Analysis of moth flame optimization optimized cascade proportional-integral-proportional-derivative controller with filter for automatic generation control system incorporating solar thermal power plant

In the fast developing electric power system network, ancillary services like automatic generation control (AGC) plays a vital and significant role to ensure good quality of power supply in the system. To distribute good quality of power, a hybrid AGC system along with an efficient and intelligent controller is compelled. So, in this article, a cascaded proportional-integral (PI)-proportional-derivative (PD) controller with filter (PI-PDF) is proposed as secondary controller for AGC system. A nature inspired optimization algorithm named as moth flame optimization (MFO) algorithm is employed for simultaneous optimization of controller gains. Initially, a two-area interconnected nonreheat thermal power system is investigated. Analysis revealed that MFO-tuned PI controller performs better than the different optimization techniques tuned PI controller for the same system and PI-PDF controller performs better than PI controller does. Then, the study is extended to a three-area interconnected hybrid system with proper generation rate constraint. Area-1 consists of solar thermal-thermal unit; area-2 consists of thermal-hydro unit and area-3 thermal-gas unit as generating sources. Performance of PI-PDF controller is compared with classical controllers such as PI, PID, PIDF, and PI-PD controller without filter. Result analysis divulges that MFO-tuned PI-PDF controller performs better than all other controllers considered in this article. Robustness of the PI-PDF controller is evaluated using parameter variations and random load variation.     

Design and performance analysis of elephant herding optimization based controller for load frequency control in thermal interconnected power system

In this present contribution, an attempt has been taken to design and analyze the performance of elephant herding optimization (EHO) based controller for load frequency control (LFC) applications of interconnected power system. The studied system is a two‐area nonreheat thermal interconnected system which is widely used in literature. A proportional‐integral‐differential controller is utilized for LFC of the studied system. EHO technique is applied to obtain the tuned set of controller parameters. The objectives considered for design of the controller are the minimization of settling times and integral‐time‐multiplied‐absolute‐error of frequency deviations (FDs) and tie‐line power deviation (TPD). The design objectives are integrated together to form a function with single objective by assigning equal weights after normalization. Several test cases of diverse set of disturbances are taken into account to test the performance of the proposed controller and the obtained results are compared with other controllers designed with differential evolution, gray wolf optimization, particle swarm optimization, teacher‐learner‐based optimization, and whale optimization algorithm. Furthermore, the time‐domain simulations of FDs and TPD are illustrated to support the tabulated results. In addition, comparative statistical analysis is presented to validate the robust behavior of the proposed controller.     

A computational intelligence based maximum power point tracking for photovoltaic power generation system with small-signal analysis

There are multiple peak functions in its output power characteristic curve of a photovoltaic (PV) array under partial shading conditions (PSCs), the perturb and observe (P&O) may fail to track the global maximum power point (GMPP). Therefore, a reliable maximum power point tracking (MPPT) technique is essential to track the GMPP within an appropriate time. This article proposes a hybrid technique by combining an evolutionary optimization technique, namely the modified invasive weed optimization (MIWO) with the conventional P&O algorithm to enhance the search performance for the maximum power output of the PV system. MIWO executes in the initial stages of the tracking followed by the P&O at the final stages in the MPPT search process. The combined approach ensures faster convergence and better search to the GMPP under rapid climate change and PSCs. The search performance of the hybrid MIWO+P&O technique is examined on a standalone PV system through both MATLAB/Simulink environment and experimentally using dSPACE (DS1103)-based real-time microcontroller hardware setup. The performance of the proposed hybrid MPPT scheme is compared with the recent state-of-the-art MPPPT techniques. In addition, the small-signal analysis of the PV system is carried out to evaluate the loop robustness of the controller design. For a given set of system parameters, simulations for the small-signal model and robustness studies are analyzed to verify the results. The overall results justify the efficacy of the proposed hybrid MPPT algorithm.     

Hybrid BOA-GWO-PSO algorithm for mitigation of congestion by optimal reactive power management

The generation of power with load optimization, particularly in the current deregulated electricity market conditions, is a very important process for improved planning and operation of the grid. In addition, it is very important for the system not to experience problems due to congestion, have tensile stability, and protection to increase the share of electricity from renewable sources with the current supply system. This article presents load balancing with the butterfly optimization algorithm (BOA) in a hybridized form to minimize and maximize loads when used in pool and hybrid markets. The methods have been designed to prevent the drawbacks of BOA and generate a better trade-off between exploration and exploitation abilities by hybridizing it with particle swarm optimization (PSO) and gray wolf optimizer (GWO). Empirical research on other algorithms shows that proposed hybrid BOA-GWO-PSO algorithm performs better and shows potential in diverse problems. These studies give it a significant advantage over BOA in general, and when it is employed to solve complex optimization problems validated on benchmark IEEE 30 bus system. A comparative analysis has been conducted to validate the potency of the hybrid BOA-GWO-PSO approach with some conventional meta-heuristic algorithms. Analysis of results by mathematical validation on 23 benchmark functions and application in congestion management by optimal reactive power management (RPM) reveal that the proposed technique has the potent to solve real world optimization problems and is competitive with recent methods reported in state-of- art literature.     

Hybrid cross entropy—cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads

The demand of energy is increasing due to the growing population of the world and improvements of technology. One of the best significant solution techniques to fulfill this energy demand is utilization of renewable energy sources (RESs). Modern power systems, which integrate RESs, such as wind, small hydro or solar energy sources need to carry out the uncertainty by the accessibility of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow (OPF) problems. This paper proposed a novel hybrid meta-heuristic algorithm entitled cross entropy—cuckoo search algorithm (CE-CSA). The application of levy flights in the cuckoo search algorithm (CSA) improves the local exploitation capability while the CE method is used in the initial stage for global exploration due to its fast convergence. The effectiveness of the proposed hybrid algorithm has been demonstrated in solving the OPF problem, considering RESs and controllable loads for different stochastic scenarios in a benchmark system to minimize the total operation cost. To verify its effectiveness, its performance is compared with the most advanced and recently proposed hybrid meta-heuristic techniques. Simulation results show that the proposed algorithm can solve the OPF problems with RESs and controllable loads efficiently and can give better solutions compared to different techniques. The conventional statistical method called analysis of variance (ANOVA) test, T ukey honestly significant difference test, and Wilcoxon sign rank test are performed for comparative analysis of different techniques. The results of this test show the validation of CE-CSA compared to different optimization techniques.     

Reactive power management by using a modified differential evolution algorithm

In this article, a modified differential evolution (MDE) algorithm is proposed and applied to provide the solution for reactive power management by incorporating the flexible alternating current transmission systems (FACTS) controllers. The proper siting of FACTS controller has been achieved with an objective to minimize the losses and to improve the loading capability. The power flow analysis is performed to determine the optimal position for FACTS controllers. These controllers are incorporated in the most heavily loaded lines and hence controls the power flow in that particular line and allow more power to be transmitted in the remaining lines. The proposed MDE algorithm uses a novel DE/best3/1/bin mutation operator to produce three temporary mutant vectors which are averaged to obtain the mutant vector. Hence, the decision vectors of a generation simultaneously move toward the three best decision vectors of the population thereby maintains a better trade between exploration and exploitation. The proposed MDE algorithm is applied on different standard test bus (i.e., IEEE30, IEEE57, and IEEE118) systems with varying active and reactive loading (i.e., 100%, 110%, and 120%). The proposed method's performance is compared to those obtained from some well-known meta-heuristic algorithms. The proposed MDE algorithm optimized FACTS controllers reduce transmission loss by 60.90% in IEEE30 bus, 49.72% in IEEE57 bus and 8.37% in IEEE118 bus test system under base loading. The statistical analysis of the obtained results is carried out using the Wilcoxon signed rank test and the Friedman and Nemenyi hypothesis test, which ensures the reliability and robustness of the proposed method.     

An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions

A hybrid technique for maximum power point tracking (MPPT) for a photovoltaic (PV) system is proposed in this paper. The proposed hybrid system is combination of Wing suit Flying Search (WFS) and modified Transient search optimization (MTSO), therefore it is called WFS-MTSO method. The proposed controller has three processes: (i) to identify the operating level of photovoltaic (uniform or in PSC), (ii) to estimate the maximal power point using WFS technique, and (iii) to ensure the photovoltaic system runs on the estimated maximum power point (MPP) by MTSO optimized cascade controller. This method begins with a sense of irradiance and temperature. The proposed photovoltaic system has two components. The first one is WFS maximal power point tracking algorithm attain maximal power point. The second one is MTSO optimized cascade controller to force the photovoltaic system to activate at maximal power point. Here, the proposed hybrid technique is utilized at MPPT to diminish tracking error and oscillation across MPP for optimizing power output. The proposed optimized cascade control improves the system efficiency by averting interruptions previously they propagate to the system. Finally, the performance of proposed hybrid system is executed on MATLAB/Simulink working platform and the performances are compared with various existing approaches. The statistical matrices, like mean, median, and standard deviation is analyzed the tracking efficiency of the proposed WFS-MTSO approach.     

Globalized and bounded Nelder‐Mead algorithm with deterministic restarts for tuning controller parameters: Method and application

This paper develops and examines an optimization algorithm for simulation‐based tuning of controller parameters. The proposed algorithm globalizes the Guin augmented variant of Nelder–Mead's nonlinear downhill simplex by deterministic restarts, linearly growing memory vector, and moving initial simplex. First, the effectiveness of the algorithm is tested using 10 complex and multimodal optimization benchmarks. The algorithm achieves global minima of all benchmarks and compares favorably against the evolutionary, swarm, and other globalized local‐search multimodal optimization algorithms in probability of finding global minimum and numerical cost. Next, the proposed algorithm is applied for tuning sliding mode controller parameters for a servo pneumatic position control application. The experimental results reveal that the system with sliding mode controller parameters tuned using the proposed algorithm targeting smooth position control with maximum possible accuracy, performs as desired and eliminates the need of manual online tuning for desired performance. The results are also compared with the performance of the same servo pneumatic system with parameters tuned using manual online tuning in an earlier published work. The system with controller parameters tuned using the proposed algorithm shows improvement in accuracy by 28.9% in sinusoidal and 42.2% in multiple step polynomials tracking.     

Study of wind-solar based combined heat and power economic dispatch problem using quasi-oppositional-based whale optimization technique

Source of fossil fuel is impoverishing in the upcoming future. Renewable energy sources (RESs) are becoming challenging conventional energy substitutes in the present scenario. In this article, an attempt has been made to utilize RESs such as wind and solar energy with combined heat and power economic dispatch problems. The intention of this presentation is to minimize conflict objectives such as fuel cost accomplished with load demand along with transmission losses while satisfying all the constraints. A new optimization technique, namely a quasi-oppositionalbased whale optimization algorithm (QOWOA) is adopted to cope up with the non-linearities of the chosen systems. The proposed technique is tested on two different nonlinear realistic power systems to achieve the satisfactory performances. The superiority of the proposed QOWOA algorithm is judged by comparing it with some recently developed metaheuristic optimization techniques.     

Development of MADB of P-I controller using LMI technique in a renewable energy based AGC system and study its application in a deregulated environment including energy storage device

In recent decades, inclusion of renewable resources is considered as one of the most promising options for the long run uninterrupted power supply without depending on conventional resources. Thus, the renewable energy generation will get more attention and massive growing, so that the goal of 40% share of electricity in the worldwide energy portfolio in 2050 would be realized. But during replacement of renewable energy by conventional energy, engineers are facing a lot of problem due to solar generation, wind generation change their characteristic rapidly with weather condition, which may cause large synchronizing imbalance between different units and generate large system delay or communication delay in large interconnected grid. In this article, the authors propose linear matrix inequalities techniques to developed margin of allowable delay for delay dependent stable hybrid system. Initially, to judge the efficacy of proposed chaotic atomic search optimization (CASO) algorithm over other evolutionary algorithms with PID controller, a thermal-hydro gas system is considered. The second part of this article is motivated by the fact that proposed CASO algorithm with P-I controller is superior in contrast to bacterial foraging algorithm technique. In addition to this, some energy storage devices such as fuel cell, aqua electrolyzer, and ultra-capacitor are used to achieve a better dynamic response within specified delay margin. Moreover, to study the impact of communication delays (delay margin) due to the loss of synchronism between solar-wind (for their unpredicted environmental features) and thermal unit (nonlinearities like generation rate constraints, boiler dynamics, and governor with the dead band) are considered in thermal unit and the simulation results verify with the effectiveness of the proposed approach on providing a balance between the delay margin and the damping performances is evaluated under deregulated environment. The simulation results help to make an inter-relation between the delay margin and the controller gain (P-I) which help the system operator in designing controllers gain for stable operation of the proposed hybrid system.     

Stabilizing a multimachine system via non-linear voltage controller with the use of a local control strategy

A new local control strategy with non-linear optimal controllers for a multimachine system is presented. Non-linear Field Voltage Controllers (NFVC) are designed with the use of the feedback linearization approach to adjust generator state variables to external reference signals resulting from optimal power flow calculation [P_i, Q_i, V_ti] in the stabilized power system. A non-linear control law has been derived for the three-dimensional [δ, ω, E′_q] one-axis model of a generator. The local control strategy has been verified for the New England 39 bus system. An extensive simulation study shows robustness of the non-linear controllers and better performance than PSS-based classical controllers. © 1997 John Wiley & Sons, Ltd.     

Model‐free online tuning of controller parameters using a globalized local search algorithm

In an earlier work, the authors proposed a globalized bounded Nelder‐Mead algorithm with deterministic restarts and a linearly growing memory vector. It was shown that the algorithm was a favorable option for solving multimodal optimization problems like controller tuning because of the greater probability of finding the global minimum and lesser numerical cost. Therefore, the algorithm was successfully used for model‐based offline tuning of sliding mode controller parameters for a servo‐pneumatic position control application. However, such offline tuning requires a sufficiently adequate system model, which, in some applications, is difficult to attain. Moreover, it is not generally appreciated as an essential requirement for controller tuning by the end user like the industry. An improvement in performance of optimization algorithm for tuning is expected if it relies on measurements coming directly from an actual physical system and not just its mathematical model. Therefore, in this paper, we apply the aforementioned algorithm for model‐free online optimization of controller parameters. The application involves the programmatic control of a real‐time interface of a physical system by the algorithm for data flow and logical decisions for optimization. For comparison with the results of the model‐based offline tuning suggested in earlier work, the sliding mode controller parameters are tuned online for the same position control application. The experimental results reveal that the system performance with controller parameters tuned online using the algorithm compares favorably to the one with model‐based offline tuning especially at higher priority level for accuracy. The improvement in system performance amounts to 21%.     

Optimal control of electrical vehicle incorporated hybrid power system with second order fractional-active disturbance rejection controller

This article proposes a novel control methodology employing a fractional-active-disturbance-rejection-controller for the combined operation of load frequency control and automatic voltage regulator of a hybrid power system. A two area hybrid power system with diverse energy sources like solar-thermal, conventional-thermal and wind sources equipped with appropriate system nonlinearities is investigated. In order to ascertain the role of modern-day electric-vehicle (EV), the hybrid power system is incorporated with EVs in both the areas. To establish an effective frequency, voltage and tie line power control of the hybrid power system, a second order fractional-active-disturbance-rejection-controller with fractional-extended state observer is modeled as secondary controller. Magnetotactic-bacteria-optimization (MBO) technique is applied to obtain optimal values of the controller gains and the hybrid system parameters. The robustness of the controller gains is tested under different system parameter changes from their nominal values. In addition, the effect of incorporating a power system stabilizer on the hybrid power system is evaluated. Further, the impact of integrating renewable sources and EVs in the hybrid power system is explored. Moreover, the stability of the hybrid power system is monitored with the inclusion of FACTS device. The developed controller operates encouragingly with reference to system stability, rapidity and accuracy in comparison to testified control strategies available in the literature. The robustness test under load-perturbation, solar-insolation, wind input variations also proves the efficiency of MBO optimized second order fractional-active-disturbance-rejection-controller gains.     

An improved neuro-adaptive-optimal control for oscillation damping in wind-integrated power systems

This article proposes an improved neuro-adaptive-optimal control scheme, based on online system identification and simultaneous control, to replace power system stabilizer in the renewable-energy-penetrated power systems. A simple, linear neural identifier, with a few adjustable connection weights is used, which ensures minimal computational burden, reduced development time, and makes the controller practically realizable. An adaptive learning rate, derived using Lyapunov stability theorems, guarantees stability of convergence of the learning algorithm as well as an optimal speed of convergence. It is demonstrated that a simple linear neural identifier, which approximates a local linear model of a system, by adjustment of its parameters online, is faithfully able to track the varying dynamics of the system. Improved oscillation-damping performance over a wide range of operating conditions and disturbances, in comparison with a well-established IEEE-PSS1A and fuzzy-logic-control-based PSS, was validated through simulation studies on a single-machine infinite-bus power system and a wind-integrated two-area power system. The computational superiority of the proposed scheme in comparison to complex and non-linear neural networks and fuzzy-logic-based control was also established. The novelty of the controller lies in its structure which, in-spite of being purely linear, performs robustly for highly complex and non-linear power system models.     

A novel power supply system for puborectalis‐like artificial anal sphincter

Puborectalis‐like artificial anal sphincter (PAAS) is an innovative new type of artificial anal sphincter (AAS). It overcomes many drawbacks and inadequacies of various previous AASs, and it has successfully been implanted in vivo for almost 3 weeks. During in vivo testing, PAAS shows its ability to retain continence with low risk of ischemia necrosis, and somehow truly helps to remodel rectal perception. However, there are still many defects that influence the long‐term implantation of PAAS, especially in the power supply system (PSS). This article presents a new designed PSS which includes a new transcutaneous energy transfer (TET) system, a heat reduction system, and a safety usage system. The new PSS reduces the total size of PAAS by at least 30%. Newly designed TET system can satisfy the Qi standard, and render a power of 3W to fulfill the requirement of fast charging and normal use of PAAS at the distance of 15.5 mm when frequency of TET system is 110 kHz, which previous TET systems can hardly achieve. Heat reduction system helps to reduce the heat generated during TET charging. It can reduce heat by 40% during the same period of time of TET charging. Safety usage system helps the user control PAAS more properly which can reduce the rate of failure of PAAS system.     

Comparative performance analysis of 2DOF state feedback controller for automatic generation control using whale optimization algorithm

This paper presents the design of two‐degree‐of‐freedom state feedback controller (2DOFSFC) for automatic generation control problem. A recently developed new metaheuristic algorithm called whale optimization algorithm is employed to optimize the parameters of 2DOFSFC. The proposed 2DOFSFC is analyzed for a two‐area interconnected thermal power system including governor dead band nonlinearity and further extended to multiunit hydrothermal power system. The supremacy of the 2DOFSFC is established comparing with proportional‐integral, proportional‐integral‐derivative (PID), and 2DOFPID controllers optimized with different competitive algorithms for the concerned system. The sensitivity analysis of the optimal 2DOFSFC is performed with uncertainty condition made by varying bias coefficient B and regulation R parameters. Furthermore, the proposed controller is also verified against random load variations and step load perturbation at different locations of the system.     

Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

In this paper, the load frequency regulation problem of 2‐area interconnected power system is resolved using the sliding mode control (SMC) methodology. Interconnected 2‐area power systems with and without doubly fed induction generator wind turbines are considered for implementing the proposed optimal control methodology. Here, a heuristic gravitational search algorithm (GSA) and its variants such as opposition learning–based GSA (OGSA), disruption‐based GSA (DGSA), and disruption based oppositional GSA (DOGSA) are employed to optimize the switching vector and feedback gains of SMC. In order to overcome the inherent chattering problem in SMC, the control signals are considered in the objective function. The robustness of optimized SMC is analyzed by the inclusion of nonlinearities such as generation rate constraint (GRC), governor deadband, and time delay during the signal processing between the control areas, which are present in the real‐time power system. The insensitiveness of the optimal controller is shown by variation in system parameters like loading condition, speed governor constant, turbine constant, and tie‐line power coefficient. Further, the optimal SMC has been studied with significant load variations and wind power penetration levels in the control areas. The potential of proposed SMC design with chattering reduction feature is shown and validated by comparing the results obtained with the other reported methods in the literature.     

Optimal decentralized control of a wind turbine and diesel generator system

This article presents a decentralized optimal controller design technique for the frequency and power control of a coupled wind turbine and diesel generator. The decentralized controller consists of two proportional-integral (PI)-lead controllers which are designed and optimized simultaneously using a quasi-Newton based optimization technique, namely, Davidon–Fletcher–Powell algorithm. The optimal PI-lead controllers are designed in such a way that there are no communication links between them. Simulation results show the superior performance of the proposed controller with a lower order structure compared to the benchmark decentralized linear-quadratic Gaussian integral controllers of orders 4 and 11. It is also shown that the proposed controller demonstrates an effective performance in damping the disturbances from load and wind power, as well as a robust performance against the parameter changes of the power system.     

A modified optimal load-frequency controller for interconnected power systems

This paper presents a modified optimal load-frequency controller for interconnected hydrothermal power systems. The weighting matrix of the optimal controller is sequentially modified to let the dominant energy open-loop poles or poles in a conventional optimal feedback system be located in the desired region. The poles selected to be shifted are determined using the concept of dominant energy modes rather than those located closer to the imaginary axis. Improper pole placement can be avoided and very good system frequency and tie-line power dynamic responses can be achieved by the proposed controller.