Topology Identification in Distribution Networks with Limited Measurements
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Solution Overview
Problem
In distribution networks, the low measurement redundancy and high number of buses make it challenging to accurately and quickly detect the status of switching devices, which is crucial for power system state estimation and outage management, as existing methods are inefficient and time-consuming.
Innovation Solution
A method is developed to identify the status of switch devices in a distribution network by deriving a probability model from historical power injections and using real-time sensor measurements to select the most likely network topology, optimizing the placement of sensors to minimize the number of measurements required for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional network topology processing is used to monitor switching device statuses, then the network connectivity can be determined, but the detection time is long and the system is time-consuming
Solution Approach 1:
The patent transforms the topology identification problem from a deterministic approach to a probabilistic one by introducing likelihood functions. The system calculates the likelihood of different topologies given the measurements, and selects the most probable topology. This parameter change from binary status monitoring to continuous probability assessment enables faster convergence and more efficient identification with limited measurements.
Solution Approach 2:
The patent replaces the conventional mechanical monitoring approach (directly monitoring all switching devices) with an information-theoretic approach using likelihood functions and probability models. Instead of mechanically tracking each device status, the system uses statistical inference to deduce the most likely topology from limited measurements, significantly reducing the time and computational resources required.
2Reliability
If the number of measurements is increased to improve topology identification accuracy, then the detection reliability improves, but the system complexity and cost increase
Solution Approach 1:
The patent applies partial action by using only the minimum necessary measurements to achieve reliable topology identification. Rather than measuring all possible quantities, the system strategically selects a subset of measurements that provide maximum information for topology determination. The likelihood-based approach efficiently utilizes these partial measurements to achieve high reliability without requiring complete system observation.
Solution Approach 2:
The patent makes the measurement system universal by designing it to handle multiple topologies and switching configurations with the same limited set of measurements. The likelihood function framework is general and can evaluate any candidate topology against the available measurements, making the system adaptable to various network configurations without requiring additional measurement devices for each scenario.
3Measurement precision
If complete network model information is used for topology processing, then the state estimation accuracy improves, but the computational burden increases
Solution Approach 1:
The patent segments the computational problem by separating the topology identification from the state estimation. First, the likelihood function identifies the most probable topology structure. Then, the state estimator uses this identified topology to compute the system state. This segmentation allows each component to focus on its specific task, reducing the overall computational burden compared to simultaneously solving for both topology and state.
Solution Approach 2:
The patent performs preliminary action by pre-calculating and storing the network admittance matrix and other system parameters that are independent of the switching configuration. When topology changes occur, the system only needs to re-evaluate the likelihood function with the pre-computed parameters, rather than recalculating everything from scratch. This preliminary preparation significantly reduces the real-time computational requirements.
Data Source
AI summary
A statistical technique is used to estimate the status of switching devices (such as circuit breakers, isolator switches and fuses) in distribution networks, using scares (i.e., limited or non-redundant) measurements. Using expected values of power consumption, and their variance, the confidence level of identifying the correct topology, or the current status of switching devices, is calculated using any given configuration of real time measurements. Different topologies are then compared in order to select the most likely topology at the prevailing time. The measurements are assumed as normally distributed random variables, and the maximum likelihood principle or a support vector machine is applied.