A Novel Energy Management Scheme using ANFIS for Independent Microgrid

This paper presents an adaptive neuro fuzzy interference system (ANFIS) based approach to tune the parameters of the static synchronous compensator (STATCOM) with frequent disturbances in load model and power input of a wind-diesel based isolated hybrid power system (IHPS). In literature, proportional integral (PI) based controller constants are optimized for voltage stability in hybrid systems due to the interaction of load disturbances and input power disturbances. These conventional controlling techniques use the integral square error (ISE) criterion with an open loop load model. An ANFIS tuned constants of a STATCOM controller for controlling the reactive power requirement to stabilize the voltage variation is proposed in the paper. Moreover, the interaction between the load and the isolated power system is developed in terms of closed loop load interaction with the system. Furthermore, a comparison of transient responses of IHPS is also presented when the system has only the STATCOM and the static compensation requirement of the induction generator is fulfilled by the fixed capacitor, dynamic compensation requirement, meanwhile, is ful-filled by STATCOM. The model is tested for a 1% step increase in reactive power load demand at t = 0 s and then a sudden change of 3% from the 1% at t = 0.01 s for a 1% step increase in power input at variable wind speed model.

In this paper, optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the parallel execution of both the ant lion optimization with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. Here, the HRES is made out of photovoltaic, wind turbine, fuel cell, and battery which is associated with the DC link and can adjust the real and reactive power. The proposed ALANN/RNN technique predicts the required control gain parameters of the HRES to maintain the power flow based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy accessibility and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. In the HRES unit, the power flow management of grid is accomplished while controlling the PI controller for producing the optimal control pulses of the DC/DC converter. The proposed method is actualized in MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques such as SPC, GA, PSO and BAT technique. The comparison results demonstrate that the occurrence of proposed approach confirms its ability for controlling the power flow in the HRES system. An optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the joined execution of both the ant lion optimization (ALO) with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. ALO is utilized to minimizing the power variations of the HRES units. ANN is used for real power prediction, and RNN is utilized for reactive power prediction.

The power flow management scheme for a microgrid (MG)-connected system utilizing a hybrid technique is suggested in this dissertation. An MG-connected system includes photovoltaic, wind turbine, micro turbine and battery storage. Due to the use of this resource, power production is intermittent and unpredictable, as well as unstable, which causes fluctuation of power in hybrid renewable energy system. To ensure the fluctuation of power, an optimal hybrid technique is suggested. The suggested hybrid technique is joint execution on ANFIS and ASOA. ANFIS stands for adaptive neuro fuzzy interference system, and ASOA stands for advanced salp swarm optimization algorithm, thus it is commonly known as the ANFASO method. In the established method, ANFIS is applied to continuously track the MG-connected system's required load. ASOA optimizes the perfect combination of MG in terms of predicted required load. The suggested methodology is used for optimal cost and to increase renewable energy sources (RESs). Constraints are RES accessibility, power demand and the storage elements. Using the MATLAB/Simulink work site, the ANFASO approach is executed and implemented compared with existing methods. The suggested method is compared with genetic algorithm (GA), BFA and the artificial bee colony algorithm (ABC), and the observed elapsed time of ABC is 37.11 seconds, BFA is 36.96 seconds and GA is 38.08 seconds. The elapsed time of the proposed technique was found to be lower (36.47 seconds) compared to existing techniques. Significant improvements regarding utilization of RES and total generation cost accuracy are attainable by utilizing the proposed approach.

In this paper, an adaptive neuro-fuzzy interference system (ANFIS) that is based on hysteresis controller is being proposed for achieving the power quality improvement. The innovatory ideas behind this methodology are the smoothness obtained with the fuzzy interpolation and the adaptability for complex problems using the neural network back propagation. In addition, the neural network renders increased control over the output voltage of the series active power filter (APF) and the output current of the shunt APF too. Here, the ANFIS is trained using the target control signals of both the series APF as well as the shunt APF and with the corresponding input source side and load side parameters of the system. During the testing time, the UPQC is controlled using the control signals that are attained from the ANFIS. With the utilisation of the proposed method, the voltage and the current perturbations are reduced and the system power quality is enhanced. The MATLAB/Simulink platforms are used to execute the proposed control technique and the presentation is examined using different types of source voltage fault conditions. The effectiveness of the proposed ANFIS-based controller is analysed through the comparison analysis with the conventional control techniques.

Efficient approach for power flow management of hybrid renewable energy system connected smart grid system is proposed in this paper. Here, the proposed approach is the combination of both the modified elephant herding optimization algorithm with tabu search algorithm named as MEHOTSA. In the proposed technique, the modified elephant herding optimization algorithm plays out the assessment procedure to establish the exact control signals for the system and builds up the control signals database for the offline way in light of the power variety between source side and the load side. The multi-objective function is shaped by the grid required active power and reactive power varieties generated based on the accessible source power. The accomplished dataset is used to work the Tabu search algorithm on the online way and it leads the control procedure in less execution time. The proposed technique-based control model enhances the control parameters of the power controller in light of the power flow varieties. By utilizing the proposed methodology, the power flow management of the smart grid system is controlled dependent on the source side and load side parameters varieties. Additionally, the proposed methodology is in charge of controlling the energy sources so as to produce the power demanded by the grid, utilizing optimally both renewable energy sources and energy storage devices. Finally, the proposed model be actualized in MATLAB/Simulink platform and the performance are compared with other techniques.

ABSTRACTThis paper presents a new Combined Modified Bat Search algorithm and artificial neural network control of grid-connected Hybrid Renewable Energy System (HRES). The HRES consists of photovoltaic, wind turbine, fuel cell, and Battery. Because of this resource utilization, the intermittent power generations are unpredictable and variable, which created a power fluctuation in HRES. To stabilize the power fluctuations, the intelligent controller is proposed. In the proposed technique, the modified bat search algorithm plays out the assessment procedure to establish the exact control signals for the system and builds up the control signals database for the offline way in light of the power variety between source side and the load side. The accomplished dataset is used to work the artificial neural network strategy on the online way and it leads the control procedure in less execution time. In the proposed control technique, the searching behavior of the bats is modified by using the efficient neighborhood search functions like crossover and mutation. Initially, the system behaviors are analyzed based on the objective function. For analyzing the power flow management, the equality and inequality constraints are defined, which specifies the availability of the renewable energy sources, power demand and the state of charge of storage elements. With this proper control, HRES is able to significantly enhance the dynamic security of the power system. In addition, Battery is utilized as an energy source to stabilize and permit the renewable power system units to keep running at a steady and stable output power. The proposed method is implemented in MATLAB/Simulink working platform and their performances are analyzed with the existing methods.

This dissertation proposes an elite converter for the optimal utilization of hybrid renewable energy sources in the microgrid-associated systems using hybrid technique. Microgrid (MG)-associated systems are photovoltaic, wind turbine and battery. The proposed elite converter is the high conversion ratio (HCR) DC–DC converter with a maximum power point tracking (MPPT) controller. The main advantage of the converter is to eliminate the problem of high switch voltage stress. The proposed hybrid technique is the joint execution of both the adaptive whale optimization algorithm (AWOA) and adaptive neuro-fuzzy interference system (ANFIS), and hence, it is named as AWO-ANFIS technique. Here, AWOA technique is utilized to establish the exact control signals based on the power variation between the source side (renewable energy sources and battery) and load side. ANFIS technique is utilized for error minimization by using the achieved dataset from AWOA technique. Here, the reference currents of the controller are managed in view of the MPPT for solar and wind energy. In the proposed technique, the objective function is defined by the system data subject to equality and inequality constraints. Finally, the proposed technique is executed in the MATLAB/Simulink working platform and the execution is compared with the existing techniques. To test the performance of the proposed method, different case studies are conducted such as variation of irradiance under constant wind speed, variation of PV power under constant irradiance and load variation under constant irradiance and wind speed. For all the cases, the response of current, voltage and power of all the sources is analyzed and compared with that of the existing techniques such as GA, PSO and WOA. The statistical analysis of the proposed technique is also analyzed. Likewise, the computation time using various trails such as 100, 250, 500 and 1000 is also analyzed. The comparison results affirm that the proposed technique has less computational time than the exiting techniques. Moreover, the proposed method is cost-effective power production of MG and effective utilization of renewable energy source without wasting the available energy.

In addition to Power Quality (PQ) problems caused by solid state devices in the distribution side of grid connected Photo Voltaic (PV) system, quality of power also suffers in the source side by the usage of converters. Generally, PQ problems mitigation strategy has turned into a critical issue in grid-connected PV systems. This paper proposes a control topology for PQ issues mitigation scheme in grid connected PV systems. The primary goal of this study is to enhance the PQ in power distribution system through eradicating the voltage interruption, voltage swell and sag as well as voltage fluctuation. To accomplish these changes, Unified Power Quality Conditioners (UPQCs) with controllers were utilized. Then the faults are mitigated by the utilization of UPQC with Tree Seeds Algorithm (TSA) — Adaptive Neuro-Fuzzy Interference System (ANFIS). At first, the normal properties of the grid-connected PV systems are dissected. From that point forward, a few interruptions are made in the system and the abnormal characteristics are analyzed. Then the abnormal characteristics of PQ problems are diminished in the utilization of UPQC and TSA–ANFIS controllers. UPQC is one of the best filters topologies which can mitigate and control all PQ problems. The combined algorithms of TSA-ANFIS are used to keep the dc-link voltage in UPQC at consistent level. The proposed hybrid approach is executed in MATLAB/Simulink platform and the performance is studied. Further, the proposed method is compared with existing Elephant Herding Optimization (EHO) technique and also with hybrid EHO-ANFIS techniques. The comparison results showed that the proposed technique is superior to the EHO and EHO-ANFIS techniques.

This manuscript proposes a hybrid strategy for power flow management under a smart grid system connected to a hybrid renewable energy system (HRES). The HRES is the combination of photovoltaic, wind turbine, fuel cell and energy storage system or battery. The proposed technique is the execution of the Integrated Harris Hawk Optimization algorithm (IH2OA). Here, Harris Hawk Optimization is the consolidation of crossover and mutation function, hence it is called IH2OA. Moreover, IH2OA is enhancing the voltage source inverter of control signals under the variation of power replace among source and load side. The numerous characteristics are made up of necessary grid active with reactive power variations created under the view of available power source. The IH2OA method ensures the location of control signals using parallel implementation as opposed to active with reactive power variation. In the IH2OA method, the control scheme-based optimizes the power controller under the power flow variation. The proposed system performed on MATLAB/Simulink platform and the efficiency are compared with diverse existing techniques.

The power quality of an electrical system is critical for industrial, commercial, and housing applications, and with the increasing use of sensitive loads, customers and utilities are beginning to pay more attention to it. A distribution static synchronous compensator (D-STATCOM) represents one of the best custom power devices (CPDs) for improving the power quality of a distribution system. The performance of this device relies upon the algorithm and strategy used for its control. Artificial intelligence was utilized to overcome these shortcomings, while a response optimizer tool was used for the tuning process. An adaptive controller design was also proposed, based on the integration of fuzzy logic with traditional proportional-integral (PI) controller. The fuzzy logic controller system was designed using the adaptive neuro fuzzy interference system (ANFIS) editor. In this work, a D-STATCOM controller was used to mitigate sag and swell problems, while the ANFIS together with the optimization method was used to improve the system response. This study was carried out using MATLAB/Simulink, and the results showed a superior and adaptive performance in mitigating voltage sag and swell problems at different loading conditions compared to the traditional PI.

In this paper, the optimal power flow management-based microgrid in hybrid renewable energy sources with hybrid proposed technique is presented. The photovoltaic, wind turbine, fuel cell and battery are also presented. The proposed technique is the combined execution of both spotted hyena optimization and elephant herding optimization. Spotted hyena optimization is utilized to optimize the combination of controller parameters based on the voltage variation. In the proposed technique, the spotted hyena optimization combined with elephant herding optimization plays out the assessment procedure to establish the exact control signals for the system and builds up the control signals for offline way in light of the power variety between source side and load side. The objective function is defined by the system data subject to equality and inequality constraints such as real and reactive power limits, power loss limit, and power balance of the system and so on. The constraint is the availability of the renewable energy sources and power demand from the load side in which the battery is used only for lighting load. By utilizing the proposed method, the power flow constraints are restored into secure limits with the reduced cost. At that point, the proposed model is executed in the Matrix Laboratory/Simulink working platform and the execution is assessed with the existing techniques. In this article, the performance analysis of proposed and existing techniques such as elephant herding optimization, particle swarm optimization, and bat algorithm are evaluated. Furthermore, the statistical analysis is also performed. The result reveals that the power flow of the hybrid renewable energy sources by the proposed method is effectively managed when compared with existing techniques.

This paper proposed an optimal control technique for power flow control of hybrid renewable energy systems (HRESs) like a combined photovoltaic and wind turbine system with energy storage. The proposed optimal control technique is the joined execution of both the whale optimization algorithm (WOA) and the artificial neural network (ANN). Here, the ANN learning process has been enhanced by utilizing the WOA optimization process with respect to the minimum error objective function and named as WOANN. The proposed WOANN predicts the required control gain parameters of the HRES to maintain the power flow, based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy source accessibility, storage element state of charge, and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. The proposed technique is implemented in the MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques.

Electricity produced by a hybrid renewable energy system (HRES) is reliable, economical, and environmentally benign. However, effective management of the load-source side is essential to enhance the reliability, affordability, and eco-friendliness of HRES. Therefore, an integrated load–source side management for techno-economic-environmental (TEE) performance improvement of HRES is developed in this paper. The factors considered are namely technical (renewable energy portion, excess energy factor, and unmet load), environmental (particulate matter and carbon emission), and economical (annualized cost of system, total net present cost, and cost of energy). To solve the HRES size optimization problem, a marine predators algorithm (MPA) is used for the first time. HRES is investigated without Load side management (LSM), with LSM, and with LSM integrated with source-side management (SSM). LSM is carried out using the peak shifting technique, which shifts time-movable loads (TMLs) to off-peak hours depending on excess energy availability, taking into account consumer comfort violations and customer behavior uncertainty. For SSM, a comparatively better energy management strategy (EMS) than the load following, cycle charging, and generator order is proposed, which improves the TEE performance of HRES. The integrated load–source side management saves 5% in NPC and 0.008 $/kWh in COE, respectively. To come up with a configuration that is more feasible, a sensitivity analysis for the HRES's component costs and macroeconomic variables is carried out. Furthermore, the optimal configuration COE is slightly higher than that of the most recent study in the literature. By contrasting the MPA result with the HRES PSO result, the accuracy of the MPA result is also confirmed.

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