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This paper investigates a reduced order model of the power system using model order reduction (MOR) approach to analyze the performance and system efficacy. In this effect, the suitability of a novel combines Student Psychology-Based Optimization (SPBO) with Grey Wolf Optimization (GWO) technique is studied under distinct reduced order transfer function (TF) scenarios. Compared to the higher-scale model, the applied methodology aims to obtain better results in terms of stability and computational effort. The quality of optimization performance index and speed of the convergence curves depicts the superiority of the proposed approach for computational burden. Moreover, to realize a higher dimensional power system, implementation of the SPBO-GWO may proffer more effectiveness and robustness as opposed to the state-of-the-art optimization techniques.

This work primarily focuses on electrical characteristics of a hybrid power system (HPS) incorporating renewable energy generation (REG) (HPSREG). The major components of HPSREG are the resources coordinated with multi-unit of photovoltaic cells, multi-unit of wind turbine generators, a diesel engine generator (DEG), energy storage system (ESS) with diverse nature and an electric vehicle (EV). The performance characteristics of HPSREG are determined by constant generation of power from the various sources as well as varying load perturbations. As the variation in load demand will introduce fluctuation in frequency and power with constant generation. In few of overcome the frequency and power deviation under both the above-mentioned generation and load demand conditions, proper control technique is required. In order to control the deviation in frequency and power, an integration in the environment of fractional order (FO) calculus for proportional–integral–derivative (PID) controller and fuzzy controller, termed with FO-Fuzzy PID controller tuned with quasi-opposition based harmonic search (QOHS) algorithm has been proposed. The results acquired with the proposed FO-Fuzzy-PID controller are then analyzed along with FO-PID and PID controller route for quantify effectiveness for the same under the considered cases to determine the effectiveness of the algorithm undertaken. Sensitivity investigation is also conducted in order to show the strength of the technique under study of differences in HPSREG parameters of magnitude.

This paper uses adaptive backstepping sliding mode control to synchronize two satellite attitude dynamics with perturbing torques. The external perturbing torques induce chaotic motion with no control inputs. The proposed control system uses Lyapunov theory and Barbalat's Lemma to guarantee the asymptotic stability of the controlled system. Simulation results confirm the effectiveness of the proposed design.

This paper presents the impact assessment of distributed generations (DGs) (such as T2and T4i.e operating at different power factors such as 0.85, 0.90, 0.95 and 0.99 leading and lagging, respectively) and flexible alternating current transmission systems (FACTS) controllers like SVC with different load models (DLMs) in distribution power system networks by using genetic algorithms (GA) from minimum total MVA intake of main substation viewpoint. The different type DGs (such as T2and T4i.e. operating at different power factors such as 0.85, 0.90, 0.95 and 0.99 leading and lagging, respectively) and FACTS controller like SVC with DLMs are considered by employing GA in DPSs form minimum total MVA intake of main substation viewpoint. Different distribution power system (DPS) performance indices viewpoint, such as minimization of real power loss, minimization of reactive power loss, improvement of voltage profile, reduce the short circuit current or MVA line capacity and reduce the environmental greenhouse gases like carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxide (NOx) and particulate matters in an emergency e.g. under fault, sudden change in field excitation of alternators or load increase in DPSs are considered. This paper also investigated the comparisons of different DGs (such as T2and T4) and FACTS controller like SVC with DLMs by employing GA in DPSs form minimum total MVA intake of main substation viewpoint. The effectiveness of the proposed methodology is tested on IEEE 37-bus distribution test system. All the simulations are done on MATLAB package toolbox. Keywords: Different load models (DLMs), Distributed generations (DGs), Static Var Compensator (SVC), Distributed generation planning, Genetic algorithm (GA), Different load modes (DLMs), DPS performance indices

This paper proposes teaching-learning-based optimization (TLBO) algorithm for congestion management (CM) in a pool based electricity market. Congestion is a principal problem that an independent system operator faces in post deregulated era. The aim of employing TLBO algorithm is to effectively relieve congestion in the line with minimum deviation in initial generation and, hence, congestion cost. Various security constraints such as load bus voltage and line loading are taken into account while dealing with this problem. Inspired by teaching–learning process of classroom, TLBO algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. It only requires common control parameters like population size and number of generation. In this paper, the proposed TLBO algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the TLBO algorithm for obtaining higher quality solution is also established. Keywords: Congestion management, Deregulation, Independent system operator (ISO), Optimal power flow, Price bids, Teaching-learning-based optimization (TLBO)

In present aspect, automatic generation control (AGC) of deregulated power system with thyristor controlled series compensator (TCSC) device is investigated. The objective is to discuss bilateral power transaction issue with the TCSC effect. A deregulated two-area power system model having two thermal units in each control area is considered for this act. A quasi-oppositional harmony search (QOHS) algorithm is being applied for the constrained optimization problem. Three cases, commonly studied in deregulation, are discussed for the effectiveness of the proposed technique. Further, sensitivity analysis is studied by varying the test system parameters up to ±25% from their rated values. The obtained simulation plots are analytically discussed with the calculation of oscillatory modes, transient details and the studied performance indices. Sugeno fuzzy logic control technique is also investigated to the studied test system. The simulation results show that the proposed QOHS based TCSC controller is quite effective in deregulated environment.

In the present scenario of all over world, the planning of distributed generations (DGs) in distribution power systems are very important issues from power system performances viewpoints. The broad categories of different types of DGs on the basis of their power delivering characteristics are considered T1, T2, T3 and T4 with different load models (DLMs) for the analysis in this paper. This paper presents the impact assessment of optimally placed different types of DGs (such as T1, T2, T3 and T4) with DLMs by employing genetic algorithm (GA) in the distribution power systems (DPSs) form total minimum real power loss of the system viewpoint. Different DPS performance parameters such as minimization of real power loss, minimization of reactive power loss, improvement of voltage profile, reduction of the short circuit current or MVA line capacity and reduction of the environmental green house gases like carbon dioxide (CO2), sulphur dioxide (SO2), nitrogen oxide (NOx) and particulate matters in emergency e.g. under fault, sudden change in field excitation of alternators or load increase in the distribution power system are considered. The contribution of the present work is to investigate the comparisons of different DGs with DLMs by excercizing GA in the distribution systems form minimum total real power loss of the system viewpoint. The effectiveness of the proposed methodology is tested on IEEE-37 bus distribution test system. The different types of DGs (such as T1, T2, T3 and T4) with DLMs have shown different behaviours for power system performance indices such as PLI, QLI, VDI, SCCI and EIRI viewpoints. The sequence of overall power system performance indices such as PLI, QLI, VDI, SCCI and EIRI are as follows: T2>T1>T4>T3. This paper presents that the overall performance of T2 type DG is better as compared to T1, T3 and T4 types DGs in the distribution system form minimum real power loss of the system viewpoint.

In competitive electricity market, congestion is a serious economic and reliability concern. Congestion is a common problem that an independent system operator faces in open access electricity market. This paper presents a reliable and efficient meta-heuristic based approach to solve congestion problem. The proposed approach of the present work employs firefly algorithm (FFA) for alleviation of transmission network congestion in a pool based electricity market via active power rescheduling of generators. FFA is a new meta-heuristic approach based on flashing patterns and behavior of fireflies. Various important security constraints such as load bus voltage and line loading have been taken into account while dealing with congestion problem. The proposed methodology may help in removing the congestion of line with minimum rescheduling cost. The numerical results of modified IEEE 30- and 57-bus test power systems are illustrated.

The present work addresses a decentralized, well tested three-area multi-unit power system for its automatic generation control (AGC) after deregulation which is characterized by price-based market operation. To match with the actual deregulated environment, as prevailing in the real one, the market structure is kept generic enough enabling to capture all possibilities occurring in real-time day-to-day power environment. In accordance to the modifications, as done in the investigated three-area power system model, the concerned objective is to intensify the deregulated AGC operation followed by load disturbances. At the present platform, three different classes of case study results are postulated for the studied test system. The first two illustrate the behavior of unilateral and bilateral based power contract transactions while the third one considers the contract violation case as it exists in present time. The contractual agreement, instituted by DISCO participation matrix, is initialized to address the power transaction contracts. In this work, a novel quasi-oppositional harmony search (QOHS) algorithm is explored and presented its significances in deregulated AGC operation. In the second phase of investigation, fast acting Sugeno fuzzy logic technique is explored for on-line, off-nominal operating conditions. For analysis purpose, both the qualitative and the quantitative aspects of the proposed QOHS are presented in reference to genetic algorithm (GA). Additionally, the sensitivity analysis is also performed to evaluate the performance of the proposed QOHS based controller. Simulation work reveals that the proposed QOHS may be, effectively, worked out to order to improve the deregulated AGC performance. It is also being observed that the proposed QOHS outperforms the GA in sense of deregulated AGC operation of power system.

In this paper, symbiotic organisms search (SOS) algorithm is proposed for the solution of optimal power flow (OPF) problem of power system equipped with flexible ac transmission systems (FACTS) devices. Inspired by interaction between organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. The performance of the proposed SOS algorithm is tested on the modified IEEE-30 bus and IEEE-57 bus test systems incorporating two types of FACTS devices, namely, thyristor controlled series capacitor and thyristor controlled phase shifter at fixed locations. The OPF problem of the present work is formulated with four different objective functions viz. (a) fuel cost minimization, (b) transmission active power loss minimization, (c) emission reduction and (d) minimization of combined economic and environmental cost. The simulation results exhibit the potential of the proposed SOS algorithm and demonstrate its effectiveness for solving the OPF problem of power system incorporating FACTS devices over the other evolutionary optimization techniques that surfaced in the recent state-of-the-art literature.

Power versus voltage curve of a uniformly shaded photovoltaic (PV) array has only one maximum power point (MPP). However, when the array is partially shaded, there may be several local MPPs and there exists only one global MPP. The commonly employed perturb and observe (P&O) algorithm works well in detecting the MPP of a PV array when the array is uniformly shaded. However, when the array is partially shaded, the operating point based on P&O method may converge to a local MPP of the array rather than the global one resulting in its under-utilization. In this paper, continuous genetic algorithm (CGA) and hybrid particle swarm optimization (HPSO) algorithm are adopted in detecting the global MPP of a partially shaded PV array. It is observed that the performance of the HPSO is better than either P&O method or the CGA in finding the global maxima of a partially shaded PV array.

The design and performance analysis of a Sugeno fuzzy logic (SFL) controller for an autonomous power system model is presented in this paper. In gravitational search algorithm (GSA), the searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. The problem of obtaining the optimal tunable parameters of the studied model is formulated as an optimization problem and the same is solved by a novel opposition based GSA (OGSA). The proposed OGSA of the present work employs opposition-based learning for population initialization and also for generation jumping. In OGSA, opposite numbers are utilized to improve the convergence rate of the basic GSA. GSA and genetic algorithm are taken for the sake of comparison. Time-domain simulation reveals that the developed OGSA-SFL based on-line, off-nominal controller parameters for the studied model give real-time on-line terminal voltage response.

Harmony search (HS) is a derivative-free real parameter optimization algorithm. It draws inspiration from the musical improvisation process of searching for a perfect state of harmony. The proposed opposition-based HS (OHS) of the present work employs opposition-based learning for harmony memory initialization and also for generation jumping. The concept of opposite number is utilized in OHS to improve the convergence rate of the HS algorithm. The potential of the proposed algorithm is assessed by means of an extensive comparative study of the numerical results on sixteen benchmark test functions. Additionally, the effectiveness of the proposed algorithm is tested for reactive power compensation of an autonomous power system. For real-time reactive power compensation of the studied model, Takagi Sugeno fuzzy logic (TSFL) is employed. Time-domain simulation reveals that the proposed OHS-TSFL yields on-line, off-nominal model parameters, resulting in real-time incremental change in terminal voltage response profile.