Voltage Sensor Placement by Simulated Annealing in Power Grids
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Solution Overview
Problem
Existing methods for optimizing the placement of voltage sensors in electrical distribution networks are either time-consuming, non-optimal, or fail to account for the limited number of sensors available, leading to suboptimal state estimation and voltage control.
Innovation Solution
A method using simulated annealing to optimize the placement of voltage sensors, which involves initializing sensor positions randomly, evaluating their performance, and iteratively adjusting their positions based on performance differences and temperature parameters, ensuring optimal sensor placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of sensors in the network is increased to improve voltage estimation accuracy, then the reliability of voltage estimation is improved, but the cost of instrumenting the network increases
Solution Approach 1:
The patent changes the parameter of sensor placement configuration from random or expert-based selection to an optimized arrangement determined by the simulated annealing algorithm. This optimization maximizes the information gain from a limited number of sensors, improving voltage estimation accuracy without increasing the sensor count. The algorithm iteratively adjusts sensor positions to achieve the best possible state estimation with the given budget constraint.
2Productivity
If expert opinion is used to determine sensor placement to improve placement efficiency, then the implementation time is reduced, but the optimality of sensor positioning cannot be guaranteed
Solution Approach 1:
The system performs self-optimization through the simulated annealing algorithm, which automatically evaluates different sensor configurations and selects the optimal placement without requiring expert intervention. The algorithm independently explores the solution space, evaluates voltage estimation accuracy for each configuration, and converges to the optimal sensor positions, ensuring both optimality and repeatability.
Solution Approach 2:
The patent implements a feedback mechanism where the simulated annealing algorithm evaluates the performance of each sensor configuration by computing voltage estimation accuracy. This feedback loop allows the algorithm to iteratively improve sensor placement by comparing different configurations and selecting those that maximize estimation reliability, ensuring optimal positioning without expert bias.
3Reliability
If iterative methods are used to explore sensor configurations to improve placement optimality, then the reliability of voltage estimation is improved, but the computational time increases
Solution Approach 1:
The simulated annealing algorithm employs periodic action by iterating through a structured sequence of configuration evaluations. Instead of exhaustive search, it periodically samples the solution space using temperature-based acceptance criteria, allowing it to escape local optima and converge to global optimum efficiently. This periodic iteration balances thorough exploration with computational efficiency.
Solution Approach 2:
The patent applies dynamics through the simulated annealing process, which dynamically adjusts the acceptance probability of sensor configurations based on a temperature parameter that decreases over time. This dynamic approach allows the algorithm to initially explore diverse configurations broadly and then progressively focus on refining the best solutions, achieving optimal placement without exhaustive computation.
Data Source
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AI summary
The invention relates to a method for optimizing the positioning of sensors in an electrical network comprising a set of nodes, the sensors being configured to provide measurements of electrical variables of the network. According to the invention, such a method comprises: a. an initialization (E1) of the positioning of the sensors, by random selection of an initial solution corresponding to a set of positions of the sensors within the nodes of the network, and an initial temperature parameter T0, b. an evaluation (E2) of a performance of the initial solution, and c. at least one iteration of evolution of the positioning of said sensors, producing from a positioning solution of rank i ≥ 0 a positioning solution of rank i+1, by simulated annealing algorithm. Such a method also comprises a placement (E8) of the sensors within the network at the positions determined by the positioning solution of rank i+1 obtained at the end of the evolution iterations.