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This paper proposes a methodology for predicting online rotor angle stability in power system operation under significant contribution from wind generation. First, a novel algorithm is developed to extract a stability index (SI) that quantifies the margin of rotor angle stability of power systems reflecting the dynamics of wind power. An approach is proposed that takes advantage of the machine learning technique and the newly defined SI. In case of a contingency, the developed algorithm is employed in parallel to find SIs for all possible instability modes. The SIs are formed as a vector and then applied to a classifier algorithm for rotor angle stability prediction. Compared to other features used in state-of-the-art methods, SI vectors are highly recognizable and thus can lead to a more accurate and reliable prediction. The proposed approach is validated on two IEEE test systems with various wind power penetration levels and compared to existing methods, followed by a discussion of results. [ABSTRACT FROM AUTHOR]

This paper presents a fast state estimator and a corresponding bad data (BD) processing architecture aimed at improving computational efficiency and maintaining high estimation accuracy of existing state estimation (SE) algorithms, simultaneously. The conventional and phasor measurements are separately processed by a three-stage SE method and a linear estimator, respectively. Then, the derived estimates are combined using estimation fusion theory. To eliminate computational bottlenecks of the conventional BD processing scheme, BD identification is moved before the second stage of supervisory control and data acquisition based SE, and bad phasor measurements or bad conventional measurements in the phasor measurement units observable area are identified and processed all at once, which can dramatically reduce the implementation time, especially for large-scale networks with multiple BD. The proposed estimator is compared to existing methods in terms of estimation accuracy and computational effort through simulation studies conducted on standard IEEE test systems. Promising simulation results show that the proposed estimator could be an effective method to obtain system states in a fast and accurate manner. [ABSTRACT FROM PUBLISHER]

Stochastic programming is a cost-effective approach to model the transmission expansion planning (TEP) considering the uncertainties of wind and load, which is known as stochastic TEP (STEP). The uncertainty can be accurately represented by a large number of scenarios, which need to be reduced to a relatively small number in order to shorten the computational time required by the STEP. The forward selection algorithm (FSA) is an accurate scenario reduction method which, however, is quite time consuming. An improved FSA (IFSA) is proposed in order to shorten the computational time. The STEP is a large-scale mixed-integer programming problem, and, therefore, is difficult to be solved directly. Benders decomposition algorithm is suitable to solve the STEP by decomposing it into master and multiple slave problems. The slave problems are nonlinear and thereby are difficult and time consuming to be solved. In this regard, a linearization method is proposed to solve the slave problems faster and to calculate the Lagrangian multipliers needed by the master problem. Two medium and a large datasets are used to demonstrate the efficiency of the IFSA and a 24-, a 300-, and a 2383-bus test systems are used to verify the efficiency of the linearization method. [ABSTRACT FROM PUBLISHER]

The recently-developed Two-sided Arnoldi and Sensitive Pole Algorithm (TSA-SPA) is effective and robust in computing the most sensitive eigenvalues with respect to control parameter changes in large-scale power systems. This paper extends the TSA-SPA to handle different system parameters, including control, system operating and network parameters. The proposed algorithm makes use of perturbation in reduced matrix obtained from Arnoldi/TSA method through linearization and successfully avoids the need for TSA-SPA to formulate the whole state matrix of the system and to explicitly calculate the elements' variations in system state matrix. A new deflation method is also proposed and adopted in the generalized algorithm to find other sensitive eigenvalues. Simulation results illustrate that the generalized algorithm is able to not only maintain the excellent properties of TSA-SPA in terms of convergence and robustness, but also consider various parameter changes effectively in large-scale power systems. [ABSTRACT FROM AUTHOR]

Due to increasing demand on adequate dynamic load modeling, aggregation algorithms for induction and synchronous motors are proposed in the literature for cases that the same types of motors are connected to a common bus. However, one problem remains unsolved: how to perform load aggregation with upstream motors if both induction and synchronous motors are connected to a common downstream bus through a transformer and/or series impedance fed by the upstream circuit. In this paper, a bus-split algorithm is proposed to address this issue, and its detailed mathematical derivation is provided. A case study using electrical transient and analysis program simulation is conducted to verify the accuracy of the algorithm. The bus-split algorithm makes load aggregation possible facility wide, and thus, it can serve as the milestone for the advance of load aggregation for industrial facilities. [ABSTRACT FROM PUBLISHER]

A novel algorithm (TSA-SPA) that combines the Two-Sided Arnoldi method (TSA) and the Sensitive Pole Algorithm (SPA) is proposed in this paper for calculation of the most sensitive eigenvalues to control parameters in large power systems. In the proposed method, first, with the shift-invert transformation precondition, TSA builds two Krylov subspaces and obtains a reduced matrix of a much smaller scale, which contains eigenvalues close to the chosen shift point. Second, SPA is adopted to realize the most sensitive eigenvalue computation. TSA-SPA can find the most sensitive eigenvalues of interest, with satisfactory reliability and convergence, in a specified frequency domain. With proper selection of sizes of Krylov subspace and the reduced matrix, the convergence to good eigenvalue approximations is practically guaranteed. Moreover, with the deflation technique, the algorithm is also capable of finding several other dominant eigentriplets which may relate to inter-area and/or local control modes. The efficiency of the proposed algorithm has been validated on small and large-scale power systems. It has been found that compared to other available sensitive pole algorithms, the proposed algorithm has more robust and reliable performance. The proposed algorithm is suitable for practical applications in large-scale power systems. [ABSTRACT FROM PUBLISHER]

Based on a quantum-inspired evolutionary algorithm for unit commitment, this paper proposed ways to advance the efficiency and robustness of the algorithm so that its capacity for application in large-scale unit commitment problems can be significantly enhanced. The paper develops an advanced quantum-inspired evolutionary unit commitment algorithm by developing a new initialization method based on unit priority list and a special Q-bit expression for ensuring diversity in the initial search area for improving the efficiency of solution searching. Different techniques such as multi-observation, single-search, and group-search are also proposed for incorporation in the advanced algorithm. The advanced algorithm is tested and compared with the earlier quantum-inspired evolutionary algorithm and a number of known methods through its applications to test systems with up to 100 generator units for a 24-h scheduling horizon. [ABSTRACT FROM PUBLISHER]

Differential evolution (DE) is a promising evolutionary algorithm for solving the optimal reactive power flow (ORPF) problem, but it requires relatively large population size to avoid premature convergence, which will increase the computational time. On the other hand, evolutionary programming (EP) has been proved to have good global search ability. Exploiting this complementary feature, a hybrid algorithm of DE and EP, denoted as DEEP, is proposed in this study to reduce the required population size. The hybridisation is designed as a novel primary–auxiliary model to minimise the additional computational cost. The effectiveness of DEEP is verified by the serial simulations on the IEEE 14-, 30-, 57-bus system test cases and the parallel simulations on the IEEE 118-bus system test case. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for solving the unit commitment (UC) problem based on quantum-inspired evolutionary algorithm (QEA). The proposed method applies QEA to handle the unit-scheduling problem and the Lambda-iteration technique to solve the economic dispatch problem. The QEA method is based on the concept and principles of quantum computing, such as quantum bits, quantum gates and superposition of states. QEA employs quantum bit representation, which has better population diversity compared with other representations used in evolutionary algorithms, and uses quantum gate to drive the population towards the best solution. The mechanism of QEA can inherently treat the balance between exploration and exploitation and also achieve better quality of solutions, even with a small population. The proposed method is applied to systems with the number of generating units in the range of 10 to 100 in a 24-hour scheduling horizon and is compared to conventional methods in the literature. Moreover, the proposed method is extended to solve a large-scale UC problem in which 100 units are scheduled over a seven-day horizon with unit ramp-rate limits considered. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A hybrid constrained genetic algorithm and particle swarm optimisation (PSO) method for the evaluation of the load flow in heavy-loaded power systems is developed. The new algorithm is applied to find the maximum loading points of three IEEE test systems. The experimental determination of the best values of the parameters for use in the PSO part of the hybrid algorithm is also reported. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is studied in detail for optimal reactive power flow (ORPF) problems. The concept, mechanism, and parameter setting of DE are discussed. Based on the IEEE 14-, 30- and 57-bus system test cases, DE is compared with some basic or improved evolutionary algorithms that have been applied to ORPF. It is found that DE is generally a good algorithm for ORPF and worthy of more attention. However, it is also found that DE requires relatively large populations to avoid premature convergence. The impact of this shortcoming is made clear in the IEEE 118-bus system test case. The effectiveness of parallel computing technology for speeding up the computation of DE-based ORPF is also analysed. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a promising evolutionary algorithm for solving optimal reactive power flow problems, but it requires relatively large population to avoid premature convergence. In order to overcome this disadvantage, a novel decomposition and coordination method based on the cooperative co-evolutionary architecture and the voltage-var sensitivity-based power system decomposition technique is proposed and incorporated with DE in this paper. It is implemented with a three-level parallel computing topology on a PC-cluster. Based on the IEEE 118-bus system test case, the effectiveness of the proposed method has been verified by comparison with the parallel basic DE not using the decomposition and coordination technique. [ABSTRACT FROM AUTHOR]