This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

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Renewable energy generation exhibits a distributed development trend, and has changed the way distribution networks operated. Due to the integration of large-scale distributed photovoltaic (PV) generation cluster and the access of electronic devices, distribution networks are suffering from more and more serious voltage risks. By using appropriate methods, PV inverters can autonomously regulate reactive power output in a distributed manner to improve voltage profile in networks. In this paper, a distributed Newton-based voltage control method for large-scale PV generation cluster in distribution networks is presented to realize distributed coordination of PV inverters, which is based on matrix splitting and approximate Newton iteration, and can fast respond to reactive power mismatch and realize voltage profiles optimization. Exhibiting a more efficient, reliable and flexible performance than existing decentralized methods in IEEE test systems with different PV penetration, the proposed method is demonstrated to be effectively to organize and control PV cluster to realize fast voltage optimization in distribution networks. i neighborhood descent direction of bus i. e j i descent direction of bus j calculated at bus i. d i local approximate Newton direction.

In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful in the reasonable planning and stable operation of the system in advance. For this purpose, a short-term load forecasting model based on frequency domain decomposition and deep learning is proposed. The original load data are decomposed into four parts as the daily and weekly periodic components and the low- and high-frequency components. Long short-term memory (LSTM) neural network is applied in the forecasting for the daily periodic, weekly periodic, and low-frequency components. The combination of isolation forest (iForest) and Mallat with the LSTM method is constructed in forecasting the high-frequency part. Finally, the four parts of the forecasting results are added together. The actual load data of a Chinese city are researched. Compared with the forecasting results of empirical mode decomposition- (EMD-) LSTM, LSTM, and recurrent neural network (RNN) methods, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of forecasting and actual values.

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