A Minor Change?: A Case Study of Carleton Place's Integration of Minor Variance Procedures into Ontario's Development Permit System

The Development Permit System has been introduce with minimal directives for establishing a decision making process. This is in opposition to the long established process for minor variances and suggests that the Development Permit System does not necessarily incorporate all of Ontario’s fundamental planning principles. From this concept, the study aimed to identify how minor variances are incorporated into the Development Permit System. In order to examine this topic, the research was based around the following research questions: • How are ‘minor variance’ applications processed within the DPS? • To what extent do the four tests of a minor variance influence the outcomes of lower level applications in the DPS approval process? A case study approach was used for this research. The single-case design employed both qualitative and quantitative research methods including a review of academic literature, court cases, and official documents, as well as a content analysis of Class 1, 1A, and 2 Development Permit application files from the Town of Carleton Place that were decided between 2011 and 2015. Upon the completion of the content analysis, it was found that minor variance issues were most commonly assigned to Class 1 applications. Planning staff generally met approval timelines and embraced their delegated approval authority, readily attaching conditions to applications in order to mitigate off-site impacts. While staff met the regulatory requirements of the DPS, ‘minor variance’ applications were largely decided on impact alone, demonstrating that the principles established by the four tests, the defining quality of the minor variance approval process, had not transferred to the Development Permit System. Alternatively, there was some evidence that the development community has not fully adjusted to the requirements of the new approvals process, as some applications were supported using a rationale containing the four tests. Subsequently, a set of four recommendations were offered which reflect the main themes established by the findings. The first two recommendations are directed towards the Province, the third to municipalities and the fourth to developers and planning consultants: 1) Amend Ontario Regulation 608/06 so that provisions under Section 4(3)(e) fall under Section 4(2). 2) Change the rhetoric from “combining elements of minor variances” to “replacing minor variances”. 3) Establish clear evaluation criteria. 4) Understand the evaluative criteria of the municipality in which you are working.

ADVANCED ISLANDING DETECTION METHODS FOR SINGLE PHASE GRID CONNECTED PV INVERTERS

Due to the growing concerns associated with fossil fuels, emphasis has been placed on clean and sustainable energy generation. This has resulted in the increase in Photovoltaics (PV) units being integrated into the utility system. The integration of PV units has raised some concerns for utility power systems, including the consequences of failing to detect islanding. Numerous methods for islanding detection have been introduced in literature. They can be categorized into local methods and remote methods. The local methods are categorically divided into passive and active methods. Active methods generally have smaller Non-Detection Zone (NDZ) but the injecting disturbances will slightly degrade the power quality and reliability of the power system. Slip Mode Frequency Shift Islanding Detection Method (SMS IDM) is an active method that uses positive feedback for islanding detection. In this method, the phase angle of the converter is controlled to have a sinusoidal function of the deviation of the Point of Common Coupling (PCC) voltage frequency from the nominal grid frequency. This method has a non-detection zone which means it fails to detect islanding for specific local load conditions. If the SMS IDM employs a different function other than the sinusoidal function for drifting the phase angle of the inverter, its non-detection zone could be smaller. In addition, Advanced Slip Mode Frequency Shift Islanding Detection Method (Advanced SMS IDM), which has been introduced in this thesis, eliminates the non-detection zone of the SMS IDM. In this method the parameters of SMS IDM change based on the local load impedance value. Moreover, the stability of the system is investigated by developing the dynamical equations of the system for two operation modes; grid connected and islanded mode. It is mathematically proven that for some loading conditions the nominal frequency is an unstable point and the operation frequency slides to another stable point, while for other loading conditions the nominal frequency is the only stable point of the system upon islanding occurring. Simulation and experimental results show the accuracy of the proposed methods in detection of islanding and verify the validity of the mathematical analysis.

Absorption Detection Using Optical Waveguide Cavities

Cavity ring-down spectroscopy is a spectroscopic method that uses a high quality optical cavity to amplify the optical loss due to the light absorption by a sample. In this presentation we highlight two applications of phase-shift cavity ring-down spectroscopy that are suited for absorption measurements in the condensed phase and make use of waveguide cavities. In the first application, a fiber loop is used as an optical cavity and the sample is introduced in a gap in the loop to allow absorption measurements of nanoliters of solution at the micromolar level. A second application involves silica microspheres as high finesse cavities. Information on the refractive index and absorption of a thin film of ethylene diamine on the surface of the microresonator is obtained simultaneously by the measurements of the wavelength shift of the cavity mode spectrum and the change in optical decay time, respectively.

Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.