{"title":"A coherency based rescheduling method for dynamic security","authors":"Wenping Li, A. Bose","doi":"10.1109/PICA.1997.599404","DOIUrl":"https://doi.org/10.1109/PICA.1997.599404","url":null,"abstract":"For power system online dynamic security analysis (DSA), preventive control or remedial action should be an integral part of the function if instability for a contingency is detected. Research done so far in online remedial action has been in rescheduling generation and most of the suggested methods for determining such preventive control use the sensitivities of the stability energy margin to the generator power injections. For any approach using the energy margin, computation time is a concern and a somewhat different approach was suggested that determines control action by maximizing coherency of the generators without the use of sensitivities. In this paper, a new coherency based sensitivity method is proposed for generation rescheduling. Different coherency indices have been defined and then compared by ranking the contingencies according to these indices as well as the energy margin index. Since the coherency indices are always functions of the rotor angles, the sensitivity trajectories of a coherency index, such as the most critical rotor angle, with respect to changes of generation can be calculated at every time step of the integration process. This paper suggests that these sensitivities calculated shortly after fault clearing be used for rescheduling the generation. The calculation of these sensitivities are obviously faster than the calculation of the energy margin sensitivities. This paper also shows, with test results using several different systems, that the rescheduling achieved by this method provides the necessary remedial action. It is also noted that this method is intuitively more direct as it uses the sensitivities of the worst affected generator angles for rescheduling.","PeriodicalId":383749,"journal":{"name":"Proceedings of the 20th International Conference on Power Industry Computer Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122479071","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":"Surrogate constraint method for optimal power flow","authors":"L. Chen, S. Matoba, H. Inabe, T. Okabe","doi":"10.1109/PICA.1997.599391","DOIUrl":"https://doi.org/10.1109/PICA.1997.599391","url":null,"abstract":"This paper represents a technique based on constraints surrogate defined by the maximum entropy principle for optimal power flow (OPF) problems. One of the obstacles impeding the OPF calculations is the problem associated with handling a large number of functional inequality constraints, which cause computational inefficiencies for large power systems. To cope with this problem, this paper proposes a methodology which aggregates all inequality constraints into one surrogate constraint with a single parameter according to the maximum entropy principle. This implementation not only reduces the scale or dimensions of OPF problems, but also improves the convergence characteristics. Several numerical examples including a practical power system are provided to show the efficiency of the proposed approach.","PeriodicalId":383749,"journal":{"name":"Proceedings of the 20th International Conference on Power Industry Computer Applications","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126701043","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 synchronous generator fuzzy excitation controller optimally designed with a genetic algorithm","authors":"J. Wen, Shijie Cheng, O. Malik","doi":"10.1109/PICA.1997.599384","DOIUrl":"https://doi.org/10.1109/PICA.1997.599384","url":null,"abstract":"Design of a fuzzy logic power system controller with satisfactory performance is not an easy task. The difficulties come from two aspects. First, design of a fuzzy logic computer mainly uses the experience of the human experts. To acquire enough heuristic knowledge from the domain experts and to represent this kind of knowledge appropriately with a set of fuzzy rules presents difficulties. Second, it is difficult to appropriately tune the parameters used in the fuzzy logic controller. These parameters are commonly determined by a \"trial and error\" method which is rather time consuming. In this paper, a genetic algorithm is introduced to design an optimal fuzzy logic controller. The proposed method has been used to design an optimal fuzzy logic excitation controller for a generating unit. Test results with the fuzzy logic controller show very satisfactory results.","PeriodicalId":383749,"journal":{"name":"Proceedings of the 20th International Conference on Power Industry Computer Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129444106","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}