ARIMA Model for Distribution Network Reliability Prediction
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Solution Overview
Problem
Current methods for assessing the reliability of power distribution networks are inaccurate due to high time complexity and excessive sample variance, especially in systems with fewer components or lower failure frequencies, making it difficult to predict and assess operation reliability effectively.
Innovation Solution
A method using the Autoregressive Integrated Moving Average (ARIMA) model to predict monthly power outage frequencies, convert nonstationary sequences into stationary ones, and establish a reliability index by considering real-time operation states and historical data, thereby improving prediction accuracy and guiding planning, design, and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional parsing method-based reliability assessment is used, then reliability relationships between components and system are described by mathematical model, but time complexity is exponentially increased along with expansion of system model
Solution Approach 1:
The patent replaces the conventional parsing method (mathematical model-based approach) with a neural network-based prediction model. The neural network is trained on historical operation data to directly predict reliability indicators, substituting the complex mathematical parsing process with a data-driven approach that achieves similar or better accuracy without exponential time complexity growth.
Solution Approach 2:
The patent creates a virtual copy of the power distribution system's reliability behavior through the neural network model. By training the network on historical data, it learns to replicate the system's reliability patterns and predict future states, avoiding the need to perform complex real-time parsing calculations on the actual system model.
2Measurement precision
If Monte Carlo sampling-based reliability assessment is used, then system reliability is assessed by computer sampling method, but sample variance is excessively large under condition of relatively fewer system components or relatively lower failure frequency
Solution Approach 1:
The patent replaces the Monte Carlo sampling method with a neural network-based prediction approach. Instead of performing numerous random simulations to estimate reliability, the trained neural network directly predicts reliability indicators with much lower variance, especially for systems with fewer components or lower failure frequencies where Monte Carlo methods struggle.
Solution Approach 2:
The patent performs preliminary action by training the neural network on extensive historical operation data before actual reliability assessment. This pre-training phase allows the model to learn system patterns and behaviors, so that during actual prediction, reliable estimates can be obtained without performing large numbers of samples during the assessment phase itself.
3Productivity
If reliability assessment is performed during operation, then real-time reliability assessment is required, but network structure frequently changes due to failures, load transfer or operation regulation which brings difficulties to real-time assessment
Solution Approach 1:
The patent applies dynamics by designing the neural network model to adapt to changing system conditions. The model processes input data that reflects current network state (including topology changes, load variations, and operation regulations) and dynamically predicts reliability indicators in real-time, accommodating the frequently changing network structure without requiring complex recalculation.
Solution Approach 2:
The patent implements feedback by using historical operation data and actual reliability outcomes to continuously improve the neural network model. The system learns from past performance and adjusts its predictions based on actual system behavior, enabling accurate real-time assessment even as the network structure evolves over time.
Data Source
AI summary
A method of predicting the operation reliability of a distribution network, the method being based on an ARIMA model. By establishing an ARIMA model predict a user monthly power outage count; convert a non-smooth element outage count time series to a smooth time series; perform regression on only lagged values of a dependent variable, and on current values and lagged values of a stochastic error term, so as to establish a user monthly power outage count model; according to a prediction result sample an outage point, and at the same time, taking into account a real time load operating state, establish a failure mode effects table based on TLOC criteria and PLOC criteria; and calculate a system recovery time for each instance of device outage, and finally obtain a whole-year reliability index.