A Hybrid Intelligent Method for Estimating Distribution Network Reconfigurations

This paper presents optimal reconfiguration of radial distribution network with integration of distributed energy resources (DER) using improved particle swarm optimization (PSO) algorithm. The benefits of DER integration in distribution system are reducing power losses, improving voltage profiles and load factors, eliminating system upgrades, and reducing environmental impacts. However, the presence of DER could also cause technical problems in voltage quality and system protection. Reconfiguration of distribution network is aimed to minimize power loss and to improve voltage quality in order to enhance the distribution system performance. In this study, reconfiguration method is based on an improved PSO. The method has been tested in a 60-bus Bantul distribution network of Yogyakarta Special Region province, Indonesia. The simulation results show the importance of reconfiguring the network for enhancing the distribution system performance in the presence of DER.

The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

The present work aims to study the development and application of Radial Basis Function (RBF) networks for predicting auger energy consumption based on input energy. The study utilized RBF networks and explored the input energy with treatments 2 (Soil moisture content), 2 (Rotary speeds), 2 (Hole depths) and 4 (Replication) based on field operations. As indicated by the results, energy input differed between the treatments but was not significant. The highest input value in transaction soil moisture content was 14.75 %, rotary speeds of 235 rpm, and hole depths of 40 cm. In comparison, the lower input energy at transaction soil moisture content was 7.9%, rotary speeds of 235 rpm, and hole depths of 20 cm. Input energy in treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9 %,235 rpm, and 20 cm) were 100.204 and 57.135 MJ. ha-1, respectively. The highest input energy shares were recorded for diesel fuel at all treatments. Furthermore, the RBF network with one hidden layer had good convergence. The output results showed 10 and five hidden neurons in a hidden layer with high accuracy for treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9%, 235 rpm, and 20 cm). In the treatment (14.75 %, 235 rpm, and 40 cm), the MSE for the training and testing sets was 0.0001 % and 0.01 % for data points with Ordinary RBF (ORBF type). The performance of the 3-10-1 architecture was better than other architectures. Finally, this research concluded that the RBF network method can forecast the input energy and energy expenditures related to the types of treatments.

Prediction of stock market indices is an interesting and challenging research problem in financial data mining area because movement of stock indices are nonlinear and they are dependent upon different constitutional and extraneous aspects. In this paper we come up with the practice of different techniques of Artificial Neural Network (ANN) in stock market prediction. Here we have selected Multilayer Perceptron model (MLP), Radial Basis Functional Network (RBFN) and an optimized Radial Basis Functional neural network. The proposed model provides an optimized set of center set to Radial Basis Functional Network for experiment and Particle swarm algorithm is used for this boosting process. Lastly in order to verify the best tactics, a comparative result of the applied models is represented.

This paper studies the reciprocal impact of distribution network protection and reconfiguration of active Nowadays, with increasing energy consumption in distribution networks and the demand of consumers to buy highly reliable power, it is of high importance to establish adapt between protection and operation of the power system. distribution system. Accordingly, a distribution network reconfiguration is carried out to find the optimal switching operations in an economic way. Since the switching operations will change network topology as well as short circuit level of buses, the protection coordination may be invalid. To address this issue, constraints of the coordination of protection relays and fuses is being formulated and added to the reconfiguration problem. Moreover, the nonlinear equations of the problem are linearized and are transformed the reconfiguration problem into Mixed-Integer Linear Programming (MILP) one to achieve the global optimal solution. The proposed method has been implemented on 33 bus distribution network. The results clearly show effectiveness of the active and reactive power management in an intelligent distribution network considering protection concepts.

Electricity distribution networks in densely populated urban areas are, in order to increase the reliability of supply, usually designed as meshed networks, though they always operate in radial configuration. Reconfiguration of distribution networks is achieved through switching operations on switching devices of distribution network branches (switching on/off of a branch). It is often the case that the existing configuration is not the configuration with minimum power losses. With the new computer program, developed by the authors, it is possible to find the network configuration that enables operation with minimum losses. The program proposes the best network configuration to the dispatcher, and the latter makes his own decision whether to change the network configuration or not. The mathematical algorithm for the network reconfiguration is presented in the paper, as well as results of the reconfiguration of the electricity distribution network of the city of Maribor, Slovenia.

This paper reviews some frequently used methods to initialize an radial basis function (RBF) network and presents systematic design procedures for pre-processing unit(s) to initialize RBF network from available input–output data sets. The pre-processing units are computationally hybrid two-step training algorithms that can be named as (1) construction of initial structure and (2) coarse-tuning of free parameters. The first step, the number, and the locations of the initial centers of RBF network can be determined. Thus, an orthogonal least squares algorithm and a modified counter propagation network can be employed for this purpose. In the second step, a coarse-tuning of free parameters is achieved by using clustering procedures. Thus, the Gustafson–Kessel and the fuzzy C-means clustering methods are evaluated for the coarse-tuning. The first two-step behaves like a pre-processing unit for the last stage (or fine-tuning stage—a gradient descent algorithm). The initialization ability of the proposed four pre-processing units (modular combination of the existing methods) is compared with three non-linear benchmarks in terms of root mean square errors. Finally, the proposed hybrid pre-processing units may initialize a fairly accurate, IF–THEN-wise readable initial model automatically and efficiently with a minimum user inference.

The present paper proposes a novel non-intrusive model reduction approach based on Proper Orthogonal Decomposition (POD), the Discrete Empirical Interpolation Method (DEIM) and Radial Basis Function (RBF) networks to efficiently predict production of oil and gas reservoirs. Provided a representative set of training reservoir scenarios, either POD or DEIM allows for effectively projecting input parameters (e.g., permeability, porosity), states (e.g., pressure, saturations) and outputs (e.g., well production curves) into a much lower dimension that retains the main features contained in the simulation system. In this work, these projections are applied across multiple levels to be able to collapse a large number of spatio-temporal correlations. It is observed that these projections can be effectively performed at a large extent regardless of the underlying geological complexity and operational constraints associated with the reservoir model. The RBF network provides a powerful means for developing learning functions from input-output relationships described by the reservoir dynamics entailed by multiple combinations of inputs and controls. In order to achieve a high degree of predictability from the resulting reduced model, the RBF network exploits locality by a means of Gaussian basis functions that are maximal at the sampled point and decrease monotonically with distance. Compared to multilayer perceptron networks (i.e., traditional artificial neural networks) RBF networks require less training and are less sensitive to the presence of noise in the data. In this regard, POD or DEIM acts as a data filter that additionally aids at designing a more compact RBF network representation suitable for targeting fast reservoir predictions. Numerical results show significant accelerations with respect to running the original simulation model on a set of field-motivated cases.

Distribution network reconfiguration problem has been known as a common issue in distribution network operation. Up to now, distribution network reconfiguration has been used for minimizing the power loss, reliability concerns and emergency problems. According to the increasing installation rate of Distributed Generations (DGs) and the rapidly growing number of Electric Vehicles (EVs), as a large responsive loads, distribution network operators have encountered new problems. These new aspects of network operation problem consist of congestion problem and require to established distribution market to execute the market process. This paper presents a novel methodology to solve this problem with the application of Distribution Locational Marginal Price (DLMP), using Genetic Algorithm (GA). The standard IEEE 33-bus network has been selected as a suitable case study and simulation results demonstrate the compatibility of proposed method. Comparison with other papers have also been addressed in case of not considering DGs and congestion issue. In the following, DGs participation in distribution electricity market, the congestion problem and numerical results has been shown and discussed.

To group microcrystalline celluloses (MCCs) using a combination of artificial neural network (ANN) and data clustering. Radial basis function (RBF) network was used to model the torque measurements of the various MCCs. Output from the RBF network was used to group the MCCs using a data clustering technique known as discrete incremental clustering (DIC). Rheological or torque profiles of various MCCs at different combinations of mixing time and water:MCC ratios were obtained using mixer torque rheometry (MTR). Correlation analysis was performed on the derived torque parameter Torque(max) and physical properties of the MCCs. Depending on the leniency of the predefined threshold parameters, the 11 MCCs can be assigned into 2 or 3 groups. Grouping results were also able to identify bulk and tapped densities as major factors governing water-MCC interaction. MCCs differed in their water retentive capacities whereby the denser Avicel PH 301 and PH 302 were more sensitive to the added water. An objective grouping of MCCs can be achieved with a combination of ANN and DIC. This aids in the preliminary assessment of new or unknown MCCs. Key properties that control the performance of MCCs in their interactions with water can be discovered.

This paper proposes an efficient method for risk assessment of carbon pricing with artificial neural network (ANN). The global warming is of main concern in the world. The power industry wants to make generation planning more flexible through the emission trading system. In this paper, an ANN-based method is proposed to predict one-step-ahead carbon pricing. As ANN, the radial base function network (RBFN) is used to approximate the nonlinear function of time-series carbon pricing. To improve the performance of RBFN, this paper makes use of preconditioned RBFN in a way that DA (Deterministic Annealing) clustering classifies learning data into some clusters and RBFN is constructed at each cluster. In addition, DA clustering is used to determine the center vectors of the Gaussian function in RBFN. Also, the Monte Carlo simulation is applied to the risk assessment of carbon pricing with the RBFN model. The risk of one-step-ahead carbon pricing is evaluated in probability. The proposed method is successfully applied to real data of the carbon pricing market.

The Using Smart Grid technology to build the policy strategy in reducing the losses in distribution network is very important in technical and economic aspect. The automation of distribution network through Smart Grid is very important in operation and future investments which needed to upgraded network grid. This technology of monitoring will help Distribution Company to address customer's requirement, optimize system and decrease losses and cost of operation and maintenance. This advantages encourages the distribution companies in Kosovo to invest and begin to implement the Smart Grid in existing system as well will be monitored from the distance in a regular time and will be operated in order to carry out optimization, automation of control, and direction of key parameters of the distribution system. This paper provides strategy using of smart grid and its conceptual technologies in a way to, identification areas with high losses in medium voltage and low voltage, Implementation of project with using of smart meters with PLC communication in low voltage network, using of smart meters for controlling of consumer load, control energy losses in Distribution Network Operator in Kosovo, measures which are proposed to be applied in order to reduce energy losses, which are much higher than in the EU states.

As the electric power system grows in size and complexity with a large number of interconnections, the assessment of the transient stability of power systems became an extremely intricate and highly non‐linear problem. Its solution needs either numerical methods involving bulk computations or specific dedicated methods to analyse dynamic non‐linear systems. Either method mostly assesses, particularly in the post‐fault condition, the critical clearing time (CCT). This parameter constitutes very complex functional relationships between the pre‐fault condition, type, and location of fault beside the clearance sequence. The available methods for evaluating such parameter had been previously reviewed. New approaches using the locally‐tuned radial basis function (RBF) network, an artificial neural network (ANN) paradigm have been recently proposed. The goal of this paper is to develop methods that can combine both neural networks and genetic algorithms (GA) into a common framework, and apply them to prediction problems. In the paper, the application of genetic algorithms in selecting the input patterns for the RBF network is proposed. Description of this combined approach and the results of its application to two power systems, one for four‐machine six‐bus system and the other for an existing system of North Sumatra. Indonesia, are also given in the paper. The attainable results show that the performance of the RBF network can be maintained and improved in spite of less features in the input patterns.

Spray drying is a unique drying process for powder production. Spray dried product must be free-flowing in order to fill the pressing dies rapidly, especially in the ceramic production. The important powder characteristics are; the particle size distribution and moisture content of the finished product that can be estimated and adjusted by the spray dryer operation, within limits, through regulation of atomizer and drying conditions. In order to estimate the moisture content of the resultant dried product, we modeled the control system of the drying process using two different Artificial Neural Network(ANN) approaches, namely the Back-Propagation Multiplayer Perceptron(BPMLP) algorithm and the Radial Basis Function(RBF) network. It was found out that the performance of both of the artificial neural network models were quite significant and the total testing error for the 100 data was 0.8 and 0.7 for the BPMLP algorithm and the RBF network respectively.

Approximation methods such as response surface method and artificial neural network (ANN) method are widely used to alleviate the computation costs in structural reliability analysis. However most of the ANN methods proposed in the literature suffer various drawbacks such as poor choice of parameter setting, poor generalization and local minimum. In this study, a support vector machine-based radial basis function (RBF) network method is proposed, in which the improved RBF model is used to approximate the limit state function and then is connected to a reliability method to estimate failure probability. Since the learning algorithm of RBF network is replaced by the support vector algorithm, the advantage of the latter, such as good generalization ability and global optimization are propagated to the former, thus the inherent drawback of RBF network can be defeated. Numerical examples are given to demonstrate the applicability of the improved RBF network method in structural reliability analysis, as well as to illustrate the validity and effectiveness of the proposed method.