Satish Kansal

Bio: Satish Kansal is an academic researcher. The author has contributed to research in topics: Automatic Generation Control & Particle swarm optimization. The author has an hindex of 2, co-authored 5 publications receiving 32 citations.

Optimal Placement of Distributed Generation Using Genetic Algorithm Approach

Abstract: Nowadays there is increasing number of small scale power generations which are connected to distribution networks termed as Distributed Generation (DG). The allocation of DG in distribution networks is intended for power flow control, improvement in stability and voltage profile, power factor correction, and reduction of line losses. This paper presents an optimization approach using Genetic Algorithm (GA) to find optimal location and size of DG in radial distribution system. The objective is to minimize the active power loss keeping the voltage profile in distribution system within defined limits. The effectuality of proposed approach is checked on IEEE 33 bus and 69 bus test systems.

Congestion Management in Deregulated Power Market – a Review

Abstract: In a deregulated power market, the optimum power flow (OPF) for an interconnected grid system is an important concern as related to transmission loss and operating constraints of power network. The increased power transaction as related to increased demand and satisfaction of those demand to the competition of generation companies (GENCOs) are resulting the stress on power network which further causes the danger to voltage security, violation of limits of line flow, increase in the line losses, large requirement of reactive power, danger to power system stability and over load of the lines i.e. congestion of power in system. It can be managed by rescheduling of generators or optimal location of distributed generation (DG) at minimum cost with minimum loss without disturbing the operating constraints. This paper reviews some of congestion management (CM) methods including the nodal pricing method, differential evolution (DE), addition of renewable energy sources, extended quadratic interior point (EQIP) based OPF, mixed integer nonlinear programming, particle swarm optimization (PSO), cost free methods and Genetic Algorithm (GA). Each technique has its own significance and potential for promotion of rescheduling of generators in a deregulated power system.

Temperature control in transformer using intelligent system

Abstract: The actual working condition of transformers plays an important role for uninterrupted power supply in power grid. There is always a pressure on power utilities to invent an effective maintenance strategy to conserve the transformer health, which should give the possibility to know transformer working condition for a long interval of time. In this paper, a fuzzy model of transformer temperature control has been developed with a set of linguistic rules which may be helpful for the control of fan speed. Some studies have taken mainly the two input parameters i.e. load and temperature. In this work, the moisture content and pressure which are the dominant parameters for assessing the condition of transformer insulation have also been taken into account. This approach offers a simple, accurate, reliable and economic system to make it feasible to detect the faults at early stages in a transformer.

Automatic Generation Control-A Review

Abstract: Automatic Generation Control is necessary for reliable & continuous operation of power system which is obtained by using different controllers. For efficient operation of the system, optimization of the different controlling parameters is necessary which can be obtained by using optimization techniques. Some of these techniques like genetic algorithm, BF, PSO etc. are discussed and a review is presented.

Application of PSO Technique in Multiarea Automatic Generation Control

Abstract: This paper proposes particle swarm optimization (PSO) technique to optimize the gains of an integral controller for automatic generation control (AGC) of a three unequal area thermal power system. Every control area takes into consideration dynamics of the thermal systems. Load frequency of interconnected multiarea thermal power system is also controlled for obtaining a better steady-state response of system. Further, results of PSO technique are compared with the bacterial-foraging (BF) technique that reveals superior performance of PSO technique over BF technique.

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

Abstract: Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Levy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (e constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

Abstract: This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks.

The Optimal Placement and Sizing of Distributed Generation in an Active Distribution Network with Several Soft Open Points

Abstract: A competent methodology based on the active power loss reduction for optimal placement and sizing of distributed generators (DGs) in an active distribution network (ADN) with several soft open points (SOPs) is proposed. A series of SOP combinations are explored to generate different network structures and they are utilized in the optimization framework to identify the possible solutions with minimum power loss under normal network conditions. Furthermore, a generalized methodology to optimize the size and the location of a predefined number of DGs with a predefined number of SOPs is presented. A case study on the modified IEEE 33 bus system with three DGs and five SOPs was conducted and hence the overall network power loss and the voltage improvement were examined. The findings reveal that the system loss of the passive network without SOPs and DGs is reduced by 79.5% using three DGs and five SOPs. In addition, this research work introduces a framework using the DG size and the impedance to the DG integration node, to propose a region where the DGs can be optimally integrated into an ADN that includes several SOPs.