Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm

Abstract

Transmission line is a vital part of the power system that connects two major points, the generation, and the distribution. For an efficient design, stable control, and steady operation of the power system, adequate knowledge of the transmission line parameters resistance, inductance, capacitance, and conductance is of great importance. These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable. This paper presents a method to optimally estimate the parameters using the input-output quantities i.e., voltages, currents, and power factor of the transmission line. The equivalent π -network model is used and the terminal data i.e., sending-end and receiving-end quantities are assumed as available measured data. The parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective function. An improved particle swarm optimization (PSO) in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line parameters. Two cases are considered for parameter estimation, the first case is when the line conductance is neglected and in the second case, the conductance is considered into account. The results obtained by the improved algorithm are compared with the standard version of the algorithm, firefly algorithm and artificial bee colony algorithm for 30 number of trials. It is concluded that the improved algorithm is tremendously sufficient in estimating the line parameters in both cases validated by low error values and statistical analysis, comparatively.

Electric bus charging load forecasting method-based on improved spectral clustering and IPSO-LSTM

“…Through the cluster analysis in the previous section, the charging load curves of four types of buses are obtained [18] . Each type of curves is accumulated according to the date and time to obtain the total daily load curve set of this type, and the training set and test set are divided according to 5:1, which are input into IPSO-LSTM neural network for training and prediction.…”

Research on Information Identification of Chaotic Map with Multi-Stability

“…The results show that the algorithm combined with a logistic map has the best performance. In addition, the JAYA algorithm [15], differential evolution algorithm [16], particle swarm optimization algorithm [17], bird swarm algorithm [18], sunflower optimization algorithm [19], butterfly optimization algorithm [20], and other intelligent optimization algorithms are utilized to identify the parameters of nonlinear systems with chaotic behavior.…”

Estimação dos parâmetros de linhas aéreas de transmissão de energia elétrica por meio de medições temporais.

“…Podem-se citar os seguintes trabalhos: em (SOLDEVILLA; HUERTA, 2018), utiliza-se o algoritmo de otimização Particle Swarm Optimization (PSO); o algoritmo genético é utilizado em (ZHANG et al, 2004); algoritmos baseados no comportamento de baleias são empregados em (HAS-SANEIN et al, 2021;SHAIKH et al, 2022a); e um algoritmo baseado no movimento de mariposas é utilizado em (SHAIKH et al, 2022b). Por fim, existem algoritmos combinados, como os apresentados em (SHOUKAT et al, 2021;PEREIRA et al, 2023a), nos quais duas técnicas são combinadas para a estimação dos parâmetros.…”