C. Christober Asir Rajan

Demand side management using expert system

Electricity supply is essential for any economic growth. The electric demand growth requires an increase of the installed capacity to meet consumption. An alternative is the better use of installed capacity. Demand side management (DSM) has been increasingly adopted by utilities as a subroutine for huge investments and as a method for resource use optimization. DSM is the ability of a grid to continue normal operation despite unplanned causalities to the operating equipment, known as contingencies. The DSM is essentially concerned with predicting the vulnerability of the current state (normal) to a set of postulated next contingencies. The system operates in three different states normal, emergency and restorative states. Recently, artificial intelligence (AI) based expert system procedures have been adopted for the grid management problem. An expert system (ES) is a computer application that solves complicated problems that would otherwise require extensive human expertise. To do so, it simulates the human reasoning process by applying specific knowledge and inferences. The system is not case oriented. It is composed of a main module, which, based on a set of rules, selects one of many possible solutions to perform DSM. The system has been tested using data from different industrial, residential and commercial applications.

Optimal Reconfiguration in Radial Distribution System Using Gravitational Search Algorithm

Abstract This article presents an innovative technique for solving network reconfiguration problems with an objective of minimizing network I2R losses for an explicit set of loads. Amid many performance standards considered for optimal network reconfiguration, voltage constraint is an important one. This problem calls for determining the best combination of feeders to be opened in the radial distribution system so it provides optimal performance in the preferred settings. In solving this problem, the gravitational search algorithm is used to reconfigure the radial distribution system; this algorithm practices an optimal pattern for sustaining the radial nature of the network at every stage of the solution, and it further allows proficient exploration of the solution space. The anticipated scheme minimizes the objective function, which has been given in the problem formulation to reduce I2R losses in addition to balancing loads in the feeders. The solution technique involves determination of the best switching combinations and calculation of power loss and voltage profile. The practicality of the anticipated technique is validated in two distribution networks, where attained results are compared by means of available literature. Correspondingly, it is seen from the results that network losses are reduced when voltage stability is enriched through network reconfiguration.

Stability and Performance Investigation of a Fuzzy-Controlled LCL Resonant Converter in an RTOS Environment

The resonant converter (RC) is finding wide applications in many space and radar power supplies. Among various RCs LCL, LCC, and LCL-T topologies are broadly used. This manuscript presents a comparative evaluation of steady-state stability of LCL, LCC, and LCL-T resonant configurations. Careful analysis favors LCL RC among the aforementioned three configurations since the stability region is good for the LCL RC over the other configurations. Also, this paper presents a comparative evaluation of proportional integral (PI) controller and fuzzy logic controller for a modified LCL RC. The aforementioned controllers are simulated using MATLAB and their performance is analyzed. The outcome of the analysis shows the superiority of fuzzy control over the conventional PI control method. The LCL RC is proposed for applications in many space and radar power supplies. Design, simulation, and experimental results for a 133-W, 50-kHz LCL RC are presented in this manuscript which provide high efficiency (greater than 89%) even for 50% of load. Efficiencies greater than 80% are obtained at significantly reduced loads (11%). In this paper, the applicability of the Philips advanced RISC machine processor LPC 2148 is also investigated for implementing the controller for an RC.

Genetic Algorithm Based Simulated Annealing Method for Solving Unit Commitment Problem in Utility System

The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Genetic Algorithms (GA’s) are general‐purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as neural section, genetic recombination and survival of the fittest. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status (“flat start”). Here the parents are obtained from a pre‐defined set of solution’s i.e. each and every solution is adjusted to meet the requirements. Then, a random recommitment is carried out with respect to the unit’s minimum down times. And SA improves the status. A...

A hybrid approach based on PSO and EP for proficient solving of Unit Commitment Problem

Unit Commitment Problem (UCP) is a nonlinear mixed integer optimization problem used in the scheduling operation of power system generating units subjected to demand and reserve requirement constraints for achieving minimum operating cost. The task of the UC problem is to determine the on/off state of the generating units at every hour interval of the planning period for optimally transmitting the load and reserve among the committed units. The importance for the necessity of a more effective optimal solution to the UCP problem is increasing with the regularly varying demand. Hereby, we propose a hybrid approach which solves the unit commitment problem subjected to necessary constraints and gives the optimal commitment of the units. The possible combination of demand and their corresponding optimal generation schedule can be determined by the PSO algorithm. Being a global optimization technique, Evolutionary Programming (EP) for solving Unit Commitment Problem, operates on a method, which encodes each units operating schedule with respect to up/down time. When the demand over a time horizon is given as input to the network it successfully gives the schedule of each units commitment that satisfies the demands of all the periods and results in minimum total cost.

Improved genetic algorithm solution to unit commitment problem

As the electrical industry restructures, many of the traditional algorithms for controlling generating units need modification or replacement. Previously utilized to schedule generation units in a manner that minimizes costs while meeting all demand, the Unit Commitment (UC) algorithm must be updated. This work deals with the Improved Genetic Algorithm (IGA) Solution to UC problem. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A simple GA implementation using the standard reproduction, cross over and mutation operators has been utilized to get optimal solution. In this paper, we have obtained the satisfactory solutions for the UC problem using the varying quality function technique and by adding problem specific operators. The idea has been implemented using technical simulation package MATLAB and the results for the same were obtained. Neyveli Thermal Power Station - II in India, demonstrates the effectiveness of the proposed approach. Numerical results are shown to compare the superiority of the cost solutions obtained using the conventional methods.

Transient stability improvement using neuro-fuzzy controller design for STATCOM

This paper presents a transient stability improvement using neural-fuzzy controller design for STATCOM with static synchronous time critical error and better damping system oscillations after a short circuit fault. This article on a STATCOM Control for transient stability improvement has proposed a system to meet with the addition of Lyapunov stability criterion to the ability and conditions as well. The performance is analyzed using digital simulation with (SMIB) with infinite bus.

An evolutionary programming based tabu search method for solving the unit commitment problem in utility system

This paper presents a new approach to solving the short-term unit commitment problem in utility system using an evolutionary programming based tabu search method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Evolutionary programming, which happens to be a global optimization technique for solving unit commitment problem, operates on a system, which is designed to encode each units operating schedule with regard to its minimum up/down time. The best population is selected by evolutionary strategy. A 66-bus utility power system with twelve generating units in India demonstrates the effectiveness of the proposed approach. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, Lagrangian relaxation and tabu search in reaching proper unit commitment.

Refined simulated annealing method for solving unit commitment problem

The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints with temperature and demand as control parameter. Neyveli Thermal Power Station - II in India, demonstrates the effectiveness of the proposed approach.

A Modified Shuffled Frog Leaping Algorithm for Long-Term Generation Maintenance Scheduling

This paper discuss a modified Shuffled frog leaping algorithm to Long-term Generation Maintenance Scheduling to Enhance the Reliability of the units. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In a monopolistic power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. In this paper present a modified Shuffled frog leaping algorithm methodology for finding the optimum preventive maintenance scheduling of generating units in power system. The objective function is to maintain the units as earlier as possible. Varies constrains such as spinning reserve, duration of maintenance and maintenance crew are being taken into account. In case study, test system consist of 24 buses with 32 thermal generating units is used.