DIAGNOSIS AND CONTROL FOR MULTI-AGENT SYSTEMS USING IMMUNE NETWORKS

ABSTRACT Soft computing (SC) i

Non-intrusive load management system for residential loads using artificial neural network based arduino microcontroller

The energy monitoring is one of the most important aspects of energy management. In fact there is a need to monitor the power consumption of a building or premises before planning technical actions to minimize the energy consumption. In traditional load monitoring method, a sensor or a group of sensors attached to every load of interest to monitor the system, which makes the system costly and complex. On the other hand, by Non-Intrusive Load Monitoring (NILM) the aggregated measurement of the building’s appliances can be used to identify and/or disaggregate the connected appliances in the building. Therefore, the method provides a simple, reliable and cost effective monitoring since it uses only one set of measuring sensors at the service entry. This thesis aims at finding a solution in the residential electrical energy management through the development of Artificial Neural Network Arduino (ANN-Arduino) NILM system for monitoring and controlling the energy consumption of the home appliances. The major goal of this research work is the development of a simplified ANN-based non-intrusive residential appliances identifier. It is a real-time ANN-Arduino NILM system for residential energy management with its performance evaluation and the calibration of the ZMPT101B voltage sensor module for accurate measurement, by using polynomial regression method. Using the sensor algorithm obtained, an error of 0.9% in the root mean square (rms) measurement of the voltage is obtained using peak-peak measurement method, in comparison to 2.5% when using instantaneous measurement method. Secondly, a residential energy consumption measurement and control system is developed using Arduino microcontroller, which accurately control the home appliances within the threshold power consumption level. The energy consumption measurement prototype has an accurate power and current measurement with error of 3.88% in current measurement when compared with the standard Fluke meter. An ANN-Arduino NILM system is also developed using steady-state signatures, which uses the feedforward ANN to identify the loads when it received the aggregated real power, rms current and power factor from the Arduino. Finally, the ANN-Arduino NILM based appliances’ management and control system is developed for keeping track of the appliances and managing their energy usage. The system accurately recognizes all the load combinations and the load controlling works within 2% time error. The overall system resulted into a new home appliances’ energy management system based on ANN-Arduino NILM that can be applied into smart electricity system at a reduced cost, reduced complexity and non-intrusively

IMPROVEMENT OF POWER QUALITY OF HYBRID GRID BY NON-LINEAR CONTROLLED DEVICE CONSIDERING TIME DELAYS AND CYBER-ATTACKS

Power Quality is defined as the ability of electrical grid to supply a clean and stable power supply. Steady-state disturbances such as harmonics, faults, voltage sags and swells, etc., deteriorate the power quality of the grid. To ensure constant voltage and frequency to consumers, power quality should be improved and maintained at a desired level. Although several methods are available to improve the power quality in traditional power grids, significant challenges exist in modern power grids, such as non-linearity, time delay and cyber-attacks issues, which need to be considered and solved. This dissertation proposes novel control methods to address the mentioned challenges and thus to improve the power quality of modern hybrid grids.In hybrid grids, the first issue is faults occurring at different points in the system. To overcome this issue, this dissertation proposes non-linear controlled methods like the Fuzzy Logic controlled Thyristor Switched Capacitor (TSC), Adaptive Neuro Fuzzy Inference System (ANFIS) controlled TSC, and Static Non-Linear controlled TSC. The next issue is the time delay introduced in the network due to its complexities and various computations required. This dissertation proposes two new methods such as the Fuzzy Logic Controller and Modified Predictor to minimize adverse effects of time delays on the power quality enhancement. The last and major issue is the cyber-security aspect of the hybrid grid. This research analyzes the effects of cyber-attacks on various components such as the Energy Storage System (ESS), the automatic voltage regulator (AVR) of the synchronous generator, the grid side converter (GSC) of the wind generator, and the voltage source converter (VSC) of Photovoltaic (PV) system, located in a hybrid power grid. Also, this dissertation proposes two new techniques such as a Non-Linear (NL) controller and a Proportional-Integral (PI) controller for mitigating the adverse effects of cyber-attacks on the mentioned devices, and a new detection and mitigation technique based on the voltage threshold for the Supercapacitor Energy System (SES). Simulation results obtained through the MATLAB/Simulink software show the effectiveness of the proposed new control methods for power quality improvement. Also, the proposed methods perform better than conventional methods

Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

Software Algorithms to Coordinate and Improve Voltage Sag Ridethrough Capabilities of Networked Industrial Processes

For those who design, operate, and troubleshoot industrial processes, electric power quality is a subject that requires much consideration. Processes that use electronic sensors, actuators, and computation devices are heavily reliant on a stable, consistent input power source. When a power quality event such as a voltage fluctuation occurs, automation equipment often behaves unpredictably and causes process malfunction or failure. Because industrial power consumers often blame their electric utility for these events, some utilities offer process susceptibility studies as a service for their customers. During a typical study, utility technicians and engineers perform in-house tests on suspect components or systems using voltage sag generating equipment. These tests determine device malfunction thresholds and establish an event failure timeline. Test results provide data for applying mitigation solutions, where the most critical or susceptible loads receive a higher priority for improvement. While effective, this approach often requires the addition of costly hardware. This study presents novel software algorithms that coordinate and improve process ridethrough capabilities of network connected industrial processes. An add-on PC interfacing with an automation network executes a routine that detects voltage sags, performs a fast measurement of sag parameters, and determines an expected process response. Rather than implement a `cure all\u27 reaction for every disturbance scenario, mitigation routines are executed based upon the expected response. Underlying design constraints of this study are to minimize or avoid the installation of conventional ridethrough hardware and adhere to a software architecture that is unintrusive to existing controllers. Voltage sag detection is performed with a real-time analysis of incoming voltages and is triggered from RMS voltage derivative threshold crossings. Having recognized the presence of a voltage sag, the algorithm determines the sag magnitude with a peak detection method, and can associate the measured magnitude/phase combination with previously recorded process data. Either the sag characteristics or historical process response data is then analyzed to determine the expected process response. Sags that can potentially force motor drives to trip offline cause the process to respond to an expected shutdown. Voltage sag magnitude/phasing combinations that have been shown to cause no process disruption are ignored. Combinations which have caused only instrument signal corruption and significant process variable deviations trigger the mitigation routine to address faulted control signals only. Drive fault mitigation responses consist of a software-only drive coast routine and an improved drive coast routine requiring the addition of basic switching hardware. Out of tolerance process errors are mitigated with output control command substitution or input signal substitution routines. Verification of software functionality is achieved with an experimental automated process - - a textile unwind/rewind system that operates at a controlled linespeed and tension. Detailed analysis and simulation is performed on both component and system-wide levels. Unmitigated and mitigated process voltage sag responses are recorded and matched with the theoretical process model. Although customization is required to apply the algorithms to the specific design of the textile tension control process, experimentation with this test bed system serves as a satisfactory proof of concept for the software routines. As a result, the methods developed in this study can improve the task of process power quality mitigation by customizing solutions for individual processes, avoiding the application of power quality mitigation solutions where they are not required, coordinating corrective actions by utilizing existing automation network functionality, and ultimately reducing the need for costly hardware installation and maintenance

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Efficient Learning Machines

Computer scienc