A hybrid BFOA–MOL approach for FACTS-based damping controller design using modified local input signal

A hybrid Bacteria Foraging Optimization Algorithm and Many Optimization Liaisons (hBFOA–MOL) is employed in this paper to design a Flexible Ac Transmission Systems (FACTS)-based damping controller for power system stability improvement. The input signal to the FACTS-based damping controller is derived from the locally measurable line active power. The design problem of the proposed controller is formulated as an optimization problem and hBFOA–MOL algorithm is employed to search for the optimal controller parameters. At the outset, this concept is applied to a Static Synchronous Series Compensator (SSSC) connected in a single-machine infinite bus (SMIB) power system and then extended to a two-area four-machine power system. The performances of the proposed controllers are evaluated in SMIB and multi-machine power system subjected to various severe disturbances. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with both local and remote signals. It is observed that the proposed controller with modified local input signal exhibits a superior damping performance in comparison to both remote and local input signals.     

A new H∞ based PSS design using numerator–denominator perturbation representation

A new H_∞ PSS design method which uses the numerator–denominator perturbation representation and includes the partial pole placement technique and a new weighting function selection method is proposed. This overcomes certain conventional H_∞ PSS design algorithm limitations. A robust PSS has been successfully designed by treating the highly non-linear characteristic of the power system as model ‘uncertainty’. The design is verified to have better performance for a wide range of system operating conditions when compared with the conventional PSS design.     

Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm

This paper presents a novel hybrid algorithm combining Firefly Algorithm (FA) and Nelder Mead (NM) simplex method for solving power system Optimal Reactive Power Dispatch (ORPD) problems. The ORPD is a very important aspect of power system operation and is a highly nonlinear, non-convex optimization problem, consisting of both continuous and discrete control variables. Like many other general purpose optimization methods, the original FA often traps into local optima and in order to overcome the shortcoming, in this paper, an efficient local search method called NM simplex subroutine is introduced in the internal architecture of the original FA algorithm. The proposed Hybrid Firefly Algorithm (HFA) avoids premature convergence of original FA by exploration with FA and exploitation with NM simplex. The proposed method is applied to determine optimal settings of generator voltages, tap positions of tap changing transformers and VAR output of shunt capacitors to optimize two different objective functions; such as minimization of real power loss and voltage deviations. The program is developed in Matlab and the proposed hybrid algorithm is examined on two standard IEEE test systems for solving the ORPD problems. For validation purpose, the results obtained with the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version of FA and other existing methods. It is revealed that the proposed hybrid method is able to provide better solutions.     

Energy procurement management for electricity retailer using new hybrid approach based on combined BICA–BPSO

These smaller consumers usually prefer to purchase at a constant price that is adjusted at most a few times per year. Electricity retailers are in business to bridge the gap between the wholesale market and these smaller consumers. This paper proposes a hybrid approach based on binary imperialist competitive algorithm (BICA) and binary particle swarm optimization (BPSO) to find the optimal energy procurement for electricity retailer with multiple procurement options such as self-generating facility, bilateral contracts and pool market purchase in restructured electricity markets. The solutions of these problems provide adequate information to obtain an energy procurement strategy for retailers. Also, the results of proposed hybrid approach are compared with conventional ICA and PSO methods. Test results show that the proposed hybrid approach is more effective and has higher capability in finding optimum solutions in comparison to ICA and PSO methods. A case study is used to illustrate the efficiency of the proposed hybrid approach.     

PID controller for automatic voltage regulator using teaching–learning based optimization technique

The present work presents teaching–learning based optimization (TLBO) algorithm as an optimization technique in the area of tuning of the classical controller installed in automatic voltage regulator (AVR). The proposed TLBO algorithm is applied with an aim to find out the optimum value of proportional integral derivative (PID) controller gains with first order low pass filter installed in the AVR. The voltage response of the AVR system, as obtained by using the proposed TLBO based PID controller with first order low pass filter, is compared to those offered by the other algorithms reported in the recent state-of-the-art literatures. The advantage of using this control strategy may be noted by providing good dynamic responses over a wide range of system parametric variations. For on-line, off-nominal operating conditions, fast acting Sugeno fuzzy logic technique is applied to obtain the on-line dynamic responses of the studied model. Furthermore, robustness analysis is also carried out to check the performance of the designed TLBO based PID controller. An analysis, based on voltage response profile, has been investigated with the variations of the model parameters. The simulation results show that the proposed TLBO based PID controller is a significant optimization tool in the subject area of the AVR system. The essence of the present work signifies that the proposed TLBO technique maybe, successfully, applied for the AVR of power system.     

Optimized PI+ load–frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units

This paper investigates a renewable energy resource’s application to the Load–Frequency Control of interconnected power system. The Proportional-Integral (PI) controllers are replaced with Proportional-Integral Plus (PI+) controllers in a two area interconnected thermal power system without/with the fast acting energy storage devices and are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network (BWNN) approaches. The energy storing devices Hydrogen generative Aqua Electroliser (HAE) with Fuel cell and Redox Flow Battery (RFB) are incorporated to the two area interconnected thermal power system to efficiently damp out the electromechanical oscillations in the power system because of their inherent efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE and RFB designed with BWNN controller reveals that the PI+ controller designed using BWNN approach is found to be superior than that of output response obtained using PI+ controller. Moreover the BWNN based PI+ controller exhibits a better transient and steady state response for the interconnected power system with Hydrogen generative Aqua Electroliser (AE) unit than that of the system with Redox Flow Battery (RFB) unit.     

A robust SMES controller strategy for mitigating power and voltage fluctuations of grid-connected hybrid PV–wind generation systems

Electrical Engineering - Recently, hybrid generation systems (HGSs) are considered to be the optimal solution for supplying remote areas with the required electrical power. HGSs contain two or more...     

Design of a 2-DOF-PID controller using an improved sine–cosine algorithm for load frequency control of a three-area system with nonlinearities

This paper proposes an improved sine–cosine algorithm (ISCA) based 2-DOF-PID controller for load frequency control. A three-area test system is built for study, while some physical constraints (nonlinearities) are considered for the investigation of a realistic power system. The proposed method is used as the parameter optimizer of the LFC controller in different scenarios. The 2-DOF-PID controllers are used because of their capability of fast disturbance rejection without significant increase of overshoot in set-point tracking. The 2-DOF-PID controllers’ efficacy is observed by examining the responses with the outcomes obtained with PID and FOPID controllers. The simulation results with the suggested scheme are correlated with some of the existing algorithms, such as SCA, SSA, ALO, and PSO in three different scenarios, i.e., a disturbance in two areas, in three areas, and in the presence of physical constraints. In addition, the study is extended to a four-area power system. Statistical analysis is performed using the Wilcoxon Sign Rank Test (WSRT) on 20 independent runs. This confirms the supremacy of the proposed method.     

Design of a 2-DOF-PID controller using an improved sine–cosine algorithm for load frequency control of a three-area system with nonlinearities

This paper proposes an improved sine–cosine algorithm (ISCA) based 2-DOF-PID controller for load frequency control. A three-area test system is built for study, while some physical constraints (nonlinearities) are considered for the investigation of a realistic power system. The proposed method is used as the parameter optimizer of the LFC controller in different scenarios. The 2-DOF-PID controllers are used because of their capability of fast disturbance rejection without significant increase of overshoot in set-point tracking. The 2-DOF-PID controllers’ efficacy is observed by examining the responses with the outcomes obtained with PID and FOPID controllers. The simulation results with the suggested scheme are correlated with some of the existing algorithms, such as SCA, SSA, ALO, and PSO in three different scenarios, i.e., a disturbance in two areas, in three areas, and in the presence of physical constraints. In addition, the study is extended to a four-area power system. Statistical analysis is performed using the Wilcoxon Sign Rank Test (WSRT) on 20 independent runs. This confirms the supremacy of the proposed method.     

A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT–MINLP method.