Parallel Mode Estimation Improvement in Power Networks based on Optimal ANFIS Approach

In this research, the issue of non-technical losses in the distribution network was addressed, which should have been detected. Distribution networks and transmission lines have different parameters, so their state estimation and modeling are completely different. This problem causes the detection of non-technical losses in the distribution network by mode conversion to suffer a series of computational complications. Therefore, an attempt has been made to improve the parameters of voltage, primary current, passing current and bus energy along with error estimation after estimating the state of the distributed network so that non-technical losses can be detected. Therefore, a hybrid approach based on observer-based fuzzy neural network filter and genetic algorithm is used. Due to the existence of errors, the fuzzy neural network filter has been used, the reason for its three-dimensionality is that the system is non-linear and the errors and states may be different at different times. This research, in one case, has estimated the state of the distribution network with the aim of detecting non-technical losses. The observer can check these different situations at different times. Also, because the problem of detecting non-technical losses in the distribution network based on state estimation is assumed as a hard problem, therefore, the distribution network environment can be assumed as a main platform as a search environment for optimization algorithms. . Therefore, the use of intelligent algorithms from the family of fuzzy neural network and evolutionary algorithms and crowd intelligence can be interesting for estimating the optimal state with the aim of detecting non-technical losses in the distribution network. Hence, genetic algorithm is also used for optimization. After considering the main parameters to estimate the system state with the proposed approach, the results obtained from the simulation indicate the improvement of the state estimation and the detection of non-technical losses in the distribution network along with the minimization of the error estimation compared to the initial state.