Asymmetric Membership Function Research Articles

Two-Stage experimental intelligent dynamic energy management of microgrid in smart cities based on demand response programs and energy storage system participation

Power variations due to uncertainties create fluctuations in voltage/frequency (V/F). Most critical microgrid’s (MG) challenge in smart cities is V/F stability considering uncertainties in different operating conditions. This study proposes an energy management platform based on an intelligent probabilistic wavelet petri neuro-fuzzy inference algorithm (IPWPNFIA) to control the V/F index in the presence of renewable energy sources (RESs) and battery energy storage system (BESS) facing with various uncertainties. The suggested approach is programmed at two central and local controller stages based on the communication system and time-of-use demand response programs execution. The suggested approach is modeled by considering asymmetric membership functions based on the BESS optimal participation to control uncertainties caused by RESs, plug-and-play operations, and load fluctuations. The proposed platform's performance is verified and compared in different scenarios with different methods. The experimental setup and results are based on the rapid control prototyping of the micro-grid platform, MATLAB/Simulink and RT-LAB software, and hardware infrastructure such as the OPAL-RT (OP5600/OP8660) System. The most important highlights of this research are: fast dynamic response, real-time control based on real data, reducing the calculation time and burden based on learning algorithms, and the suitable coordination to adjust the protection equipment pick-up time.

Interval type-2 fuzzy neural networks with asymmetric MFs based on the twice optimization algorithm for nonlinear system identification

This paper proposes a novel algorithm twice optimization for interval type-2 fuzzy neural networks with asymmetric membership functions (TOIT2FNN-AMF), for nonlinear system identification problems. The proposed TOIT2FNN-AMF uses an asymmetric Gaussian interval type-2 membership function to enhance the network’s ability to describe and solve nonlinear and uncertain problems. The twice optimization algorithm consists of structure learning and parameter learning. Firstly, this paper proposes a multi-strategy adaptive differential evolution (MSADE) algorithm as the first optimization algorithm, which is used to determine the structure and the initial values of the parameters of the TOIT2FNN-AMF. It applies the root mean square error (RMSE) of the TOIT2FNN-AMF as the fitness function to determine the structure (number of rules) and initial parameters of the IT2FNN by searching for the RMSE values under different structures. When the fitness value reaches the minimum, that is, the RMSE value of the TOIT2FNN-AMF, the corresponding number will become the optimal one of fuzzy rules of the TOIT2FNN-AMF. Then, the second optimization algorithm of the TOIT2FNN-AMF turns into a hybrid optimization algorithm composed of an adaptive moment estimation (Adam) algorithm and recursive least squares (RLS) algorithm. Adam is used to optimize the antecedent parameters of TOIT2FNN-AMF rules, so as to maintain rapid convergence without generating oscillation during the training process; RLS is used to optimize the consequent parameters of TOIT2FNN-AMF rules, so that the network parameters can be optimized rapidly. In this way, the problems of excessive parameters to be adjusted and excessive slow convergence of the network can be solved. Finally, this paper evaluates the proposed TOIT2FNN-AMF by testing on problems of nonlinear system identification and chaotic time-series prediction. The simulation results are compared with those of similar methods in the existing literatures, which demonstrates that the proposed TOIT2FNN-AMF model yields a lower RMSE value and a simpler network structure than the other type-2 fuzzy neural networks (T2FNNs).

An experimental performance verification of continuous mixed P‐norm based adaptive asymmetrical fuzzy logic controller for single stage photovoltaic grid integration

AbstractThis paper deals with a new continuous mixed p‐norm (CMPN) based adaptive asymmetrical fuzzy logic controller (AAFLC) method for single stage grid integrated photovoltaic (PV) system as an inverter control strategy. Here, asymmetrical membership functions are employed to compensate the non‐linear characteristics of the PV module with effective system improvement. The proposed CMPN based algorithm provides online updates of the fuzzy logic controller (FLC) with rapid convergence velocity under varying operating conditions. The performance of a single‐stage grid integrated PV system controlled using CMPN based AAFLC has been investigated using the dSPACE DS1104 platform under steady and dynamic operating situations. Extensive practical analysis has been presented for verification of effective proposed inverter controller design under changing environmental conditions.

Voltage Stabilization Control for Microgrid With Asymmetric Membership Function-Based Wavelet Petri Fuzzy Neural Network

Voltage stabilization is an important task for the microgrid power control. The long transient response of voltage caused by the grid transition from the grid-connected mode to the islanded mode or the power variations would deteriorate the operation of the voltage protective relay. This would lead to the difficulty of integration for the renewable energy and the storage system. To solve this problem, the asymmetric membership function based wavelet petri fuzzy neural network (AMFWPFNN) controller is proposed for the voltage stabilization control of storage system in this paper to provide fast response speed and mitigate the transient impact. To investigate the performance of the proposed microgrid controller and examine the compliance with the settings in IEC Std. 60255, Cimei Island in Taiwan is studied. Through the hardware in the loop (HIL) mechanism built with OPAL-RT real-time simulator, the effectiveness of proposed controller can be verified.

Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control

An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d-axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq-axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision-making process as well as strategic planning. In this study, a prototype asymmetric-based neuro-fuzzy network (AGFINN) architecture has been implemented for short-term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well-established learning-based models.

Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia

In this article, a real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed. The estimated moment of inertia will be used in the online design of an integral–proportional (IP) speed controller to achieve the gains autotuning of the IPMSM servo drive. In the proposed method, the dynamic analysis of a field-oriented control IPMSM servo drive system with an IP speed controller is constructed first. Then, a heuristic approach using the PPFNN-AMF is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the PPFNN-AMF are devised and derivated. Furthermore, an IPMSM servo drive based on a high-performance digital signal processor is developed. Finally, from the experimental results, the gains of the IP speed controller can be tuned online effectively at different operating conditions with robust control characteristics.

Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

In this work, a novel self-organizing interval type-2 fuzzy cerebellar model articulation controller design for chaotic systems synchronization is presented. A function-link network and an asymmetric-membership function are employed to improve the learning ability and flexibility of the proposed network. The gradient-descent method is used to derive the adaptive laws for online updating the network parameters. Suitable learning rates for adaptive laws can be achieved by employing the modified Jaya algorithm. Furthermore, the proposed self-organizing algorithm is used to generate new fuzzy rules or delete less-active fuzzy rules. The control system's exponential stability is analyzed by applying the Lyapunov theory. Finally, numerical simulations on the synchronization of four-dimensional chaotic systems (considering external disturbances and system uncertainties) are conducted to validate the efficiency and performance of the proposed method.

A Modified Grey Wolf Optimizer for Optimum Parameters of Multilayer Type-2 Asymmetric Fuzzy Controller

This study presents a modified algorithm of the grey wolf optimizer to solve the problem of learning rate selection in the multilayer type-2 asymmetric fuzzy controller (MT2AFC). The improvements of our modified optimizer are: the best position of the swarm is memorized, thus making the alpha wolves only update when a better position appears in the next iteration; search performance is enhanced by giving more freedom to update the grey wolf position. The proposed optimizer algorithm is then applied to optimize the suitable learning rates for the proposed controller. The multilayer type-2 asymmetric membership function is used in the fuzzy control network to enhance the learning ability and flexibility of the designed network architecture. The gradient descent method is used to adjust the parameters of the proposed MT2AFC controller online. The stability of the system is guaranteed using the Lyapunov approach. Besides, the self-evolving algorithm is used to construct the network structure autonomously. Ultimately, the numerical simulations of the chaotic synchronization systems are carried out to verify the effectiveness of our proposed method.

Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF

A novel method is proposed to compensate the three-phase unbalanced currents of power grid under three-phase unbalanced load for a two-stage photovoltaic (PV) power system without the augmentation of active power filter. The PV power system is composed of an interleaved dc/dc converter and a three-level neutral-point clamped inverter. Moreover, the PV power system possesses the smart inverter function, in which the output active and reactive powers of the PV inverter are predetermined by a power factor according to grid codes of the utilities. In the proposed method, $dq0$ -axis compensation currents are obtained through low-pass filters to compensate the three-phase unbalanced currents of power grid. Furthermore, in order to improve the control performance of the dc bus voltage of the PV power system under unbalanced load variation condition, an online-trained compensatory neural fuzzy network with an asymmetric membership function (CFNN-AMF) is proposed to replace the traditional proportional-integral controller for the dc bus voltage control. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. In addition, the dimensions of the Gaussian membership functions are directly extended to AMFs. Additionally, the proposed controllers of the PV power system are implemented by two control platforms using floating-point digital signal processor. Finally, excellent compensation performance for the three-phase currents of power grid under three-phase unbalanced load can be achieved from the experimental results.

EFFICIENT ADAPTATION OF FUZZY CONTROLLER FOR SMOOTH SENDING RATE TO AVOID CONGESTION IN MULTICAST NETWORKS

This paper prefers a fuzzy-logic-based sending rate adaption scheme named FSR (Fuzzy Sending Rate) intending to improve the evenness of TCPFriendly Multicast Congestion Control (TFMCC). To mitigate fluctuation of sending rate for TFMCC sender, FSR intends, five actions and link utilization for tuning sending rate and uses a fuzzy controller to determine which operation should be reaped according to the feedback information from CLR (current limiting receiver). Asymmetrical membership functions and biased fuzzy inference rules make FSR as friendly to TCP flows as TFMCC. Simulation results show that FSR has exceptional smoothness and fine TCP Friendliness.

Efficient Adaptation of Fuzzy Controller for Smooth Sending Rate to Avoid Congestion in Multicast Networks

This paper prefers a fuzzy-logic-based sending rate adaption scheme named FSR(Fuzzy Sending Rate) intending to improve the evenness of TCPFriendly Multicast Congestion Control (TFMCC). To mitigate fluctuation of sending rate for TFMCC sender, FSR intends, five actions and link utilization for tuning sending rate and uses a fuzzy controller to determine which operation should be reaped according to the feedback information from CLR (current limiting receiver). Asymmetrical membership functions and biased fuzzy inference rules make FSR as friendly to TCP flows as TFMCC. Simulation results show that FSR has exceptional smoothness and fine TCP Friendliness.

Моделювання інвестиційної політики банку методами нечіткої логіки

Проаналізовано моделі, які використовують в банківській діяльності, починаючи від робіт Еджворта до моделей нечіткої логіки. Зазначено їх переваги та недоліки. Обґрунтовано вибір моделі нечіткої логіки як інструменту моделювання. Розглянуто задачу оцінки інвестиційної політики банку, для якої визначено вид функції приналежності, що є одним із найважливіших питань у теорії нечітких множин. За функцію приналежності обрано несиметричну функцію Гауса, що зумовлено специфікою побудови таких функцій економічних показників, для яких у багатьох випадках відомо тільки три значення: найімовірніше, максимально та мінімально можливі. Запропоновано використати несиметричну функцію приналежності, сформовану на основі функції Гауса з різними значеннями середньоквадратичного відхилення. Проведено чисельні експерименти оцінки інвестиційного проекту на основі трикутних функцій приналежності та запропонованих несиметричних гаусоїд. Для розрахунку параметрів несиметричної функції Гауса спочатку побудовано наближення – трикутна функція приналежності. Виходячи з параметрів трикутної функції, розраховано середньоквадратичні відхилення для лівої та правої частин функції Гауса. Показано, що під час використання несиметричної функції Гауса потрібні більші початкові інвестиції для того, щоб проект був прибутковим. Зроблено висновок щодо необхідності ретельного підбору виду функцій приналежності для зменшення інвестиційного ризику.

A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation

A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive loads. Moreover, the DC-link voltage regulation control of the DSTATCOM is essential especially under load variation conditions. Therefore, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under the variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a new online trained compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Furthermore, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. In addition, the network structure and online learning algorithm of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller to improve the power quality and maintain the constant DC-link voltage under the change of nonlinear and linear inductive loads have been demonstrated by some experimental results.

Self-organising interval type-2 fuzzy neural network with asymmetric membership functions and its application

For the identification and modelling problems of a nonlinear system with complex uncertainties, a self-organising interval type-2 fuzzy neural network structure with asymmetric membership functions (SIT2FNN-AMF) is developed. First, a fuzzy c-means algorithm with four fuzzifier parameters is used to partition the input data to obtain the uncertainty means and widths of the fuzzy rule antecedent; then, according to the cluster validity criterion, the number of fuzzy rules is determined. Thus, identifications of the structure and rule antecedent parameters are automatically completed. The consequent part uses the Mamdani model, and the initial value of the consequent parameter is an interval random number. The fuzzy rule parameters are tuned by the gradient descent method. Finally, the proposed SIT2FNN-AMF is applied to simulations of nonlinear system identification and soft-sensing model for ethylene cracking furnace yield. The comparison of simulation results obtained with a conventional fuzzy neural network and interval type-2 fuzzy neural network verifies the performance of the proposed SIT2FNN-AMF.

DC-Link Voltage Regulation Using RPFNN-AMF for Three-Phase Active Power Filter

Due to the instantaneous power following into or out of the DC-link capacitor in a three-phase shunt active power filter (APF), the DC-link voltage regulation control plays an important role in the shunt APF especially under nonlinear load change. In this paper, for the purpose of improving the DC-link voltage regulation control in the shunt APF under nonlinear load variation and reducing the total harmonic distortion of the current effectively, a novel recurrent probabilistic fuzzy neural network with an asymmetric membership function (RPFNN-AMF) controller is developed to substitute for the conventional proportional-integral controller. Moreover, the network structure, the online learning algorithm, and the convergence analysis of the proposed RPFNN-AMF are detailedly introduced. Finally, the effectiveness and feasibility of the shunt APF using the proposed RPFNN-AMF controller for the DC-link voltage regulation control and the compensation of harmonic current are verified by some experimental results.

Intelligent tracking control of a PMLSM using self‐evolving probabilistic fuzzy neural network

This study presents a self-evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN-AMF) controller for the position servo control of a permanent magnet linear synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field-oriented control. Subsequently, an SPFNN-AMF control system, which integrates the advantages of self-evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time-varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN-AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN-AMF for controlling the PMLSM servo drive system.

FUZZY DATA CLUSTERING IN THE RANK SCALE BASED ON A DOUBLE NEO-FUZZY NEURON

Context. A task of data classification under conditions of clusters’ overlapping is considered in this article. Besides that, it’s assumed that information to be processed is given in the rank scale. Objective. It’s proposed to use a double neo-fuzzy neuron for classification which is a modification of a traditional neo-fuzzy neuron with specially designed asymmetrical membership functions and improved approximating properties. Method. The double neo-fuzzy neuron (just like the traditional one) is designated for processing data given the scale of natural numbers. However, the situation may become complicated greatly if source data is not given in the numerical scale but in the ordinal one which is a quite common case for a wide variety of practical tasks. Results. A gradient minimization procedure with a variable learning step parameter was used for learning the double neo-fuzzy neuron. The proposed approach to fuzzy classification for data given in the ordinal scale based on the double neo-fuzzy neuron which is learnt with the help of a high-speed algorithm possesses additional smoothing properties. The clustering accuracy for a training sample and the test one as well as the system’s learning speed were measured during experiments. The proposed architecture of the double neo-fuzzy neuron is a sort of compromise between a traditional neo-fuzzy neuron and its extended modification. This architecture demonstrates good performance in those cases when the results’ accuracy has more influence compared to the elapsed time used for data processing. Conclusions. Experimental implementation (for both artificial and real-world data) proved efficiency of the proposed techniques. During the experiments, properties of the proposed system were studied which confirmed usability of the proposed system for a wide range of Data Mining tasks.

Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC-) based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.

Investigation of the influence of the symmetry of membership functions in the fuzzy controller on the quality of MPPT regulation in the photovoltaic system

The paper presents the results of the investigation of the influence of asymmetric membership functions in a fuzzy controller on the quality of MPPT regulation in the photovoltaic system. Comparison of the quality of fuzzy MPPT regulation with symmetrical and asymmetric membership functions is carried out. The results of the research showed the advantage of using asymmetric membership functions in the MPPT algorithm with fuzzy logic.