminates this by connecting directly to insurers, saving $1-3M annually for large plants.
Equipment in combined cycle plants that captures exhaust heat from gas turbines to produce steam for additional power generation. A common source of boiler/machinery claims.
Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) that monitor and control plant operations. Primary target for cyber attacks.
Laboratory test that analyzes gases dissolved in transformer oil to detect internal faults. Results are categorized as Normal, Caution, or Abnormal.