To address the difficulty of effectively sensing weakly distorted electric fields generated during the early stage of insulation degradation in power equipment, this paper proposes a miniaturized high-sensitivity multilayer flexible electric field sensor optimized using an improved GOOSE algorithm. First, based on the electric field coupling mechanism, the enhancement effect of the flexible arc-shaped structure on local distorted electric fields is analyzed, and an equivalent model that discretizes the flexible curved surface into parallel-plate micro-elements is established. Furthermore, a multilayer sensor equivalent circuit model considering the effects of parasitic capacitance and conductive ink electrode resistance is developed, and the relationship between sensor sensitivity and structural parameters is derived. Subsequently, an electric field–circuit-coupled simulation model is established in COMSOL Multiphysics to systematically investigate the influence of parameters such as electrode layer number, electrode thickness, dielectric layer thickness, and electrode side length on the output response. The results show that the multilayer structure can effectively improve the equivalent sensing capacitance and electric field coupling capability, while the electrode thickness and dielectric layer thickness exhibit significant nonlinear effects on the output voltage. To achieve the coordinated optimization of high sensitivity and miniaturization, Tent chaotic mapping initialization and nonlinear dynamic adaptive inertia weight are introduced to improve the traditional GOOSE algorithm, and a multi-objective optimization model is constructed with the objectives of maximizing the output voltage and minimizing the electrode area. The optimization results indicate that the improved GOOSE algorithm achieves faster convergence and better optimization stability. Among the Pareto solution sets with different electrode layer numbers, the seven-layer structure exhibits the best size–performance trade-off. The final optimized design achieves an electrode area of only 1.746 mm2 and an output voltage of 2.565 × 10−6 V. These results demonstrate that the proposed multilayer flexible structure and improved GOOSE optimization method can significantly enhance the response capability to weak electric fields while maintaining device miniaturization, providing a new sensor design concept and optimization approach for non-contact detection of early-stage insulation degradation in power equipment.
Junpeng Dang, Chuanxu Yang, Yang Li et al.· Applied Sciences· 0 citations
The extensive deployment of the Power Internet of Things (PIoT) relies on dual-mode communication (HPLC + HRF) for robust data acquisition. However, under massive bursty traffic, conventional static MAC superframe scheduling struggles to reconcile high throughput with stringent reliability constraints. To mitigate this, we propose a dynamic adaptive scheduling scheme. Initially, a joint PHY-MAC layer dual-mode system architecture is proposed. At the MAC layer, a dual-link parallel multiplexing contention access mechanism is applied; at the physical layer, a capacity bottleneck determination model is established, incorporating log-normal–Bernoulli–Gaussian mixed noise and multipath fading. Subsequently, an extended two-dimensional Markov chain analytically derives key performance indicators, including equivalent collision probability, joint outage probability, access delay, and network throughput. Building upon this, a Q-learning-based algorithm is proposed. By constructing an asymmetric penalty–reward function, the central coordinator (CCO) autonomously optimizes the Contention Access Period (CAP) to Contention-Free Period (CFP) ratio under dynamic node scales. Simulations demonstrate this methodology effectively averts channel congestion during extreme concurrent traffic surges. Ultimately, it strictly preserves service reliability while substantially augmenting the concurrent carrying capacity and resource utilization of the dual-mode network.
Yue Zhao, Bo Jiang, Zhixiong Chen· Electronics· 1 citation
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