An international research team has developed a particle swarm optimization (PSO) algorithm based on quantum computing for real-time maximum power point tracking (MPPT) implementation in PV systems.
The scientists explained that the quantum version of the PSO algorithm capitalizes on the high speed of quantum computing and reduces the interval of random numbers in subsequent stages to avoid premature convergence. “A detailed implementation of the quantum aspect of the solution is provided using qubit states instead of classic particles and performing qubit spins using the y-axis rotation gates to perform the moves on the qubit states to search for the optimal solution,” they explained.
In quantum computing, a qubit or quantum bit is a basic unit of quantum information. It is the quantum mechanical analog of a bit in classical computing based on binary digits.
The proposed quantum particle swarm optimization (QPSO) algorithm is based on Schrödinger's equation – a differential equation that defines the behavior of wavefunctions in quantum mechanics.
“Simulating the behavior of human intelligence, rather than that of a flock of birds or a school of fish, necessitates capturing the thought processes of a complex social organism, which cannot be adequately described by a linear evolution equation,” the researchers said, seeking to describe the working principle of the algorithm. “It is believed that human thinking is as uncertain as a particle with quantum behaviors.”
The group tested the performance of the algorithm through Matlab Simulink software in a simulated PV array relying on four 213 W solar modules. It found the QPSO algorithm shows a strong ability to maintain performance close to that of conventional PSO across different environmental conditions. “It performs well in power optimization and maintaining system activity, as indicated by the power output and duty cycle values under both optimal and challenging conditions,” the academics added, noting that the proposed algorithm also requires “more prominent” computational demands.
They also found that, although the power achieved by the conventional PSO algorithm was approximately 0.15% higher than that attained by the QPSO algorithm under the same conditions, the QPSO was able to beat the conventional PSO in more challenging conditions.
“Specifically, the quantum algorithm generates 3.33% more power in higher temperature tests and 0.89% more power in partial shading tests,” they emphasized. “Additionally, the quantum algorithm displays lower duty cycles, with a reduction of 3.9% in normal operating conditions, 0.162% in high-temperature tests, and 0.54% in partial shading tests.”
The new algorithm was described in the study “Quantum maximum power point tracking (QMPPT) for optimal solar energy extraction,” published in Systems and Soft Computing. The research group included scientists from Algeria's École Nationale Polytechnique and its École Nationale Supérieure de Technologies, Canada's Université du Québec à Trois-Rivières, and the Norwegian Research Centre.
“Despite the classical algorithm's marginal advantage in power output under normal conditions, the quantum algorithm illustrates superior performance across all other metrics, achieving higher power values and consistently lower duty cycle records, indicating more excellent general efficiency,” the scientists concluded. “Future work could explore adaptive algorithms that dynamically adjust to changing environmental conditions, enhancing efficiency and reliability.”
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