Ke-yan Liu

Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II

An improved nondominated sorting genetic algorithm-II (INSGA-II) has been proposed for optimal planning of multiple distributed generation (DG) units in this paper. First, multiobjective functions that take minimum line loss, minimum voltage deviation, and maximal voltage stability margin into consideration have been formed. Then, using the proposed INSGA-II algorithm to solve the multiobjective planning problem has been described in detail. The improved sorting strategy and the novel truncation strategy based on hierarchical agglomerative clustering are utilized to keep the diversity of population. In order to strengthen the global optimal searching capability, the mutation and recombination strategies in differential evolution are introduced to replace the original one. In addition, a tradeoff method based on fuzzy set theory is used to obtain the best compromise solution from the Pareto-optimal set. Finally, several experiments have been made on the IEEE 33-bus test case and multiple actual test cases with the consideration of multiple DG units. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been proved.

A Trust Region SQP Method for Coordinated Voltage Control in Smart Distribution Grid

This paper investigates the coordinated voltage control problem for smart distribution grid with the integration of distributed generation (DG). By actively participating in voltage control together with under-load tap changer and shunt capacitors, DG can operate more effectively in the distribution network. The objective of the proposed control method is to minimize the active power loss in the distribution system and to decrease the number of switching device operations while maintaining the grid voltage within the allowable range. Nondispatchable and dispatchable DG are both considered in the control method. To solve the mixed integer nonlinear programming problem, the trust region sequential quadratic programming method is integrated with the branch and bound approach to iteratively approximate the optimization with trust region guidance. Numerical tests on a standard 10-kV distribution system, a real 10-kV distribution system in the Sichuan province of China, and the IEEE 13-bus demonstrate the applicability of the proposed coordinated voltage control method.

A Big Data Online Cleaning Algorithm Based on Dynamic Outlier Detection

To effectively clean the large-scale, mixed and inaccurate monitoring or collective data, reduce the cost of data cache and ensure the consistent deviation detection on timing data of each cycle, a big data online cleaning algorithm based on dynamic outlier detection has been proposed. The data cleaning method is improved by local outliner detection upon density, sampling cluster uniformly dilution Euclidean distance matrix retaining some corrections into next cycle of cleaning, which avoids a sampling causing overall cleaning deviation and reduces amount of calculation within data cleaning stable time, enhancing the speed greatly. Finally, the distributed solutions on online cleaning algorithm based on Hadoop platform.

Improved Multiobjective Harmony Search Algorithm with Application to Placement and Sizing of Distributed Generation

To solve the comprehensive multiobjective optimization problem, this study proposes an improved metaheuristic searching algorithm with combination of harmony search and the fast nondominated sorting approach. This is a kind of the novel intelligent optimization algorithm for multiobjective harmony search (MOHS). The detailed description and the algorithm formulating are discussed. Taking the optimal placement and sizing issue of distributed generation (DG) in distributed power system as one example, the solving procedure of the proposed method is given. Simulation result on modified IEEE 33-bus test system and comparison with NSGA-II algorithm has proved that the proposed MOHS can get promising results for engineering application.

A parallel algorithm of optimal power flow on Hadoop platform

Application of smart grid leads to significant increase in scale and data of the power systems, bringing new challenges to the calculation of optimal power flow. However, the existing parallel algorithms, such as MPI-based solutions, suffer from high computational complexity. In this paper, we propose a parallel algorithm of optimal power flow based on Map-Reduce framework. More concretely, the node reordering in our algorithm can greatly accelerate solution speed of the linear equations meanwhile fit well with Map-Reduce programming specifications. Moreover, we determine the appropriate formats for input, intermediate and output data sets and partition the algorithm into separate map/reduce tasks. This facilitates our algorithm to be executed in parallel on a large number of computing nodes. The proposed algorithm is verified on a Hadoop cluster. The experimental results demonstrate that the effectiveness of the propose algorithm.

Classification of Massive User Load Characteristics in Distribution Network Based on Agglomerative Hierarchical Algorithm

In order to improve primary energy utilization, achieve economical operation of distribution network, comprehensively consider the concentration / compensation needs of various groups under typical load levels, and to gain understanding of characteristics of different types of user loads, the present paper proposes a hierarchical cluster algorithm to enhance the cohesion of a distribution feeder load characteristic clustering algorithm circumstances. This will serve to ultimately provide effective guidance for electricity energy conservation as well as to better realize peak load shifting. By cutting distribution network load time sequence data in longitudinal manner, relevant feature were extracted to achieve user load characteristics classification based on hierarchical clustering algorithm. Such classification will therefore assist to optimize distribution network scheduling. It is therefore an effective way to enhance accuracy and effectiveness of relevant power distribution decision-making.

Optimal placement and sizing of distributed generation in distribution power system based on multi-objective harmony search algorithm

This study deals with the optimal placement and sizing issue in distribution power system with distributed generation (DG). This issue is a sophisticated multi-objective optimization problem with constraints of investment and power system operation demands. In this paper, minimum power loss, minimum voltage deviation and maximal voltage stability margin are taken into account for the optimization of DG planning. To solve the comprehensive multi-objective optimization problem, a meta-heuristic searching algorithm harmony search (HS) is improved with the fast non-dominated sorting approach, which forms the novel intelligent optimization algorithm called multi-objective harmony search (MOHS). Simulation on IEEE 33-bus test system and comparisons with some other multi-objective evolutionary algorithms yield promising results for optimal placement and sizing of DG based on MOHS.

Research on Power Quality Disturbance Signal Classification Based on Random Matrix Theory

In this paper, a method of power quality disturbance classification based on random matrix theory (RMT) is proposed. The method utilizes the power quality disturbance signal to construct a random matrix. By analyzing the mean spectral radius (MSR) variation of the random matrix, the type and time of occurrence of power quality disturbance are classified. In this paper, the random matrix theory is used to analyze the voltage sag, swell and interrupt perturbation signals to classify the occurrence time, duration of the disturbance signal and the depth of voltage sag or swell. Examples show that the method has strong anti-noise ability.

Solving coordinated voltage optimization in distribution network based on a robust sequential quadratic programming algorithm

This paper investigates the optimal operation problem for distribution networks with the integration of distributed generation (DG). By considering the objectives of minimal line loss, minimal voltage deviation and maximum DG active power output, the proposed operational optimization formulation is a multi-object optimization problem. Through normalization of each objective function, the multi-objective optimization is transformed to single objective optimization. To solve such a non-convex problem, the trust region robust sequential quadratic programming (TRR-SQP) method is proposed which iteratively approximates by a quadratic programming with the trust region guidance. Numerical tests on IEEE 33-bus, and multiple actual systems show the applicability, and comparisons with the primal-dual interior point method and sequential linear programming method are provided.

Clustering algorithm of power load curves in distribution network based on analysis of matrix characteristic roots

In order to solve the problem of determining the clustering number of power load curves, this paper presented a clustering method based on the analysis of matrix characteristic roots. The clustering number is determined by the analysis result and the method is applied to the power load curves clustering. Firstly, the unified mathematical expression of the same type of load curves is established based on the changing pattern of load curves. Then, the equivalent relation between the clustering number of load curves and the number of larger characteristic roots is demonstrated. Besides, two criterions are presented to calculate the number of larger characteristic roots; and the number is the clustering number. After that, the load matrix is standardized and clustered by K-means algorithm. At last, the clustering result is evaluated by two indexes, the distance within classes and the distance between classes; and case simulation is given to prove the effectiveness and reasonability of the method proposed.