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Leak location using correlation-based acoustic method is a common practice in gas-filled pipelines, which will be extremely effective when the leakage-induced acoustic wave has a constant propagation speed. This premise is often not satisfied as result of multi-modal and dispersive nature which will cause acoustic speed to vary with frequency and modal type. In this work, the multi-modal identification based on selection of coherent frequency band is proposed for improving leak location in gas pipelines. The dispersive and multi-modal characteristics are studied by theory analysis and experimental validation to give understanding of the reason that correlation-based leak location has a larger error at times. The results demonstrate that the multi-modal type can be identified from law of acoustic speed variation by selection of coherent frequency band and multi-modal and dispersive characteristics are the main cause of a larger leak location error.

To solve the non-differentiable optimal power flow (OPF) problems with multiple contradictory objectives, a modified pigeon-inspired optimization algorithm (MPIO) is put forward in this paper. Combining with the common-used penalty function method (PFM), the MPIO-PFM algorithm is proposed and applied to optimize the active power loss, emission and fuel cost (with valve-point loadings) of power system. Eight simulation trials carried out on MATLAB software validate MPIO-PFM algorithm can obtain superior Pareto Frontier (PF) comparing with the typical NSGA-II algorithm. Nevertheless, some Pareto solutions obtained by MPIO-PFM algorithm cannot satisfy all system constraints due to the difficulty in choosing the proper penalty coefficients. Thus, an innovative approach named as constraint-objective sorting rule (COSR) is presented in this paper. The bi-objective and tri-objective trials implemented on IEEE 30-node, 57-node and 118-node systems demonstrate that the Pareto optimal set (POS) obtained by MPIO-COSR algorithm realizes zero-violation of various system constraints. Furthermore, the generational-distance and hyper-volume indexes quantitatively illustrate that in contrast to NSGA-II and MPIO-PFM methods, the MPIO-COSR algorithm can determine the evenly-distributed PFs with satisfactory-diversity. The intelligent MPIO-COSR algorithm provides an effective way to handle the non-convex MOOPF problems.

To improve state-of-health (SOH) estimation and remaining useful life (RUL) prediction, a prognostic framework shared by multiple batteries is proposed. A variant long-short-term memory (LSTM) neural network (NN), called AST-LSTM NN, is designed to guarantee the performance of proposed framework. Firstly, the input and forget gates are coupled by a fixed connection, which leads simultaneous determination of old information and new data. Secondly, the element-wise product of the new inputs and the historical cell states is conducted for screening out more beneficial information. Thirdly, a peephole connection from the “constant error carousel” (CEC) is added into the output gate to shield the unwanted error signals. AST-LSTM NNs, with mapping structures of many-to-one and one-to-one, are well-trained separately for the prediction of SOH and RUL. Compared with other data-driven methods, the experiments carried on NASA dataset demonstrate our method hits lower average root mean square, 0.0216, and conjunct error, 0.0831, for SOH and RUL, respectively.

The correlation-based location methods are widely used in leak detection of the pipelines assuming that the acoustic speed has been known and constant. In practice, the acoustic speed is frequency-varying due to the dispersions of gas-leak-induced acoustic waves, and thus the assumption is not supported. In this work, a location scheme based on cross-time–frequency spectrum (CTFS) is intended for the gas-leak-induced acoustic waves with frequency-varying acoustic speed. In the scheme, the CTFS is obtained by the one-dimensional Fourier transform of the time domain convolution between the kernel function in correlation domain and the instantaneous cross-correlation of the two spatially separately collected acoustic signals on either sides of a leakage. Then, the time difference of arrival (TDOA) and the corresponding frequency information are extracted simultaneously when the CTFS reaches the maximum value. The resulting peak frequency is used to online determine the frequency-dependant acoustic speed in combination with the known dispersive curve of gas-leak-induced dominated mode. Finally, the gas leakage is located by the TDOA and the frequency-dependant acoustic speed of real-time determination instead of constant acoustic speed. Consequently, for the proposed scheme, the constant acoustic speed is no longer a prerequisite. The proposed scheme has been experimentally validated in leak detection of gas pipelines and results demonstrate that the average relative location errors are reduced by six times compared with the commonly used correlation-based location method.