article · IET Generation Transmission & Distribution
Accurate transmission line parameter estimation is critical for power flow, network planning, economics, dispatch, and system stability analysis. Traditional optimisation techniques often face limitations regarding precision, accuracy, computational time, slow convergence, and entrapment in local optima. To tackle these issues, a hybrid optimisation technique combines the Salp Swarm Algorithm with the Sine Cosine Algorithm, designated as HSSASCA. The method utilises the Sine Cosine Algorithm after the Salp Swarm Algorithm to expand search space exploration, ensure effective exploitation, and accelerate convergence rates. Evaluated across six test systems alongside benchmark functions, the hybrid method was compared against several existing algorithms, including firefly optimisation, Grey Wolf Optimisation, and symbiotic organisms search. Statistical assessments and convergence curves show that the hybrid approach delivers superior search efficiency, higher convergence accuracy, and improved avoidance of local optima.
Power grid operations such as dispatch, economic planning, and stability management depend directly on exact transmission line parameters. Inaccurate or slow computational calculations can compromise network analysis. By resolving mathematical bottlenecks found in standard optimisation tools, advanced algorithmic methods help generate the precise technical data necessary to support stable and reliable electricity transmission systems.
The technique is aimed at power system software developers, electrical utilities, and grid operators seeking more accurate transmission line parameter estimation. Tested against benchmark functions and six simulation test systems, the research is at an algorithmic, early testing stage. Moving toward commercial application would require integrating the algorithm into power flow and energy management software suites and validating its performance on live operational grid telemetry.
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Abstract Power flow, planning, economics, dispatch, and stability analysis rely on accurate transmission line parameters (TLPE). Standard optimization methods are employed to develop such analyses and obtain TLPE. Additionally, these methods have limitations, including precision, accuracy, and time complexity. It is challenging to find improved solutions using standard optimization methods due to slow convergence and limitations in identifying local optima. Concerned with these challenges, the study suggest a new application for an effective hybrid optimization method capable of addressing such limitations. The hybrid algorithm, named the Salp Swarm Algorithm with Sine Cosine Algorithm (HSSASCA), that aims to tackle the issues of slow convergence and local optima. The Sine Cosine Algorithm (SCA) is employed after the Salp Swarm Algorithm (SSA), and Salp integration is utilized to successfully explore and analyze the search space. To enhance the performance of HSSASCA, the hybrid technique aims to provide expanded exploration capabilities, effective exploitation of the search space, and a better convergence rate. These key features position the HSSASCA algorithm as an effective solution to complex optimization problems. To assess the efficiency of the HSSASCA algorithm, six different test systems are employed. Initially, the evaluation of exploration, exploitation, and minimized local optima is conducted using the CEC 2019 benchmark functions. Secondly, efficiency monitoring and verification of HSSASCA across different scenarios occur by comparing it with established optimization algorithms such as SSA, SCA, firefly optimization algorithm (FFO), Grey Wolf Optimization (GWO), student psychology‐based optimization (SPBO), and Symbiotic Organisms Search (SOS). Finally, statistical analysis is performed, revealing that the HSSASCA outperforms SSA, SCA, FFO, GWO, SPBO, and SOS. In terms of statistical results and convergence curves, the HSSASCA demonstrates superior performance in searching efficiency, convergence accuracy, and local optimum avoidance ability.
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DOI: 10.1049/gtd2.13157
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