Improved BPSO for optimal PMU placement

Optimal phasor measurement unit (PMU) placement involves the process of minimizing the number of PMU needed while ensuring entire power system network completely observable. This paper presents the improved binary particle swarm (IBPSO) method that converges faster and also manage to maximize the measurement redundancy compared to the existing BPSO method. This method is applied to IEEE-30 bus system for the case of considering zero-injection bus and its effectiveness is verified by the simulation results done by using MATLAB software

Optimal PMU placement using topology transformation method in power systems

Optimal phasor measurement units (PMUs) placement involves the process of minimizing the number of PMUs needed while ensuring the entire power system completely observable. A power system is identified observable when the voltages of all buses in the power system are known. This paper proposes selection rules for topology transformation method that involves a merging process of zero-injection bus with one of its neighbors. The result from the merging process influenced by the selection of bus selected to merge with the zero-injection bus. The proposed method will determine the best candidate bus to merge with zero-injection bus according to the three rules created in order to determine the minimum number of PMUs required for full observability of the power system. In addition, this paper also considered the case of power flow measurements. The problem formulated as integer linear programming (ILP). The simulation for the proposed method tested by using MATLAB for different IEEE bus systems. The explanation of the proposed method is demonstrated by using IEEE 14-bus system. The results obtained in this paper proved the effectiveness of the proposed method since the number of PMUs obtained are comparable with other available techniques

Integrated mutation strategy with modified binary PSO algorithm for optimal PMU placement

Optimal phasor measurement units (PMUs) placement refers to the strategic placement of PMUs to achieve the full observability of power systems with a minimum number of PMUs. A strategic placement is needed because of the economic or technical restriction that hinders the deployment of PMUs on every bus. A modified version of the binary particle swarm optimization (BPSO) method is proposed in this paper by integrating a mutation strategy and the V-shaped sigmoid function for placing the PMUs that maintains the full power system observability in the presence of zero-injection bus, single PMU loss and PMU’s channel limits while maximizing the measurement redundancy. The solution that has the highest measurement redundancy was selected as the best placement of PMUs. The use of mutation strategy and V-shaped sigmoid function in this paper improves the population diversity, thereby minimizing the chance of the particles being trapped in the local optima, consequently leading to a quality solution. In order to validate its effectiveness, the results obtained by the proposed method are compared with other published techniques to demonstrate the accuracy and validity of the proposed technique. The results of the IEEE 300-bus system show that the proposed method effectively managed to reduce the number of PMUs needed

Water uptake and transport studies in PVP-PMMA hydrogels

Hydrogels comprising 90 wt.% polyvinylpyrrolidone and 10 wt.% poly(methyl methacrylate) have been soaked in distilled water. By taking the mass change for different soaking times the number of water molecules entering the hydrogel can be calculated. A maximum in conductivity has been observed in the conductivity-soaking time plot. The increase and decrease in conductivity over time imply that some water molecules in the hydrogel are mobile (infrared band at 1598 cm-1) and immobile water band observed at 1687 cm-1. These can also be implied from the plot for number density of conductivity contributing water molecules versus soaking time. From the results shown water molecules diffuses at a faster rate when there are less conductivity contributing water molecules

Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling

Bas, Eren/0000-0002-0263-8804; Egrioglu, Erol/0000-0003-4301-4149WOS: 000434268000024Single multiplicative neuron model and multilayer perceptron have been commonly used for time series prediction problem. Having a simple structure and features of easily applicable differentiates the single multiplicative neuron model from the multilayer perception. While, multilayer perceptron just as many other artificial neural networks are data-based methods, single multiplicative neuron model has a model structure due to it is composed of a single neuron. Multilayer perceptron can highly compliance with data by changing its architecture, though single multiplicative neuron model, in this respect, is insufficient. In this study, to overcome this problem of single multiplicative neuron model, a new model that its weights and biases are obtained by way of autoregressive equations is proposed. Since the time indexes are considered to determine weights and biases from the autoregressive models, the proposed neural network can be evaluated as a data-based model. To show the performance and capability of the proposed method, various implementations have been executed over some well-known data sets. And the obtained results demonstrate that data-based proposed method has outstanding forecasting performance