Yijia Cao

A multiagent-based particle swarm optimization approach for optimal reactive power dispatch

Reactive power dispatch in power systems is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, a solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented. This method integrates the multiagent system (MAS) and the particle swarm optimization (PSO) algorithm. An agent in MAPSO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbors, and it can also learn by using its knowledge. Making use of these agent-agent interactions and evolution mechanism of PSO, MAPSO realizes the purpose of optimizing the value of objective function. MAPSO applied to optimal reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system. Simulation results show that the proposed approach converges to better solutions much faster than the earlier reported approaches. The optimization strategy is general and can be used to solve other power system optimization problems as well.

Optimal reactive power dispatch using an adaptive genetic algorithm

This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, pc and pm, are varied depending on the fitness values of the solutions and the normalized fitness distances between the solutions in the evolution process to prevent premature convergence and refine the convergence performance of genetic algorithms. The AGA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system.

A note on stability of analog neural networks with time delays

This note presents a generalized sufficient condition which guarantees stability of analog neural networks with time delays. The condition is derived using a Lyapunov functional and the stability criterion is stated as: the equilibrium of analog neural networks with delays is globally asymptotically stable if the product of the norm of connection matrix and the maximum neuronal gain is less than one.

A nonlinear variable structure stabilizer for power system stability

A nonlinear variable structure stabilizer is proposed in this paper. Design of this stabilizer involves the nonlinear transformation technique, the variable structure control technique and linear system theory. Performance of the proposed nonlinear variable structure controller in a single machine connected to an infinite bus power system and a multimachine system with multimode oscillations is simulated. The responses of the system with the proposed stabilizer are compared with those obtained with some other kinds of stabilizers when the system is subjected to a variety of disturbances. Simulation results show that the nonlinear variable structure stabilizer gives satisfactory dynamic performance and good robustness. >

Identification of Vulnerable Lines in Power Grid Based on Complex Network Theory

Some critical lines can have important impact on the large-scale blackouts and cascading failures in power grid. Based on the newest progress in the field of complex network, a new vulnerability index called weighted line betweenness is proposed as vulnerability index in this paper. The weighted line betweenness of one line is defined as the sum of the loads acted on this line, which are brought by the shortest electric paths between generator nodes and load nodes that passing through this line. We revise the weighted line betweenness by increasing the betweenness to the highest betweenness in the neighboring lines before revision. Vulnerability analysis has been carried out on the IEEE 39 bus system and Huazhong-Chuanyu power grid. The time domain simulation results verify that the weighted line betweenness can not only identify the most critical lines but also find out those light loaded but critical lines due to their special position in the power grid.

Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm

In this paper, Message Passing Interface (MPI) based parallel computation and particle swarm optimization (PSO) algorithm are combined to form the parallel particle swarm optimization (PPSO) method for solving the dynamic optimal reactive power dispatch (DORPD) problem in power systems. In the proposed algorithm, the DORPD problem is divided into smaller ones, which can be carried out concurrently by multi-processors. This method is evaluated on a group of IEEE power systems test cases with time-varying loads in which the control of the generator terminal voltages, tap position of transformers and reactive power sources are involved to minimize the transmission power loss and the costs of adjusting the control devices. The simulation results demonstrate the accuracy of the PPSO algorithm and its capability of greatly reducing the runtimes of the DORPD programs.

An Efficient Implementation of Automatic Differentiation in Interior Point Optimal Power Flow

This paper presents an improved implementation of automatic differentiation (AD) technique in rectangular interior point optimal power flow (OPF). Distinguished from the existing implementation of AD, the proposed implementation adds a subroutine to identify all constant first-order and second-order derivates by AD and form a list of constant derivates before the processing of iterations. At every iteration of interior point OPF algorithm, only the changing derivates are updated by AD tool. An excellent AD software-ADC-is used as a basic AD tool to finish the proposed implementation. A user-defined model interface is provided with AD technique to enhance performance and flexibility. Numerical studies on several large-scale power systems indicate that the proposed implementation of AD can compete with hand code in execution speed without loss of maintainability and flexibility of AD codes. This paper demonstrates that AD technique has an application potential in online operating environments of power systems instead of hand-coded derivates, and greatly relieves the burdens of software developers.

A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network

Short term load forecasting (STLF) has an essential role in the operation of electric power systems. In recent years, artificial neural networks (ANN) are more commonly used for load forecasting. However, there still exist some difficulties in choosing the input variables and selecting an appropriate architecture of the networks. This paper presents a novel fuzzy-rough sets based ANN for STLF. The fuzzy-rough sets theory is first employed to perform input selection and determine the initial weights of ANN. In the sequel, an improved k-nearest neighbor (K-NN) method is used for the selection of similar days in history as the training set of ANN. Then ANN module is trained using historical daily load and weather data selected to perform the final forecast. To demonstrate the effectiveness of the approach, short-term load forecasting was performed on the Hang Zhou Electric Power Company in China, and the testing results show that the proposed model is feasible and promising for load forecasting

Improved particle swam optimization algorithm for OPF problems

This work presents the solution of the optimal power flow (OPF) using particle swarm optimization (PSO) technique. The main goal of this paper is to verify the viability of using PSO problem composed by the different objective functions. Incorporation of nonstationary multistage assignment penalty function in solving OFF problems can significantly improve the convergence and gain more accurate values. The proposed PSO method is demonstrated and compared with linear programming (LP) approach and genetic algorithm (GA) approach on the standard IEEE 30-bus system. The results show that the proposed PSO method is capable of obtaining higher quality solutions efficiently in OFF problem.

Optimization of control parameters in genetic algorithms: A stochastic approach

This paper proposes a stochastic approach for optimization of control parameters ( probabilities of crossover and mutation ) in genetic algorithms ( GAs ) . The genetic search can be modelled as a controlled Markovian process, the transition of which depends on the control parameters. A stochastic optimization problem is formed for control of GA parameters, based on a given performance index of populations and analysed as a controlled Markovian process during the genetic search. The optimal values of control parameters can be found from a recursive estimation of control parameters, which is obtained by introducing a stochastic gradient of the performance index and using a stochastic approximation algorithm. The algorithm possesses the capability of finding the stochastic gradient and adapting the control parameters in the direction of descent. A non-stationary Markov model is developed to investigate asymptotic convergence properties of the proposed genetic algorithm. It is proved that the proposed geneti...