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With the development of economy, the utilization rate of natural gas in urban areas increases year by year, and so does the incidence of gas events. Therefore, it is very important to set the location of duty points reasonably. The selection of duty points is related to the distribution of emergency needs. However, most of the existing emergency facility site selection methods divide the entire area into grid forms and use GIS software for site selection, ignoring the rationality of regional division and the randomness of natural gas events in the distribution of the entire demand space. This paper uses Birch clustering algorithm for spatial clustering based on the consequence characteristics of natural gas events and relevant historical data. Considering the randomness of urban transportation and gas events, the opportunity constraint model of gas company duty point under uncertain environment is constructed, and the demand weight is determined combined with the pipeline hidden danger risk assessment level. The SA algorithm with global optimization ability and PSO with random search ability were used to solve the model under different risk coefficients, and the optimal position of the guard point was obtained. By comparing the experimental data with the mean value model and the P-median model, the model established in this paper is more suitable for the actual environment and has advantages over other models. [ABSTRACT FROM AUTHOR]

Aiming at the problem of load distribution during multi-pass cold rolling of nuclear zirconium alloy strip, the load distribution model with good plate shape is established by the self-adaptive particle swarm optimization (SAPSO) algorithm, considering the main constraint conditions including rolling force, reduction, and torque in cold rolling process. Based on the penalty function method transforming the constraint problem into the unconstrained problem, the particle swarm optimization algorithm (PSO) combined with self-adaptive inertia weight factor optimized the load distribution model is developed to improve the local search ability of the particle swarm optimization algorithm. Compared with the original nuclear zirconium alloy cold rolling schedule, the simulation results of load distribution based on the SAPSO algorithm can keep good plate shape in multi-pass cold rolling process with the high prediction accuracy. The industrial experiments demonstrate that the proportional crown difference value is consistent, and the plate shape flatness is good. [ABSTRACT FROM AUTHOR]

The loss of power and voltage can affect distribution networks that have a significant number of distributed power resources and electric vehicles. The present study focuses on a hybrid method to model multi-objective coordination optimisation problems for dis- tributed power generation and charging and discharging of electric vehicles in a distribution system. An improved simulated annealing based particle swarm optimisation (SAPSO) algorithm is employed to solve the proposed multi-objective optimisation problem with two objective functions including the minimal power loss index and minimal voltage deviation index. The proposed method is simulated on IEEE 33-node distribution systems and IEEE-118 nodes large scale distribution systems to demonstrate the performance and effectiveness of the technique. The simulation results indicate that the power loss and node voltage deviation are significantly reduced via the coordination optimisation of the power of distributed generations and charging and discharging power of electric vehicles.With the methodology supposed in this paper, thousands of EVs can be accessed to the distribution network in a slow charging mode.

Nodes of wireless sensor networks due to environmental influences and deliberate attacks by nodes in the network, nodes of wireless sensor network are prone to death. In this paper, based on the idea of algorithm fusion, this paper proposes a research method for survivability of wireless sensor networks based on tenacity. A hybrid algorithm based on simulated annealing algorithm and particle swarm optimization algorithm is used to calculate the value of the tenacity of the network node. The optimized algorithm reduces the time and complexity of calculating tenacity. In this paper, the wireless sensor network is simulated and verified. The experimental results demonstrate the feasibility of evaluating the survivability of wireless sensor networks. The proposed method can effectively improve network lifetime and data transmission capability. [ABSTRACT FROM AUTHOR]

In order to reduce the environmental noise and the influence of multi point leakage signal， improving pipeline leak location accuracy， a blind source separation algorithm is proposed， which is rithm and particle swarm algorithm is embedded into the memory of the blind source separation algorithm ,combined with the probabilistic jumping property of random search for the maximum likelihood objective function in the solution space, by iterating the global optimization to improve the circuitous search methods, the convergence speed and the precision of separation. The MSAPSO blind source separation algorithm is applied to the multi holes leak location experiment of urban pressure pipeline. The results show that the improved MSAPSO blind source separation algorithm reduces the pipeline multi-point leak location error from 7. 7 % to 3. 3%. [ABSTRACT FROM AUTHOR]

The estimation of the photovoltaic (PV) inverter model parameters could lay the foundation for analyzing the grid-connected operation of PV generation system. In this paper, the control parameters to be identified are determined first through the analysis of the double loop control system structure of the PV inverter. The concerned parameters includes four proportional and integral coefficients ( k PU , k IU , k PI and k II ) and the filtering inductance ( L ). Then a parameter identification model based on simulated annealing-particle swarm optimization (SAPSO) algorithm is proposed to identify the control parameters in MATLAB environment. The comparison between the identification results of the SAPSO and the other optimization algorithms indicates that SAPSO identification model possesses high accuracy and fast convergence speed. The simulation results with identified values under the disturbance of temperature and irradiance demonstrate that SAPSO identification model has a strong generalization ability. [ABSTRACT FROM AUTHOR]

Electricity prices have rather complex features such as high volatility, high frequency, nonlinearity, mean reversion and non-stationarity that make forecasting very difficult. However, accurate electricity price forecasting is essential to market traders, retailers, and generation companies. To improve prediction accuracy using each model’s unique features, this paper proposes a hybrid approach that combines the wavelet transform, the kernel extreme learning machine (KELM) based on self-adapting particle swarm optimization and an auto regressive moving average (ARMA). Self-adaptive particle swarm optimization (SAPSO) is adopted to search for the optimal kernel parameters of the KELM. After testing the wavelet decomposition components, stationary series as new input sets are predicted by the ARMA model and non-stationary series are predicted by the SAPSO-KELM model. The performance of the proposed method is evaluated by using electricity price data from the Pennsylvania-New Jersey-Maryland (PJM), Australian and Spanish markets. The experimental results show that the developed method has more accurate prediction, better generality and practicability than individual methods and other hybrid methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an (SAPSO) Self-Adaptive Particle Swarm Optimization algorithm to solve the base station positioning problem. This algorithm is used to minimize the distance between the base station and cluster head owing to maximizing the energy. Moreover, the proposed algorithm is compared with many other conventional algorithms such as Artificial Bee Colony-Dynamic Scout bee (ABC-DS) algorithm, Genetic algorithm (GA), Particle swarm optimization (PSO) and Firefly with Dual Update Process (FFDUP) algorithm. Also, empowering SAPSO helps to find the optimal location of the base station to conserve energy in a noteworthy manner to maximize the network lifetime and prolong network connectivity regarding minimizing distance, delay and maximizing the security, energy. Our simulation results demonstrate that the proposed SAPSO algorithm performs better than the other conventional algorithm. [ABSTRACT FROM AUTHOR]

Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal-gas outburst prediction in deep coal mines, this study proposes a deep coal-gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal-gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal-gas outburst risks.

• Advanced computational model aims at developing efficient solution techniques. • Complex features of electricity price can be well captured by the proposed model. • The proposed model can adjust its parameters adaptively according to the changing of input sample. • Validation and verification of numerical models through the real electricity markets. With the large-scale renewable energy integration into the power grid, the features of electricity price has become more complex, which makes the existing models hard to obtain a satisfactory results. Hence, more accurate and stable forecasting models need to be developed. In this paper, a new adaptive hybrid model based on variational mode decomposition (VMD), self-adaptive particle swarm optimization (SAPSO), seasonal autoregressive integrated moving average (SARIMA) and deep belief network (DBN) is proposed for short term electricity price forecasting. The effectiveness of the proposed model is verified by using data from Australian, Pennsylvania-New Jersey-Maryland (PJM) and Spanish electricity markets. Empirical results show that the proposed model can significantly improve the forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]