Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

A systematic literature review of machine learning methods applied to predictive maintenance

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.

New inspirations in swarm intelligence: a survey

https://www.matlabi.ir/wp-content/uploads/bank_papers/p%20paper/p107_Matlabi.blogfa.com.pdf

Multiobjective particle swarm optimization for environmental/economic dispatch problem

This paper presents an effective method of congestion management in power systems. Congestions or overloads in transmission network are alleviated by generation rescheduling and/or load shedding of participating generators and loads. The two conflicting objectives 1) alleviation of overload and 2) minimization of cost of operation are optimized to provide pareto-optimal solutions. A multiobjective particle swarm optimization (MOPSO) method is used to solve this complex nonlinear optimization problem. A realistic frequency and voltage dependent load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics is used to solve this problem. The proposed algorithm is tested on IEEE 30-bus system, IEEE 118-bus system, and Northern Region Electricity Board, India (NREB) 390-bus system with smooth as well as nonsmooth cost functions due to valve point loading effect.

Congestion Management Using Multiobjective Particle Swarm Optimization

This paper presents a multi-objective optimal power flow technique using particle swarm optimization. Two conflicting objectives, generation cost, and environmental pollution are minimized simultaneously. A multi-objective particle swarm optimization method is used to solve this highly nonlinear and non-convex optimization problem. A diversity preserving technique is incorporated to generate evenly distributed Pareto optimal solutions. A fuzzy membership function is proposed to choose a compromise solution from the set of Pareto optimal solutions. The algorithm is tested on IEEE 30 and 118 bus systems and its effectiveness is illustrated. Copyright © 2010 John Wiley & Sons, Ltd.

A multi‐objective optimal power flow using particle swarm optimization

The Indian electricity sector, despite having the world's fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional DSM strategies may not be suitable for India as the local conditions usually favor only inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present nPlug, a smart plug that sits between the wall socket and deferrable loads such as water heaters, washing machines, and electric vehicles. nPlugs combine real-time sensing and analytics to infer peak periods as well as supply-demand imbalance and reschedule attached appliances in a decentralized manner to alleviate peaks whenever possible. They do not require any manual intervention by the end consumer nor any enhancements to the appliances or existing infrastructure. Some of nPlug's capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage.

nPlug: a smart plug for alleviating peak loads

Summary form only given. This paper presents an effective method of congestion management in power systems. Congestions or overloads in transmission network are alleviated by generation rescheduling and/or load shedding of participating generators and loads. The two conflicting objectives (1) alleviation of overload and (2) minimization of cost of operation are optimized to provide Pareto-optimal solutions. A multiobjective particle swarm optimization (MOPSO) method is used to solve this complex nonlinear optimization problem. A realistic frequency and voltage dependent load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics is used to solve this problem. The proposed algorithm is tested on IEEE 30-bus system, IEEE 118-bus system, and Northern Region Electricity Board, India (NREB) 390-bus system with smooth as well as nonsmooth cost functions due to valve point loading effect.

Congestion management using multiobjective particle swarm optimization

This paper proposes a novel hybrid state estimation method using traditional SCADA (Supervisory Control And Data Acquisition) and newly deployed limited PMU (Phasor Measurement Unit) measurements. System states are estimated when a set of SCADA and/or PMU measurements come in. As PMU measurements come much faster (typically one sample in 20ms) than SCADA measurements (typically one sample in 10 seconds), in between two SCADA measurements, system states of PMU unobservable buses are interpolated using an interpolation matrix (H)live PMU measurements. In between two SCADA samples, if PMU measurements change significantly, pre-computed interpolation matrix (H) is compensated with a sensitivity change matrix (ΔH) and system states are estimated using the corrected interpolation matrix. In order to compute the ΔH, the method classified the measurement set into four sub-sets i.e. PMU measurements, SCADA measurements of PMU boundary buses with significant change, SCADA measurements adjacent to the selected boundary buses, and remaining SCADA measurements and run a modified weighted least square method with different weights corresponding to each sub-set of measurements. This compensation improves the estimation accuracy significantly. Effectiveness of the proposed scheme is evaluated on a number of IEEE benchmark test systems and evaluation results are presented in this paper.

Jagabondhu Hazra

Papers

Congestion Management Using Multiobjective Particle Swarm Optimization

A multi‐objective optimal power flow using particle swarm optimization

nPlug: a smart plug for alleviating peak loads

Congestion management using multiobjective particle swarm optimization

Real-time hybrid state estimation incorporating SCADA and PMU measurements