2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)最新文献

{"title":"Total optimization of smart community using sequence-based deterministic initialization and k-means based initial searching points generation","authors":"M. Sato, Y. Fukuyama","doi":"10.1109/ISAP.2017.8071387","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071387","url":null,"abstract":"This paper proposes total optimization of smart community (SC) using sequence-based deterministic initialization and k-means based initial searching points generation. In this paper, energy supply models such as electric power utility, natural gas utility, drinking water plant, and waste water treatment plant, and energy consumption models such as industry, building, residence, and railroad are utilized. Using the SC model, energy costs, actual electric power at peak load hours, and the amount of CO2 emission of the whole SC is minimized. Differential Evolutionary Particle Swarm Optimization (DEEPSO) is applied as the optimization technique with the proposed initial searching points generation method based on the sequence-based deterministic initialization and k-means. The proposed method is applied to a model of Toyama city, which is a moderately-sized city in Japan. Optimal operation by the proposed method is compared with that by an initial searching points generation method using pseudo-random number generator (PRNG) and the proposed method.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132223046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Average electrical distance-based bus clustering method for network equivalence","authors":"Muhammad Nauman Rafiq, D. Sharma, Di Wu, John N. Jiang, C. Kang","doi":"10.1109/ISAP.2017.8071381","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071381","url":null,"abstract":"Network equivalence is useful for electrical market analysis in large power networks. Generation shift factors (GSF)-based bus clustering methods have been used for network equivalence to analyze the impact of changes in transactions between operating areas or congestion zones on transmission line flows. However, GSFs are sensitive to the location change of slack generator. Equivalent networks based on GSF bus clustering methods change with the location of slack generator, which may increase the complexity of market behavior analysis in the equivalent networks. In this paper, we present a new bus clustering method for network equivalence based on the average electrical distance (AED) from buses to a designated transmission line. AED is independent of the location changes of slack generator in a power system, which can reduce the complexity of market behavior analysis in the equivalent network. The efficacy of the proposed method is demonstrated by comparing the proposed bus clustering method and GSF-based clustering method on the IEEE 39-bus system.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128672275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online intelligent technique for preventing relay maloperation under stressed conditions","authors":"Sayari Das, B. K. Panigrahi","doi":"10.1109/ISAP.2017.8071422","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071422","url":null,"abstract":"Various power system blackouts have been caused due to the maloperation of distance relays during stressed conditions like power swing and voltage instability. Thus differentiating fault from stressed conditions and making the protection scheme intelligent enough to stop the relay maloperations has become very important. There are a few computational intelligent techniques proposed in the literature for preventing relay maloperations. However with the increase in size and complexity of the power systems there have been situations during which there is change in network topology or system parameters. An online intelligent technique: online sequential extreme learning machine (OSELM) has been suggested in this paper which under such real time situations successfully furnishes accurate results. This online computational intelligence technique based on synchronized wide area measurements has been implemented to develop a classifier that differentiates fault from power swing and voltage instability. Potential of this online tool in preventing relay maloperations has been validated by comparing it with other offline intelligent techniques during real time scenario.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121869893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A graph model for enhancing situational awareness in power systems","authors":"Mirjavad Hashemi Gavgani, S. Eftekharnejad","doi":"10.1109/ISAP.2017.8071427","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071427","url":null,"abstract":"As societies are becoming more dependent on the power grids, the security issues and blackout threats are more emphasized. This paper proposes a new graph model for online visualization and assessment of power grid security. The proposed model integrates topology and power flow information to estimate and visualize interdependencies between the lines in the form of line dependency graph (LDG) and immediate threats graph (ITG). These models enable the system operator to predict the impact of line outage and identify the most vulnerable and critical links in the power system. Line Vulnerability Index (LVI) and Line Criticality Index (LCI) are introduced as two indices extracted from LDG to aid the operator in decision making and contingency selection. This package can be useful in enhancing situational awareness in power grid operation by visualization and estimation of system threats. The proposed approach is tested for security analysis of IEEE 30-bus and IEEE 118-bus systems and the results are discussed.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130057132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agent-based distributed underfrequency load shedding","authors":"Jing Xie, Chen-Ching Liu, M. Sforna","doi":"10.1109/ISAP.2017.8071393","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071393","url":null,"abstract":"A distributed underfrequency load shedding (UFLS) scheme is proposed as a last resort to mitigate the frequency decline. In the agent-based scheme, Paxos and average-consensus protocols are utilized by agents to reach an agreement. The monitoring, estimation, and distribution steps are presented in detail for implementation of the multi-agent system (MAS). Simulation results are provided to validate the performance of the proposed agent-based UFLS scheme, which is being implemented as a demonstrative application of the RIAPS (Resilient Information Architecture Platform for the Smart grid).","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129597694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-kernel assimilation for prediction intervals in nodal short term load forecasting","authors":"M. Alamaniotis, L. Tsoukalas","doi":"10.1109/ISAP.2017.8071377","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071377","url":null,"abstract":"Utilization of intelligent systems for information and decision making is of paramount significance toward implementing a smart and sustainable power grid. Nodal load forecasting is an aspect that can greatly benefit from the use of intelligent methods. In this paper, a multi-kernel method is proposed for load forecasting in power systems. In particular, the method adopts a set of kernel-modeled Gaussian process regressors that are subsequently compounded to provide a predictive distribution over the future values of a node's load. The compound predictive distribution is taken by the assimilation of the individual Gaussian processes using a genetic algorithm. In addition, the forecasting horizon varies at each step and is determined by the amount of uncertainty in the forecasted values. The proposed method is applied on a set of historical real-world load demand datasets taken from a node in US metropolitan area. Results exhibit that the assimilated models provide prediction intervals of less variance forecasts than the individual regressors. In addition, the proposed method provided forecast intervals in which a high number of actual forecasts fall within the limits of the interval.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic identification, clustering and reporting of recurrent faults in electric distribution feeders","authors":"Karthick Manivinnan, C. Benner, B. Russell, J. Wischkaemper","doi":"10.1109/ISAP.2017.8071426","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071426","url":null,"abstract":"Latent power line conditions, such as vegetation intrusion and apparatus that have failed or are in the process of failing can cause recurring fault events. Many such conditions are influenced by other factors such as wind and moisture, and therefore cause fault events only intermittently. These conditions are difficult to detect and locate with conventional technologies. Fault current and arcing from recurrent faults can cause further damage to already weak apparatus, ultimately leading to a catastrophic failure, at which time there may be more consequential damage to apparatus, including burned-down lines. For more than a decade, Texas A&M researchers have instrumented dozens of feeders using sensitive, high-fidelity waveform recorders to document numerous apparatus failure conditions, including multiple instances in which failing apparatus and other factors have caused recurring faults and momentary interruptions, spread over significant periods of time, without causing sustained outages. A series of related faults can escape notice when an unmonitored, pole-mount recloser is the interrupting device, unless customers report momentary interruptions, and experience indicates this often does not happen. Even if customers report individual momentary interruptions, the utility may not recognize that the multiple interruptions are related to each other, particularly if time intervals between operations are sufficiently long for operator memories to fade. Awareness of recurrent fault conditions would enable utilities to make timely, proactive repairs, thus avoiding additional faults and interruptions, as well as potentially preventing more catastrophic failures (e.g., equipment damage, downed conductors, fires). This paper describes an on-line, automated method to mine, cluster and report recurrent faults to utility operators in a near real-time fashion. This paper also documents one of multiple real-world examples where the methodology described in this paper was successfully used by utilities to locate and fix problematic components and prevent further faults.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124156802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Merging conventional and phasor measurements in state estimation: A multi-criteria perspective","authors":"B. Tavares, V. Freitas, Vladimiro Miranda, Antonio Simões Costa","doi":"10.1109/ISAP.2017.8071423","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071423","url":null,"abstract":"This paper presents a new proposal for sensor fusion in power system state estimation, analyzing the case of data sets composed of conventional measurements and phasor measurements from PMUs. The approach is based on multiple criteria decision-making concepts. The equivalence of an L1 metric in the attribute space to the results from a Bar-Shalom-Campo fusion model is established. The paper shows that the new fusion proposal allows understanding the consequences of attributing different levels of confidence or trust to both systems. A case study provides insight into the new model.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131534400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unit commitment using gravitational search algorithm with holomorphic embedded approach","authors":"A. Shukla, J. Momoh, S. Singh","doi":"10.1109/ISAP.2017.8071396","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071396","url":null,"abstract":"Volatility of load demand and electricity price has set a challenging task In the operational planning and controls of modem power system. Therefore, power utilities face challenge to serve the load demand at minimum operating cost by performing a proper scheduling of the generating units. To perform such a task, unit commitment plays a vital role and significant amount cost is saved per year. In this paper, Gravitational Search Algorithm (GSA) is utilized for Unit Commitment problem. Optimum scheduling of the generating units is obtained using GSA and Holomorphic Embedded Load Flow method for handling load flow operation, generators real power output and network losses for each time period. IEEE 30-bus systems is considered to check the performance of the proposed method and simulation results are compared to other techniques available in the literature.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130750708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A targeted attack for enhancing resiliency of intelligent intrusion detection modules in energy cyber physical systems","authors":"M. E. Hariri, Eric Harmon, H. Habib, T. Youssef, Osama A. Mohammed","doi":"10.1109/ISAP.2017.8071363","DOIUrl":"https://doi.org/10.1109/ISAP.2017.8071363","url":null,"abstract":"Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time-critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware to trigger false positives in the neural network's response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network's ability to learn and adapt to these attacks.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130627556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}