Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Design and Control of Power Converters 2019

In this book, 20 papers focused on different fields of power electronics are gathered. Approximately half of the papers are focused on different control issues and techniques, ranging from the computer-aided design of digital compensators to more specific approaches such as fuzzy or sliding control techniques. The rest of the papers are focused on the design of novel topologies. The fields in which these controls and topologies are applied are varied: MMCs, photovoltaic systems, supercapacitors and traction systems, LEDs, wireless power transfer, etc

Planning and Management of a solar power-based distribution system

This thesis is aimed at the response of the power system network to the integration of solar photovoltaic (PV) generation and battery energy storage systems (BESS). Any solar power–based system integrated into a grid has voltage fluctuations that must be controlled through adaptive and robust control algorithms. The siting of battery in a distribution system affects system performance, including voltage regulation, system losses and cost minimization. In particular, here the aim is to analyse how the present-day schemes and technologies affect voltages, and their control, in the network. Another focus is on the optimal placement of BESS to facilitate system loss minimisation and cost reduction in the system. The battery placement optimisation is achieved through the minimisation of the losses in, and the cost of, the system. The voltage regulation is achieved through two control algorithms: Synchronous Reference Frame theory (SRFT) and adaptive linear neural network (ADALINE), which are subsequently modified by incorporation of fuzzy logic into the control system. Both battery placement optimisation and improvements to voltage regulation are shown to improve performance of the system. A further aim of this work is to improve cooperation between present day grid regulation equipment and schemes and the conventional methods through advancements in the control techniques. The aims of this thesis are as follows: 1. It is essential to place BESSs optimally. The aim of the thesis is to study and enhance the method of the optimal siting of battery energy storage in the presence of renewable energy–based power generating sources (RES)– such as solar PV – in a low-voltage power system network. A model for optimisation is developed to potentially find the battery site that enhances the hosting capability of the RES of the power system network. Among the essential points of this technique are its accuracy and robust nature. The fitness function includes the minimisation of the cost of operation and of system losses. 2. The second research objective is to examine the power control techniques of the inverter that might be leading to the voltage quality issues during unbalanced voltage scenarios, especially with solar PV–based generation in the power system. As such, after the implementation of the suggested coordination of the control mechanism into the grid under study, the variations in the voltage due to the solar PV variability dynamics are regulated more quickly and more precisely compared with the control schemes employed in the past. This substantially minimises the voltage fluctuations in time and amplitude, helps in mitigating hunting phenomena in voltage and provides alternative to the unnecessary control operations existing in the system

Control and estimation techniques applied to smart microgrids : a review

DATA AVAILABILITY : No data was used for the research described in the article.The performance of microgrid operation requires hierarchical control and estimation schemes that coordinate and monitor the system dynamics within the expected manipulated and control variables. Smart grid technologies possess innovative tools and frameworks to model the dynamic behaviour of microgrids regardless of their types, structures, etc. Various control and estimation technologies are reviewed for developing dynamic models of smart microgrids. The hierarchical system of a microgrid control consists of three architectural layers, primary, secondary and tertiary, which need to be supported by real-time monitoring and measurement environment of the system variables and parameters. Various control and estimation schemes have been devised to handle the dynamic performance of microgrids in the function of control layers requirement. Firstly, control schemes in the innovative grid environment are evaluated to understand the dynamics of the developed technologies. Six control technologies, linear, non-linear, robust, predictive, intelligent and adaptive, are mainly used to model the control design within the layer(s) regardless of the types of microgrids. Secondly, the estimation technologies are evaluated based on the state of variables, locations and modelling of microgrids that can efficiently support the performance of the controllers and operating microgrids. Finally, a future vision for designing hierarchical and architectural control techniques for the optimal operation of intelligent microgrids is also provided. Therefore, this study will serve as a fundamental conceptual framework to select a perfect optimal design modelling strategy and policy-making decisions to control, monitor and protect the innovative electrical network.http://www.elsevier.com/locate/rserhj2023Electrical, Electronic and Computer Engineerin

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Reverse Engineering of Short Circuit Analyses

The electrical distribution system has evolved with embedded computer systems that can better manage the electrical fault that occurred around the feeders. Such random events can affect the reliability indices of overall systems. Computerized management system for distribution operation has been improving with the advanced sensing technologies. The general research question is here to articulate is the responsiveness for utility crew to pinpoint the exact location of a fault based on the SCADA fault indicators from pole-mounted feeder remote terminal units (FRTUs). This has been a tricky question because it relies on the information received from the sensors that can conclude fault with logic\u27s of over currents. The merit of this work can benefit at large the grid reliability because of time-saving in searching the exact location of a fault. The main contribution of this thesis is to utilize the 3-phase unbalanced power flow method to incrementally search for narrowing the localization of electrical short circuits. This is known as the reversal of the typical short circuit approach where a location of the fault is presumed. The 3 topological configurations of simulation studied in this thesis exhibit the typical radial configuration of a distribution feeder have been researched based on unidirectional and bidirectional power flow simulation. The exact fault location is carried in two steps. Firstly, a bisection search algorithm has been employed. Secondly, an incremental adjustment to match the simulated currents of fault with the measurements is conducted. Finally, the sensitivity analysis of a search can be improved with the proposed algorithm that leads to matching of telemetered and calculated values. The analysis of exact fault location is carried in unidirectional and bidirectional flow of power. Distributed energy resources (DER) such as residential PV at a household level as well the wind energy changes affect the protective relaying within a feeder as well as the reconfigurability of the switching sequences. Furthermost, the bidirectionality of power flow in an unbalanced manner would also be a challenging issue to deal with the power quality in automation. Finally, the simulation results based on unidirectional and bidirectional power flow are extensively discussed along with the future scope

Performance Analysis of Photovoltaic Fed Distributed Static Compensator for Power Quality Improvement

Owing to rising demand for electricity, shortage of fossil fuels, reliability issues, high transmission and distribution losses, presently many countries are looking forward to integrate the renewable energy sources into existing electricity grid. This kind of distributed generation provides power at a location close to the residential or commercial consumers with low transmission and distribution costs. Among other micro sources, solar photovoltaic (PV) systems are penetrating rapidly due to its ability to provide necessary dc voltage and decreasing capital cost. On the other hand, the distribution systems are confronting serious power quality issues because of various nonlinear loads and impromptu expansion. The power quality issues incorporate harmonic currents, high reactive power burden, and load unbalance and so on. The custom power device widely used to improve these power quality issues is the distributed static compensator (DSTATCOM). For continuous and effective compensation of power quality issues in a grid connected solar photovoltaic distribution system, the solar inverters are designed to operate as a DSTATCOM thus by increasing the efficiency and reducing the cost of the system. The solar inverters are interfaced with grid through an L-type or LCL-type ac passive filters. Due to the voltage drop across these passive filters a high amount of voltage is maintained across the dc-link of the solar inverter so that the power can flow from PV source to grid and an effective compensation can be achieved. So in the thesis a new topology has been proposed for PV-DSTATCOM to reduce the dc-link voltage which inherently reduces the cost and rating of the solar inverter. The new LCLC-type PV-DSTATCOM is implemented both in simulation and hardware for extensive study. From the obtained results, the LCLC-type PV-DSTATCOM found to be more effective than L-type and LCL-type PV-DSTATCOM. Selection of proper reference compensation current extraction scheme plays the most crucial role in DSTATCOM performance. This thesis describes three time-domain schemes viz. Instantaneous active and reactive power (p-q), modified p-q, and IcosΦ schemes. The objective is to bring down the source current THD below 5%, to satisfy the IEEE-519 Standard recommendations on harmonic limits. Comparative evaluation shows that, IcosΦ scheme is the best PV-DSTATCOM control scheme irrespective of supply and load conditions. In the view of the fact that the filtering parameters of the PV-DSTATCOM and gains of the PI controller are designed using a linearized mathematical model of the system. Such a design may not yield satisfactory results under changing operating conditions due to the complex, nonlinear and time-varying nature of power system networks. To overcome this, evolutionary algorithms have been adopted and an algorithm-specific control parameter independent optimization tool (JAYA) is proposed. The JAYA optimization algorithm overcomes the drawbacks of both grenade explosion method (GEM) and teaching learning based optimization (TLBO), and accelerate the convergence of optimization problem. Extensive simulation studies and real-time investigations are performed for comparative assessment of proposed implementation of GEM, TLBO and JAYA optimization on PV-DSTATCOM. This validates that, the PV-DSTATCOM employing JAYA offers superior harmonic compensation compared to other alternatives, by lowering down the source current THD to drastically small values. Another indispensable aspect of PV-DSTATCOM is that due to parameter variation and nonlinearity present in the system, the reference current generated by the reference compensation current extraction scheme get altered for a changing operating conditions. So a sliding mode controller (SMC) based p-q theory is proposed in the dissertation to reduce these effects. To validate the efficacy of the implemented sliding mode controller for the power quality improvement, the performance of the proposed system with both linear and non-linear controller are observed and compared by taking total harmonic distortion as performance index. From the obtained simulation and experimentation results it is concluded that the SMC based LCLC-type PV-DSTATCOM performs better in all critical operating conditions

Smart grid optimized operation driven by reinforcement learning

This thesis focuses on the development of a reinforcement learning model for the operation and demand response control of a smart grid. First, a generic problem is formulated to define the demand response control. Then a study case is proposed with different locations of distributed energy resources and flexible components for reducing the cost associated with its grid con- sumption and safety management. The potential application of different deep reinforcement learning models with different activation functions and network shapes, among them, will be compared and analysed for the grid operation. The goal is to find a deep reinforcement learning model to optimize the demand side of energy management of a smart grid, that achieves better results than other existing approaches. Finally, a new policy for deep reinforcement learning algorithms will be proposed. This will provide a tool to guide the energy management of elec- trical distribution grids with high penetration of renewable energy sources