To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although well-matched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more meta-heuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers. An attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal sizing and siting of DG in distribution networks. The present study can contribute meaningful knowledge and assist as a reference for investigators and utility engineers on issues to be considered for optimal sizing and siting of DG units in distribution systems.

Optimal sizing and siting techniques for distributed generation in distribution systems: A review

An overview of numerical and mathematical modelling-based distributed generation (DG) system optimisation techniques is presented in this review paper. The objective is to compare different aspects of these two broad classes of DG optimisation techniques, explore their applications, and identify potential research directions from reviewed studies. Introductory descriptions of general electrical power system and DG system are first provided, followed by reviews on renewable resource assessment, load demand analysis, model formulation, and optimisation techniques. In renewable resource assessment model review, uncertain solar and wind energy resources are emphasised whereas applications of forecasting models have been highlighted based on their prediction horizons, computational power requirement, and training data intensity. For DG optimisation framework, (solar, wind and tidal) power generator, energy storage and energy balance models are discussed; in optimisation technique section, both numerical and mathematical modelling optimisation methods are reviewed, analysed and criticised with recommendations for their improvements. In overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

A new meta-heuristic optimizer: Pathfinder algorithm

Power loss minimization and voltage stability improvement are important areas of power systems due to existing transmission line contingency, financial loss of utility and power system blackouts. Optimal allocation (i.e. siting and sizing) of Distributed Generation (DG) is one of the best ways to strengthen the efficiency of power system among capacitor placement and network reconfiguration. Power system operators and researchers put forward their efforts to solve the distribution system problem related to power loss, energy loss, voltage profile, and voltage stability based on optimal DG allocation. Furthermore, optimal DG allocation secures distribution system from unwanted events and allows the operator to run the system in islanding mode. In this paper a comprehensive study is carried out for optimum DG placement considering minimization of power/energy losses, enhancement of voltage stability, and improvement of voltage profile. An attempt has been made to summarize the existing approaches and present a detailed discussion which can help the energy planners in deciding which objective and planning factors need more attention for optimum DG allocation for a given location or in a given scenario.

A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system

Distribution system provides a link between the high voltage transmission system and low voltage consumers thus I2R loss in a distributed system is high because of low voltage and high current. Distribution companies (DISCOs) have an economic enticement to reduce losses in their networks. Usually, this enticement is the cost difference between real and standard losses. Therefore, if real losses are higher than the standard ones, the DISCOs are economically penalized or if the opposite happens, they obtain a profit. Thus loss minimization problem is a well researched topic and all previous approaches vary from each other by selection of tool for loss minimization and thereafter either in their problem formulation or problem solution methods employed. Many methods of loss reduction exist like feeder reconfiguration, capacitor placement, high voltage distribution system, conductor grading, Distributed Generator (DG) Allocation etc. This paper gives a bibliographical survey, general background and comparative analysis of three most commonly used techniques (i) Capacitor Placement, (ii) Feeder Reconfiguration, (iii) and DG Allocation for loss minimization in distribution network based on over 147 published articles, so that new researchers can easily find literature particularly in this area.

Loss minimization techniques used in distribution network: bibliographical survey

Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm

Optimal location and sizing of distributed generation unit using intelligent water drop algorithm

A novel initiative towards the digital image processing technique by the application of histogram of oriented gradients (HOG) feature descriptor using the OpenCV library coded with the High-level programming language Python, booted with the help of Raspberry Pi microcontroller fitted with a RaspiCam to capture moving images of objects passing under it has been presented in this paper. The project utilizes image samples in top-view which are used to set predefined models containing a great number of motion variations for identification of humans entering a room through a door or gate. Pair of Passive Infra-Red (PIR) sensors has been used to instruct the system to capture images of incoming or outgoing objects that cross it. This method of image detection combined with a sensor feedback has been used along with an ability to send data via bluetooth to local servers for security or record purposes.

Human detector and counter using raspberry Pi microcontroller

Distributed generation is the process of generating electricity from small energy sources and connecting it to the distribution network or on the customer's side of the meter The placement of distributed generation and the size of the distribution generation is important Determining the optimal location and sizing of a Distributed Generation (DG) Unit is being presented in this paper The radial distributed network has been considered A robust stochastic optimization technique based on the movement and intelligence of swarms called Particle Swarm Optimization has been implemented to obtain optimal solution of the DG placement problem Reduction of line losses in the radial distribution network is the main objective of this paper

Determining the optimal location and sizing of distributed generation Unit using Particle Swarm Optimization algorithm

Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different
faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain and time-frequency domain features are then extracted which are used to implement the neural networks through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found to be 95.7% and using DNN has been found to be 100%.

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D. Rama Prabha

Papers

Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm

Optimal location and sizing of distributed generation unit using intelligent water drop algorithm

Human detector and counter using raspberry Pi microcontroller

Determining the optimal location and sizing of distributed generation Unit using Particle Swarm Optimization algorithm

Application of EMD, ANN and DNN for Self-Aligning Bearing Fault Diagnosis