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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. [ABSTRACT FROM AUTHOR]

In recent years, micro combined cooling, heating and power generation (mCCHP) systems have attracted much attention in the energy demand side sector. The input energy of a mCCHP system is natural gas, while the outputs include heating, cooling and electricity energy. The mCCHP system is deemed as a possible solution for households with multiple energy demands. Given this background, a mCCHP based comprehensive energy solution for households is proposed in this paper. First, the mathematical model of a home energy hub (HEH) is presented to describe the inputs, outputs, conversion and consumption process of multiple energies in households. Then, electrical loads and thermal demands are classified and modeled in detail, and the coordination and complementation between electricity and natural gas are studied. Afterwards, the concept of thermal comfort is introduced and a robust optimization model for HEH is developed considering electricity price uncertainties. Finally, a household using a mCCHP as the energy conversion device is studied. The simulation results show that the comprehensive energy solution proposed in this work can realize multiple kinds of energy supplies for households with the minimized total energy cost. [ABSTRACT FROM AUTHOR]

Performance of busbar insulators is affected by the accumulation of pollutants on their surfaces. The combined action of both the chemical and natural dust pollutants accumulated on the surfaces of the insulators results in the likelihood of surface flashovers. In this paper, the pollution level in terms of equivalent salt deposit density (ESDD) and chemical analyses of a 12-kV generator busbar insulators are carried out to identify the impacts of pollutants presented on the insulators. It is found by actual measurements that the ESDD values of five types of busbar insulators are 0.569, 1.66, 1.61, 1.58, and 2.44mg/cm2, respectively, and all of these values are beyond the acceptable limits. New mathematical models are proposed in between ESDD and chemical anions/cations using a multivariable linear regression analysis. A positive correlation is found between ESDD and cations for the T1-type insulator, whereas for the T5-type insulator, it is found between ESDD and anions. [ABSTRACT FROM AUTHOR]