County Distribution Grid Planning Using K-Means and Whale Optimization
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Solution Overview
Problem
Conventional county power distribution network gridding planning methods are high in cost and unreasonable in division, failing to consider the impact of distributed resource access and landform variations.
Innovation Solution
A county power distribution network gridding planning method based on digital resource integration technology, utilizing geographic information, K-means clustering, and a self-adaptive multi-target whale optimization algorithm for primary, secondary, and third division of power supply grids, optimizing grid division and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional grid division method is used based only on terrain, then the division is simple to implement, but it does not consider the impact of distributed resource access and landform variations, leading to unreasonable division
Solution Approach 1:
The patent applies segmentation by dividing the power distribution network into multiple hierarchical levels: first dividing the county into several grid regions based on terrain, then further dividing each grid into smaller basic grids. This multi-level segmentation allows the system to consider both broad terrain characteristics and local distributed resource variations, resolving the contradiction between implementation simplicity and division rationality.
Solution Approach 2:
The patent implements local quality by allowing different grid division strategies for different regions based on their specific characteristics. Each grid region is divided according to its own terrain features, distributed resource distribution, and load characteristics, rather than applying a uniform division method across the entire county. This enables reasonable division that adapts to local conditions while maintaining overall system coherence.
2Manufacturing precision
If distributed resource access is fully considered in grid division, then the division becomes more reasonable and optimized, but the planning cost and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by first performing terrain-based grid division to establish a preliminary framework, then iteratively optimizing the division based on distributed resource access patterns. This staged approach allows the system to start with a simple, implementable baseline and progressively refine it, reducing overall planning complexity while achieving reasonable division that considers distributed resources.
Solution Approach 2:
The patent implements dynamics by using an iterative optimization process that adjusts grid divisions based on distributed resource access patterns. The system dynamically refines the initial terrain-based division through multiple optimization iterations, allowing the division scheme to adapt to distributed resource distributions without requiring complete re-planning from scratch, thus managing complexity while improving rationality.
3Reliability
If multiple optimization algorithms are used for grid division, then the resource absorption rate and network reliability improve, but the computational time and algorithm complexity increase
Solution Approach 1:
The patent applies segmentation to the optimization process by dividing it into three distinct stages: primary division using terrain data, secondary division using K-means clustering algorithm, and tertiary division using self-adaptive multi-target whale optimization algorithm. This segmented optimization approach allows each algorithm to focus on specific aspects of grid division, improving network reliability through comprehensive optimization while managing computational time by distributing the optimization workload across multiple specialized algorithms rather than using a single complex algorithm.
Data Source
AI summary
Disclosed is a county power distribution network gridding planning method and system based on a digital resource integration technology, comprising: acquiring geographic information of a county region, and conducting primary division of power supply grids; conducting secondary division of the power supply grids by using a K-means clustering algorithm; and selecting an optimal planning strategy and conducting third division of the power supply grids through a self-adaptive multi-target whale optimization algorithm. By studying the development and evolution mechanism of a county power distribution network in terms of time, space and resources, the planning and operation methods of the county power distribution network are combined to carry out resource digital integration, and conduct grid division on the county power distribution network. From the aspect of economy and safety, the multi-target collaboration planning model of the power distribution network is established, and an optimal planning scheme is selected.