PV Cell Parameter Identification Using IEO and BP Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing photovoltaic cell parameter identification methods struggle with inaccurate results and are prone to local optima, especially under changing operating conditions and limited measurement data.
Innovation Solution
A photovoltaic cell parameter identification method using an improved equilibrium optimizer (IEO) algorithm, which includes establishing a PV cell model, utilizing a BP neural network for data prediction, and employing a root mean square error (RMSE) as an objective function, along with an IEO algorithm for parameter identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deterministic methods are used for parameter identification, then identification accuracy is improved, but the method becomes sensitive to initial conditions and gradient information, making it easy to be trapped into local optimum
Solution Approach 1:
The algorithm performs preliminary actions by maintaining a population of candidate solutions and pre-calculating equilibrium positions before the main optimization process. This allows the algorithm to explore multiple potential optimal points simultaneously, reducing the risk of getting trapped in local optima while maintaining high identification accuracy.
Solution Approach 2:
The equilibrium optimizer algorithm introduces dynamic behavior by simulating the natural equilibrium process where particles continuously adjust their positions based on balance between exploration and exploitation phases. This dynamic adaptation allows the algorithm to escape local optima and converge to global optimum more reliably.
2Device complexity
If analysis method is used for parameter identification, then structural simplicity is improved, but identification accuracy deteriorates under changing operating environment
Solution Approach 1:
The patent replaces traditional mechanical calculation methods with an intelligent optimization algorithm that uses equilibrium concepts. This substitution maintains relative structural simplicity while dramatically improving identification accuracy under changing operating conditions through adaptive search mechanisms.
Solution Approach 2:
The algorithm dynamically changes parameters during the optimization process, adjusting exploration and exploitation rates based on iteration progress. This parameter adaptation allows the method to maintain simplicity while achieving high accuracy across varying operating environments.
3Measurement precision
If deterministic methods are used for parameter identification, then identification accuracy is improved, but computation time increases due to strict model characteristics requirements
Solution Approach 1:
The algorithm applies partial action by using a subset of available data for each iteration's equilibrium calculation, rather than processing all data exhaustively. This approach maintains high identification accuracy while significantly reducing computation time per iteration.
Solution Approach 2:
The optimization process uses periodic action by alternating between exploration and exploitation phases, and between different equilibrium calculation steps. This periodic structure allows the algorithm to achieve high accuracy through multiple coarse-to-fine refinement cycles rather than requiring excessive computation in a single pass.
Data Source
AI summary
The invention discloses a method of photovoltaic cell parameter identification based on the improved equilibrium optimizer algorithm, which comprises: step 1, establishing PV cell model and fitness function; step 2, based on the measured output I-V data, predicting output data of PV cell by a BP neural network; step 3, identifying PV cell parameters by using IEO algorithm until convergence conditions of the IEO algorithm are reached, and finally outputting the optimal identified parameters. Solving technical problems of the existing technology such as, cannot reach the optimal parameter identification, and being easy to be trapped into the local optimal.