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Considering the problem of difficulty in transmission and storage due to a large amount of data in the water-supply network monitoring system based on a wireless sensor network (WSN), we propose a sparse representation of the water-supply network monitoring data by using compressed sensing (CS) method. At the same time, aiming at the problem of low leakage identification accuracy caused by information loss under compressed sensing, we propose a leak identification method for a water-supply pipe network based on compressed sensing and deep residual neural network (ResNet). Firstly, under the condition that the observation matrix ensures the integrity of signal information, the compressed sensing theory is used to compress and observe leakage signals to obtain observation data, to reduce the redundant information and volume of the data. At the same time, the observation data is preprocessed to realize the transformation of a one-dimensional signal to a two-dimensional matrix. Then the residual neural network is trained by using the two-dimensional data to realize the automatic, efficient, and accurate leak identification under different leakage apertures. Experimental results show that the proposed method can obtain relatively high accuracy and greatly reduce the training time of ResNet by using compressed data. When the Compression rate (CR) is 70% and the observation matrix is a Gaussian random matrix, the average accuracy is 96.67% and the training time is only 50% compared to uncompressed data. The research work provides a new intelligent leak identification under different leak apertures using WSN and has important application prospects in saving water resources.

Leak location in water-supply pipelines is of great significance in order to preserve water resources and reduce economic losses. Cross-correlation (CC) based leak location is a popular and effective method in water-supply pipelines (WSP). However, with a decrease of signal to noise ratio (SNR), the errors of time-delay estimation (TDE) based on CC will become larger making it almost impossible to determine a leakage position. Hence, this work proposes leak location based on a combination of phase difference spectrum and variational mode decomposition (PDS-VMD) of leakage-induced vibration signal under low SNR for WSP. Firstly, the leakage-induced vibration signal is decomposed into several intrinsic mode functions (IMFs) by VMD, where the characteristic frequency band is determined by PDS of cross spectrum of two leakage signals. Then, the energy ratio of leakage signal in characteristic frequency band serves as a guideline to select effective IMF components from the decomposed IMFs. Finally, the selective IMFs are reconstituted into a new signal which can be used to determine a leak position using CC based TDE. In order to verify the effectiveness of the proposed leak location algorithm, the method based on PDS-VMD is compared with that using CC, combination of CC coefficient and VMD (CCC-VMD) using both simulation and experiment. The simulation and experimental results demonstrate that the proposed PDS-VMD method is more suitable for leak location in WSP, which provides immunity to both broadband and narrow band noise under low SNR.

In this paper, an Improved Ant Colony Optimization (IACO) algorithm optimized fuzzy PID (FPID) controller is proposed for Load Frequency Control of multi area systems. The nonlinear incremental evaporation rate and improvement of pheromone increment updating are proposed in the IACO algorithm to improve the quality of solution. And, a modified objective function using integral time multiply absolute error (ITAE), overshoot, undershoot and settling time with appropriate weight coefficients is proposed to improve the performance of the controller. Initially, a two-area non-reheat thermal system is applied and the FPID controller parameters are optimized by the IACO algorithm with five different objective functions. The modified objective function has better performances than four conventional objective functions. To demonstrate the robustness of the proposed control method, sensitivity analysis is implemented under wide variation of operating conditions and system parameters. Further, the proposed approach is also extended to two-area four-sources hydro thermal power system with/without High Voltage Direct Current (HVDC) link. The superiority of the proposed approach is shown by comparing the results with ZN, GA and hPSO-PS algorithms. The robustness of the proposed method is verified under random load disturbances. Finally, the proposed approach is extended to a two-area power system with governor dead band nonlinearity and results show that the proposed approach can cope with nonlinearity well. The results obtained from all simulations show that the proposed algorithm and modified objective function achieves better performances, such as minimum objective values (ITAE = 0.0255, ITSE = 9.30e-5, ISE = 1.91e-4 and IAE = 0.0273) obtained for the two-area non-reheat thermal system.

To improve the accuracy of short term wind speed forecasting, a novel principle-subordinate prediction model based on Conv-LSTM network and BPNN is designed in this paper. The proposed model combines deep learning algorithms and improved neural network to deal with the problem of wind speed forecasting. In this model, the prediction sequence of each subseries is obtained by Conv-LSTM to form more smoother and characteristic series; the BPNN, optimized by MWOA (Modified Whale Optimization Algorithm), is trained with the reconstruction sequence, which processed for the prediction sequence by invert-EMD (inverse empirical model decomposition). Before prediction, singular spectrum analysis (SSA) and complete ensemble empirical model decomposition adaptive noise (CEEMDAN) are adopted to de-noise and decompose the original wind speed data into several subseries. This process is beneficial to improve the ratio of signal to noise and simply the features of wind speed data. In addition, the Conv-LSTM is tested on three datasets, the results proved that data process is advantaged to obtain higher quality wind speed datasets, and convolutional layer can deep extract the characteristics of each subseries, it can facilitate LSTM to make accurate prediction. The final prediction results, compared with other five different models, demonstrate that the proposed model can achieve higher precision. Such as the performance evaluation matrix (MAPE = 2.62%, RMSE = 0.151) are smallest obtained from experiments on three wind different wind speed datasets.

The short-term hydrothermal scheduling (STHS) is a complicated optimization problem, considering valve-point effect (VPE) and transmission losses, it exhibits the characteristics of nonlinearities and non-smoothness which increase the difficulty of solving the problem. Responding to these challenges, a hybrid algorithm that comprises chaotic local search technique, grey wolf optimizer, and dragonfly algorithm (CGWO-DA) is proposed to settle the STHS problem. The grey wolf optimizer (GWO) has strong local search ability with the slow convergence rate. The dragonfly algorithm (DA) owns superior global search capability and fast convergence rate, however, it exerts premature convergence and is easy to fall into local optimum. Using the chaotic local search as a supplement, the hybrid algorithm combined by two excellent algorithms can improve the shortcoming of each algorithm and enhance the exploration and exploitation of the algorithm simultaneously. In addition, two novel constraints handling strategies are presented to resolve the complicated equality constrains and guarantee the feasibility of solutions. Three systems have been tested by the proposed methodology. The solutions are compared with the ones from other available literatures, the feasibility and effectiveness of the proposed approach are well demonstrated, and the findings of case studies show that the proposed method has great competitive advantages to obtain better scheduling schemes.

Underground pipelines are vital means of transporting fluid resources like water, oil and gas. The process of locating buried pipelines of interest is an essential prerequisite for pipeline maintenance and repair. Acoustic pipe localization methods, as effective trenchless detection techniques, have been implemented in locating underground utilities and shown to be very promising in plastic pipeline localization. This paper presents a comprehensive review of current acoustic methods and recent advances in the localization of buried pipelines. Investigations are conducted from multiple perspectives including the wave propagation mechanism in buried pipe systems, the principles behind each method along with advantages and limitations, representative acoustic locators in commercial markets, the condition of buried pipes, as well as selection of preferred methods for locating pipelines based on the applicability of existing localization techniques. In addition, the key features of each method are summarized and suggestions for future work are proposed. Acoustic methods for locating underground pipelines have proven to be useful and effective supplements to existing localization techniques. It has been highlighted that the ability of acoustic methods to locate non-metallic objects should be of particular practical value. While this paper focuses on a specific application associated with pipeline localization, many acoustic methods are feasible across a wide range of underground infrastructures.

Increasingly mature computer technology is very conducive to solve the complex optimizations of power system. To effectively deal with the many-objective optimal power flow (MOOPF) problems, a novel NSGA-FA algorithm which alleviates the limitation of local optimum is put forward in this paper. The NSGA-FA algorithm combines the special sorting rule of non-dominated sorting genetic algorithm-III (NSGA-III) and the location-updating mechanism of many-objective firefly algorithm (MFA). Furthermore, the optimal elite guidance (E-guide) mechanism and the non-duplicate elite solution storage (NDES) strategy are proposed to optimize operation-efficiency and solution-diversity of NSGA-FA algorithm. The applicability and preponderance of NSGA-FA algorithm compared with NSGA-III and MFA methods are evaluated by both bi-objective and tri-objective MOOPF experiments on IEEE 30-bus and 57-bus systems. Furthermore, the hyper-volume (HV) metric and the detailed results of five simulation trials intuitively indicate that the presented NSGA-FA algorithm achieves the more preferable Pareto front (PF) with superior-diversity and fast-convergence. In general, the suggested NSGA-FA algorithm provides an innovative idea for the application of computer technology on the economic operation of electric systems. [ABSTRACT FROM AUTHOR]

The commonly used cross-correlation technique for leak location requires that the acoustic speed is known and invariable. In practice, the gas leakage-induced acoustic waves propagate along multiple paths including in-pipe gas and pipe wall, and the acoustic waves in different transmission paths exhibit different acoustic speeds and different dispersive behaviors, which bring a great challenge for leak detection and location in the gas pipelines. In this study, based on the vibration theory of cylindrical elastic thin shell, the wave number formulae in different transmission paths are derived to predict the acoustic speeds and the acoustical coupling between the in-pipe gas and the pipe wall is analyzed to determine the dominant transmission path. In addition, the velocity dispersions in the dominant transmission path are suppressed by selection of a characteristic frequency band of the gas leakage-induced acoustic waves. The theoretical predictions are verified in the experiment and the results show that the theoretical acoustic speed is slightly larger than the measured acoustic speed. Thus, the theoretical acoustic speed formula is modified considering the effect of the structural loss factor and consequently the location error using the modified acoustic speed is reduced by two times compared to that using the theoretical acoustic speed. [ABSTRACT FROM AUTHOR]

Computer technology provides new possibilities for handling the multi-objective optimal reactive power dispatch (MOORPD) problems with high-dimension and non-differentiability. In this paper, a novel constraint stratification ranking methodology integrated on dynamic balance imperialist competition algorithm (ICA) is proposed to deal with MOORPD problem, and this methodology is called NCSR-DBICA. The proposed methodology includes three sub-methods to solve three typical problems encountered when dealing with MOORPD problems with ICA. These typical problems include the original ICA which is easy to fall into local optimum, it is difficult to handle the constraints in MOORPD problem and there is no effective solution set ranking method. Thus, the dynamic balance strategy (DBS) is proposed to improve the searching ability of ICA, the constraint-based country stratification mechanism (CCSM) is proposed to deal with the constraint problem, and a novel ranking method (NRM) is proposed to solve the ranking problem. To verify the effectiveness of the improved method, the NCSR-DBICA, MOICA-FS, NSGA-III, NSGA-II and MOPSO-CD were tested on three test systems. Simulation results show that NCSR-DBICA can find better results, especially in large scale systems. In addition, two indicators: Generational Distance (GD) and Hyper-volume (HV), were selected to evaluate the diversity, stability and convergence of the above algorithms. The evaluation results also verify the superiority of NCSR-DBICA. [ABSTRACT FROM AUTHOR]

Computer technology provides new possibilities for handling the many-objective optimal power flow (MOOPF) problems with high-dimension and non-differentiability. As one of typical intelligent algorithms, the novel multi-objective artificial fish swarm algorithm (NMAFSA) is proposed to solve the MOOPF problems and realize the economical operation of power systems. The NMAFSA algorithm, which combines with optimal solution guidance (OSG) principle and non-inferior retention (NIR) mechanism, is effective to reduce the fuel cost, emission and power loss. Compared with the representative many-objective particle swarm optimization (MPSO) and non-dominated sorting genetic algorithm-â…¡ (NSGA-â…¡), the superiority and adaptability of presented NMAFSA algorithm are validated. Six simulation trials are carried out on MATLAB software, including the dual-objective and triple-objective optimizations on three different scale power systems. Detailed results demonstrate that the suggested NMAFSA algorithm with stable-operation and fast-convergence has great potential to deal with the MOOPF problems more efficiently. Furthermore, the generation distance (GD) index also quantitatively proves that the NMAFSA algorithm can obtain the well-distributed Pareto front (PF). [ABSTRACT FROM AUTHOR]

Overuse of non-renewable energy has seriously affected environment, wind power is of vital significance to alleviate the energy crisis and protect the environment. The forecasting accuracy of wind speed has a positive correlation with the effective utilization rate of wind energy. BP neural network has advantages over traditional prediction models in dealing with nonlinear problems, but it has different generalization ability to different data. Therefore, we proposed a hybrid forecasting model based on singular spectrum analysis (SSA), fuzzy c-means clustering (FCM) and improved sparrow search algorithm (ISSA-BP). Firstly, after the raw wind speed data are obtained, singular spectrum analysis is employed to de-noise, so as to improve the data quality. Secondly, the input dataset of BP is divided into several categories using FCM, and the number of classifications for two different datasets is obtained through multiple experiments. Thirdly, since the selection of parameters largely determines the performance of BP neural network, the improved sparrow search algorithm (ISSA) is adopted to optimize the weights and thresholds. Then different ISSA-BP models are built for each class of input datasets. Finally, the class of forecasting input is determined and the corresponding ISSA-BP is used for forecasting. The experimental results of two cases showed that the proposed model was not only suitable for one-step forecasting, but also improved the accuracy of multi-step forecasting. The ultimate experimental results, in comparison with other six different models, demonstrate that the proposed model can acquire higher prediction precision. [ABSTRACT FROM AUTHOR]

Water cycle algorithm (WCA) is a heuristic algorithm proposed in recent years. To overcome the insufficiency of standard WCA algorithm in solving the non-convex optimal power flow (OPF) problems, the multi-objective novel improved water cycle algorithm (MONIWCA) is proposed in this paper. The evaporation process is improved in WCA by introducing evaporation rate and the normal distribution optimization mechanism is used to mutate the individual position. The modified WCA also adopts a constraint-based strategy to ensure zero constraint violations. In order to obtain a high-quality Pareto optimal solution set (POS) and select the best compromise solution (BCs), a global ranking strategy is proposed. The global ranking strategy includes the novel constraint handling method, the rank index calculation and the BCs on fuzzy satisfaction theory to deal with the complex constraints of the optimization problem. The MONIWCA has been simulated under the constraints of zero violations on IEEE 30, IEEE 57 and IEEE 118 standard test systems, including six dual-objective cases and one tri- objective case. The simulation results are compared with the multi-objective particle swarm optimization (MOPSO). The results show that the improved method can effectively solve the MOOPF problem, not only to obtain a uniform continuous Pareto solution set but also to achieve a better compromise solution. In addition, the two performance indicators of the generational distance (GD) and the spacing (SP) also show that the MONIWCA algorithm has uniform distribution, high convergence and strong stability. [ABSTRACT FROM AUTHOR]