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arXiv:2401.09800 · Scientific Reports

Power system fault diagnosis with quantum computing and efficient gate decomposition

Xiang Fei, Huan Qi Zhao, Xiyuan Zhou, Junhua Zhao, Ting Shu, Fushuan Wen

Published Jul 23, 2024• 17 citations
arXivHTMLPDF

Abstract

Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the quantum approximate optimization algorithm. The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is utilized. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.

DOI
10.1038/s41598-024-67922-w

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Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the quantum approximate optimization algorithm. The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is utilized. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.
In line with UN Sustainable Development Goal 7, maintaining grid stability in the face of growing energy demand and clean energy integration is a significant worldwide concern. The speed, scalability, and real-time adaptability of traditional fault detection techniques-which rely on SCADA polling and heuristic algorithms-are constrained. For such combinatorial tasks, quantum computing provides a fundamentally quicker alternative by utilizing amplitude amplification and qubit superposition. In this paper, a distributed quantum model for identifying defective power grid components using Grover's technique is presented. In order to reduce search complexity fromO(n)O(n)O(n)toO(n/K)O(\sqrt{n / K})O(n/K​)utilizingKKKnodes, grid elements are encoded as qubits, and defect detection is carried out in parallel across quantum nodes. In order to preserve stability after discovery, a quantum-classical approach reallocates power. Our theoretical solution is scalable for future energy systems because to its increased efficiency and reduced qubit requirements. This research provides a solid basis for incorporating quantum computing into intelligent, environmentally friendly grid architecture.
The reliable and efficient operation of power systems is crucial for the proper functioning of modern society. However, the large and complex nature of power systems presents significant computational challenges for their operation and planning. Traditional computational techniques are insufficient in addressing these complexities, and novel approaches are necessary to enhance the efficiency and effectiveness of power system operations and planning. Although quantum computing algorithms have shown the potential in completing significant computing tasks faster than traditional computers, their application to power systems remains largely unexplored. This dissertation aims to investigate advanced solution techniques for power system optimization problems by proposing and implementing hybrid distributed-quantum computing-based optimization approaches. To address the current limitations of noisy quantum computers and their practical application, a multi-block alternating direction method of multiplier (ADMM) is proposed for solving mixed-integer linear programming (MILP) problems. The proposed algorithm is applied to a large-scale transmission expansion planning problem to demonstrate its performance. Furthermore, the quantum approximate optimization algorithm (QAOA) is integrated into the ADMM algorithm as a hybrid quantum-classical method for solving the MILP problem. The effectiveness of this approach is demonstrated through simulations and numerical experiments on a generation scheduling problem. To reduce the quantum-classical iteration of the QAOA algorithm, a recurrent neural network is designed to estimate the variational parameters of the quantum circuit with an appropriate sampling technique. The simulation results exhibit the effectiveness of the proposed model.
Abstract Failure probability estimation and identifying failure paths are crucial for ensuring the reliable operation of complex systems like autonomous ships. Fault tree Analysis is a widely used method to assess potential causes of system failure. However, as system complexity increases, identifying minimal cut sets becomes computationally challenging. This study explores the use of quantum computing to overcome these limitations. By encoding fault events as qubits, quantum computing leverages superposition and entanglement to accelerate computations. We developed a quantum fault tree circuit and applied the quantum amplitude amplification (QAA) algorithm to efficiently estimate failure probabilities and identify the minimal cut sets. Using IBM’s Qiskit environment, we implemented and tested the methodology on the power system of an autonomous ship. The results demonstrate the potential of quantum computing to enhance failure risk analysis, enabling efficient assessment of large, complex systems beyond the capabilities of classical computers.
Scenario-based stochastic optimization is a prevalent method for the economic dispatch of renewables-rich power systems. To reduce the time and space complexities for generating massive scenarios and solving pertinent mixed-integer stochastic optimization efficiently, quantum computing can provide critical assistance. To this end, we first propose the unified quantum amplitude estimation (UQAE) to generate massive scenarios with quadratic acceleration by a few qubits, after which the dimension of state variables embedded in the two-stage stochastic optimization also decreases exponentially. Then, by utilizing quantum-classical Benders decomposition, we accelerate the mixed integer master problem in the first stage on quantum processing units, while convex subproblems are solved on central processing units. Especially considering limited qubits in the noisy intermediate-scale quantum era, we propose to reduce the scale of master problem by scenario clustering and cut selection using the customized quantum approximation optimization algorithm (QAOA). Mathematically, we not only prove the decreases of complexities by UQAE-QAOA but also give the feasible ranges of penalty factors for transforming constrained problems into Ising models. Finally, we perform numerical experiments on modified IEEE systems to test the efficiency and accuracy of UQAE-QAOA, as well as the scalability of the designed quantum scheme.
The rapid development of quantum computing has provided a ground-breaking tool for solving problems that are insurmountable on classical computers. In the power system domain, quantum computing is still in the nascent stage, with limited but ongoing studies. The Quantum Approximation Optimization Algorithm (QAOA) has been applied in solving several NPhard problems and can be further studied to address challenging problems in power systems. Key applications include contingency analysis, optimal power flow, and unit commitment, among others. With the significant integration of renewable energy, the intermittent nature of renewable energy introduces substantial volatility to the system. An overhead contingency analysis is required to evaluate the possible system outage scenarios, which will identify critical contingencies to maintain system stability. However, the contingency analysis is a prototypical NP-Hard problem, requiring substantial classical computation resources. In this work, QAOA is developed for theN−kN-kN−kcontingency analysis on line contingency as well as the general contingency scenarios, with the aim of minimizing the shedding of the power load while ensuring the power flow constraints. A tailored objective function is developed to encode theN−kN-kN−kcontingency condition as well as the power flow loss constraints into a problem Hamiltonian for QAOA computation. The developed method is verified on IEEE 9- and 14-bus systems compared with classical methods as the baseline. In addition, a QAOA parameter study including the number of QAOA parameters, the number of repetitions of the quantum circuits execution, and the number of iteration steps of the classical optimization algorithm, is conducted to offer insights for the parameter tuning under different problem sizes. This work is anticipated to establish a quantum framework for solving critical and complex power system problems in the Noisy intermediate-scale quantum (NISQ) era.
Detection and isolation of faults is a crucial step in the physical realization of quantum circuits. Even though quantum gates and circuits compute reversible functions, the standard techniques of automatic test pattern generation (ATPG) for classical reversible circuits are not directly applicable to quantum circuits. For faulty quantum circuits under the widely accepted single fault assumption, we show that their behavior can be fully characterized by the (single) faulty gate and the corresponding fault model. This allows us to efficiently determine test input states as well as measurement strategy for fault detection and diagnosis. Building on top of these, we design randomized algorithms which are able to detect every nontrivial single-gate fault with minimal probability of error. We also describe similar algorithms for fault diagnosis. We evaluate our algorithms by the number of output samples that needs to be collected and the probability of error. Both of these can be related to the eigenvalues of the operators corresponding to the circuit gates. We experimentally compare all our strategies with the state-of-the-art ATPG techniques for quantum circuits under the “single missing faulty gate” model and demonstrate that significant improvement is possible if we can exploit the quantum nature of circuits.