An Improved Fuzzy Parallel Distributed –Like Controller for Multi-Input Multi-Output Twin Rotor System

Twin Rotor Multi Input Multi Output (MIMO) System (TRMS) is a laboratory set-up design for which it has been used for control experiments, control theories developments, and applications of the autonomous helicopter. Fuzzy Logic Control (FLC) has been widely used with different control schemes to cope with control objectives of TRMS. In this work, Self Tuning Fuzzy PD-like Controller (STFPDC) is proposed to make the response of FLC more robust to the interactions and the non-linearity of the process in terms of less rising time, settling time and overshoot. Adaptive Neuro Fuzzy Inference System (ANFIS) based Fuzzy Subtractive Clustering Method (FSCM) was used to remodel the proposed STFPDC to achieve the control objectives on TRMS with less number of rules. MATLAB/SIMULINK was involved to achieve the simulations in this work. The results showed the proposed controller could simplify the STFPDC to reduce the number of rules from 392 to 73, which is even less than the original FLC that has 196 rules. The conclusion of this work is improving FLC response by using STFPDC and reducing the number of rules used to achieve this improvement by using ANFIS based on FSCM modeling. For future works, it is recommended to develop an optimization algorithm which achieves best selection for the range of influence which gives best response with less number of rules

Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation

Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem \cite{BestounKamalFuzzy2017}. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system \cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.Comment: 21 page

Optimal power generation in microgrids using agent-based technology

The existing power grids that form the basis of the respective electrical power infrastructures for various states and nations around the world, are expected to undergo a period of rapid change in the near future. The key element driving this change is the emergence of the Smartgrid. The Smartgrid paradigm represents a transition towards an intelligent, digitally enhanced, two-way power delivery grid. The aim of the Smartgrid is to promote and enhance the e_cient management and operation of the power generation and delivery facilities, by incorporating advanced communications, information technology, automation, and control methodologies into the power grid proper. Smartgrid\u27s are currently an active topic for research, where the research is strongly focused on developing new technologies such as: demand response, power generation management, pricing modelling and energy markets participation, power quality, and self-healing scenarios. In recent times, in both the United States of America and Europe, many new projects have begun which are specifically directed towards developing “Smartgrid” technologies. In Australia, the Federal Government has recently initiated funding plans to promote the commercialisation of renewable energy. In order to exploit these developments, Edith Cowan University (ECU); which is a High Voltage (HV) customer for the major utility network of Western Australia, and which owns its own transformers and Low Voltage (LV) network; is planning to integrate renewable energy suppliers within its LV network. The aim of this research is to introduce a smart decision making system, which can manage the operation of disparate power generation sources installed on a LV network (microgrid); such as that owned by ECU on its campuses. The proposed energy management system is to gather data in real-time, and it must be capable of anticipating and optimising energy needs for each operational scenario that the microgrid might be expected to experience. The system must take into account risk levels, while systematically favouring low economic and environmental costs. A management system application, based on autonomous and distributed controllers, is investigated in a virtual environment. The virtual environment being a full-scale simulation of ECU\u27s microgrid; with solar panels, wind turbines, storage devices, gas gen-sets, and utility supply. Hence the simulation studies were conducted on the basis of realistic demand trends and weather conditions data. The major factors for reducing the cost of generation in the case study, were identified as being: 1) demand forecasting; 2) generation scheduling; 3) markets participation; and 4) autonomous strategies configuration, which is required to cope with the unpredictable operation scenarios in LV networks. Due to the high uncertainty inherent within the operational scenarios; an Artificial Intelligence (AI) deployment for managing the distributed sub-systems was identified as being an ideal mechanism for achieving the above mentioned objectives. Consequently it is proposed that Multi-Agent System (MAS) technology be deployed, to enable the system to respond dynamically to the unpredictable operational conditions by updating the method of analysis. The proposed system is to behave in a strategic manner when dealing with the expected operational scenarios, by aiming to achieve the lowest possible cost of power generation for the microgrid. The simulated system is based on realistic operational scenarios, which have been scaled to suit the size and type of load in the case study. The distributed intelligent modules have proven to be successful in achieving the potential benefits of the dynamic operational conditions, by minimising the cost of power generation. The distributed intelligent modules, which form the basis of the proposed management systems, have been designed to perform the following functions: 1. Provide accurate demand forecasts through the utilisation of an AI-based adaptive demand forecasting model. The novel demand-forecast modelling technique, which was introduced to model demand in the case study, has been utilised to supply reasonably accurate demand forecasts to other stages of processing in the management system. The forecasts are generated from this model, by monitoring and controlling the forecasting error to ensure consistent and satisfactory forecasts. 2. Make optimum decisions concerning the operation of the power generators by considering the economic and the environmental costs. In order to deal with the complexity of the operational conditions, a smart and adaptive generation scheduling method was implemented for the case study. The method was primarily applied to control the charging/ discharging process of the Storage Devices (SDs) among the other generators. The proposed method aims at controlling the resources, and extracting the benefit of having an hourly based variable generation cost. 3. Integrate the microgrid into the electricity market, in order to enable the microgrid to offer its spinning and non-spinning power generation reserve as Ancillary Services (AS) to the grid. To this end, studying the operational mechanisms of the Australian market was essential prior to building the proposed market participation rules which form an integral part of the proposed management system. As a result we used the market data, by approaching the market operators to create a semi-realistic competitive market environment for our simulations. Consequently, a smart and adaptive pricing mechanism, that adapts the AS prices to the amount of electricity on offer, and the level of demand in the market has been presented. The motivation for introducing the proposed management system, is to achieve a transition plan for current microgrids, so that they can have a commercial connection to the future Smartgrid. The results obtained in this work show that there is a signi_cant economic and environmental advantage to be gained from utilising intelligence when managing electricity generation within a power grid. As a consequence, selecting the appropriate management strategy is fundamental to the success of the proposed management system. In conclusion, modelling of the proposed strategies using MAS technology has proven to be a successful approach, and one that is able to reflect the human attitude; in making critical decisions and in reducing the cost of generation

Upgrade a Medium Size Enterprise Power System with Wind and Solar Sources: Design, Financial and Environmental Perspectives

Efficient generation and distribution are crucial for economic power production. In this paper we discuss the planning and design of upgrading a medium size enterprise power system by installing Distributed Energy Resources (DERs), with particular emphasis on economic viability and environmental benefits. The planning for this project considers both conventional grid and SmartGrid connections. Project planning, installation challenges and governmental support of renewable energy projects in Australia are discussed. It is found that upgrading a medium size enterprise power system with DERs can yield reasonable levels of energy cost savings and greenhouse gas mitigation with both conventional grid and SmartGrid connections, but that SmartGrid connection can deliver better outcomes

Unlocking market secrets: Revealing wholesale electricity market price dynamics with a novel application of spectrum analysis

Understanding market participants\u27 competitive behaviour is essential for optimising financial performance in liberalised electricity markets. However, this is challenging due to complex market structures, generation dependent on different primary energy sources and lack of transparency. This paper introduces a novel approach using power spectrum analysis applied to wholesale electricity markets to uncover hidden patterns. Applying this novel method to the Western Australian Wholesale Electricity Market (WEM) revealed periodic cycles in different fuel types and technologies that offered insights into competitor behaviour not immediately evident in the dataset. Surprisingly, the approach uncovered that in a power system with high penetration of renewable generation, there is a weak price response to demand changes, challenging assumptions about the direct link between demand and price formation. These insights could be applied gain a competitive edge in capital investment decisions and tactical bidding behaviour

Fuzzy Interference System in Energy Demand Prediction

No abstract available

Fuzzy Inference System in Energy Demand Prediction

No abstract available

ANFIS: Self-tuning Fuzzy PD Controller for Twin Rotor MIMO System

This work presents a self-tuning fuzzy PD controller for solving the control challenges of twin rotor MIMO system. The controller is made adaptive through output scaling factor adjustment of the updating factor, . The value of is calculated directly from a fuzzy rule base defined as error and change of error of the controlled variable. A combination of adaptive neural fuzzy inference system and fuzzy subtractive clustering method was used, where the objective was to improve its time response, while reducing its computational complexity. Simulation results show performance improvement in comparison with that of the previous method. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc

Tuning Fuzzy Systems to Achieve Economic Dispatch for Microgrids

In this paper, a Tuning Fuzzy System (TFS) is used to improve the energy demand forecasting for a medium-size microgrid. As a case study, the energy demand of the Joondalup Campus of Edith Cowan University (ECU) in Western Australia is modelled. The developed model is required to perform economic dispatch for the ECU microgrid in islanding mode. To achieve an active economic dispatch demand prediction model, actual load readings are considered. A fuzzy tuning mechanism is added to the prediction model to enhance the prediction accuracy based on actual load changes. The demand prediction is modelled by a Fuzzy Subtractive Clustering Method (FSCM) based Adaptive Neuro Fuzzy Inference System (ANFIS). Three years of historical load data which includes timing information is used to develop and verify the prediction model. The TFS is developed from the knowledge of the error between the actual and predicted demand values to tune the prediction output. The results show that the TFS can successfully tune the prediction values and reduce the error in the subsequent prediction iterations. Simulation results show that the proposed prediction model can be used for performing economic dispatch in the microgrid

System strength shortfall challenges for renewable energy-based power systems: A review

Renewable energy sources such as wind farms and solar power plants are replacing conventional coal-based synchronous generators (SGs) to achieve net-zero carbon emissions worldwide. SGs play an important role in enhancing system strength in a power system to make it more stable during voltage/frequency disruptions. However, traditional coal-fired SGs are being decommissioned in many parts of the world, owing to stringent environmental regulations and low levelized cost of energy of renewables. Consequently, maintaining system strength in a renewable energy-dominated power system has become a major challenge, and without adequate mitigation techniques, low system strength can potentially cause widespread power outages. This paper provides an overview of system strength and its measurement techniques in a power system with a large number of renewable energy sources (RESs), for example solar and wind farms. The review includes the system strength measurement techniques, mitigation approaches, and future challenges